Chapter 3

FACULTEIT LANDBOUWKUNDIGE EN TOEGEPASTE BIOLOGISCHE WETENSCHAPPEN ACADEMIC YEAR 2001 - 2002

METHODOLOGY FOR TREE SPECIES DIVERSIFICATION PLANNING FOR AFRICAN AGROECOSYSTEMS METHODOLOGIE VOOR DIVERSIFICATIEPLANNING VOOR BOOMSOORTEN IN AFRIKAANSE AGROECOSYSTEMEN DOOR

IR.

ROELAND KINDT

THESIS SUBMITTED IN FULFILMENT OF THE REQUIREMENT FOR THE DEGREE OF DOCTOR (PHD) IN APPLIED BIOLOGICAL SCIENCES PROEFSCHRIFT VOORGEDRAGEN VOOR HET BEHALEN VAN DE GRAAD VAN DOCTOR IN DE TOEGEPASTE BIOLOGISCHE WETENSCHAPPEN

OP GEZAG VAN RECTOR: PROF. DR. A. DE LEENHEER

PROMOTOR PROF. DR. IR. P. VAN DAMME CO-PROMOTOR DR. A. J. SIMONS

DEAN PROF. DR. IR. O. VAN CLEEMPUT

The author, the promotor and co-promotor give authorisation to consult and to copy parts of this work for personal use only. Any other use is limited by Laws of Copyright. Permission to reproduce any material contained in this work should be obtained from the author. De auteur, de promotor en de co-promotor geven de toelating dit doctoraatswerk voor consultatie beschikbaar te stellen, en delen ervan te copiëren voor persoonlijk gebruik. Elk ander gebruik valt onder de beperkingen van het auteursrecht, in het bijzonder met betrekking tot de verplichting uitdrukkelijk de bron vermelden bij het aanhalen van de resultaten uit dit werk.

Prof. Dr. ir. P. Van Damme

Dr. A. J. Simons

ir. R. Kindt

Promotor

Co-promotor

Author

(PVD & RK) Ghent University Faculty of Agricultural and Applied Biological Sciences Department Plant Production Laboratory of Tropical and Subtropical Agronomy and Ethnobotany Coupure links 653, B-9000 Ghent, Belgium [email protected] & (AJS & RK) International Centre for Research in Agroforestry (ICRAF) Domestication of Agroforestry Trees Programme PO Box 30677, Nairobi, Kenya [email protected] [email protected]

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CONTENTS ACKNOWLEDGMENTS

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SAMENVATTING VAN HET PROEFSCHRIFT (summary)

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CHAPTER 1

1

Introduction R Kindt CHAPTER 2

21

The Relationship between Species Diversity within an Ecosystem and its Stability and Productivity R Kindt CHAPTER 3

33

Description of the Survey Areas and Data Collection Methods R Kindt CHAPTER 4

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Methodology R Kindt CHAPTER 5

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The Study of On-Farm Tree Species Diversity in Western Kenya to Plan for Agroecosystem Diversification R Kindt, AJ Simons & P Van Damme CHAPTER 6

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The Study of Random and Proximity-based Tree Species Diversity on Farms in Western Kenya Using Exact Species Accumulation Curves R Kindt, P Van Damme & AJ Simons

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CHAPTER 7

101

Studying On-farm Tree Species Richness and Evenness in Western Kenya with Diversity Profiles R Kindt, P Van Damme & AJ Simons CHAPTER 8

117

Do Farm Characteristics Explain Differences in Tree Species Diversity among Western Kenyan Farms? R Kindt, AJ Simons & P Van Damme CHAPTER 9

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Comparison of Tree Species Composition of Farms in Western Kenya Using Constrained Ordination. I. Analysis for Separate Use-groups R Kindt, P Van Damme, AJ Simons & H Beeckman CHAPTER 10

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Comparison of Tree Species Composition of Farms in Western Kenya Using Constrained Ordination. II. Analysis for Separate Niches R Kindt, P Van Damme, AJ Simons & H Beeckman CHAPTER 11

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Farmers’ Decision-making in Managing On-farm Tree Diversity R Kindt, P Van Mele & P Van Damme CHAPTER 12

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Intraspecific Diversity of Trees on Farms in Western Kenya – Results from Simulations of Individual-based Metapopulation Dynamics R Kindt, AJ Simons, P Van Damme & D Reheul CHAPTER 13 Comparing Species Richness and Evenness Contributions to On-Farm Tree Diversity for Datasets with Varying Sample Sizes from Kenya, Uganda, Cameroon, and Nigeria with Randomised Diversity Profiles R Kindt, A Degrande, L Turyomurugyendo, C Mbosso, P Van Damme & AJ Simons

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207

CHAPTER 14

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Species Diversity on Farms in Cameroonian, Kenyan and Ugandan Landscapes R Kindt, J Weber, A Lengkeek, JM Boffa, A Degrande, JG Jourget, D Van Oijen , C Mbosso, P Van Damme & AJ Simons CHAPTER 15

251

General Conclusions and Recommendations for Future Research and Development Activities R Kindt LITERATURE LIST

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APPENDIX I

285

Matrices with Information on Tree Species Identities, Farm Characteristics and Species Distribution over Farms in Western Kenya APPENDIX II

313

Questionnaires Used in the Western Kenyan Surveys

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ACKNOWLEDGMENTS Many people have helped me to complete this thesis. I sincerely hope that I managed to mention all of them in this section. If you do not see yourself mentioned, this only reflects a lapse in my short-term memory. First, I want to thank my wife Gladys Mumbi Wacira for her patience with me when I was bringing some work home and to tolerate when I was traveling outside of Nairobi or to faraway places in my mind. I have enjoyed all the time that we have shared and I am looking forward to many years in the future, especially now that we are trying to expand our family. I am very grateful to my supervisor in the International Centre for Research in Agroforestry, Dr. Tony Simons, for providing me with the possibility of conducting the research that lead to results listed in this thesis, and as co-promotor for intellectually stimulating me and helping me to complete this thesis. Prof. Dr. ir. Patrick Van Damme has been of equal importance to the success of this thesis. I am very grateful for his supervision and the time and detail that he has devoted to providing comments on this work. I appreciate the farmers of Cameroon, Kenya, Nigeria, and Uganda, who provided time and information during data collection for this thesis, very much. I hope that I have been able to demonstrate the diversity of their knowledge in this thesis, and that its results may contribute to improvement of their livelihood. I am very grateful for Joseph Njeri who assisted greatly during data collection in western Kenya. I thank Prof. Dr. ir. Hans Beeckman, Prof. Dr. ir. Dirk Reheul and Dr. ir. Paul Van Mele for contributions to some chapters of this thesis. I also feel very grateful for the comments given by Paul on the general information requirements for this thesis. I am very grateful for the review of the doctoral manuscript by the members of the examination committee: Prof. Dr. ir. M. Verloo (Gent University, Belgium, chair), Prof. Dr. ir. Patrick Van Damme (Gent University, promotor), Dr. Tony Simons (ICRAF, Nairobi, co-promotor), Prof. Dr. ir. Hans Beeckman (Gent University), Prof. Dr. ir. Bernard De Baets (Gent University), Prof. Dr. Paul Goetghebeur (Gent University), Prof. Dr. ir. Robert De Wulf (Gent University), Prof. Dr. ir. François Malaisse (Université de Gembloux, Belgium), Prof. Dr. Jean-Pierre Ottoy (Gent University), Dr. ir. Olivier Thas (Gent University) and Dr. ir. Paul Van Mele (CABI, UK). I wish to thank VVOB (the Flemish Association for Development Co-operation and technical Assistance) for seconding me to ICRAF. Of the VVOB headquarters office, I especially would like to thank Dr. ir. Jaak Lenvain and Dr. Rudy Poulussen. Lut Laenen-Fox has been extremely helpful as representative for VVOB in Kenya. I would like to thank Prof. Dr. ir. Jef Coosemans for the comments that he provided in his capacity as scientific advisor for VVOB associates in ICRAF. I also feel very grateful towards DFID, USAID, and IFAD who provided funding that enabled to conduct fieldwork in Cameroon, Kenya, Nigeria, and Uganda. Joris, Veerle, Said, and Aron De Wolf always managed to provide me with a very warm welcome in Kisumu, and making my fieldwork in western Kenya very enjoyable. I appreciate Joris’ assistance with methodological issues of planning and analysis very much.

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Very important to the conceptualization and completion of this thesis have been colleagues within ICRAF. I would like to acknowledge them in alphabetical order: Kwesi Atta-Krah, Liz Betser, Jean-Marc Boffa, Wim Buysse, Richard Coe, Ian Dawson, Ann Degrande, Joris De Wolf, Steve Franzel, Anne-Marie Izac, Hannah Jaenicke, Bashir Jama, Ard Lengkeek, Jens-Peter Barnekow Lillesø, Hans Lindqvist, Charlie Mbosso, Amadou Niang, Frank Place, Jane Poole, Thomas Raussen, Ralph Rommelse, Ralph Roothaert, Stephen Ruigu, Zac Tchoundjeu, Levand Turyomurugyendo, Tom Vandenbosch, Meine Van Noordwijk, Bruno Verbist, François Verdeaux, Grégoire Vincent, John Weber, James Were, and Robert Zomer. I also thank all former and present members of the ICRAF Management Committee and Board of Trustees, especially Dennis Garrity, Anne-Marie Izac, Roger Leakey and Pedro Sanchez, for their support. I am very grateful for the comments and suggestions provided by the members of the Advisory Committee to ICRAF on Genetic Resource Activities and the members of the Board Commissioned External Review of the ICRAF Tree Domestication Programme. In ICRAF, I also appreciated the assistance of many people who are not researchers but who have assisted me throughout the years. These people are too many to list here, but I would like to give special mention to Stella Muasya and Josina Kimotho. Of the Laboratory of Tropical and Subtropical Agronomy and Ethnobotany of the Faculty of Agricultural and Applied Biological Sciences of the University of Ghent, I thank Annita Goethals, ir. Tinneke Dirckx and ir. Xavier Scheldeman for assistance that they provided with administrative issues. I would like to thank my VVOB-colleagues at ICRAF for their companionship and assistance: ir. Wim Buysse, ir. Bruno Cammaert, ir. Ann Degrande, ir. Elke Delvoye, Dr. ir. Johan Desaeger, ir. Joris De Wolf, ir. Tom Vandenbosch, ir. Bruno Verbist, ir. Inge Vissers and ir. Katty Wauters. The assistance of ir. Jan Beniest at ICRAF has also been greatly appreciated. Last but not least, I would like to thank my parents, Daniel Kindt and Rita Vermander, my brothers Wouter and Brecht, my Belgian and Kenyan extended families, and friends from Belgium, Kenya and other countries, for their friendship and support.

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SAMENVATTING VAN HET PROEFSCHRIFT In het eerste hoofdstuk worden de 27 hypotheses die onderzocht werden belicht. Ook wordt het onderzoek gesitueerd binnen de onderzoeksagenda van ICRAF rond het beheer van natuurlijke rijkdommen in verschillende tropische regio’s. Het onderzoek was vooral exploratief – met verschillende numerische-ecologie technieken werden secties binnen agro-ecosystemen van significant lagere diversiteit gecategorizeerd die later het mikpunt kunnen zijn voor diversificatieinitiatieven. In het tweede hoofdstuk worden de resultaten van een literatuurstudie naar het verband tussen de diversiteit van een ecosysteem en zijn stabiliteit en productiviteit voorgesteld. Verschillende ecologische experimenten en modellen hebben aangetoond dat er een positieve relatie bestaat. Deze relatie is echter afhankelijk van spatio-temporele diversiteit en diversiteit in karakteristieken van de soorten in het systeem. Het derde hoofdstuk beschrijft de zes studiegebieden in Kenya, Uganda, Kameroen en Nigeria. De gerandomizeerd-gestratificeerde monstername van boerderijen op basis van afstand tot bosreservaten en socio-economische parameters wordt toegelicht. Het vierde hoofdstuk belicht de gebruikte analyze-technieken, waaronder enkele nieuwe methodes. Het vijfde hoofdstuk onderzoekt hoe de belangrijkste gebruiken van bomen over boerderijen verdeeld zijn. Een positieve relatie tussen het aantal soorten op een boerderij, en het aantal gebruiken van soorten op deze boerderij wordt bewezen. Niet iedere boer gebruikt dezelfde soort op dezelfde manier – zowel soorten als informatie over gebruiken kunnen wijdser verspreid worden in het gebied. In hoofdstuk zes wordt de accumulatie van soorten met gebiedsgrootte geanalyseerd met een snellere en meer accurate manier dan de gangbare methode. Gebruiken met lagere alpha- en/of beta-diversiteit werden in kaart gebracht. Het zevende hoofdstuk introduceert een nieuwe techniek om de diversiteit van een systeem te ontbinden in de invloeden van soortenrijkdom, proportionele gelijkheid en grootte-orde van het proefgebied. Met de methode worden gebruiken binnen het West-Kenyaanse landschap van lagere diversiteit – rijkdom en/of proportionele gelijkheid - geselecteerd. In hoofdstuk acht wordt de relatie tussen karakteristieken van boerderijen en hun diversiteit onderzocht door middel van multiple regressiemodellen. Significante relaties worden aangetoond, maar niet alle variatie kon gemodelleerd worden. Hoofdstukken negen en tien bekijken verschillen en soortencompositie van boerderijen via een combinatie van twee nieuwe ordinatietechnieken. Een significant gedeelte van de variatie wordt gemodelleerd. Uitzonderlijke boerderijen worden aangetroffen met een soortensamenstelling die niet typisch is voor andere boerderijen van hetzelfde type. In hoofdstuk elf worden de resultaten voorgesteld van participatorisch onderzoek naar de manier waarop boeren de diversiteit op hun boerderijen willen beheren. De algemene resultaten toonden aan dat boeren meer bomendiversiteit willen, vooral omdat ze de elkaar aanvullende karakteristieken van verschillende boomsoorten appreciëren. Hoofdstuk twaalf onderzoekt de dynamiek van genetische diversiteit in metapopulaties van de meeste onderzochte boomsoorten. De meeste boomsoorten kwamen geaggregeerd voor in het landschap. Hoewel de simulatiemodellen geplaagd werden door de momenteel beperkte kennis inzake reproductieve ecologie van de meeste tropische boomsoorten, toonden deze modellen aan

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dat een gestratifieerde collectiemethode van zaden, zaailingen of stekken door boeren de genetische erosie van deze soorten kan beperken. In hoofdstukken dertien en veertien worden verschillende diversiteitsfacetten die in eerdere hoofdstukken onderzocht werden voor westelijk Kenya ook onderzocht voor de andere studiegebieden. Hoofdstuk vijftien bekijkt de hypotheses uit hoofdstuk een en bespreekt hoe de aanzienlijke diversiteit die aangetroffen werd in de studiegebieden gebruikt kan worden binnen initiatieven die deze systemen verder willen diversifiëren. De thesis wordt afgesloten met een literatuurlijst, informatie gecollecteerd in westelijk Kenya en de formulieren die gebruikt werden tijdens de inventarissen en interviews.

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CHAPTER 1 INTRODUCTION R KINDT

INTRODUCTION The research findings that are presented in this thesis are the results of activities that were conducted by R. Kindt and colleagues in the International Centre for Research in Agroforestry. R. Kindt was seconded to ICRAF by the Flemish Association for Development Co-operation and Technical Assistance (VVOB). A short introduction is given first about the mission of ICRAF, after which a description of the domestication concept developed by ICRAF is presented. Finally, an introduction is given to some of the data analysis methods employed in this thesis.

1.1. History and Mission of ICRAF ICRAF was established as the International Council for Research in Agroforestry in 1978 to promote agroforestry research in developing countries, with a focus on research, training, and information activities in sub-Saharan Africa. It joined the Consultative Group on International Agricultural Research in 1991 to conduct strategic research on agroforestry on a global scale, changing its name from Council to Centre and expanding into Southeast Asia and Latin America. Currently, ICRAF has active regional programmes in Eastern and Central Africa, Humid West Africa, Southern Africa, the Sahel, Southeast Asia and Latin America, whereas its headquarters are located in Nairobi. In 2002, ICRAF will change its name to the World Agroforestry Centre and adopt the tagline ‘transforming lives and landscapes‘ as an indication of its evolution into a 21st century institute. The mission of ICRAF is to conduct innovative research and development on agroforestry, strengthen the capacity of its partners, enhance worldwide recognition of the human and environmental benefits of agroforestry, and provide scientific leadership in the field of integrated natural resource management. The mission will be accomplished through the combination of the best of science with farmer knowledge in a wide range of strategic alliances along the researchdevelopment continuum (ICRAF 2000). Agroforestry refers to the growing of trees on farms to improve livelihoods and protect the environment. The technical definition of agroforestry adopted by ICRAF is (Leakey 1996): Agroforestry is a dynamic, ecologically-based, natural resource management practice that, through the integration of trees on farms and in the agricultural landscape, diversifies and sustains production for increased social, economic and environmental benefits. Simons (1996) mentions that this definition conveys the idea that various agroforestry practices play different roles in the ecological succession towards ‘climax agroforests’ at a landscape scale. The term ‘Trees’ in the agroforestry definition refers to woody or ligneous plants, which include ‘trees’ (woody plants with a single main stem, ≥7m), ‘small trees’ (< 7 m), ‘shrubs’ (multistemmed woody plants), ‘lianas’ or ‘woody vines’ (woody plants that generally require a support), and ‘bamboos’. Agroforestry is a form of natural resource management (NRM) – the management of resources (natural capital) generated by natural biogeochemical processes and solar energy that produce flows of desirable products and services. Agroforestry provides economic and ecosystem functions at multiple geographical scales ranging from the farm, watershed/village/landscape, national/regional to the global scale. At the global scale, for instance, incorporating trees into farming systems leads to carbon sequestration, greenhouse gas regulation, biodiversity conservation, poverty reduction, and decreased human migration. At the farm level, agroforestry leads to greater prosperity by providing marketable products such as lumber, building poles, firewood, fodder, fruits and medicines. At the farm level, trees also improve soil fertility by fixing atmospheric nitrogen and recycling nutrients, holding moisture where it is needed, reducing soil erosion, and helping to ensure the stability of future production (ICRAF 2000).

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CHAPTER 1 ICRAF’s integrated natural resources management (INRM) agenda builds on the results of the Green Revolution, but differs from it in four ways (ICRAF 2000): ·

the farmers whose needs are addressed are the poor in marginal or depleted environments;

·

the research includes beneficiaries other than farmers, such as community-level landusers, national and global policy makers, and the concerned public;

·

the approach focuses on heterogeneous environments, which require a flexible range of management options; and

·

the method builds upon the production and ecosystem service functions that natural capital fulfils in agriculture. Such functions increase productivity while ensuring the stability of these increases.

The INRM agenda of ICRAF corresponds to the advocacy of a “greener revolution”, an environmentally sustainable revolution, that considers gains and losses in values of ecosystem goods and services (see Chapter 2) by converting natural ecosystems to agriculture, now that human population size and per capita consumption are assumed to be the biggest drivers of global environmental change (Tilman et al. 2001a).

1.2. Tree Domestication in ICRAF The Tree Domestication Programme of ICRAF’s Research Division conducts research on the domestication of trees and landscapes, operating under the following definition of domestication (Simons in prep. – this is an updated definition from Simons 1997): Domesticating agroforestry trees involves accelerated and human induced evolution to bring species into wider cultivation through a farmer-driven or market-led process. This is a science-based and iterative procedure involving the identification, production, management, and adoption of high quality germplasm. High quality germplasm in agroforestry incorporates dimensions of productivity, fitness of purpose, viability, and diversity. Strategies for individual species vary according to their functional use, biology, management alternatives, and target environments. Domestication can occur at any point along the continuum from the wild to the genetically transformed state. The intensity of domestication activities warranted for a single species will be dictated by a combination of biological, scientific, policy, economic, and social factors. In tandem with species strategies are approaches to domesticate landscapes by investigating and modifying the uses, values, intraspecific diversity, ecological functions, numbers, and niches of both planted and naturally regenerated trees The research documented in this thesis has contributed to the ‘domestication of the landscape’ concept and methodology that was recently developed in ICRAF. In contrast to domestication of agroforestry species aimed at accelerated evolution of individual species, domestication of the landscape proposes modifications to agroecosystems based on diversity already present in these systems. Simons (in prep.) distinguished four potential interventions of landscape domestication: (i) “replacement” of a tree by a tree of the same species, (ii) “substitution” of a tree by a tree of a different species, (iii) “addition” of new trees, or (iv) modifications in tree “management”. Several analysis methods of diversity in the investigated agroecosystems, and how this information can be utilized to design interventions are documented in the various chapters. As the research presented in this thesis was conducted within the objectives of ICRAF’s Tree Domestication Programme of diversification of the species composition of agricultural landscapes, the general objectives of the investigations of this thesis can be summarized as:

3

INTRODUCTION ·

to document the distribution of species diversity in several landscapes

·

to investigate possibilities for diversification of these landscapes

Palmer et al. (2001) differentiated between species that are globally important agroforestry species for domestication, regionally important species, and ‘shield species’ that are collectively important for farm incomes and the maintenance of ecosystem services. They further stated that on-farm management of genetic resources of shield species was a vitally important component of a domestication strategy. Genetic diversity contributes to landscape biodiversity as biodiversity can be structured into genes, populations, species, functional ecological groups, and ecosystems. Molecular methods that sample genetic material and allow estimations of genetic diversity in tree populations have been used for priority species for domestication in ICRAF (examples of such research conducted in ICRAF are Dawson & Powell 1999; Russell et al. 1999 and Lowe et al. 2000). For practical reasons, genetic diversity per se has not been sampled during the surveys documented in this thesis. However, information on origins and types of germplasm (propagules for sexual multiplication of plants such as seeds or seedlings, or vegetative multiplication such as cuttings or grafts), on the distribution of trees, and general reproductive biology characteristics of tropical tree species enabled simulations of genetic diversity dynamics for various scenarios of on-farm germplasm management. Actual sampling of genetic diversity and measurement of reproductive ecology characteristics of individual species would obviously have allowed for better investigations, but time and resources did not permit this and thus a decision was made to focus on the interspecific component of landscape diversity.

1.3. Format and Sequence of the Chapters Most of the chapters of the thesis were developed as articles in the required formats of the targeted journals for submission. Here, the articles have been modified into a common format. To avoid duplication, the sections that described the study areas and the methods were removed and added into separate chapters (Chapter 3 & 4). For the same reason of presenting information in a more condensed format, all literature is combined into a common bibliographic list. Chapter 15 presents some general conclusions from the investigations, and makes suggestions for future research. The objective of the following chapter (Chapter 1) is to outline the objectives of the investigations within the context of the objectives of ICRAF, and to demonstrate how the various chapters address various aspects pertaining to diversity and diversification of agroecosystems. Since scientific knowledge on the influence of species’ diversity on ecosystem productivity and stability evolved substantially during the research period, and to better document the results of a literature survey on the subject, one chapter specifically documented the role of diversity in ecosystems (Chapter 2). We realized that our investigations were performed on anthropogenic systems (agroecosystems), and that the typical measure of productivity of a natural ecosystem as biomass differs from the economical measurement of agricultural productivity. However, we included Chapter 2 since we referred to the role of diversity in ecosystem functioning (which included agroecosystems) explicitly when discussing options for diversification in various chapters. Although Chapter 2 appears separately, information on the function of diversity was not removed from the other chapters. Figure 1.1 depicts a general sequence by which some of the analyses were conducted. The onfarm surveys aimed at collecting information about all tree species that occurred on selected farms. All information was stored in Microsoft Access databases that were designed for data storage and analysis. Data analysis often started by constructing a sites × species matrix that contained the abundance (number of trees) of each species on each farm (ecological data matrices

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CHAPTER 1 such as sites × species matrices are the typical basis for many ecological analyses: Legendre & Legendre 1998 pp. 52-53). Some matrices only listed trees that provided a specific product or service (summarized here as use), only listing an abundance > 0 for a particular cell in case the farmer had communicated to use the species for that particular purpose on his or her farm. Other matrices only listed trees that occurred in a specific on-farm niche, defined by the location on the farm and the establishment pattern of trees in that location (Chapter 10). A substantial part of this thesis deals with comparisons among and between these matrices through statistics that describe differences in diversity. For example, in Chapter 7, diversity profiles are calculated from matrices that each only included trees that provided a specific use. Another example is Chapter 8, where the diversity of fruit trees are compared among farms, based on calculations from the fruit matrix. Thus, comparisons among and within uses or niches actually refer to comparisons among or within matrices. Figure 1.2 provides an illustration of these two types of comparison modes. With the sites × species matrix as the basis for many analyses, identification of all tree species that occurred on each farm was crucial. Species identification in the field posed some serious problems in some cases, however. Not all species could be confirmed to botanical species when analyses were made, although herbarium samples were taken (deposited in ICRAF), vernacular names were collected, and repeated visits were made in the field with species identification guides. In Kenya, Beentje (1994) was used as the essential reference for indigenous species, and Mabberley (1997) for exotic (and naturalized – which were still classified ‘exotic’ in this thesis) species. We used the sequence of botanical families by Cronquist (1981). The major identification problem was often the lack of reproductive organs, while farmers had removed some trees before these could be identified in the field or by herbarium specimens. However, information of table I.1 and table I.3 (Appendix I), and the information stored in the databases, will allow later investigation of the potential influence of species identification on the results. As many methods used logarithmic scales to compare the diversity of various systems, the influence of unidentified species on the results is expected to be minimal, however. In addition, this thesis has focused more on analysis methods rather than the results of these analyses – although results of the analyses have lead to a current field-testing phase of landscape domestication in the target areas. For some types of data analysis presented in this thesis, appropriate software was not available. To perform these analyses, several programs were written in FORTRAN by the author. The source code for these programs is provided in Appendix III.

1.4. Hypotheses Tested in Chapters 5 - 14 Chapters 5-12 describe various analyses that were conducted on data collected from western Kenya to test a range of hypotheses. Chapters 13 and 14 test hypotheses on data collected from a larger set of surveys in four countries. Here, is listed the 27 hypotheses that were tested in these chapters, and what the consequences of accepting or rejecting a hypothesis would be in terms of conducting biodiversity surveys or planning the management of on-farm diversity. Some hypotheses were tested in several chapters, and were therefore provided with the same number. Most often, hypotheses dealt with the possibility of obtaining some ranking from low to high diversity within or among the matrices used as inputs in the investigations. As diversity can be measured in many different ways – which is demonstrated throughout this thesis – many different diversity rankings are theoretically possible. The consequence of the existence of rankings is that certain development or research projects that are designed to enhance the onfarm diversity of tree species will have the possibility to concentrate their efforts on subsections of lower biodiversity of a landscape. One justification to concentrate on subsections of lower diversity is that, on average, larger effects are expected from diversification of a system of low

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INTRODUCTION diversity. For example, the effect of adding one new species where only one species is present is expected to be greater than adding one new species to ecosystems where already 50 species are present – in situations where diversity has an effect on the functioning of the ecosystem. In case a project aims at improving the functioning of the ecosystem through diversification, for instance by improving the stability of its production, then efforts of adding one species would be more efficient if such species was added to a system of lower diversity. Thus, to maximize the effect of diversification, it is better to introduce a species in a subsection where diversity is currently low. Diversification where diversity is low could also be justified from the perspective of limiting risk. For example, an agroecosystem with two species that provide timber and fifteen species that produce firewood could be categorized as more risky of failing to produce timber than failing to produce firewood due to potential pests, diseases, or other factors that affect production. One of conditions of this relationship, however, is that the fifteen species that provide firewood do not have a substantially higher risk (in the order of 15/2) of not producing that product than that associated with the two species that provide timber. Obviously, species affect the functioning of an ecosystem through their interactions with the environment and with the other species, and not merely by their presence or absence. Therefore, fewer species may offer a larger range in these interaction traits than offered by more species. Thus, the points presented above imply that more species offer a larger set of species’ characteristics, as provided for instance when each species has a random set of traits. In addition, the same number of species may offer a large or a small variation in traits, depending on the distribution of trees among those species. In cases where 99% of trees belong to the same species, then the variation in traits will be low, whereas when each species has the same abundance, a larger variation in traits will be offered. Thus, evenness in the abundance of species will also effect the variation in traits and by consequence the functioning of the ecosystem. Therefore, making species more evenly distributed may also effect ecosystem functioning. As diversity can conceptually be dissociated into richness (the number of species) and evenness (the equality among abundances of the component species), diversification can also be dissociated into enhancing richness (adding more species) and enhancing evenness (making the abundance distribution more even). Diversification, in the context of this thesis, should therefore not be equated to species addition. The effect of diversity (and thus diversification) on ecosystem processes, and conditions for positive effects of diversity on these processes, are detailed in Chapter 2. In summary, a large section of this thesis was devoted to categorizing the diversity of subsections of agricultural ecosystems. In case subsections of smaller diversity can be identified, then diversification efforts that are targeted towards these subsections are expected to have larger effects than random introductions of species or trees would have. The following hypotheses were posed: Chapter 5 documents how sites × species matrices can be formed based on information on species’ uses collected from each farm. As the formation of such matrices was an important step before the analyses of Chapters 6 - 9 could be conducted, the various options available to form these matrices were scrutinized. Subsequently, some simple statistics that rank categories of uses in terms of diversity were calculated from these matrices.

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CHAPTER 1 H1

There is a positive relationship between the number of species on a farm, and the number of uses of these species on the farm ► Accepting this hypothesis would indicate that farmers establish more species so that more uses can be obtained from trees on their farms. In case the objective of a project would be to maximize the number of species that are grown on farms (e.g. for conservation-through-use), then farmers would prefer to grow species for a large range of purposes, rather than growing many species for a limited number of purposes. A positive relationship could indicate that redundancy (the average number of species per use) would not increase much with species richness, and, therefore, that diversification within a use may still be important even if many species and uses are encountered on a farm.

H2

The number of uses of a species in the entire survey equals the number of uses of a species on each farm ► Accepting this hypothesis implies that the collection of ethnobotanical information can be simplified, as information of the distribution of species over farms and information of the uses of a species will suffice to obtain the information on the uses of a species on each particular farm. In other words, the use matrix described above can be constructed by means of a sites × species matrix, and a general list with all the uses of each species. In case the hypothesis is rejected, then information will need to be collected for the uses of each species on each farm. Accepting this hypothesis could mean that farmers do not share information on species’ uses well.

H3

Recording information only on the main use of each species on a farm provides an adequate representation of the distribution of diversity of species of similar on-farm use ► Accepting this hypothesis implies that the collection of ethnobotanical information can be simplified, as only information on the main use of a species on a farm will suffice to obtain a representative use matrix. Rejecting the hypothesis means that information will need to be collected for all uses of each species on each farm

H4

Alpha diversity, i.e. the average number of species per farm, is the same for each use ► Rejecting this hypothesis implies that uses can be ranked from low to high average diversity, using the diversity statistic of alpha diversity. Under such conditions, development projects could target those uses of low diversity, for instance when the aim would be to have at least two species for each use.

H5

Gamma diversity, i.e. the total number of species observed in the survey, is the same for each use ► Rejecting this hypothesis implies that uses can be ranked from low to high total (landscape-scale level) diversity. Development projects could then target uses of low diversity in the entire landscape (these are not necessarily the same uses with the smallest diversity on the average farm as investigated by hypothesis 4). A project could for example set the objective of having a minimum of five species for every use that occurs in a specific area (the relationship between diversity and function applies to each spatial scale, not only at the smallest scale, as described in Chapter 2).

7

INTRODUCTION H6

Evenness, as expressed by the proportion of the most abundant species in the entire survey, is the same for each use ► Rejecting this hypothesis implies that uses can be ranked from low to high total landscape-scale diversity. Here, however, diversity was not expressed in terms of richness (as for hypothesis 5), but in terms of evenness in the abundances of the component species. The objective of a project could for instance be that within each use not more than 50% trees belong to the same species. As for hypotheses 5 and 6, such objective could be linked to limit risks when one species would not provide the product or service at a given moment.

H7

Species density, the average number of trees per species per ha, is the same for each grouping of species of similar on-farm use ► Rejecting this hypothesis means that uses can be ranked from low to high density. In case the risk of genetic erosion (the loss of genetic diversity) would be positively correlated with density (through effects from limited geneflow), and assuming no effects from reproductive biology then rankings in terms of density would also be rankings in terms of risk for genetic erosion. Certain projects could use this information to focus their efforts on uses with high risks for genetic erosion.

Chapter 6 documents species accumulation patterns, which are associated with the average species richness of 1, 2, 3, ... accumulated sites, and spatial distribution patterns of species richness. In contrast with earlier research, a novel approach was employed where a formula was specified that allows the calculation of the average richness from information on the frequency of each species, whereas in earlier research accumulation curves were obtained through randomization. Species accumulation curves provide information on alpha diversity, the average number of species per farm, and beta diversity, providing information on differentiation in species composition among sites. The two parameter model, S = cAz

where S stands for species richness, c for alpha diversity, A for the area sampled and z for beta diversity is widely used in the ecological and biogeographical literature (see Chapter 6), and describes how species richness at specific spatial scale (e.g. A = 10 ha) is determined by alpha and beta diversity. This chapter complements chapter 5 through a sample-scale specific measurement of beta diversity. H4

Alpha diversity, i.e. the average number of species per farm, is the same for each use ► See Chapter 5

H5

Gamma diversity, i.e. the total number of species observed in the survey, is the same for each use ► See Chapter 5

8

CHAPTER 1 H8

Beta diversity, i.e. the average difference in species composition among farms, is independent of sample size ► The consequence of accepting this hypothesis is that species accumulation, the average increase of species richness when species richness is measured on a larger number of randomly selected farms, can be categorized by two parameters only, through the model specified above. When species accumulation is more complex, beta diversity will need to be categorized for each intermediate scale (2, 3, 4 ... farms) in between 1 farm and all farms. The consequence for the design of interventions is that in systems with high beta diversity, alpha diversity can be enhanced relatively easily by distributing more widely some of the species that are only present in a subsection of the landscape now. In systems with low beta diversity, substantial enhanced alpha diversity could only be possible through introduction of new species in the landscape.

H9

Sample size has no influence on the rank-order in terms of species richness between uses ► The consequence of rejecting this hypothesis means that one use may contain more species on 10 farms than another use, while the other use may contain more species on 50 farms than the first use. Therefore, neither the first nor the second use can be identified as the use of lowest diversity, without specifying at which spatial scale diversity is measured. Projects that aim at enhancing diversity will need to specify at which scale diversity needs to be enhanced, in order to be able to select the uses of lowest diversity.

H10

Villages contain fewer species than expected by random chance ► Accepting this hypothesis means that farms within villages are more similar in species composition than expected when species would be distributed at random over farms. This means that that species are aggregated within villages. Under these conditions, distributing species and information on their use more randomly in the landscape – for instance through farmer-farmer exchange visits – will enhance the species richness of each village. ► Aggregation of species within villages probably reflects the history of cultivation of particular species in an area.

Chapter 7 describes a new mathematical procedure by which diversity can be dissociated into species richness (the number of species) and evenness (in the abundance distribution). Changes in diversity when scaling from one farm to the complete survey are investigated. Chapter 7 complements Chapter 5 through a more accurate measurement of evenness. All analyses in this chapter are done for the entire survey. Diversity is thus calculated for all species and all trees encountered. H5

Gamma diversity, i.e. the total number of species observed in the survey, is the same for each use ► See Chapter 5

H11

Diversity, as measured for the species composition of the entire survey, is the same for each use ► Rejecting this hypothesis implies that uses can be ranked from low to high landscape-scale diversity. In contrast with Chapter 5 (hypotheses 5 & 6), the rank-order that will be obtained will not be dependent on the type of diversity statistic that is used, i.e. statements that one use is more diverse do not need to be accompanied by “... when diversity was measured as...”. Rejecting the hypothesis allows projects to identify uses of lowest diversity.

9

INTRODUCTION H12

Evenness, as measured for the species composition of the entire survey, is the same for each use ► Rejecting this hypothesis implies that uses can be ranked from low to high total landscape-scale evenness. Rejecting the hypothesis allows projects to identify uses of lowest evenness. ► Hypotheses 11 and 12 will not be accepted or rejected simultaneously for all theoretical cases. When one use has substantially higher richness, it will also have substantially higher diversity than a second use that could have higher evenness. In other words, higher evenness and higher richness imply higher diversity, but higher diversity does not imply higher evenness and higher richness.

H13

A use with higher gamma diversity will also have higher evenness ► Accepting this hypothesis implies that uses can be ranked from low to high total landscape-scale diversity, only by measuring gamma diversity. Under these conditions, measurement of diversity and diversification can be approached easily mathematically, as projects should only be concerned with the number of species, not with the differences in the abundance distribution.

Whereas Chapters 5-7 deal with methods of comparing the diversity among sites × species matrices (see above), Chapters 8-10 deal with methods of comparing the diversity within matrices. Chapters 8-10 investigate whether important differences in diversity can be detected among the rows of these matrices, thus among the farms. The analyses of Chapters 8-10 were repeated for each matrix. As the relationships between characteristics of farms with diversity are investigated in a statistical manner, those results should apply to a wider area, allowing for extrapolation of the results and design of area-wide interventions. Chapter 8 shows how much of the diversity of an individual farm can be explained by multiple linear regression analysis with socio-economic characteristics of the farm and the location of farms in relation to forests as explanatory variables. The regression analyses allowed to investigate whether factors such as the gender of the head of the household, wealth, family size, education levels, or proximity to forests, could influence the diversity of tree species on a farm. H14

Socio-economic characteristics and the spatial location of the farm both explain variation in diversity and abundance among farms ► Accepting this hypothesis means that socio-economic factors influence on-farm diversity. Under these conditions, development projects have various options to target their efforts at subsections of the landscape, depending on the objectives of these projects. In case a project would prefer to design interventions that would benefit a predefined group of clients (e.g. poor farmers, female-headed households), then those uses could be targeted for which the selected types of clients have substantially lower diversity than other types of clients. Another pathway for these projects could be to select the uses of lowest diversity first (potentially by using one of the methods documented in the previous chapters), and then target the interventions at the types of farms that have lowest diversity within these uses. Both pathways would lead to the formulation of objectives of such project as for example “enhancing fruit-tree diversity on female-headed farms”, which could be justifiable as farms of that type would be substantially lower in diversity.

10

CHAPTER 1 H15

The same area of land, consisting of many or few randomly-accumulated farms, has the same diversity and abundance ► Rejecting this hypothesis means that fragmentation of farmland in an area has an influence on the diversity encountered in that area. Such pattern has implications for the conservation of species on farms. It may also influence ecosystem functioning, as a different number of species would be present on the same unit of land after x years. The hypothesis tests whether diversification or simplification may already happen in a landscape without the direct influence of development projects. It could be that further subdivision of farms would lead to fewer species that are maintained in this landscape, or the reverse phenomenon of more species in a fragmented landscape could be observed. Projects should consider such temporal dynamics in species diversity in their planning, especially for trees that take several years to reach maturity. ► This hypothesis is linked to hypotheses 5, 8 & 9, adding a temporal dimension to the investigations – despite the fact that our surveys were conducted within a limited time frame which did not allow for direct investigations of the dynamics of diversity over time. Given the fact that it takes several years to alter the tree composition of a landscape, projects should ideally measure their impact by comparing the expected diversity when no interventions would have been done with the changes they provoked.

Chapters 9 and 10 are examples of constrained ordination techniques, where the relationship between farm characteristics and the species composition of these farms is analyzed. Such methods offer an alternative way of measuring beta diversity (Chapter 6), and provide information on the most important species in which farms of various types differ. Analyses in Chapter 9 were repeated for each group of species of similar on-farm use, while analyses were done in Chapter 10 for trees that were established in the same on-farm niche. H16

Socio-economic characteristics and the spatial location of the farm both explain variation in species composition among farms ► Accepting this hypothesis means that socio-economic factors influence on-farm composition. For example, poorer farmers may have fewer trees of species x on their farms than richer farmers. Similarly as discussed for hypothesis 14, development projects then can target their efforts at subsections of the landscape. In case a project would for example define its objectives of “enhancing fruit-tree diversity on femaleheaded farms” (either based on results of methods as described in Chapter 8, or not), then the conditions under which hypothesis 16 is accepted could show that the species composition of female-headed farms differs substantially from that of male-headed farms. In such case, the diversity on female-headed farms would be enhanced by promoting species on those farms that feature more on male-headed farms for the moment. A project could, however, also choose to promote species that do not dominate any subsections of the landscape for the moment.

11

INTRODUCTION H17

Relationships between farm characteristics and the abundance of a particular species corresponds to the relationships between farm characteristics and the relative species composition of the farm ► Accepting this hypothesis means that the interpretation of ordination results is straightforward. Rejecting the hypothesis means that a farm with a large abundance of a particular species is not necessarily a farm where this species dominates its composition. It is possible that farm x has more trees of species a, but that the species composition of farm y is more dominated by species a. For instance, the 100 trees of species a on farm x may constitute 10% of all trees on this farm, while the 50 trees of the species on farm y may constitute 90% of all trees on that farm. When the hypothesis is accepted, the design of interventions is simpler. The hypothesis is directly linked to hypothesis 16 as it implies that increasing the abundance of species x on subsections of the landscape where this species has lower abundance would directly result in a more homogeneous species composition for the entire landscape, without altering the abundance of other species. Otherwise, in the example that we provided, increasing the abundance of farm y from 50 to 60 trees would result in farm y differing more in species composition with farm x. Making the species composition more homogeneous could be a strategy of enhancing the evenness of species in a landscape.

Chapter 11 provides some evidence of farmer management of interspecific diversity based on open-ended interviews with farmers. This chapter investigates whether farmers already appreciate diversity within certain uses, and what the reasons for their appreciation are. These reasons are not necessarily those of enhancing the functioning of the agroecosystem as mentioned above. Therefore, observing local reasons for diversity could provide another pathway by which diversity could be promoted in the area – either for the local reasons that farmers provide, or for the ecological reasons stemming from ecological relationships between diversity, stability and productivity. H18

The diversity of species that is present on farms is desired by farmers ► Rejecting this hypothesis means that the diversity that is present on farms is not necessarily desired by farmers. This hypothesis was tested because many tree species can regenerate spontaneously, and could therefore be encountered because they were only tolerated by farmers but not actively desired. Rejecting this hypothesis has implications for the design of projects. For instance, when only one out of thirty species used for timber is desired by farmers for this purpose, then a project aimed at diversifying may still opt to concentrate efforts on timber trees, as diversity of timber trees could decline rapidly.

H19

For a particular use, farmers have reasons to maintain more than one species ► Accepting this hypothesis means that farmers are managing diversity within uses. Under these situations, development projects could find it easier to promote diversity, as diversification would correspond to farmers’ knowledge, perceptions, and practices. Possibly, win-win situations could occur when a diversification project would promote alternative species that would be appreciated by farmers, and that would enhance agroecosystem function.

12

CHAPTER 1 H20

For a particular use, farmers have reasons not to maintain species in perfectly even distributions ► Accepting this hypothesis means that farmers are not willing to establish species in the most diverse distribution that is theoretically possible where each species has the same number of trees. Projects that aim to diversify a landscape should incorporate information on the abundance distribution that is desired by farmers. ► Previous projects have often recorded information on preference rankings of among species. Differences in preference will probably result in differences among the abundance of each species. Obtaining a preference ranking, however, does not mean that farmers would only want to establish the most preferred species (hypothesis 19). Hypothesis 20 implies that more even distributions may be promoted, but that perfectly even distributions may not be realistic.

Chapter 12 investigates consequences of present or potential management methods of intraspecific diversity based on an individual-based metapopulation model. A metapopulation model describes the distribution of individuals within subpopulations (a metapopulation is a group of subpopulations, in a similar way as a population is a group of individuals), with higher chances of geneflow within subpopulations than among subpopulations. As stated above (section 1.2), most of this thesis concentrated on interspecific rather than intraspecific diversity. However, since information was collected on origins and types of germplasm for each tree encountered in the landscape, some simulations of how genetic diversity would change were possible. With the focus on species in this chapter, some analyses of this chapter focused on differences among columns (=species) of the overall sites × species matrix, whereas Chapters 8-10 looked at differences among rows (=farms) of such matrices. H21

Species are aggregated within farms and villages ► Accepting this hypothesis means that more trees of the same species will be encountered on the same farm and the same village, than expected from a random distribution. The implication of such situation is that geneflow is expected to be spatially structured. In situations where a tree is expected to have a higher chance of obtaining pollen from trees that occur on the same farm than from trees that occur on different farms, and where trees are spatially aggregated, a metapopulation model will describe the actual situation better. ► This hypothesis is related to hypothesis 7, which dealt with species density. Spatial aggregation of species, as investigated through hypothesis 21, means that although species density may be very low (e.g. < 1 tree/ha), each tree may exchange genes with many conspecific trees in case most of these trees occur in the direct surroundings. Thus, species density may not be a very accurate criterion to investigate the chances of genetic erosion, because it is still possible that subpopulation sizes will be relatively large under situations where species density is low.

13

INTRODUCTION H22

Changes in the way in which germplasm is collected will affect genetic diversity ► Whereas farmers have little control over geneflow by pollen, a major difference with natural ecosystems is that farmers have direct control over geneflow by seed or cuttings. For situations where species are aggregated (hypothesis 21), geneflow will differ drastically where farmers collect most seeds or cuttings from trees that occur on their own farm to collection schemes where most germplasm is collected from other farms. Accepting hypothesis 22 implies that when germplasm collection patterns are changed, genetic diversity dynamics would be different, by consequence. Projects that would aim to limit genetic erosion could then advocate for the best germplasm collection systems in an area. For example, bulking all seed that is collected in a village and letting farmers select at random from this bulk, rather than collecting only seed from the own farm, could limit genetic erosion drastically.

H23

Farmers prefer to collect most trees from the own farm ► Accepting this hypothesis has implications for the expected geneflow when projects would not change the current preferences and practices of farmers in terms of germplasm acquisition. The existence of preferences of the own farm would indicate that projects that aim to change anthropogenic geneflow in an area should address these preferences, for example by explaining the negative consequences of current practices. The balance between the benefits that farmers obtain by collecting from the own farm and the benefits of collecting from other sources should be investigated, if projects aim at altering current practices. ► Information on the currently preferred origins to investigate influences on genetic diversity is only relevant when hypothesis 22 can be accepted.

Chapters 13 and 14 provide a synthesis of results from several surveys. These chapters used some of the analysis methods of the previous chapters. In Chapter 13, information from Cameroon, Nigeria, south-western Uganda, and western Kenya is used. In this chapter, we compared results from different surveys that collected information from a different number of farms, a different number of individuals, and a different area. We investigated whether sample size had an influence on diversity. In case such influence is detected, comparisons will only be meaningful if based on the same sample size, for example 50 farms for each survey, or 10,000 trees for each survey.

14

CHAPTER 1 H24

The diversity rank-order of surveys depends on whether comparisons are based on an equal sample size of the same number of sites, the same number of trees, or the same area ► In case this hypothesis holds true, then ranking one use as less diverse than another use will depend on the sample size that is used. A development project should then select the most appropriate sample size that is used to compare diversity. For example, if the project would be more concerned with the results for farmers, then comparing results based on the same number of farms may be more appropriate. If a project would attach higher value to diversity per unit area, then the reference basis of the same area would be more relevant. ► This hypothesis will only be relevant when surveys differ in the number of sites, numbers of individuals/site and average area/site. Otherwise, a reference basis of the same number of sites will automatically be a reference of the same number of individuals and area. Typically, biodiversity surveys differ in individuals/site, and therefore testing hypothesis 24 is relevant in most situations. ► This hypothesis is related to hypothesis 11, which compared diversity among matrices that were all obtained from the same sample size of 201 farms and 116 ha. Thus, implicitly hypothesis 11 is based on the sample size of the same number of farms and the same area that is sampled. Therefore, when hypothesis 24 would be accepted for the comparisons of Chapter 7, a different rank-order would be obtained of the uses if based on the same number of trees.

H25

Distance from forests influences diversity of villages ► If this hypothesis is accepted, then development projects could design interventions based on distance to forests. For example, if a project would aim at diversifying the fruit tree diversity of villages, whereas villages close to a forest would have higher diversity, then the project could decide to target villages that are further away from forests. ► This hypothesis is related to hypothesis 14. The analyses differ in that hypothesis 14 compares the diversity of each individual farm based on the location of the village to which they belong, whereas hypothesis 25 compares the total diversity observed in each village.

In Chapter 14, information is analyzed from Cameroon, central Kenya, central Uganda, and western Kenya. Some analyses that were presented in previous chapters for western Kenya were also reported in this chapter. Analyses were repeated for each use and for each niche in each survey. H1

There is a positive relationship between the number of species on a farm, and the number of uses of these species on the farm ► See Chapter 5

H4

Alpha diversity, i.e. the average number of species per farm, is the same for each use ► See Chapter 5

H5

Gamma diversity, i.e. the total number of species observed in the survey, is the same for each use ► See Chapter 5

15

INTRODUCTION H6

Evenness, as expressed by the proportion of the most abundant species in the entire survey, is the same for each use ► See Chapter 5

H8

Beta diversity, i.e. the average difference in species composition among farms, is independent of sample size ► See Chapter 6

H9

Sample size has no influence on the rank-order in terms of species richness between uses ► See Chapter 6

H10

Villages contain fewer species than expected by random chance ► See Chapter 6

H11

Diversity, as measured for the species composition of the entire survey, is the same for each use ► See Chapter 7

H12

Evenness, as measured for the species composition of the entire survey, is the same for each use ► See Chapter 7

H13

A use with higher gamma diversity will also have higher evenness ► See Chapter 7

H14

Socio-economic characteristics and the spatial location of the farm both explain variation in diversity and abundance among farms ► See Chapter 8

H16

Socio-economic characteristics and the spatial location of the farm both explain variation in species composition among farms ► See Chapter 9 ► We only tested the influence of spatial location, not socio-economic characteristics, on species composition, however.

H26

The diversity rank-order of gamma diversity of surveys depends on whether comparisons are based on an equal sample size of the same number of sites or the same number of trees ► In case this hypothesis holds true, then ranking one use as less diverse than another use will depend on the sample size that is used. A development project should then select the most appropriate sample size that is used to compare diversity. ► Although the implication of this hypothesis is similar to that of hypothesis 24, the method by which we investigated this hypothesis differed from the method used in Chapter 13, and not only because hypothesis 24 tested diversity and not only richness. In Chapter 13, we always accumulated farms until we obtained a fixed reference value of x farms, y individual trees, or z ha sampled. In Chapter 14, we either accumulated farms or individual trees. For the accumulation of individual trees, trees were selected at random from the overall pool of individuals. The sampling analogy would be that for accumulation of farms, farms were picked at random from a large bag containing all farms, while for accumulation of individuals, individuals were picked at random from a large bag containing individual trees.

16

CHAPTER 1 H27

A larger percentage of on-farm trees are exotic than the percentage of exotic species on-farm ► Accepting this hypothesis means that the percentage of exotic species poorly reflects the percentage of exotic trees. A development project that is focused on conservation-through-use might focus more on the percentage of trees than the percentage of species. This hypothesis shows that the correct statistic should be used. ► This analysis is actually related to the analysis of richness and evenness, but now not comparing subsections based on use or niche, but considering two groups of trees: exotics and indigenous (or naturalized) trees.

17

Farm

Diversity profiles

Rank-abundance and densityabundance plots Species accumulation curves A c c u m u l a t i o n

Diversity profile accumulation patterns

Average diversity statistic

R a n d o m i z e

D A T A B A S E

S u m

Species

Use or niche Subset Species F a r m s

•Spatial position •Gender head •Size •...

Species

•Uses •Exotic / Indigenous •...

Trees •Number

•Planted / Retained •Niche •Age •...

Regression

Diversity statistic F a r m s

Characteristics

F a r m s

Constrained ordination

Figure 1.1. Linkages between the farms × species matrix and various analysis methods used in this thesis.

Only trees of that species that provide fruit on that farm

60

Species accumulation

Species accumulation

Timber

Fruit

Species

Species

F a r m s

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fruit timber

Only trees of that species that provide timber on that farm

40

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0 0

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carpap

Ebuchiebe Shimutu Mutambi Madidi

0.7 0.6 0.5 0.4 0.3 0.2

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0.1 0.0

F a r m s

Ordination

-0.1 -0.2

syzcum

psigua

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manind

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Figure 1.2. Some analyses in this thesis were comparisons among matrices (example of comparison between species accumulation for fruit and for timber, Chapter 6), while other analyses were comparisons within matrices (example of constrained ordination of farms for fruit, Chapter 9)

CHAPTER 2 THE RELATIONSHIP BETWEEN SPECIES DIVERSITY WITHIN AN ECOSYSTEM AND ITS STABILITY AND PRODUCTIVITY R KINDT

ECOSYSTEM DIVERSITY, PRODUCTIVITY AND STABILITY One of the objectives of tree domestication is to diversify agroecosystems to render them more stable and more productive (ICRAF 2000). The literature information that is presented here indicates that, in general, there is a positive relationship between ecosystem diversity (D), and ecosystem stability (S) and productivity (P). The literature, however, lists specific conditions under which the D~S and D~P relationships exist. Most often in the listed studies, diversity is measured by species richness, the number of species present in the ecosystem, although some other measures of diversity have also been employed (formulas for diversity indices, such as the Shannon diversity index, are listed in Chapter 6). In D~P and D~S studies, productivity is most often measured in terms of biomass (either above-ground or total biomass). Most often in studies of agricultural systems, however, productivity is measured as (harvested) yield, which is expressed in monetary units (typically US$/ha). Recently, natural ecosystems have also been evaluated economically (e.g. Bockstael et al. 1995; Ayensu et al. 1999; Costanza et al. 1999; Nunes & van den Berg 2001). Such studies include information on the value of ecosystem goods (e.g. food, timber, genetic resources, medicines) and services (e.g. water purification, flood control, coastline stabilization, carbon sequestration, pollination, waste treatment, biodiversity conservation, soil generation, disease regulation, maintenance of air quality, aesthetic and cultural benefits), since trade-offs among those goods and services have become the global rule rather than the exception. For example, a country may increase food supply by converting a forest to agriculture, but will, in consequence, decrease the supply of clean water, timber, biodiversity, and flood control (Ayensu et al. 1999). Nunes & van den Berg (2001) state that the available economic valuation estimates provide a very incomplete perspective on (at best the lower bounds of) the value of biodiversity changes, since not all benefits are validated. In this chapter and elsewhere in this thesis, however, ecosystem productivity was measured as biomass. Making certain assumptions on the link between species’ biomass and economic value, however, shows that D~P would apply both to the biomass and the economic value of an ecosystem (see below). Greater stability in D~P and D~S studies is frequently measured as reduced (temporal) variation in biomass. However, stability of an ecosystem under perturbations can also be measured in terms of persistence (time before change), resistance (the degree of change) or resilience (time to return to equilibrium) (Pimm 1984). Organisms may therefore contribute differently to different aspects of stability – trees, for example, are less resilient but more persistent than short-lived organisms on a time scale measured in years (Pimm 1984). Agroforestry systems may therefore be more persistent but less resilient than agricultural systems without trees. For this thesis, the stability measure of decrease in biomass variation was adopted, especially since this was the measure used in most investigations by mechanistic models constructed to study D~P and D~S in parallel.

2.1. The Relationship between Species Diversity and Ecosystem Stability in Surveys and Ecological Experiments Elton (1958) and Hutchinson (1959) provide theoretical grounds to prove positive D~S. Arguments that these authors used included the observation that simple population models had higher oscillations, the observation that small oceanic islands were more easily invaded, and the theoretical insight that more diverse systems provided more alternative pathways for energy to reach consumers. However, despite these longstanding theoretical grounds, positive D~S was still described as controversial at the end of the 20th century, because experiments and models had led to contradictory conclusions (Tilman et al. 1996). Johnson et al. (1996) indicated that four hypotheses had been proposed for D~S: (i) the diversity-stability hypothesis referring to a (linear)

22

CHAPTER 2 increment of stability with diversity; (ii) the rivet hypothesis pointing to a catastrophic collapse of stability below a certain diversity threshold level; (iii) the redundancy hypothesis referring to compensating species behaviours (increments in their abundance) in systems of moderate diversity, with decrease in stability below a specific diversity threshold; and (iv) the idiosyncratic hypothesis referring to no relationship between diversity and productivity. Chapin et al. (1998) also formulated positive D~S in the form of a hypothesis for further testing in more complex experiments than the ones that had been conducted already. Among the earlier field studies that investigated the form of the relationship, Frank & McNaughton (1991) documented a positive relationship (R2=0.58) between the Shannon diversity index and 1 minus the cumulated changes in species proportions before and after drought. Rodríguez & Gómez-Sal (1994), using the same measures, demonstrated a negative relationship (R2=0.38). Tilman & Downing (1994) and Tilman (1996) showed a positive relationship (R2=0.21-0.22) between species richness and the relative rate of change in plant community biomass before and after drought. Besides yielding contradictory results, the regression models used in the above studies only explained small amounts of variation (expressed by the R2 parameter, the coefficient of multiple determination = the square of the multiple correlation coefficient R; Legendre & Legendre 1998, p. 524) of stability by using diversity as an explanatory factor. This was especially the case in the two last studies where less than 50% in stability was explained. The low amount of explained variation for positive D~S as in Tilman (1996) indicated that a number of species-poor systems had relatively high stability, and vice versa that some species-rich systems had lower stability. Such pattern indicates a higher chance for systems with higher diversity to be more stable, but no certainty that the more diverse system will be more stable. The observed pattern further shows that pre-conditions exist for positive D~S, as otherwise all diverse systems would have had higher stability. Johnson et al. (1996), who reviewed a number of D~S studies of grasslands, hypothesised that seasonality was the underlying process, for example. Cottingham et al. (2001) mentioned that it is often difficult to isolate the effects of species richness from those of the identity of particular species used in experiments. Models (see below) have demonstrated that diversity in characteristics of species is more important than species diversity per se.

2.2. The Relationship between Species Diversity and Ecosystem Productivity in Surveys and Experiments Darwin already hypothesised that more diverse plant communities would have more effective resource exploitation and thus greater community productivity (McNaughton 1994). However, as for D~S, D~P studies have led to contradictory results. The Waide et al. (1999) survey of ecological literature of over 200 studies (excluding intensively managed systems) found that 30 % documented unimodal, 26 % positive linear, 12 % negative linear, and 32 % non significant D~P. Classification of studies on ecological, geographical, or taxonomic scales also resulted in a mixture of relationships. For example, the respective percentages for studies (n=50) within plant communities were 24, 22, 12, and 42 %. Methodological problems may have influenced some results, since Oksanen (1996) warned that the relationship between abundance and biomass would be unimodal when fixed quadrat sample sizes are used for measurements, excluding any relationships between species. Loreau et al. (2001) expect that unimodal patterns of P~D in multilocational studies are caused by co-varying factors that influence productivity, such as soil fertility, climate, or herbivory – whereas experiments should investigate whether diversity alone has any effect. In any case, D~P was also listed at the end of the 1990s as a research area that needed further investigation (Chapin et al. 1998).

23

ECOSYSTEM DIVERSITY, PRODUCTIVITY AND STABILITY In analogy to D~S studies, analysis of D~P experiments, besides yielding contradictory results, managed only to explain relatively low percentages of variation. The Tilman et al. (1996) documentation of a positive relationship between exp(Shannon index) and total plant cover had R2=0.18. In a multiple linear regression analysis of the same experiment, Tilman et al. (1997a) found that only functional group diversity (for a definition of functional ecological groups, see below), and not species diversity, significantly contributed to productivity, but with R2=0.09. The positive relationships found by Hooper & Vitousek (1998) between functional group richness and the reduction of inorganic N pools had R2=0.26-0.35. Naeem et al. (1994) reported a positive link between species richness of different growing chamber communities and cover, and a negative link with the percentage of transmittance of photosynthetic radiation. At the end of the study, the standard error bars for productivity of communities with high (31 species) and medium (15 species) richness overlapped, however. The negative relationship between species richness and harvested biomass documented by Smith & Rushton (1994) had R2=0.26. Conclusions about low percentages of variation can be similar as for D~S: even in situations where there is a general positive D~P, there is no guarantee that a particular system of higher diversity will be more productive. Therefore, specific conditions for a positive D~P of a particular system must exist. The study that has ignited much recent controversy about D~P is the BIODEPTH panEuropean grassland experiment, from which a positive relationship between diversity (species richness and functional group richness) and above ground biomass was reported that explained 17.7 % of variation (Hector et al. 1999). The controversy focused on the possibilities that D~P resulted from ‘overyielding’ or from species’ sampling effects (Huston et al. 2000; Kaiser 2000a, 2000b; Purvis & Hector 2000). Overyielding is defined as a synergistic relationship among species so that the productivity of the mixture is higher than that of any species growing in isolation. The ‘sampling effect’ is based on a model whereby the biomass of a species mixture equals the monoculture biomass of the most productive species (Loreau & Hector 2001; the dominance scenario described by Yachi & Loreau 1999 – section 2.3). Such model results in D~P as random species mixtures that contain more species will have greater chances of sampling the more productive species. However, under the sampling effect, each combination of species that contains the most productive species will have the highest productivity – including monocultures of the most productive species. The controversy originated from experimental limitations since not every possible combination of species could be tested because of the enormous number of species combinations that are possible (the number of combinations can be calculated as St!/(Sm! (St-Sm)!), where St stands for total species richness and Sm for species richness in the mixture). Loreau et al. (2001) mention that testing the hypothesis that there is a minimal set of complementary species that can explain diversity effects ideally requires replicated testing of the performance of all species combinations at all diversity levels, which is often difficult. They suggest that future biodiversity experiments should focus on what individual species do, in order to investigate the hypothesis that a few dominant species suffice to explain D~P. Loreau & Hector (2001), analyzing a subset of BIODEPTH data, distinguished between ‘selection effects’ and ‘complementarity effects’ of biodiversity, and were able to demonstrate positive D~P through complementarity. Complementarity among species can result from niche differentiation (resource partitioning) or facilitation among species (positive interactions). Complementarity can be negative, however, as it is measured as the change in the average relative yield of the mixture (formulas and a theoretical example of the calculation of selection and complementarity effects have been provided in section 2.7). Loreau & Hector (2001) described the sampling effect as too restrictive as a general alternative to complementarity. One reason they provided is that a community may not be dominated by a single species. Another reason they listed stated that sampling may also result in selection of complementary species and not necessarily only of those species that are more productive in monocultures. They therefore used the selection effect described by Price (1970) for evolutionary genetics, which can be calculated as

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CHAPTER 2 the difference between the net biodiversity effect (calculated as observed yield – yield expected from monoculture yields and species’ proportions, see section 2.7) and the complementarity effect. In response to the recent controversy, Tilman et al. (2001b) investigated D~P in a 7-year grassland experiment. At the end of the experiment, total and above-ground biomass of 16species plots was 22% and 27% greater than for 8-species plots, respectively. In statistical analyses where species with small monoculture biomass were removed, total biomass remained significantly dependent on species number. The hypotheses that D~P resulted solely from (i) short-lived transients caused by species with high growth rates; (ii) sampling effects of lowviability species; or (iii) sampling effects of the most productive species, could be rejected, thus indicating that niche complementarity contributed significantly to D~P. Distinguishing between selection effects and complementarity effects would also benefit analysis of crop mixture studies. Trenbath (1974) found that species mixtures produced more than monocultures in 20 percent of 572 experiments. Other examples of crop mixtures performing better have been provided in Dover & Talbot (1987) and Trenbath (1999). Swift et al. (1996) stated that intercrops might perform better, but that better performance could not be generalised as it depended on how much the component crops use resources differently. Zhu et al. (2000) demonstrated that growing two different rice varieties resulted in higher productivity as damages by diseases were reduced.

2.3. Investigation of Biodiversity Effects in Model Ecosystems Because in biodiversity experiments often only a fraction of all possible species combinations can be tested, theoretical studies provide a more extensive method to test biodiversity effects. Models are also ideally suited to investigate processes that potentially contribute to D~S or D~P, since it are these processes that are simulated in the model ecosystems. Recent models have shown that incorporation of differentiation in species’ characteristics (corresponding to environmental heterogeneity) is the underlying mechanism that explains D~S and D~P. Tilman (1994) stated that continuous interspecific trade-offs between species abilities (related to species morphology, physiology and behaviour) to deal with different environmental constraints would result in a potentially unlimited number of species that can coexist in a habitat with spatial heterogeneity in resource supply. The models and theory can be expanded to incorporate intraspecific differentiation, since the models are simply based on heterogeneity in characteristics of component individuals, not on the mechanisms of geneflow and evolution within the ecosystems. Tilman et al. (1997c) concluded, based on three models of randomly chosen species competing for a single or two resources, that average productivity would increase and variability would decrease if mixtures contained more species. For competition for a single resource, a monoculture of the most productive species would have resulted in the same productivity – the ‘sampling effect’. For heterogeneous habitats, some of the more diverse communities always had higher productivity than less diverse communities. The underlying effect of D~P and D~S in the three models was variation among species in parameters that describe the way in which they obtain resources. Loreau (1998) built a model based on an ecosystem limited by a single nutrient, with species differing in resource use efficiency and resource depletion zones. Productivity increased with diversity based on complementarity and higher mean resource-use intensity at higher diversity levels. Richness had diminishing effects on productivity.

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ECOSYSTEM DIVERSITY, PRODUCTIVITY AND STABILITY Yachi & Loreau (1999) demonstrated the ‘insurance effect’ of species richness on ecosystem productivity in fluctuating environments through a reduction in temporal variance of productivity and an increase in the temporal mean of productivity in case of ‘dominance’. Dominance indicates situations where ecosystem productivity can be approximated by the most productive species, while equivalence implies that ecosystem productivity will be the average productivity of individual species. In real ecosystems, productivity is expected to lie in between both dominance and equivalence extremes. Asynchronicity of species responses to environmental conditions was the basis for the diversity effects, so that perfect correlations of species responses resulted in no biodiversity effect. For the dominant productivity scenario, increasing the variance of species responses decreased ecosystem redundancy (= the smallest species richness level where the maximum temporal mean of productivity is reached). Nijs & Roy (2000) built another model involving different species that differed in nutrient uptake competing for a single resource,. These authors demonstrated that more species-rich and more inter-specifically different communities had higher productivity. Their model showed the sampling effect where species vary in their exponential growth characteristics. Ives et al. (2000) expanded their investigations of D~S and D~P (which also demonstrated the insurance effect, Ives et al. 1999) to model ecosystems with several trophic levels (predator-prey relationships). They demonstrated that variability in prey or predator densities primarily depended on the number of species-environment interactions rather than the number of species-species interactions. The reduction in variance was eliminated when all species experienced environmental variation in the same way. All the models cited above have demonstrated that differences in species-environment interaction and environmental heterogeneity result in D~S and D~P for random species mixtures in environments with random characteristics. Tilman (2001) explained that random species mixtures were needed to test pure effects of diversity. Berendse (1994) pointed out that natural communities are not assembled at random, but species by species, and that continuous selection affects interactions among species. McCann (2000) mentioned that the condition of generally weak bioenergetic consumer-resource interactions needed to be added so that diversity could enhance stability. Such statements indicate that, although D~S and D~P have been demonstrated for theoretical ecosystems, understanding of the functioning of an actual ecosystem will need to be based on characterization of species, environment and speciesenvironment interactions. The concept that other species are part of the environment of a particular species needs to be modeled as well to simulate effects such as facilitation among species. Effects of the spatio-temporal distribution of environmental characteristics on D~S and D~P will need to be explored more extensively, rather than assuming random distribution. The multi-species model of Norberg et al. (2001) (reviewed in Tilman 2001) tackled some of the problems mentioned in the previous paragraph. They simulated the link between evolutionary dynamics and ecosystem dynamics by modeling the influence of temporal dynamics of the environment on distribution of species’ abundances. Biomass of each species was a function of its phenotype, the value of the environmental variable, total biomass of the community, and external sources (typically dispersal). Their model demonstrated that an adaptive ecosystem would have about 6 % higher productivity than an ecosystem consisting of the single species with the highest possible long-term productivity. Short-term productivity was reduced by increased phenotypic variance of the species in the ecosystem. A net positive D~P was caused as phenotypic variance results in a higher rate at which traits can track environmental change. External inputs were necessary to maintain phenotypic variance, which indicated that sites should not be isolated from the surrounding area.

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CHAPTER 2 2.4. Functional Ecological Groups Studies of species-rich ecosystems often attempt to reduce complexity by investigating functional groups rather than individual species. The findings from recent models on the importance of differences between species characteristics in understanding D~P and D~S also point to the possibility of clustering species with similar characteristics in functional groups. Functional groups can be defined as clusters of species that play the same role in maintaining and regulating ecosystem processes (Gitay et al. 1996). When one species is removed from its functional group, other species may increase in abundance taking over its role. Schulze & Mooney (1994) point out that functional groups will be differently defined depending on the processes that are being studied. Norberg et al. (2001) define functional groups as clusters of species that share similar resources and predators. Colasanti et al. (2001) differentiate between functional groups and functional types, where one species may be simultaneously a member of several functional groups, but not of several functional types. Keystone species can be seen as monospecific functional groups. Where macro-level functional integrity (such as productivity) of ecosystems may be preserved by maintaining functional groups, their structural integrity (such as resilience) may be weakened when diversity within groups is diminished (De Leo & Levin 1997). Chapin et al. (1997) make a similar analysis as De Leo & Levin (1997), and state that as time scales increase, ecosystems will experience a wider range of conditions, and that the importance of diversity among functionally similar species will increase. They conclude, therefore, that no two species are ecologically redundant, even if they appear similar in their ecosystem effects under one particular set of environmental conditions. A debate however exists in the conservation literature on whether the functional group concept could be used to plan conservation. Gitay et al. (1996) for example pointed out that not all functions of an individual species within an ecosystem are known, so that the effects of species removals cannot be predicted, while Walker (1995) stated that the functional group concept was necessary for practical conservation planning. The discussion on functional groups is actually focused on the question whether all but one of the species within the same functional group are redundant (i.e. ‘absolute redundancy’ within functional groups), or whether functional groups are merely collections of species with similar ecosystem functions. The models with two limited resources presented by Tilman et al. (1997c), and small variation in species characteristics in the model developed by Yachi & Loreau (1999), are theoretical cases where absolute redundancy of a species may not occur. Increasing the diversity for systems with moderate or even high diversity may still result in small enhancements of stability or productivity. Notwithstanding the question whether absolute redundancy occurs in a specific ecosystem, models have demonstrated saturating effects of increased biodiversity, resulting from a process whereby each extra species added to a mixture where each species has random characteristics, will on average differ less from the species that are already present. Holmes (1998) stated that, because the effects of biodiversity on ecosystem functioning saturated, the biggest benefits of biodiversity enhancement could be expected for ecosystems that are at present lowest in diversity, that is to say particularly agroecosystems and forest plantations. Tilman (1999b) stated that total primary productivity could be expected to increase 35-70% when increasing plant species diversity from one to about 20 species through a combined sampling effect and niche differentiation, implicitly acknowledging limited effects of increasing diversity beyond 20 species. Cottingham et al. (2001 eq. 1) described that variability of an ecosystem (var(E)) can be dissociated into the sum of the variance of the individual species and their summed covariances: N ö æ N i -1 var(E ) = å var(Si ) + 2çç åå cov(Si , S j ) ÷÷ . i =1 ø è i =1 j =1

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ECOSYSTEM DIVERSITY, PRODUCTIVITY AND STABILITY The consequence of this dissociation is that when species vary independently, their covariance is zero, and the variance of the ecosystem equals the sum of the variances of species. However, when species do not vary independently, overall variability may increase or decrease. Therefore, factors that change the summed variances or covariances should be the major focus of D~S studies. Cottingham et al. (2001), therefore, advocated calculating variances and covariances for each experimental unit to investigate how species covariances changed across diversity gradients. Van Noordwijk & Ong (1999), based on theoretical consideration of variance dissociation, concluded that where covariance was zero, system variance would decrease with N-1. Increasing N will result in smaller effects on system variance under these conditions. Considering the role of species characteristics in D~S and D~P, enhancing diversity by introducing species with characteristics that are less correlated with the characteristics of species that are already present will result in larger effects. Information on functional groups (clusters based on speciesenvironment relationships) can therefore guide diversification if such information is used to select species with characteristics that are currently rare in the ecosystem and for which there is an ecosystem niche.

2.5. The Role of Disturbance The vulnerability of complex ecosystems such as tropical rainforests and coral reefs has been cited as evidence for a negative D~S (e.g. Dover & Talbot 1987: 24). As models have demonstrated that diversity effects are based on variation in species characteristics’ in respect to environmental characteristics, disturbance effects should also be investigated within the context of species-environment interaction. Disturbance has been listed as an explanatory factor for diversity. Reice (1994) stated that natural systems are usually not in equilibrium and that habitat disturbance and heterogeneity result in the enhancement of biodiversity. Phillips et al. (1994) found that dynamism (the mean of recruitment and mortality, indicating small-scale disturbance) explained most of the variation in species richness among 25 mature forests from all major tropical regions, and that more dynamic forests were more species rich and more productive. Wills et al. (1997) found that intraspecific negative density-dependent effects (for example a negative correlation between the basal area of conspecifics, and the ratio of (births-deaths)/total of trees of the species) were more common than interspecific effects in maintaining diversity in rainforests. They listed biotic effects (especially pests) as more likely causes for diversity rather than abiotic effects (such as nutrient removal and toxicity), and therefore advocated for the retention of all pathogens to preserve rainforest biodiversity. Givnish (1999) proposes that “random drift over evolutionary time in the relative effectiveness of density-dependent control by specialist natural enemies may account both for the observed distribution of tropical tree abundance and for the repeated dominance of particular taxa in separate but ecologically similar sites”. Nevo (2001) lists evidence that genetic diversity is maintained by environmental diversity, and that this pattern surprisingly also occurs for non-coding levels of DNA. The intermediate disturbance theory is based on the competitive exclusion principle whereby superior competitors ultimately replace all other organisms that are more superior in colonization. The highest richness of species that vary in colonization and competition traits then occurs at intermediate levels of disturbance (Van der Maarel 1993; Weithoff et al. 2001). Tilman (1994) points out that successional dynamics also explain the maintenance of biotic diversity because trade-offs exist between colonization and competition traits of species. Recurring disturbances in an infinitely large habitat then allow for the persistence of an unlimited number of species. McNaughton (1994) indicated that diversity would act as a stabiliser of ecosystem function if higher diversity resulted from greater niche packing, with disturbances creating niches

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CHAPTER 2 that exist or existed elsewhere in the landscape followed by establishment of suitable species in new niches. Loreau et al. (2001) mention that diversity loss at a regional scale or dispersal limitations due to landscape fragmentation may reduce the pool of potential colonists and limit the potential to change species composition to locally adjust to environmental changes. Novel disturbances may not initiate succession, and such disturbances would not lead to higher biodiversity. Tilman & Lehman (2001) mention that novel environmental changes may lead to substantial losses of species richness, especially where species abundances are limited by multiple environmental factors. Woodruff (2001) identified the current anthropogenic homogenisation of biotas as the major driver for the current biodiversity crisis, when global rates of species extinction are at an all time high when calculated for the last 65 million years. Chapin et al. (1997) mention that the current global extinction rate is 100 – 1000 times greater than prehuman levels. Tilman & Lehman (2001) mention that novel environmental constraints would create conditions for new diversification. Kirchner & Weil (2000) and Novacek & Cleland (2001) state, however, that recoveries from mass extinctions in the fossil record are measured in millions of years. Kirchner (2002) expects that our planet will be biologically depleted beyond the lifespan of the entire human species, since investigations of the geological record revealed that evolution did not accelerate quickly in response to rapid extinction. A possible explanation for this process is that extinction does not only eliminate species, but also ecological niches. Ecosystems must, therefore, first increase in complexity so that there are new niches – new roles for new organisms in the ecosystem – to fill, before new species can evolve. In analogy, Thompson (1999) stated that the history of evolution and biodiversity was fundamentally a history of coevolution, since most living organisms required a combination of their own genetic machinery and that of one or more other species to survive and reproduce. Although disturbance that maintains spatio-temporal environmental heterogeneity in the ecosystem drives D~P and D~S, Ives et al. (1999) stated that higher species richness was also an insurance against unknown future changes. Pachepsky et al (2001) demonstrated that a relationship between time of reproduction and (progeny per time unit/lifetime) and weak stochastic disturbance reproduced natural biodiversity patterns (a log-normal abundance distribution of species or plant physiological types), while initial environmental heterogeneity in nutrient availability was not necessary. Tilman (2000) listed four reasons why species could coexist, based on interspecific trade-offs between (i) competitive abilities and dispersal abilities; (ii) competitive abilities and susceptibility to disease; (iii) abilities to live off average conditions and abilities to exploit resource pulses; and (iv) abilities to compete for alternative resources in a heterogeneous landscape. Based on the modeling work of Pachepsky et al. (2001), trade-offs in time of reproduction and fecundity could be added as a fifth reason. Pachepsky et al. (2001), by modeling plant physiological types rather than species, further demonstrated that trade-offs in individual traits enabled coexistence and that intraspecific variation should therefore not be ignored. It is possible that other trade-offs enable coexistence of various species or plant types – the role of disturbance is then to remove some individuals and enable the establishment of new species or plant types. Disturbance leading to greater diversity, and diversity leading to greater stability (D~S), may seem paradoxical. However, Van der Maarel (1993) listed disturbance ‘for’ stability as the best description for the relationship between both factors. Patchiness, time-scale, and intensity of disturbance were listed by this author as major factors in the relationship between disturbance and stability, with disturbances of low intensity and small extent leading to stable communities, while highly intense disturbances at the landscape level may not result in stability. Van Noordwijk & Ong (1999) and Loreau et al. (2001) also pointed at the various scales in ecosystems, where stability at lower level is not necessary for stability at higher levels and vice versa. The role of disturbance in D~S and D~P primarily lays in maintaining diversity both of species (individual traits) and environment. Any successful effort to constrain the natural variability of a system

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ECOSYSTEM DIVERSITY, PRODUCTIVITY AND STABILITY could eventually lead to its fragility (De Leo & Levin 1997). Scheffer et al. (2001) also state that natural disturbance promotes diversity and renewal processes in ecosystems. These authors further advocate to concentrate conservation efforts more on eliminating gradual changes that affect ecosystem resilience since alternative stable states exist for many major ecosystems. Whereas natural disturbances lead to ecosystem stability, slow changes in variables of land use, nutrient stocks, soil properties and biomass of long-lived organisms may lead to catastrophic shifts between ecosystem states, from which recovery to the alternative ecosystem state will not be easy since hysteresis is involved.

2.6. Conclusions Ecological theoretical considerations, surveys, experiments and models have demonstrated that there is a positive, but saturating and conditional, relationship between D~S and D~P. Conditions for positive D~S and D~P include diversity and trade-offs in traits of species (or individuals), diversity in environmental characteristics, and disturbance that maintains turnover of species and spatio-temporal variation. Diversity of species that are more similar in traits will have smaller effects on S and P, than similar diversity that offers a larger variation in traits. Differences in the variation of species traits at a fixed diversity level were probably responsible for the low percentages of explained variation in experiments, while larger variation in traits at lower species diversity would result in negative D~S and D~P in some surveys. To understand D~S and D~P in ecological surveys and experiments (i.e. in the field), not only species diversity should be measured, but also heterogeneity in species’ traits, characteristics of the environment, and the relationships between species’ and environmental characteristics – factors that are not as easily measured as species richness and yield. Cottingham et al. (2001) concluded that future efforts should focus not only on demonstrating that diversity has an effect on stability, but also on identifying when this phenomenon is likely to occur. Loreau et al. (2001) state that experiments are needed in which both diversity and environmental fluctuations are controlled. Whether heterogeneity is better measured by the discrete distribution of categories of species and environmental conditions, or by documenting the variance in characteristics of species and the environment, is an issue that needs to be resolved. Norberg et al. (2001) proposed to use phenotypic variance as ‘D’, whereas Tilman (2001) proposed a hybrid measurement by combining species richness and phenotypic variance. Similarly to the options of classification or ordination of vegetation, the nature of spatio-temporal arrangement in the focus ecosystem will probably determine the best abstraction method. The studies have indicated that in a heterogeneous environment, diversity of species will lead to larger stability and productivity. The models have, however, also demonstrated the reverse phenomenon, that in a homogeneous environment, choice of appropriate species at low diversity levels will enable stable and productive ecosystems. Agroecosystem managers have two options when attempting to increase stability and productivity in a heterogeneous environment with low species diversity: (i) to increase species diversity (mimic the diversity of the natural system); or (ii) to reduce environmental heterogeneity. However, the practical questions for the first option are where and when which species should be introduced – Van Noordwijk et al. (1994), discussing the “not to put all eggs in one basket” option for agroecosystem management stated that the practical questions were ‘which baskets?’ and ‘how to allocate eggs?’ rather than ‘how many baskets?’. For the second option, the practical question is how all heterogeneity, including pest and disease dynamics, can be reduced in a sustainable matter and what the consequences of global climatic changes would be. In the introduction of this chapter, we stated that D~P would also have been recorded if productivity had been measured economically, rather than in terms of biomass, in case certain

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CHAPTER 2 assumptions were made on the relationship between the biomass of a species and its economic value. The ecological models assumed random variation among growth rates of component species. (Such variation among species lead to the analytical problems of distinguishing between sampling and complementarity effects in biodiversity experiments, as documented in section 2.2.) Conceptually, growth rates could be interpreted as those for the economical value of each species rather than for their biomass, if we assume that a (random) fraction of total biomass is harvested for every species and that a (random) species-specific value is obtained for each unit of harvested biomass. Possibly, the species with highest biomass would not have the highest economic value. However, as long as trade-offs exist among species in their environmental responses, then the models would document positive D~P under conditions of a fluctuating environment. These conditions exclude the case where the same species always has the highest productivity, including the situation where only one species has economic value. Theoretically, dynamics of supply and demand could counterbalance changes in economic value in relation to changes in biomass (i.e. a higher economic value when less biomass is produced, whereas we assumed a fixed value per unit of harvested biomass). Some sets of market dynamics could then result in the case where one species always has the highest productivity. Therefore, the absence of such market dynamics are an additional condition for positive D~P, when P is measured in economical units. For agroecosystem management, other factors than the stability or the productivity of the complete agroecosystem may be important. Farmers may for instance opt to produce various products and services that are needed by an individual household, which do not necessarily constitute largest potential productivity. Concentration on those species with the largest productivity or profitability could be very risky in a fluctuating market environment. The findings on saturating D~S and D~P and consideration of diversification to limit risks in agroforestry production systems, strongly advocate to increase diversity especially where diversity is low. In analogy to the biodiversity hotspot approach to conservation (Myers et al. 2000), spots of low diversity could be identified in agroecosystems for which diversification would result in largest effects on stability and productivity.

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ECOSYSTEM DIVERSITY, PRODUCTIVITY AND STABILITY 2.7. Addendum: Additive Partitioning of Biodiversity Effects Loreau & Hector (2001) proposed partitioning the net effect of biodiversity in ecosystem productivity into complementarity and selection effects to study the function of biodiversity. Complementarity and selection effects are based on information on the yield observed of each species (i) in monocultures (Mi) and in the mixture (YOi), and the proportion of each species in the mixture (RYEi). Table 2.1 provides information on calculation of complementarity and selection from these statistics through a theoretical example. Based on the figures, it can be demonstrated that the net biodiversity effect for the mixture, YO-YE = 40, can be partitioned into the complementarity effect, 3 (∆RY)average (M)average = 3 (0.1)(200) = 60, and the selection effect, 3 cov(∆RY, M) = -20. Table 2.1. Calculation of statistics used to calculate complementarity and selection effects. M (1) species1 species2 species3 total average

RYE (2) 100 200 300 200

YO (3) 0.5 0.4 0.1

70 100 30 200

YE RYO ∆RY (4)=(2)(1) (5)=(3)/(1) (6)=(5)-(2) 50 0.7 0.2 80 0.5 0.1 30 0.1 0 160 0.1

M: monoculture yield; (R)Y(o/e): (relative) observed/expected yield

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∆Y (7)=(3)-(4)=(5)(1)-(2)(1)=[(5)-(2)](1)=(6)(1) 20 20 0 40

CHAPTER 3 DESCRIPTION OF THE SURVEY AREAS COLLECTION METHODS R KINDT

AND

DATA

SURVEY AREAS Information that was analyzed in this thesis was collected during six surveys of on-farm tree diversity conducted in tropical Africa during 1998 - 2001. Figures 3.1 - 3.4 show the locations of the study sites. Since the selection of farms within each agroecosystem was partially based on their relative position to nearby forests, the location of major forest zones has been included in figure 3.2. Information was collected from complete farms defined as all land that was managed by a household. Although information was ultimately available from six locations in four countries (Cameroon, central Kenya, central Uganda, Nigeria, south-western Uganda, and western Kenya), most analyses that are reported in this thesis (Chapters 5 - 12) were based on data from western Kenya only. This was the location of the pilot survey for on-farm diversity studies. For this reason, information is more extensively provided for the western Kenyan site. Additional description of the study sites, especially for the other surveys, can be found in the cited references. Questionnaires and databases used for data collection, storage, and analysis were similar in the various studies.

3.1. Western Kenya 3.1.1. Study Area Complete tree species inventories were made in 201 farms in the Vihiga and Kakamega districts of western Kenya in the period of January 1998 – April 1999. The farms consisted of 50 (in one case 51 – we discovered during the survey that one farmer had already handed over part of his farm to his son) randomly selected households in four villages. In a follow-up survey on 78 of the 201 inventoried farms (May 1999 – April 2000), farmers were interviewed more in depth about the management of interspecific diversity of trees on their farms (Chapter 11). For the second survey, it was intended to collect information on all 50 farms of one village, and a subset of 68 farms of the other villages. The subset of farms of each village was selected in a randomly-stratified manner using information on socio-economic characteristics and species richness collected in the first survey. Due to time constraints caused by delay in data collection and entry (the last interviews were only completed in December 2001), information could only be analyzed for 78 of the 118 selected farms (table 3.1). Figure 3.3 shows that the study area belongs to the Victoria Basin forest-savanna mosaic (AT0721). This ecoregion is noted for its high species diversity and endemism which results from the mixture of habitat types. These include more than 310 tree species, 280 species of birds, 220 species of butterflies, and 100 species of moths. The forest habitats in the ecoregion have been mostly replaced by savanna, farmland, and pasture. The remaining forest patches are small and fragmented. (source: http://www.nationalgeographic.com/wildworld/terrestrial.html). Warner (1995) describes common features of farming systems in eastern Africa. Most systems provide security of land tenure, rural households experience labour shortages and a growing dependency on off-farm income, and trees are widely planted throughout the region. Households place more reliance on New World crops such as maize (Zea mays), cassava (Manihot esculenta), potatoes (Solanum tuberosum), sweet potatoes (Ipomoea batatas), and beans (Phaseolus vulgaris), while beer brewing is everywhere important. The study area lays within the East and Central African Bimodal Highlands, characterised by altitudes above 1000 m above sea level (all altitudes recorded in this chapter are above sea level) and bimodal rainfall of more than 1000 mm per annum (Hoekstra 1988). Warner (1995) provided some general features of this zone, which includes Burundi, Kenya, Rwanda, and Uganda. Trees are retained for fuel in the homestead area, exotic fruit trees are preferably planted in the

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CHAPTER 3 homestead area, while indigenous and exotic trees (especially Markhamia spp., Ficus spp., Euphorbia spp. and Grevillea spp.) are planted on boundaries, in fields and in homesteads. Farmers prefer to plant trees that address several of their needs for poles, construction materials, fuel, and soil fertility improvement. Selected indigenous trees are retained. In some systems, indigenous trees are expected to grow naturally and should not be planted. The highlands of Kenya cover an area of 85,000 km2 and accommodate 8 - 10 million inhabitants, corresponding to 15% of Kenya’s total land area and 40-50% of its total population (Hoekstra 1988). The annual rainfall in the area is positively correlated to altitude, except where features such as lakes interfere (Warner 1993). Of the total area, 59% consists of undulating landscapes with slopes between 2-8%. The soils are erosion prone because of the high land use intensity (Shepherd et al. 1997). Holmgren et al. (1994) described the Bimodal Highlands in Kenya as an area where tree cover on farms is positively correlated with population density and has thus been increasing over the last decades. Carter & Crowley (unpublished results) provide a description of changes in landuse in western Kenya from 1900-1990. Landuse changed drastically in the area from 1915 onwards through measures from the colonial administration. These measures included the formation of the North Kavirondo Native Reserve to which the African population was restricted, the imposition of taxes, and the restriction of Africans to cotton (Gossypium spp.), maize (Zea mays), and sesame (Sesamum indicum) as cash crops. Before that period, agriculture had been characterized by mixed farming, with sorghum (Sorghum bicolor), millet (Pennisetum glaucum), indigenous vegetables, maize, and bananas (Musa spp.) as the staple foods, and sweet potatoes (Ipomoea batatas) as hunger foods. Because of the colonial measures and the introduction of the iron hoe, the area under maize expanded and intensified, leading to land degradation, clearance of forest, and food shortages. The imposed shift of focus in the farming system to cash generation, in combination with population growth, also lead to male migration (as not enough cash could be generated through maize), the adoption of exotic vegetables for cash, and the increase of area under bananas as food supplement and cash. Farmers also started growing trees for the fuelwood market of Kisumu City. The colonial government tried to counter the negative effects of their measures by making soil conservation compulsory, by initiating afforestation, by stimulating farmers to grow root crops to increase food security, and by gazetting much of the southern part of Kakamega Forest, but negative effects prevail until today. Consolidation and limited expansion of land holdings, the conversion of bush, scrub and riparian woodland to arable land, the decline of numbers of trees in land, and the increase of trees in hedges characterized the period after Independence. In the same period, crossbred cattle were introduced for milk with Napier grass (Pennisetum purpureum) as feed (which also became a cash crop). The study area is inhabited predominantly by the Luhya (Luyia) ethnic group and belongs to agroecological zone Upper Midlands 1 (Tea-Coffee Zone) described by Jaetzhold and Schmidt (1983). UM1 is a zone with permanent cropping possibilities consisting of two or three seasons. In the zone, altitude ranges 1500-1800 m, annual mean temperature ranges 18.1-20.4 ˚C, and annual rainfall ranges 1600-2000 mm. The limitation to one ethnic group and one agro-ecological zone was a deliberate choice to limit potential influences on diversity characteristics observed. Four villages were selected within the area, each located in a different stratum as identified by Bradley et al. (1985) and Bradley (1991), through the interpretation of low level aerial photographs and confirmed during an on-farm survey. Table 3.1 summarises information on each stratum and lists the villages where the interviews were undertaken. The selection of villages coincided with a gradient towards the Kakamega Forest National Reserve (0º10’-0º21’ N (UTM 18.43-38.70), 34º47’-34º58’ E (UTM 698.48-718.88)). Kakamega Forest is the easternmost remnant of the Guineo-Congolian rain forest that in the past millennia stretched across the entire expanse of central Africa. The reserve is situated at an altitude of 1400

35

SURVEY AREAS - 2300 m and encompasses an area of 24,000 ha. Up to 20% of all Kenyan plant and animal species occur only in this forest, including 75% of all butterflies. The reserve contains a unique mixture of highland and central African lowland species. Some 380 species of plants occur in swamps, riverine, and hardwood forest areas, glades and the shallow forest around the edge of the reserve. There are over 150 documented species of woody trees, shrubs and vines, 170 species of herbs (of which 60 are orchids) and 62 species of ferns. There are over 350 species of birds that reside in the forest, including rare snake-eating birds, while several primate species are also present (Cox et al. 1999; Ipalei 1999). 3.1.2. Socio-economic Characteristics Various surveys had previously collected information on household characteristics. David & Swinkels (1994) provide one of the more recent sources of household characteristics of Vihiga and Kisumu districts. Results of this survey (n=75) showed that 87% of households had at least one cow. Households with crossbred cattle were wealthier. Over half of the household heads were 51 or older. The average number of resident family members was 7.3 persons for Vihiga, of which 3.7 were adults (> 15 years old). For both districts, 13% of heads of household had not received any education while 32% had received some form of secondary education. Heads of household were 71% male, 12% de jure female (single, divorced or widowed woman) and 17% de facto female (husband being away for long periods). Using permanent houses, corrugated iron roofed houses and thatch-roofed houses as wealth indicators categorised 5% of households as wealthy, 73% as average, and 22% below average, respectively. Data collected by Kindt (1997) through a survey in Vihiga and Kakamega districts (n=53) gave similar results for the percentages of heads of household older than 51, male-headed households and thatch-roofed houses, and for education levels of heads. Data from this survey differed for wealth indicators with 12% permanent houses as opposed to the 5% reported by David & Swinkels (1994). Table 3.2 shows quantitative household and farm characteristics used as explanatory variables in regression (Chapters 8 & 14) and ordination (Chapter 9, 10 & 14) analyses, while table 3.3 gives an overview of the respective qualitative characteristics. The analyses could not be done for the complete dataset because of missing values in household characteristics – the complete set of explanatory characteristics was available on only 183 of the 201 inventoried farms (tables 3.2 and 3.3). For the regression and ordination analyses, qualitative factors were analysed by s-1 dummy explanatory variables coding for the s states of the factor. Quantitative variables were ranged within the [0,1] interval by subtracting the minimum value and subsequently dividing by the maximum value. Comparing values presented in tables 3.2 and 3.3 with those collected in other surveys revealed that a reasonable socio-economic cross-section of the population was interviewed. Farm sizes as communicated by farmers were not verified in the field, but averages for Ebuchiebe (0.52 ha), Shimutu (0.93 ha) and Mutambi (0.42 ha) corresponded to the averages of the strata presented in table 3.1. An exception was the average farm size in Madidi of 0.40 ha. In comparison with characteristics from previous surveys, the present survey contained fewer households with cattle (71%), fewer household heads were older than 51 (38%), the number of resident household members was smaller (5.8), fewer household heads had received some secondary education (18%) and fewer male-headed households were encountered. The observed differences were however not very large. For each explanatory variable, the coefficient of multiple determination (R2) was calculated using the other variables as explanatory variables in a regression model. This statistic expresses the amount of variance in the response variable that can be explained by explanatory variables (Legendre & Legendre 1998, p. 524). When the variance inflation factor of a variable (defined as (1-R2)-1) is large (> 20 i.e. R2 > 0.95), then its regression coefficient is unstable (ter

36

CHAPTER 3 Braak 1986). As can be seen from tables 3.2 and 3.3, no variable had large R2 and, therefore, variance inflation factors were low. Data averages for villages (not presented) showed that household characteristics were not evenly distributed over villages. Shimutu village was most different from the other three villages with more cattle (but fewer crossbreeds), fewer residents, lower education levels, and more maleheaded households than the other villages. Comparing the household characteristics of the second survey (tables 3.2 and 3.3), revealed that the subset of villages still represented a reasonable socio-economic cross-section of the population, despite the fact that information was not analyzed for all 118 farms for which the second interview was planned (see section 3.1.1). Only variables that coded for villages had moderate inflation factors due to the strong correlation between the dummy variable for Ebuchiebe and Shinyalu (R2 = 0.77). The strong correlation was the result from the fact that only four farms belonged to other villages. 3.1.3. Data Collection on Tree Species For every tree species encountered on a farm, information was collected on the abundance in particular on-farm niches of the various cohorts (all trees established in the same period) by participatory interviews with household members involving farm walks, tree counting by the interviewer, and data recording using pre-tested questionnaires (Appendix II). On-farm niches for trees refer to the location on the farm and the establishment pattern of trees at the location. The niches that were distinguished for western Kenya were for example trees in the homestead area, trees mixed in cropland, trees on contours in cropland, trees on external boundaries of the farm, trees on internal boundaries on the farm, trees in woodlots, and trees in fallows. Free responses on tree uses were obtained on a species-by-species basis. These answers were postcoded during data entry in the databases that were created for data analysis and storage. Respondents were also requested to name the main use of the species on the farm. Tree circumferences were measured 30 cm above ground (recommended for agroforestry species, MacDicken et al. 1991; Stewart & Salazar 1992) for every cohort. From tree circumferences and cohort abundance, cross-sectional area of species was calculated. Information was provided by the farming household on the origins and the forms of germplasm of each tree cohort, and the expected replacement age of each cohort. Origins of germplasm were postcoded in categories including the own farm, other farms of the same village and outside the village. Forms of germplasm were classified as sexual with known origin of the mother tree (seeds and seedlings), wildlings (natural regeneration), and cuttings. Farmers were asked to provide the preferred form and origin of germplasm for each species. Where farmers did not have an opinion on the preferred form and origin, calculations for metapopulation dynamics (Chapter 12) used the most frequently preferred forms and origins of the species within each village, if these could be determined. In a follow-up survey, a subset of farmers (table 3.1) was interviewed about desired modifications in tree composition using a mapping tool that documented present tree composition in various on-farm niches. The respondents were also asked for reasons for these modifications, why they had not been done, and specifically about the reasons related to desired tree diversity within each use category (Chapter 11). The questionnaire for this second survey is provided in Appendix II.

37

SURVEY AREAS 3.2. Cameroon and Nigeria This part of the study was targeted at the humid forest zone of West and Central Africa, with four case-study villages in Southern Cameroon and two in Southeast Nigeria. The zone is located between 10°N and 6°S, and 30°W and 35°E and is characterized by altitudes of less than 1000 m above sea level. Annual rainfall ranges between 1400 mm and 4000 mm with bimodal distribution. The main daily temperature varies between 24 and 27°C. The soils are mainly ferric acrisols. The vegetation goes from matured secondary rain forest to degraded forest depending on demographic pressure and cultivation intensity. Cropping systems of the region are mainly based on slash-and-burn practice and produce food crops for home consumption and local markets. Tree crops, such as cocoa and coffee are usually cultivated as small-scale plantations mixed with other fruit trees, medicinal plants and timber species (Degrande et al. in prep.). The case-study communities (located in four villages in Cameroon and two villages in Nigeria) were selected against a range of socio-economic (ethnic group, demographic pressure, access to markets, land availability) and biophysical criteria (agro-ecology) and households were sampled based on a participatory wealth-ranking exercise (Degrande et al. in prep.). During the wealthranking exercise, two men and two women from each village ranked all households in five wellbeing categories. From each category, four households were chosen at random, except for one village due to its small size. Table 3.4 provides some information on the villages where the surveys were conducted. Wholefarm inventories of all exotic and indigenous trees, whether planted or not, were conducted in two Cameroonian villages in the period of 1999 - 2000. Previous (1998) fruit tree inventories were done in all villages for a study specifically targeted at the management of fruit trees. The definition of “farm” used in the study includes homegardens, food crop fields, fallow land, cocoa and coffee plantations, oil palm fields and/or small orchards, managed by a household. Tables 3.5 and 3.6 show information on the farm characteristics that were used in multiple regression analyses (Chapter 14). The only variables with moderate variance inflation factors (R2 > 0.60) were those that coded for male heads and for the origin of the household head with respect to the village (indigenous/allogenous) due to the strong correlation between these factors (R2 = 0.75).

3.3. South-western Uganda (Kabale) The on-farm tree diversity survey was conducted in the Kigezi Highlands in parts of Kabale district, located in western Uganda (bordering Rwanda). The Kigezi Highlands are part of the East and Central African Bimodal Highlands (see section 3.1.1). The Kigezi Highlands are primarily an agricultural zone. The altitude ranges from 1200 to about 2800 m, annual mean minimum and maximum temperatures are 10 and 23°C, respectively, and annual bimodal rainfall ranges 1000-1500 mm. The district, especially where farms were surveyed, is characterized by ragged terrain with flat-topped hills dissected by steep-sided valleys. Gardens in valleys are usually reclaimed swamps. The area is heavily terraced as a result of colonial by-law enforcement, although terraces are increasingly crushed to provide productive soils to lower parts. Soils are fine textured and easily leached and eroded, especially on lower and mid-hill slopes. The landholding per household in Kabale ranges from 0.2 to 9.1 ha and is scattered over several hills in 2-20 plots (averaging 7) (Turyomurugyendo in prep.). Figure 3.5 provides a map with the locations of the 928 plots where the survey was conducted in 2000 and 2001. Five parishes (clusters of villages) were selected on the gradient between the Bwindi Impenetrable National Park (BINP) and Kabale town, with two parishes adjacent to

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CHAPTER 3 BINP, two parishes adjacent to Kabale town, and one parish at intermediate position in the gradient (names and positions are provided in table 3.7). BINP is located from 0°53' - 1°08' S and 29°35' - 29°50'E and has an area of 32,092 ha. The park borders with Zaire to the west and its distance by road to the nearest main town of Kabale to the south-east is 29 km. Bwindi is characterised by steep hills and narrow valleys, with a general incline from the northern and western areas (below 1750 m), to the south-western corner (above 2250 m). The forest gets the name 'impenetrable' from the dense cover of herbs, vines, and shrubs inhabiting the valley bottoms. Bwindi is one of the few large expanses of forest in East Africa where lowland and montane vegetation communities meet. Combined with its probable role as a Pleistocene refuge, this situation has led to an extremely high biodiversity. Current evidence indicates that Bwindi is the most diverse forest in East Africa for tree species (more than 200 species) and ferns (more than 104 species), as well as other taxa (fauna). In recognition, Bwindi was selected by IUCN's Plant Programme as one of the 29 forests in Africa most important for conserving plant diversity. Bwindi is believed to hold the richest faunal community in East Africa, including over 214 species of forest birds (336 species in total), 120 species of mammals (including 7 species of diurnal primates), and 202 species of butterflies (84% of the country's total). Highly significant is the presence of almost half of the world's population of mountain gorillas Gorilla gorilla berengei (about 300 out of 650) (Turyomurugyendo in prep.). Parish leaders were requested to stratify villages. Criteria that leaders used included distance to BINP, distance to a lake, and differences in soil characteristics or temperature. Four villages were selected at random within the various strata. Based on a list provided by the village leader (or based on a list of vaccinations in one instance), two male-headed and two female-headed households were selected at random within each village. Tables 3.8 and 3.9 provide an overview of socio-economic characteristics that were collected during the survey. The wealth ranking (1→5 = poorest→richest) was calculated as the average ranking on three criteria (type of house, livestock numbers and furniture) (Guinand 1996). Farm sizes were calculated from the estimated length and width of each plot by counting steps. Household and farm characteristics were not used in multiple regressions, since ethnobotanical information was not available when the article that synthesised results from the various survey areas (Chapter 14) was written. The only variables with moderate variance inflation factors (R2 > 0.60) were those that reflected the gender of the household head. As most farms were maleheaded, de jure female household heads formed a strongly correlated category with the maleheaded category (R2 = 0.82).

3.4. Central Kenya (Meru) A survey was conducted that collected information on tree species diversity in central Meru district (one of the 13 districts in the Kenyan Eastern Province), adjacent to Mount Kenya. This study area belongs to the East and Central African Bimodal Highlands (see section 3.1.1). Meru people predominantly practise mixed farming, i.e. crop cultivation and animal husbandry. Maize (Zea mays), beans (Phaseolus vulgaris), potatoes (Solanum tuberosum), sorghum (Sorghum bicolor), pigeon peas (Vicia faba), green grams (Vigna radiata), cassava (Manihot esculenta), yams (Dioscorea spp.), arrowroots (Maranta arundinacea) and millet (Pennisetum glaucum) are used as staple crops. Cash crops include coffee (Coffea arabica), tea (Camellia sinensis), tobacco (Nicotiana tabacum), cotton (Gossypium spp.), miraa (Catha edulis), and macadamia nuts (Macadamia integrifolia). There are six gazetted forests in Central Meru district covering a total area of 86,955 ha, including Mount Kenya (Lengkeek et al. in prep.).

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SURVEY AREAS Mount Kenya (5199 m) is the second highest mountain in Africa after Kilimanjaro and is a vital water catchment on which seven million people depend. The forest zone hosts important populations of several threatened animal species. Mount Kenya became an International Biosphere Reserve in 1978, while Mount Kenya National Park / National Forest was inscribed on the IUCN World Heritage List in 1997. The Natural Forest (70,520 ha) is located between 1600 and 3100 m. There are 81 endemic higher plant species to Mt. Kenya, which are possibly relic forms of plants that were more widely distributed during the Pleistocene period. Vegetation varies with altitude and rainfall, with a rich alpine and sub-alpine flora. The lower limit of the indigenous forest on Mt. Kenya now mainly stands at 2000 - 2500 m. Upland Gallery Forest (1700 - 2300 m) contains a more humid rainforest on the east side in which truly giant trees, many with massive buttressed trunks, are common. The Bamboo zone (2300 - 2600 m) includes the most extensive stands of dense bamboo in East Africa, forming a distinct crescent around the mountain. The Hagenia-Hypericum Parkland (2600 - 3500 m), Giant Heather (2900 - 3200 m), Afro-alpine (3200 – 4000 m), and Nival (> 4000 m) zones form the other altitude-dependent vegetation zones (Lengkeek et al. in prep.). The survey followed the framework of participatory on-farm species screening trials that were implemented earlier. For these trials, farmers were selected from ‘catchment groups’ that implemented a common land management plan under co-ordination of the Ministry of Agriculture (MoA). For the trials, three groups were selected within relatively similar agroecological zones (Upper Midlands 2 and 3), and based on different location towards the forest (0, 12 and 25 km). Some information on conditions of the catchment groups is presented in table 3.10 (Lengkeek & Carsan in prep.). Farmers were selected that were willing to participate in tree planting trials, and that had at least one goat, sheep, or pig because the experiments included fodder species. The chair of the catchment group and MoA staff were requested to select four poor, four intermediate and four rich farmers in a randomly-stratified manner. They used wealth criteria of farm size, type of house, and number of animals. The request specified to select within each wealth group two male-headed and two female-headed farmers, but the exercise resulted in some wealth groups that included only one female-headed farmer (Lengkeek & Carsan in prep.). The species diversity studies were conducted in 2001 on 35 farms that had joined the species trials. Of only 32 farms, a complete set of socio-economic criteria that was used in regression analysis was available (tables 3.11 and 3.12). Education level was measured as 0 (no education), 1 (primary 1-4), 2 (primary 5-8), 3 (secondary) and 4 (adult education). Tables 3.9 and 3.10 show the quantitative and qualitative socio-economic characteristics that were used. The only variables with moderate variance inflation factors (R2 > 0.60) were farm size and wealth. The higher variance inflation was not caused by strong correlation of both factors, as ANOVA indicated important influences of the other variables as well in explaining the variance of farm size or wealth.

3.5. Central Uganda (Mabira) The survey in Central Uganda collected information in 2001 from 105 farms that were arranged in five ‘axes’ that started from the Mabira Forest Reserve at angles of about 72°. The Mabira Forest Reserve is located in between the town of Kampala (45 km) and Jinja (20 km), at 1070 1340 m, and in the Ugandan Mukono District. The study area is located within the East and Central African Bimodal Highlands (see section 3.1.1). The reserve consists of a main block of the Mabira Main Forest Reserve (29,950 ha), the Namawanyi/Namananga Forest Reserve (256

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CHAPTER 3 ha), and the Kalangala Falls. It is estimated that Mabira has 312 species of trees and shrubs, 287 of birds, 23 of small mammals, 218 of butterflies and 97 of moths (Boffa et al. in prep.). On each axis, one village was selected within a distance of less than 1 km to the Mabira Forest Reserve, one village between 5-7 km to this forest, and one village at a distance between 12-19 km. Table 3.13 provides the names of the villages and their locations. Exceptionally, farms belonged to two villages for the position furthest from the forest on axis 4, therefore the survey was conducted in 16 villages. Within each village, a randomly-stratified sample was taken of a male-headed and female-headed household nested within three wealth categories. A seventh farm was selected within each village from which the head was known to be a forest user, somebody who regularly collected forest products (often for medicinal use). Tables 3.14 and 3.15 show the quantitative and qualitative socio-economic characteristics used as explanatory variables in regression analysis. Because information was missing, only 97 households had complete information for the variables that were used in the statistical analyses. The only variables with R2 larger than 50% were the age of the household head or partner, and the years that the present head was head of the household. These larger R2 mainly resulted from positive correlation between the two variables (R2=0.44).

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SURVEY AREAS Table 3.1. Information on the strata of Kakamega and Vihiga districts, and information on the villages (arranged West-East or far-close to Kakamega Forest) where the interviews were held Villages (average (UTM) coordinates)

Strata and information from Bradley et al. (1991, pp. 121-126)

Ebuchiebe (0º 5.52’ (10.15) N, 34º 35.81’ (677.71) E)

Stratum A1 has 185 homesteads km­2, extremely small farms (0.53 ha), with most families having at least one adult male working away, the residual family members concentrating on food crops. Woody biomass amounts to 21.2% of groundcover, of which 59.9% present on-farm.

Madidi (0º 3.66’ (6.74) N, 34º 40.32’ (686.08) E)

Stratum E is characterised by average values (112 homesteads km­2, farm size of 0.87 ha, 20.6% of groundcover by woody biomass of which 45.6% on-farm).

50 (46) 0 (0)

Mutambi (0º 4.34’ (8.00) N, 34º 45.81’ (696.27) E)

Stratum A2 is similar to A1 in terms of farm sizes (0.49 ha) and population density (195 homesteads km­2). Woody biomass accounts to 23.8% of groundcover, of which 67.2% is present on-farm.

50 (44) 6 (4)

Shimutu (0º 11.28’ (20.79) N, 34º 48.89’ (701.98) E)

Stratum F2 has 74 homesteads km­2, average farm sizes of 1.12 ha, and residual forest in valley bottoms. On-farm (17.6%) woody biomass (23.8% groundcover) is significantly smaller than in the other areas.

51 (45) 23 (21)

Households interviewed in first and second survey ‡ 50 (48) 49 (47)

‡ Figures between brackets indicate households where complete socio-economic data were available; summary statistics for these households are provided in tables 3.2 and 3.3

Table 3.2. Quantitative household characteristics (before ranging in the 0-1 interval, except for R2) in the western Kenyan surveys Survey

Statistic

1 Average (n=183) Minimum Maximum St. dev. R2 2 Average (n=72) Minimum Maximum St. dev. R2

Farm size (acres) (1 acre = 0.4005 ha) 1.4 0.25 5.5 1.02 0.44 1.6 0.25 5 1.02 0.45

Number of crossbred cattle

Number of local cattle

Number of cattle

0.16 0 5 0.67 0.36 -

1.6 0 10 1.74 0.27 -

1.6 0 10 2.04 0.45

Number of years household head 21.0 0 78 17.11 0.27 19.6 0 78 16.80 0.29

Maximum age of head and partner

Number of resident children

Level of schooling

53.5 19 86 13.90 0.45 53.5 19 86 14.38 0.62

4.2 0 12 2.52 0.12 4.4 0 10 2.46 0.28

5.0 0 12 3.59 0.39 4.5 0 12 3.09 0.44

(St. dev.: standard deviation, R2: variance explained by other variables, Level of schooling was coded as 0 for no education, 1 through 8 for Standard 1 to 8 and 9 through 12 for Form 1 to 4)

Table 3.3. Qualitative household characteristics of the western Kenyan surveys Values Ebuchiebe Shimutu Mutambi Madidi Ebuchiebe Shimutu Other

Village Number 48 45 44 46 47 21 4

%

R2

26 25 24 25

0.42 0.54 0.37 -

65 29 6

0.83 0.84 -

Type of household head Values Number % Survey 1 (n=183) Male 114 62 Female de jure 28 15 Female de facto 41 23 Male Female de jure Female de facto

Survey 2 (n=72) 46 64 10 14 16 22

R2

Values

Type of house Number %

0.47 0.43 -

Permanent Iron roofed Thatch roofed

20 117 46

0.54 0.44 -

Permanent Iron roofed Thatch roofed

10 41 21

11 64 25 14 57 29

R2 0.35 0.16 0.49 0.28

(R2: variance explained by other variables, only provided for the factors with dummy coding; female (de jure): widowed or divorced female household head; female (de facto): female head because husband lives away)

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CHAPTER 3 Table 3.4. Information on the study villages in Cameroon and Nigeria (source: Degrande et al. in prep.) (n=112) Village (Country) Chopfarm (Cameroon) Elig-Nkouma (Cameroon) Nko’ovos (Cameroon) Makenene (Cameroon) Ilile (Nigeria) Uguwaji (Nigeria)

Geographical Position 3°57' N 9°15' E 4°06' N 11°24' E 2°55' N 11°21' E 4°52' N 10°48' E 5°19' N 6°55' E 6°25' N 7°32' E

Inventory

Farms

Agro-ecology Low montane Forest

Population density Fairly low

Average farm size 2.1 ha

Only fruit trees All trees

14 20

Degraded humid forest

Medium

2.6 ha

All trees

19

Humid forest

Low

6.0 ha

Only fruit trees Only fruit trees Only fruit trees

19

Transition between humid forest and savanna Degraded humid forest

High

6.2 ha

High

0.7 ha

Transition between forest and savanna

Medium

0.8 ha

20 20

Table 3.5. Quantitative household characteristics (before ranging in the 0-1 interval, except for R2) for the Cameroonian villages with complete inventories (n=39) Statistic Average Minimum Maximum St. dev. R2

Farm size (ha) 4.3 0.11 15.02 3.74 0.39

Wealth ranking 3.1 1 5 1.39 0.22

Age of head 46.9 22 80 14.12 0.29

Size of household 7.2 1 25 5.19 0.34

Education level 1.5 0 3 0.64 0.41

Table 3.6. Qualitative household characteristics for the Cameroonian villages with complete inventories (n=39) Characteristic Further from forest Type of household head Indigenous in village Use of hired labour

Category coded ‘1’ Elig-Nkouma Male Yes Yes

Farms 20 31 32 28

% 51 79 82 72

R2 0.42 0.63 0.65 0.18

Category coded ‘0’ Nko'ovos Female No No

Farms 19 8 7 11

% 49 21 18 28

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SURVEY AREAS Table 3.7. Information on the study villages in south-western Uganda (source: Turyomurugyendo in prep.) (n=80) Parish

Villages

Position

Kashasha Kitojo Kagarama Nangara Butoboore

Ihunga, Katojo, Kitahurira, Ndeego Bitanwa, Kyogo, Nkukuru, Nyakaranga Hamurambi, Ishanga, Kaashekye, Murambo Butoboore, Ikamiro, Kihorongwa, Rugoma Kyantobi, Nyakabungo, Nyakihanga, Rukinda

Close to forest Close to forest Far from forest Far from forest Intermediate from forest

Average Latitude 1° 8‘ S 1° 1’ S 1° 15’ S 1° 11’ S 1° 12’ S

Average Longitude 29° 48’ E 29° 47’ E 29° 55’ E 29° 58’ E 29° 55’ E

Number of farms 16 16 16 16 16

Table 3.8. Quantitative household characteristics (before ranging in the 0-1 interval, except for R2) for the south-western Ugandan survey (n=80) Statistic Average Minimum Maximum St. dev. R2

Farm size (ha) Wealth ranking Maximum age of head and partner 5.2 2.6 46.1 0.27 1.17 22 45.87 4.50 79 7.75 0.67 16.14 0.40 0.40 0.64

Number of children

Number of plots

Years head

Education level

3.7 0 11 2.41 0.28

11.6 2 35 6.42 0.56

14.4 1 60 12.27 0.55

3.2 0 12 3.73 0.43

Table 3.9. Qualitative household characteristics for the south-western Ugandan survey (n=80) Values Kashasha Kitojo Kagarama Nangara Butoboore

44

Parishes Number 16 16 16 16 16

% 20 20 20 20 20

R2 0.60 0.53 0.50 0.43 -

Values Male Female de jure Female de facto

Type of household head Number % 37 46 39 49 4 5

R2 0.85 0.86 -

CHAPTER 3 Table 3.10. Description of farmer groups for land management for the central Kenyan survey (n=40) (source: Lengkeek & Carson in prep.) Descriptor Name Location Agro-ecological zone Mean annual rainfall (mm) Total Population in 1999 Average farm size of trials (acres) Soils Distance to the forest Altitude (m) Longitude Latitude Number of farms *

Igoji Igoji Upper Midlands 2 (Coffee Zone) 500 – 2200 45,066 5.4 Well drained, very deep loam to clay 25 km 1353 - 1586 37° 40’ E 0° 11’ S 12 (10, 10)

Catchment group Kigane Nchoroiboro Nkubu Ruiri Upper Midlands 2 Upper Midlands 3 (Coffee Zone) (Marginal Coffee Zone) 500 - 2200 500 - 1800 54,924 40,604 3.1 6.0 Well drained, extremely Well drained, moderately deep dark reddish deep loam clay brown firm cracking clay with humic topsoil 12 km 0 km 1497 – 1674 1524 – 1761 37° 39’ E 37° 38’ E 0° 04’ S 0° 09’ N 12 (11, 11) 16 (14, 11)

Figures between brackets indicate farms where biodiversity survey was conducted, and the number of farms of the biodiversity survey where complete socio-economic data were available; summary statistics for the latter category of households are provided in tables 3.11 and 3.12 *

Table 3.11. Quantitative household characteristics (before ranging in the 0-1 interval, except for R2) for the central Kenyan survey (n=32) Statistic Average Minimum Maximum St. dev. R2

Farm size (ha) 1.6 0.22 6.92 1.35 0.65

Distance from forest (km) 11.9 0 25 10.28 0.30

Age of trial participant 48.9 30 65 10.10 0.43

Number of children 4.6 0 10 2.15 0.13

Wealth

Education level

1.7 1 3 0.74 0.63

2.3 0 4 1.03 0.39

Table 3.12. Qualitative household characteristics for the central Kenyan survey (n=32) Characteristic Type of household head Income generation from the farm Off-farm employment

Category coded ‘1’ Male Yes Yes

Farms 20 17 8

% 63 53 25

R2 0.21 0.36 0.22

Category coded ‘0’ Female No No

Farms 12 15 24

% 38 47 75

45

SURVEY AREAS Table 3.13. Information on the study villages in central Uganda (source: Boffa et al. in prep.) (n=105) Village name

Axis

Kiwaala Kayanja Mpoma Kitayunja Ntonto Mindi Kkungu Bukasa Kiteredde Kyambogo Kiira Kalega Namwendwa Kitoola Buwuma Mutwe

1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5

Distance from Mabira Forest < 1 km 5-7 km 12-19 km < 1 km 5-7 km 12-19 km < 1 km 5-7 km 12-19 km < 1 km 5-7 km 12-19 km 12-19 km < 1 km 5-7 km 12-19 km

Average Latitude

Average Longitude

Number of farms ‡

0° 28‘ N 0° 23’ N 0° 26’ N 0° 34’ N 0° 38’ N 0° 40’ N 0° 31’ N 0° 34’ N 0° 44’ N 0° 30’ N 0° 28’ N 0° 21’ N 0° 30’ N 0° 22’ N 0° 21’ N 0° 16’ N

32° 54’ E 32° 52’ E 32° 44’ E 32° 56’ E 32° 21’ E 32° 51’ E 33° 02’ E 33° 04’ E 33° 02’ E 33° 01’ E 33° 09’ E 33° 13’ E 33° 04’ E 32° 59’ E 33° 05’ E 32° 59’ E

7 (6) 7 (7) 7 (6) 7 (7) 7 (7) 7 (7) 7 (7) 7 (7) 7 (5) 7 (7) 7 (7) 4 (4) 3 (3) 7 (7) 7 (4) 7 (6)

‡ Figures between brackets indicate households where complete socio-economic data were available; summary statistics for these households are provided in tables 3.14 and 3.15

Table 3.14. Quantitative household characteristics (before ranging in the 0-1 interval, except for R2) for the central Ugandan survey (n=97) Statistic

Wealth ranking

Average Minimum Maximum St. dev. R2

2.0 1 3 0.80 0.06

Maximum age of head and partner 45.6 19 76 14.23 0.52

Number of children

Years head

4.7 0 20 3.57 0.15

20.2 1 55 13.44 0.53

Table 3.15. Qualitative household characteristics for the central Ugandan survey (n=97) Values Close Far Intermediate

46

Distance to forest Number % 34 35 31 32 32 33

R2 0.28 0.29 -

Type of household head Values Number % Male 54 56 Female de jure 13 13 Female de facto 30 31

R2 0.32 0.22 -

Values Yes No

Forest user Number 15 82

% 15 85

R2 0.11 -

a

b

c Figure 3.1. Location of the study sites. Average locations of villages are shown, which were based on the coordinates of individual farms and/or plots. a: locations within Africa; b: locations in Cameroon (4 villages) and Nigeria (2 villages); c: locations in Uganda (west) and Kenya (east).

a e

b

c

d

Figure 3.2. Location of the study sites. Average locations of villages are shown, which were based on the coordinates of individual farms and/or plots. a: locations in Uganda (west) and Kenya (east) (=Figure 3.1c); b: locations in south-western Uganda; c: locations in central Uganda; d: locations in western Kenya; e: locations in central Kenya; b-e: relative position of the edge (centre for c) of the nearest forest to the villages where the surveys were conducted

SUDAN

ETHIOPIA

SOMALIA

Lake Turkana Mount Elgon Rift Valley Aberdares Mount Kenya Mount Kilimanjaro

UGANDA

Lake Victoria RWANDA KENYA

AT0108 East African montane forests

BURUNDI

DRC

AT0101 Albertine Rift montane forests

TANZANIA

AT0721 Victoria Basin forest-savanna mosaic

Figure 3.3. Location of the eastern African study sites (circles) on a map of the terrestrial ecosystems of the world (URL http://www.nationalgeographic.com/wildworld/terrestrial.html). The ecosystems to which the study locations belonged are indicated in the legend, descriptions of the ecosystems can be found at the website.

SURVEY AREAS

Figure 3.4. Satellite image of the East African area portrayed in figure 3.3. Mountains, Rift Valley and lakes indicated in figure 3.3 can clearly be observed. (URL: http://www.nationalgeographic.com/mapmachine).

9.89*106

Butoboore Kagarama Kashasha Kitojo Nangara

9.88*106

9.87*106

9.86*106

9.85*106 8.0*105

8.1*105

8.2*105

8.3*105

8.4*105

Figure 3.5. Distribution of plots of the surveyed farms in south-western Uganda. Coordinates are UTM (WGS 1984). Symbols refer to the parish to which the plot with the homestead area belongs.

50

CHAPTER 4 METHODOLOGY R KINDT

METHODOLOGY This chapter summarises the data analysis methods that were used to arrive at the results reported in Chapters 5-14. The specific methods used in each chapter were indicated there for fast crossreferencing.

4.1. Formulation of Use-group and Niche Matrices Analogous to the concept of ecological functional groups (species that play the same role in maintaining and regulating ecosystem processes, Gitay et al. 1996), use-groups were defined as groups of species providing similar products or services to the farm household. Studying usegroups is similar to studying functional groups – redundancy within use-groups can be studied to estimate how well groups are buffered against species losses. Groups with low redundancy can be identified. Use-group delineation and composition followed the information provided by each household, since uses were listed for all species occurring on each farm. Free responses were obtained on tree uses that were postcoded during data entry and checking. Species could contribute to several use-groups. Farms × species matrices were formed for the main use-groups, expressed as the percentage of farms on which they occurred – other use-groups occurred on fewer farms. Table 14.1 (Chapter 14) lists the main use-groups and farm percentages. Several farms × species matrices were formed following several rules for inserting abundance > 0 in a specific matrix cell. These matrices formed the basis for several subsequent analyses (Chapter 1, figure 1.1 & 1.2). The consequences of using various rules to formulate these matrices were one of the main areas researched in Chapter 5. Use-groups (i.e. matrices) defined by species occurrence and use as recorded at individual farms are referred to as Urec. Abundance > 0 was recorded for a cell in case the specific farmer (listed in rows) had communicated to use the particular species (listed in columns) for the particular use (product or service). Urec was used in Chapters 13 & 14, except for the western Kenyan survey that used Uadj (see below). Because it is possible that not all actual uses of a particular species were recorded for each farm (e.g. missing data – see Chapter 5), information was adjusted by always including species in a group if more than half of and more than five households where the species occurred mentioned the particular use. Use-group definition based on this adjustment is referred to as Uadj. Uadj was used in Chapters 6-9 & 11. The intensity of data collection makes duplication of the survey in other locations difficult. Data collection in other locations could be shortened by only recording the main use of the species at each farm, or by attributing all potential uses of a species to each farm. These use-group definitions are referred to Umain and Uall, respectively. The effects of these simplifications in data collection were investigated. Niche matrices were formed in an analogous way as Urec. These matrices only included abundance > 0 for a cell in case the particular species (listed in columns) occurred in a specific niche on a particular farm (listed in rows). Table 14.1 (Chapter 14) lists the main niches that were encountered in the surveys – other niches occurred on a lower percentage of farms than specified in this table.

52

CHAPTER 4 4.2. Measurement of Diversity 4.2.1. Alpha and Gamma Diversity Species richness (S) refers to the number of species that were encountered on a specific farm, in a specific village, or in a specific survey. Alpha diversity was analysed by taking the average number of species per farm (Savg = Ŝ1 of section 4.2.2) across all farms (i.e. n=201 for western Kenya). 95% confidence interval limits were calculated by (Hayek & Buzas 1997 eq. 4.4) as mˆ ± 1.96

sˆ . 201

Use-group gamma diversity was analysed by the total number of species in the survey (Stot). For Stot, confidence interval limits could not be calculated because only one value was obtained. 4.2.2. Species Accumulation Curves Species accumulation curves show the trend in which additional species are encountered when a larger area is sampled. The S = cA z

model proposed by Arrhenius (1921) is often used to describe the relationship between area (A) and species richness (S). These curves can be calculated by two main approaches (Condit et al. 1996; Chazdon et al. 1999). One approach involves calculating the average number of species from a nested series of expanding quadrats. The other approach calculates the average number of species by randomly adding new sites. (Yet another approach involves random accumulation of individuals rather than sites – this approach is briefly outlined in the discussion of Chapter 14.) We compared species accumulation patterns by randomly sampling farms (‘random richness’), with patterns resulting from randomly sampling farms within the same village, before sampling randomly from other villages (‘proximity-based richness’). The second village sampled belonged to the same half of the survey area. Chances of sampling individual species were calculated by a new approach based on the hypergeometric distribution. Where species i occurs on fi of Ftot sites, the expected average species richness after N random site additions for Stot species equals ŜN =

S tot

å i =1

Ftot - f i - a + 1 )= Õ Ftot - a + 1 a =1 N

(1 -

S tot

å i =1

æ F - f i ö æ Ftot ö ÷÷ çç ÷÷ ). (1 - çç tot è N ø è N ø

This equation can be deducted by estimating the chance of not sampling species i in fi randomly selected sites. The last part of the equation corresponds to the rarefaction method that was developed by Sanders (1968) and corrected by Hurlbert (1971), applied to a dataset documenting presence (1) or absence (0) of each species on each site. A program (ExactS) can be obtained from the authors that calculates ŜN from a sites × species matrix over the entire range of accumulated sites (Kindt 2001a). The accuracy of this new method, and especially the gains in calculation time have been detailed in table 4.1 and figure 4.2, calculating the average richness for 100 randomly accumulated farms for all species (i.e. table I.3). Simulations were conducted on a Micron Millennium computer with a x86 Family 6 Model 5 Stepping 2 Genuine Intel processor with 130,468 kb RAM. Speciesrichref Kindt (2001i, see below) was used to calculate the species richness for s randomizations. Table 4.1 and figure 4.2 show that the average species richness is quickly approximated, as are the 95%

53

METHODOLOGY confidence interval and standard deviation. For s ≥ 100,000, a smooth and symmetrical histogramme is obtained. For s ≥ 10,000, the species richness that was most frequently encountered (i.e. the peak value of the histogramme) was closest to the average species richness. It has to be remarked here that even 1,000,000 randomizations only reflect a small fraction of all possible combinations of 100 farms out of a total of 201. In total, approximately 1.8 E59 combinations can be obtained. It would take 9.8 E56 seconds or 3.1 E49 years to calculate all values at a speed of 0.005457 seconds per randomization (calculated as the average for 1,000,000 randomizations, table 4.1), which would enable to calculate the exact value. It is therefore more practical to calculate the exact average species richness through the new approach introduced here based on the hypergeometric distribution which only takes 0.17 seconds. Comparisons with other software packages that calculate species accumulation curves based on randomizations showed that not as accurate results could be obtained for Biodiversity Professional (maximum 50 randomizations, McAleece et al. 1997) and PC-ORD (fixed at 500 randomizations, McCune & Mefford 1999) since the number of randomizations is restricted in these packages. Substantially longer calculation time is required for EstimateS (Colwell 1997), which calculates estimators for regional species richness as well, however. The standard deviation that is provided in PC-ORD and EstimateS allows for reliable calculation of the 95% confidence interval. These programs do not provide the histogramme of values obtained during randomizations. In addition, they do not provide maximum or minimum values. The maximum value can be an important value when planning to minimise areas needed for conservation (see Chapter 15). We can therefore recommend using ExactS to calculate the exact average species richness of subsamples, and Speciesrichref if confidence intervals and ranges of values are also of interest. Proximity-based richness was calculated by multiplying ŜN within the village with the probability of the village sequence. Proximity-based ŜN after N (≤50) random site additions for example equals 4

å

(vj Ftot ŜNj)

i =1

with vj the number of farms within village j and ŜNj the expected average species richness in village j. Parameter values zN of the non-linear model ŜN= cN z N were calculated for 2 ≤ N ≤ 201. By equalling c to the average richness of a farm (the expected value for Ŝ1), the exponent zN can be calculated for each number of accumulated farms as: zN = ln(ŜN c-1) (ln(N))-1. Assuming that AN = N A1, above model is related to the S=cAz model proposed by Arrhenius (1921) which describes the relationship between area and S. Although we encountered substantial variation in farm sizes (coefficient of variation=0.72), our approach calculated ŜA for accumulations of the average farm size (Chapter 6). Parameter c can be defined as alpha diversity as it describes the average site diversity, and parameter z as beta diversity as it expresses the differences in species composition among sites (Chapter 6). 4.2.3. Randomization Tests on the Influence of Sample Size on Species Richness Using a Monte-Carlo approach, random and ‘randomly-clustered’ richness was calculated for specific sample sizes (table 6.2 in Chapter 6; table 14.2 in Chapter 14) based on 100,000 random farm sequences. One of the sample sizes corresponded to the size of the smallest village of the respective survey.

54

CHAPTER 4 The average random richness was calculated by randomization to check the values obtained through the hypergeometric distribution. The Speciesrichref program developed to calculate random richness (Kindt 2001i) provided besides the average richness of s (e.g. 100,000) random site sequences also the range of values (minimum - maximum), and 95 % confidence interval limits. Confidence intervals were determined by selecting the thresholds with counts equal (or just below) 2.5% permutations and equal (are just above) 97.5% of permutations. Unlike average random richness, which only depends on the frequency of each species, these latter statistics also depend on the aggregation of species within sites, and can therefore only be calculated through a randomization procedure (see Chapter 6). Figure 4.2 and table 4.1 (discussed in the section 4.2.2) shows the influence of the number of randomizations on the results. Randomly-clustered richness was calculated by grouping farms first in four random subsamples corresponding to the size of the villages sampled, before calculating the average, ranges and 95 % confidence interval limits of the average richness of the four subsamples (Kindt 2001h, Speciesrichvillage). This approach could be conceptualised as creating ‘artificial villages’ by randomly assigning farms to these villages. Such approach is a null model analysis as specified in Gotelli (2001), an investigation that compares a statistic (here: species richness) calculated for the real data set with the same statistic of null communities generated by a set of rules for randomisation from the real data set. For our null communities we kept the sizes of the four subsamples equal to the sizes of the actual villages, and thus eliminated sample-scale effects on diversity. The ranges of randomly-clustered richness were compared to proximity-based ŜN, as the latter value corresponds to the actual distribution of farms and associated richness in villages. As for random richness, the influence of the number of randomizations on the results was investigated. The results of this analysis presented in figure 4.3 and table 4.2 show that, as for random richness, the average species richness is quickly approximated, as is the 95% confidence interval. For s ≥ 100,000, a smooth and symmetrical histogramme is obtained. For s ≥ 10,000, the species richness that was most frequently encountered (i.e. the peak value of the histogramme) was closest to the average species richness. The results show that the average richness of four clusters of 50 farms closely approximates the exact average richness of 50 randomly accumulated farms (see Chapter 6). A smaller range of values is obtained for randomly-clustered richness compared to random richness (figures 4.2 & 4.3, Chapter 6). 4.2.4. Diversity Profiles Although many equate diversity to species richness (the number of species encountered), diversity is a function of the number of species, and the evenness in distribution of species’ abundances (Magurran 1988 pp. 7-8; Purvis & Hector 2000). We developed a new method that discriminates between both facets of diversity. The Rényi series provides diversity profile values (Hα) based on a scale parameter value (“scale”) α (≥ 0 and α ≠ 1) (Tóthmérész 1995 eq. 1; Legendre & Legendre 1998 eq. 6.31; Rennols & Laumonier 2000 eq. 1):

(

)

log å pia , Ha = 1-a

where pi = proportion of item i (abundance/total). In section 4.7, we demonstrated how Rényi profiles were calculated for four theoretical ecosystems. We used base e to calculate all logarithms. It can be demonstrated that values of the Rényi profile at the respective scales of 0, ≈1, 2 and ∞ are related to species richness S H0 = ln(S),

55

METHODOLOGY Shannon H (Magurran 1988 eq. 2.17; Condit et al. 1996 eq. 3; Legendre & Legendre 1998 eq. 6.1) H1 = H = -å pi log pi

Simpson D-1 (Magurran 1988 eq. 2.27; Legendre & Legendre 1998 eq. 6.41)

H 2 = ln( D -1 ) = ln(å pi2 ) -1 ) , and Berger-Parker d-1 (Magurran 1988 eq. 2.31) -1 H ¥ = ln(d -1 ) = ln( pmax )

diversity indices. System a is more diverse than system b if all values of the diversity profile corresponding to system a are larger. Systems that have intersecting profiles consist of one system that is richer but not more evenly distributed. We separated the contributions of evenness on profile values by investigating the difference Hα-H0 = ln(Eα) = ln(eHα/S). We defined system a as more evenly distributed than system b in case all values of ln(Eα) are larger, and referred to a plot of ln(Eα) against α as an evenness profile (see discussion of Chapter 6). Eα = 1 corresponds to systems that are perfectly evenly distributed – these systems will have perfectly horizontal diversity profiles. Since H∞ is only determined by the proportion of the dominant (=most abundant) species, E∞ provides an insight in the contribution of the dominant species to evenness. Therefore, systems with larger E∞ have a more evenly distributed dominant species. Systems with intersecting evenness profiles consist of one system where the dominant species is more evenly distributed but the other species less evenly. The values of the series for the various species groups were calculated for scales αÎ{0, 0.1, 0.25, 0.5, 0.75, 1.25, 1.5, 1.75, 2, 2.5, 3, 10, 100, 200}. Species proportions were calculated for the complete sample (201 farms). Values were calculated for H1 and H∞ (since we expanded the Rényi series to include these values) by Shannon and Berger-Parker diversity indices directly. After expanding the Rényi series to include the Shannon index, the ratio ln(E1) / ln(E∞) provides an indication of the evenness in distribution of the other species (excluding the dominant species). H∞ was the only value for which confidence intervals for the expected values for the survey area could be calculated as it is obtained solely from the proportion of the dominant species (i.e. the Berger-Parker diversity index), while other values in the diversity series include an effect of species richness. The calculation of the confidence interval for this statistic is outlined in section 4.2.6. 4.2.5. Accumulation Patterns of Diversity Profiles Similarly to the randomization approach to calculating random richness (section 4.2.3), we used a Monte-Carlo approach of random site additions to obtain accumulation surfaces for diversity profiles by calculating the average Hα for each accumulated number of sites for the scales we differentiated for the complete sample (section 4.2.4). This was a new approach to analyzing the effects of sample size on diversity.

56

CHAPTER 4 We calculated average values and ranges (minimum and maximum) for the entire range of accumulated farms (1→n) based on 10000 random site sequences. The FORTRAN programme developed to carry out the computations (Kindt 2001d) can be obtained from the author. Average values, ranges (minimum and maximum) and 95% confidence intervals were calculated for diversity and evenness profiles for 100,000 random farm sequences (Kindt 2001e, 2001f, 2001g). Since different patterns of profile accumulation will result from differences in the distribution of species abundances over farms, comparisons of diversity and evenness of several locations are only meaningful if they are based on the same sample size (see Chapter 6). As indicated in table 13.1, we had three options how equal sample sizes could be determined: (a) based on the number of farms sampled (Fref), (b) based on the area sampled (Aref), and (c) based on the number of trees sampled (Nref). In Chapter 13, we calculated profiles for each country and for each village based on the country or village with the smallest sample size on Fref, Aref and Nref (table 13.1). For example, for comparisons among countries, Fref = 39 as fewest farms were sampled in Cameroon, and Aref = 115.72 ha as the total area surveyed was smallest in Kenya. In Chapters 6 & 7, we only considered sample sizes determined by the number of farms (Fref). Chapter 13 was the only chapter where also Aref and Nref were considered. Because the average farm size and average farm abundance varied among countries and among villages, calculations based on Fref, Aref and Nref were expected to yield different results. For example, villages with greater tree density will reach the Nref at fewer farms. It is easy to calculate average values for Fref as the exact reference can be reached within each random sequence. It is unlikely, however, to reach the exact Aref and Nref by randomly adding farms from surveys that did not standardise these sample sizes. Therefore, we calculated profiles for Aref and Nref by selecting the accumulated number of farms within each random sequence that was closest to the reference value. In some cases, the first randomly selected farm may contain more than twice the reference value, in which case zero is the closest number of farms to the reference value. In such cases, the diversity profile can not be calculated. It is also impossible to calculate diversity profiles where the accumulated number of farms contains no trees, although such cases did not occur in our datasets. We calculated average diversity profiles based on the number of valid sequences (determined by S>0). We counted the number of farm sequences that yielded profile values equal or above thresholds ranging from 0 to ln(S) (the maximum value possible at each scale) in steps of 0.01. We similarly counted the number of farm sequences that yielded evenness values above thresholds ranging from -ln(S) to 0. These data allowed determination of 95% confidence intervals by selecting the thresholds with counts equal (or just below) 2500 and equal (are just above) 97,500. The maximum and minimum profile values obtained were also recorded. Figure 4.4 and table 4.3 show that accurate values for average profile values are quickly obtained for subsamples of 100 farms (Fref). For randomizations ≥ 10,000, smooth histogrammes are obtained. The 0.01 interval that was most frequently observed (i.e. the peak of the histogrammes) contained the average profile value. An increasing scale of the diversity profile corresponds to a wider histogramme (see Chapter 7 & 13). The width of the histogramme increased with the number of randomizations – in case the maximum values would be of particular interested, then a large number of randomizations will be needed (see section 4.2.2 and Chapter 15). The analysis presented in figure 4.4 indicates that the 100,000 randomizations used throughout this thesis produced reliable results for average profile values and 95% confidence intervals. 4.2.6. Diversity and Evenness Statistics Although a complete characterisation of the diversity and evenness of a system involves calculation of diversity and evenness profiles (section 4.2.4, Chapter 7), we used specific diversity

57

METHODOLOGY and evenness statistics in some chapters to simplify calculations or comparisons. The Shannon diversity index H, Simpson diversity index D-1 and inverse Berger-Parker index d-1, which are all values at specific scales of the Rényi series Hα (section 4.2.4) were calculated directly from information on species’ frequencies. In Chapter 8, logarithmic species richness, Shannon, and logarithmic Simpson and inverse Berger-Parker indices were calculated for each farm and use-group. As ln(0) can not be determined, the range of values obtained only span farms where the use-group occurred. The statistic ln(S+1) included all farms. An alternative inverse Berger-Parker index (d-1csa) was calculated based on cross-sectional areas of the dominant species and use-group, not on abundance as the other diversity indices and diversity profiles. 95% confidence interval limits for the frequency of the dominant species (cluster sampling) were calculated by (Hayek & Buzas 1997 eq. 8.28) n

p ± 1.96

åm

2 i

( pi - p ) 2

i =1

(n - 1)m 2 n -1

,

mi = abundance of farm i; pi = species frequency at farm i; p = species frequency in the survey; n = number of farms; m = total abundance. These were used to calculate confidence limits for d-1 and d-1csa. The reciprocal values of the confidence interval limits were reported as confidence interval limits for d-1, natural logarithms of these reciprocal values as confidence interval limits for H∞. The inverse Berger-Parker index was also calculated for the dominant species from the broken stick distribution with the same species richness as each use-group (d-1bs). Broken stick distribution corresponds to random distribution of individuals over species (Hayek & Buzas 1997; Legendre & Legendre 1998). The formula used to calculate the expected frequency of the dominant species for a group with Stot species was (Legendre & Legendre 1998 eq. 6.49) Stot ö -1 æ Stot ç å x -1 ÷ . è x =1 ø

The broken stick value is less strict than the inverse Berger-Parker index based on completely even distribution. This value equals Stot because completely evenly distributed species each have a frequency of Stot-1. As the Berger-Parker is only determined by the frequency of the dominant species, it expresses evenness of a system. Evenness of species distribution was also analyzed by J (Magurran 1988 eq. 2.22; Legendre & Legendre 1998 eq. 6.44) J=

H ln(S )

and E (Legendre & Legendre 1998 eq. 6.50 and 6.49) E=

H - å E ( yi ) ln E ( yi ) S

with E ( yi ) = S -1 å x -1 x =i

58

CHAPTER 4 indices based on the Shannon diversity for equal distribution and for broken-stick distributions for the same species richness, respectively. These indices were only calculated for households where species richness was higher than one to avoid division by zero. 4.3. Use-group Abundance and Density Use-group species abundance (Navg) was calculated by the average of individual species abundance on farms. Use-group density (Davg) was calculated by dividing species abundance on farm by farm size. Species density for use-groups at the survey scale (SDtot) was calculated by Davg/Stot. Species density was calculated in a similar matter after removing the dominant species from each use-group (SDtot-d). In Chapter 8, in analogy to species richness (section 4.2.6), ln (N) and ln (N+1) indicate calculations of species richness and tree abundance for households where the use-group was present, and all households including those with zero abundance, respectively.

4.4. Regression, Correlation and Non-Parametric Analysis 4.4.1. Correlations Correlation parameters were calculated by the S-Plus 2000 (Professional Release 1) package. Following Kolmogorov-Smirnov goodness of fit tests (S-Plus) for normality, non-parametric Spearman ρ and Kendall τ rank correlation and significance tests were calculated in case the distributional assumptions for Pearson’s correlation r were not met. In Chapter 8, correlations were calculated between several diversity statistics (section 4.2.6) for the same farm and same use-group, and between the Shannon index of the same farm and different use-groups. High correlation values for the first aspect would indicate that the choice of diversity index did not influence diversity rankings very much (i.e. that the ranking at one specific scale of the Rényi series was the same as at another scale of the Rényi series). High correlation values for the second aspect would indicate the possibility of classifying farms as more diverse for a number of groups based on diversity information for only one group. 4.4.2. Robust MM Regression Regression analysis for most figures used the Robust MM Regression technique provided by SPlus. The robust fit is minimally influenced by outliers in the dependent and independent variables, and can be used when random variation in the data is not Gaussian. In one case (figure 5.3), results of robust regression based on the natural logarithm (y~a+bln(x)) were presented because substantially more variation was explained by that approach. 4.4.3. Multiple, Stepwise and Partial Regression Analysis In Chapter 8, the influence of farm characteristics on farm diversity statistics (H, ln (S), J and ln (N+1), section 4.2.6) as response variables were studied using multiple linear regressions (S-Plus). Qualitative factors were coded as dummy explanatory variables. Quantitative variables were ranged within the [0,1] interval by subtracting the maximum value and subsequently dividing by the maximum value (see Chapter 3). Regression coefficients can then be compared more easily, whereas the significance of regressions is not affected. S-Plus was used to select explanatory

59

METHODOLOGY variables using stepwise linear regression (S-Plus uses the Akaike Information Criterion to guide backward and forward selection). Where residuals of calculated models were not normally distributed (Kolmogorov-Smirnov goodness-of-fit test in S-Plus), the significance of coefficients was calculated by 9999 permutations of the residuals of the full regression model by the randomisation programme of Legendre (1999a) based on Anderson & Legendre (1999). Before using this programme, attempts were made to normalise response variables using the Box-Cox normalisation procedure. By coding for separate villages, and because multiple regression coefficients are partial regression coefficients, the influence of farm and household characteristics independent from their broader geographic location can be evaluated. The proportion of variation that is exclusively explained by spatial and non-spatial explanatory variables was calculated by partial linear regression as outlined by Legendre & Legendre (1998 pp. 528-532). We used a similar multiple regression approach in Chapter 14 to test whether certain farm characteristics could explain the species richness or abundance in use-groups. Since all farms were included in the calculations, including those where particular uses contained no trees, response variables were transformed to ln(X+1). We constructed a general model for all usegroups by including dummy variables for the use category (table 14.3). 4.4.4. Tests for Differences among Farms and Use-Groups In Chapter 8, multiple regression (section 4.4.3.) was also used to investigate whether significant differences existed among the average values for ln (S), H, J, E, ln (d-1), and ln (N) for different use-groups and farms (section 4.2.6) by coding the different use-groups as dummy variables. Where residuals of calculated models were not normally distributed, we calculated 99,999 permutations by Legendre (1999a). The significance of differences between groups was also calculated in Chapter 8 by the nonparametric Kruskal-Wallis rank tests (S-Plus) and Multi-Response Permutation Procedures (PCORD 4 package, McCune & Mefford 1999). MRPP provide a chance-corrected within-group agreement statistic (A), which is a descriptor of within-group homogeneity. When A equals 1, then all items are identical within groups, while A equaling zero means that group heterogeneity equals expectations by chance. 4.4.5. More Detailed Analysis of the Influences of Farm Size on Diversity and Abundance In Chapter 8, the influence of farm size on farm diversity (species richness S and Shannon diversity H) was studied here by fitting the models S = aN b

and H = aN b

(N: farm size/smallest farm size) in S-Plus. Since parameters a can be calculated directly as average diversity of farms of the smallest size, models with prefixed parameter a and models where this parameter was estimated were compared. The influence of farm size was further evaluated by calculating H, S and N for random combinations of farms of the same size and subsequently comparing values for the same

60

CHAPTER 4 combined area size. Because of the large number of combinations possible to calculate diversity statistics of accumulated farms of the same size (there are for instance 1820 = 16! (12! 4!)-1 possibilities of combining 4 of the 16 farms of 0.25 acres), the programme EstimateS 5 (Colwell 1997) was used. This program calculates average H, S, and N for random additions of sites; results presented here were based on 5000 randomisations. For S, the exact average species richness for randomly accumulated sites was calculated as well (section 4.4).

4.5. Analysis of Species Composition 4.5.1. Ordination Method Differences in species composition and influences of farm characteristics were investigated by canonical ordination. Ordinations were conducted for all species occurring on a farm and for each use-group separately. All 183 farms (out of the 201 surveyed) with complete information on environmental characteristics were included in the analysis, including those farms where a particular use-group was not represented. Differences in species composition can be analyzed by plotting each farm by its species abundances on axes representing each species and subsequently investigating how close farms are to one another in the multidimensional space. Ordination methods seek a representation in a lower number of dimensions of this space. Ordinations of farms were obtained through the transformed data approach described by Legendre & Gallagher (2001). The transformed data approach provides an alternative to the distance-based Redundancy Analysis (RDA) approach of Legendre & Anderson (1999). RDA is a canonical ordination method in which the ordination axes are combinations of explanatory variables included in a second matrix. The ordination method further follows the principal components analysis (PCA) methodology so that consecutively calculated axes represent lower amounts of variance. RDA was based on transformed farm – species abundance matrices in which the Euclidean distance between sites equalled their Hellinger distance (Rao 1995), one of several ecological distance measures that have been developed to express difference in species composition (e.g. Legendre & Legendre pp. 274-288). The Hellinger distance between site (farm) 1 and 2 is calculated by é n1 j n ù - 2j ú ê å n2+ úû j =1 ë ê n1+ S

2

(nxj abundance of species j at site x; S: species richness). When transforming each abundance value by

nxi , nx+ the Euclidean distance between site vectors of the transformed matrix will equal the Hellinger distance of the original data. Only species occurring on more than five farms were included in the calculations, and their abundance was transformed by ln (nxj+1) before the Hellinger distance transformation. Legendre & Legendre (1998) and Legendre & Gallagher (2001) described the Hellinger distance as a more meaningful distance measure than those used under classical RDA (Euclidean distance) or canonical correspondence analysis (Chi-square distance), especially when

61

METHODOLOGY less weight is to be given to rare species. The transformed data method is also to be preferred over CCA if richer sites are not to be given higher weight. The program used for the Hellinger transformations was written by Legendre (1999b). For RDA, both polynomial (PRDA) and linear (LRDA) forms were calculated (Makarenkov & Legendre 1999, 2002). The polynomial (non-linear) form allows for second-order relationships between ordination axes and environmental characteristics, whereas the linear (classical) form only includes first-order relationships. The program used for RDA (PRDA and LRDA) was Polynomial RdaCca (Makarenkov & Legendre 1998). Only ordination figures obtained through PRDA have been included. Eigenvectors were scaled to length 1, so that in ordination diagrammes, distances among sites (farms) approximated the Hellinger distance. This is RDA scaling type 1 as outlined in Legendre & Legendre (1998 pp. 585586). Projection of a site at right angle on a species vector approximates its value (Legendre & Legendre 1998 pp. 585-586). Sites (farms) were represented by symbols with size corresponding to the number of farms with the same species composition. Sites where the group was not represented correspond to the origin before centring. Explanatory variables were represented differently when they referred to categorical or continuous variables (tables 3.2 and 3.3). Categorical variables were represented by the centroids of the fitted scores of sites belonging to the respective category. Classes of nominal variables are better displayed by centroid points (ter Braak & Smilauer 1998 p. 167). An advantage of centroid representation is that centroids for all states (including the one state that is a linear combination of the other states) can be calculated easily. Centroids can be interpreted in a similar way as individual site scores in terms of species composition. The vector connecting two states indicates the expected change in species composition if a farm would change from one state to the other (ter Braak & Smilauer 1998 p. 170). Continuous variables were represented by the multiple correlation coefficients of environmental variables and their squared value with fitted site scores of each axis (Makarenkov & Legendre 1999; Makarenkov & Legendre submitted). Linear (Pearson) correlations between environmental characteristics and fitted site scores were also calculated (represented with suffix ‘1’ in the diagrammes). Subsequently, vector lengths were multiplied by five; this does not change their interpretation, but improves clarity of diagrammes (Legendre & Legendre 1998 p. 586). Angles between species and continuous variable vectors reflect their (multiple) correlations. Correlations were calculated between site scores and fitted site scores for the axes represented (species-environment correlation). In ordination diagrammes, the circle of equilibrium contributions with radius 2 / n (n: number of species) was represented. According to Legendre & Legendre (1998 p. 402), only descriptors (species) whose vector lengths exceed the circle (represented by an ellipsoid when the axes do not have the same scale) significantly contribute to the formation of two PCA axes. Although RDA calculates the PCA solution on fitted values of species variables after multiple regression and not on original species values, we only included vectors in the diagrammes whose length exceeded the equilibrium circle. For the same reason of enhancing clarity of diagrammes, only centroids at distance of 0.2 2 / n from the origin were included. Variance explained by the first (two) axes of RDA was compared to the variance explained by the first (two) axes in PCA; this is an expression of the level of constraint introduced by canonical ordination. The correlation was calculated between site scores on the first RDA and PCA axis. For PCA, also the number of axes that should be considered following the broken stick distribution criterion (the number of axes with eigenvalues larger than the broken stick values) was provided (Legendre & Legendre 1998 p. 410).

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CHAPTER 4 4.5.2. Partial Ordination Independent contribution of sets of explanatory variables on the ordination was investigated by partial canonical analysis (Borcard et al. 1992; Legendre & Legendre 1998 pp. 605-612; ter Braak & Smilauer 1998 p. 124). This approach is similar to partial linear regression (sectin 4.4.3). The variance explained by only using village information (Vv), and the respective variance by only using household characteristics (Vh) were obtained. Some of the total variance (Vt, calculated when using both sets of variables) may have been explained jointly by both sets. The variance that was explained exclusively by village information can be obtained as Vve=Vt-Vh, and the respective variance for household characteristics as Vhe=Vt-Vv. Joint variance explained equals Vj=Vt-Vve-Vbe=Vt-(Vt-Vh)-(Vt-Vv)=Vv-Vve=Vh-Vhe. Vj will have a negative sign when Vt 10 trees ha-1.

5.4. Discussion The results indicate a complex pattern of distribution of species and uses over farms. Because the basic management unit is the farm, the most relevant option to analyse use-group diversity seems to be to base analysis on species distribution over households and information collected on all uses of the species at the particular farm (i.e. Uadj or Uadj). There was a large difference and low correlation between the total number of uses of a species, and the average number of uses per species per farm. This phenomenon could be interpreted in terms of gaps in knowledge on how particular species could be used, or as a lack in perceived need for a certain use. The lack in perceived need for the use could result from the fact that the species is only used when no other (better) species can provide the use (i.e. species of last resort). Other possibilities could be that the use responds to rare needs only occurring in a few households, or because the use is only needed infrequently on farms. Another area where the difference between the actual and potential uses emerged was in household occurrence of certain use-groups. For instance, medicinal use was observed in 112 farms (56%), but given farm species composition potentially occurred in 199 farms (99%). The information provided in Figure 7 and Table 1 therefore points to potential interventions. One strategy could be to increase the frequency of some use-groups in the landscape where these uses do not occur. Groups of medium occurrence could be selected for wider distribution such as medicine, soil fertility, or fodder occurring now on respectively 112 (56%), 136 (68%) and 47 farms (23%) (Uadj). A combined strategy could involve also targeting those farms with a low total number of groups (for instance lower than eight uses per household, Figure 8) and supplement additional uses to these farms. Such interventions would increase the alpha diversity of the more widely distributed use-groups and could also increase gamma diversity provided that new species contribute to the group. One question that needs to be solved, however, is in how far households that use none of the trees on their farms for a particular use would wish to obtain the use from their own farms. For instance, 23% of households obtained tree fodder from their farms, while 71% of households had cattle (the number of small ruminants was not recorded). The question to be solved is how many of these 71% see the need to obtain tree fodder from their own farms to feed their animals, and whether this number equals the 23% that currently obtain tree fodder from their farms.

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AGROECOSYSTEM DIVERSIFICATION Some farmers could indeed choose to use other fodder sources, for instance grass from the own farm or tree fodder from other farms. A related question is how many farmers without livestock could produce and sell fodder – maybe even more than 71% of farmers could see this as a valid option. A second-generation survey is currently underway focusing on these and similar questions, collecting information on so-called farmer-desired diversity per farm. Because the desire of households to realize a particular use on their own farm is currently unknown, it seems more appropriate to focus on use-groups with medium occurrence than use-groups with low occurrence, as the latter use-groups could be limited to rare needs. The question whether differences Uadj and Uall are the result of gaps in knowledge on uses of species, rarity of need for the use, the choice to obtain the product from off-farm sources (for example through purchase from other farmers) or from alternative on-farm sources thus remains unanswered. Both average number of uses per species and use-group frequencies highlight the potential value of extension messages on alternative uses of species (in case the hypothesis of gaps in knowledge on species uses holds true). Widespread extension of information on potential uses of species that do not occur on all farms at present could result in increased on-farm diversity if this information would encourage farmers to incorporate new species in their farm. An increment of alpha diversity (Savg) would buffer use-groups of individual farms better against random low performance of individual species resulting from heterogeneity of environmental conditions, which could include weather conditions, diseases, pests, or market failures. Arnold (1995) stated that farmers plant trees in pursuit of their livelihood goals of income generation, risk management, household food security and optimum use of available land, labour and capital. Warner (1995) stated that farmers plant trees based on their needs for tree products, decline in access to off-farm resources, lack of alternative products, increasing market opportunities and declining on-farm labour. The analysis presented here does not distinguish whether a particular use-group has more on-farm consumption or more sale value. Although we collected such information, we did not make the distinction as it could be equally important to diversify usegroups mainly used for sale and use-groups mainly for direct use. Groups with low alpha diversity (Savg) were identified and could be targeted for diversification efforts. Gamma diversity (Stot) provides suggestions on how alpha diversity can be improved: for those groups with higher gamma diversity (belonging to pools A, B, and possibly C), a wider distribution of existing species within the area would offer one method of enhancing alpha diversity. For groups with low gamma diversity, the solution would rather be to introduce new species or to promote alternative uses for species that are already present. Increasing gamma diversity could also result in increased stability and productivity at the landscape level (under conditions as discussed in the introduction). Therefore, identification of use-groups with low gamma diversity and subsequent increment of gamma diversity could have a direct impact at this level. High alpha diversity of Uall groups suggests that promotion of a higher number of alternative uses would increase alpha diversity to reasonable levels for most use-groups. The difference between the Uadj and Uall groups potentially points to farmer-perceived redundancy within groups – some species could provide a certain use, but are not used in particular households because other species serve the same purpose. The questions to be resolved, however, when promoting additional species are: (i) the quality of production of these species; (ii) the complementarity in production by these species with species that are already used (for instance seasonal differences); and (iii) differences in species’ traits which are the basis for enhancement of the stability of production. Information on richness and evenness of species abundance distribution within use-groups revealed that the evenness of many groups was very low. The evenness of the majority of usegroups was lower than the evenness associated with broken stick distribution, a reference-value that could be used as a target for evenness increment. Even for the scenario where all possible

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CHAPTER 5 products are obtained from the species that are present on-farm (Uall), low evenness values prevailed. This means that even if all species would be used, evenness would still be low. Fewer use-groups had high evenness than the number of groups with high richness. From a diversification point of view, increment of evenness thus seems to be a higher priority. Low evenness implies that groups have weak buffering of performance against the loss of the dominant species, while disturbances might target species with large abundance (see for instance Wills et al. 1997). Species densities in many groups were very low, especially if the dominant species was removed from the group. The large effect of removing the dominant species on calculated average species densities is another indication of the need for more even distribution of species. As described in Kindt & Lengkeek (1999), most species in the western Kenya survey area occur in very low densities. The inverse J-shape of species rank-abundance plots is also the typical situation for tropical rain forests (Kohyama 1991; Magnussen & Boyle 1995). Because of this distribution pattern, most species occur in considerably lower densities than calculated even when the dominant species is removed from each group. Therefore, an analysis of the specific density of each species is needed. The average density per species (SDtot) showed that, in case species were completely evenly distributed (i.e. when each species would have the same density Davg), 10 use-groups would have more than 1 tree ha-1. This figure is higher than the typical density of canopy trees of tropical rain forests of one or fewer adults per hectare (Chase et al. 1996). It may not be realistic, however, to promote perfectly even distribution of species over groups, for instance in those cases when farmers prefer particular species. Therefore, some species may be maintained in very small densities in the landscape. Small plant population sizes result in rapid genetic erosion with an expected loss of genetic diversity per generation of minimum (2 × population size)-1 (the cases where expected losses are smaller, listed for example by Caballero 1994, do not apply). Simons et al. (1994) stated that genetic variation must be maintained in domesticated agroforestry species to allow for a variety of user needs and environmental conditions. To evaluate the influence of small densities on the genetic variation that can be maintained in on-farm tree populations, information is also needed on the spatial distribution of these species in the area. Species are aggregated when the average density of conspecifics in close proximity to each tree is higher than overall density. Condit et al. (2000) reported that nearly every tree species in six tropical forests had an aggregated distribution, and that the rarest species were the most aggregated. For species with small landscape densities, genetic erosion would be limited by encouraging farmers to grow these species in an aggregated pattern, which would also correspond with the natural distribution of species. Information on population sizes of species is also needed to evaluate the potential of their conservation through use. Conservation through use may be the best option for many species considering forest decline in the area (Kindt & Lengkeek 1999). The genetic diversity of these species will need to be maintained to allow for long-term conservation. Small population sizes, however, should not be a reason not to use particular species in agroecosystems. In such cases, however, continuous interventions (germplasm inputs) will be needed to ensure genetic diversity is maintained, and genetic diversity will need to be conserved elsewhere either through in situ or ex situ approaches. This type of intervention for low-density species could be considered as strict domestication of the landscape (analogous to the strict definition of domestication sensu Harlan 1975) as these species will depend on human interventions to survive in the agroecosystem. The analyses focused on species richness and diversity, while productivity is also related to abundance. A diverse use-group may still not cover all demands for products or services that are needed on an individual farm, while a less diverse use-group could provide surplus. Linked to this observation is the fact that use-groups were not validated in terms of importance to the farm household (e.g. economic importance, importance for food security). Diversification could be

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AGROECOSYSTEM DIVERSIFICATION targeted towards more important use-groups, rather than targeted towards those groups which have low diversity. Because of small farm sizes and since the current average number of uses per household is ten, the decision could be made not to promote all uses. Decreasing the number of uses per farm could result in higher profitability per farm. An analogy is the criterion introduced by Van Noordwijk et al. (1997) of the shape of the relationship between biodiversity and profitability. If the shape of the trade-off curve indicates that initial diversity loss would result in large gains in profitability, then these authors suggest that a segregation (specialisation) approach may be more appropriate – if increment of profitability is the major goal for the landscape. Similarly, Van Noordwijk & Ong (1999) indicated that the value of diversity in agroecosystems strongly depended on the ability of farmers to derive value from a large number of components, and not from one dominating component. With fewer use-groups on a farm, their within-group diversity could be increased to higher levels than possible with many use-groups. However, reduction in the number of use-groups per farm could result in substantially greater risks to individual farmers. The evenness of inter-group diversity was not investigated, but such investigation could provide insights in potential reduction of use-group number that would allow for greater evenness of uses. Reduction of use-groups on farms does not necessarily lead to reduction of diversity at the landscape level (i.e. beta diversity increment can buffer for alpha diversity decrease), so that stability at the landscape scale may not suffer. Interestingly, species population size arguments as presented above also advocate for approaches that focus on both alpha and beta diversity. Some other aspects relating to diversity, and productivity and stability that require further investigation are listed below. ·

One aspect that was not included in the analysis is that not all trees of a species necessarily contribute to the uses listed for the species. For instance, only half of the trees of a species used for firewood (156 or 89% of the 175 species encountered, and 15.2 or 92% of the average 16.6 species occurring per farm were used for firewood) could be actually used for that purpose. Linking species abundance to productivity should also incorporate information on intra-specific variation in productivity, while different parts of each tree may be used for a particular purpose. The analysis presented, therefore, still includes an aspect of potential use rather than actual use. With the purpose of detecting groups of low diversity, this level of detail may not be necessary.

·

The local value of diversity was not included in the analysis, whereas local values may include other components than productivity. Thrupp (1998) mentioned values of agrobiodiversity in addition to increased productivity and stability, including diversification of income opportunities to farmers, reducing risks, increasing efficiency of resource use, increasing nutritional values, and provision of sources of medicines and vitamins. Swift et al. (1996) mention the diverse set of goals that farming communities may have such as minimising risks, attaining minimum production or preserving cultural traditions. A second-generation survey attempts to document farmers’ expectations related to the diversity within the different usegroups. A related issue is the implementation of agroecosystem diversification. Harrington (1996) distinguished between agroecosystem diversification through design (involving participatory research on indigenous knowledge about system diversity) and demand-led diversification (where farmers follow market signals to diversify their farming systems). He pointed out that the former approach had not led to widespread adoption by farmers, while the second approach does not necessarily foster sustainable management. He advocated for a combination of both approaches. Swift et al. (1996) contrasted agroecosystem design methods of introducing small changes into traditional agricultural practices with the total conversion of a natural ecosystem into systems that only contained desired elements irrespective of background ecological conditions. They distinguished a third approach where the

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CHAPTER 5 agroecosystem is reconstructed using biological diversity rather than rejecting it. Information on areas in the agroecosystem where tree diversity is lower is not the only information necessary for successful implementation of participatory agroecosystem diversification. Reasons may exist why low diversity of some groups is locally preferred, so that provision of greater options to farmers (new information on how species that are present can be used, or wider distribution of species) would not lead to greater diversification. We believe, however, that the approach presented offers important information on interventions points on which diversification could focus, even if interventions would only indicate that local management is not constrained by the number of options that are currently available. ·

The analyses were conducted at the landscape level (comparing alpha and gamma diversity), whereas differences in diversity also occur among individual farms. Obviously, greater differences among farmers result in larger gamma diversity, but only average alpha diversity was considered. Kindt et al. (Chapter 8) analysed diversity statistics of individual farms and their differences in species composition since individual farms (or villages) are the true targets for increasing diversity.

·

The aspect of intra-specific diversity was discussed above. Another aspect that could be taken into consideration for domestication of species at the landscape scale could be supra-specific diversity. For instance, when buffering against diseases is sought, diversifying botanical families may be more effective than only diversifying species within genera.

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AGROECOSYSTEM DIVERSIFICATION Table 5.1. Characteristics of the 12 most frequent use-groups based on various group definitions (see methods). Use-group occur. Firewood Fruit Timber Shade Boundary demarcation Construction Medicine Ornamental Soil fertility improvement Charcoal Beverage Fodder

2834 920 623 554 473 355 239 194 156 124 74 46

Urec Uadj farm sp. avg. occur. farm oc. oc. 201 14.1 3053 201 201 4.6 944 201 184 3.4 775 196 157 3.5 597 166 191 2.5 571 197 191 1.9 389 197 108 2.2 251 112 87 2.2 208 96 106 1.5 196 136 80 1.6 124 80 62 1.2 74 62 40 1.2 53 47

Sp. occur. Avg. 15.2 594 4.7 906 4.0 288 3.6 172 2.9 411 2.0 283 2.2 158 2.2 74 1.4 89 1.6 18 1.2 74 1.1 25

Umain farm sp. avg. occur. oc. 175 3.4 3165 201 4.5 948 130 2.2 1623 105 1.6 2795 186 2.2 1852 182 1.6 1415 86 1.8 1374 51 1.5 1616 76 1.2 1170 16 1.1 1498 62 1.2 74 23 1.1 454

Uall farm sp. avg. oc. 201 15.7 201 4.7 201 8.1 201 13.9 201 9.2 201 7.0 199 6.9 200 8.1 200 5.9 201 7.5 62 1.2 199 2.3

occur.: number of times the use was mentioned; farm oc.: number of households where the use was mentioned; sp. avg.: average number of species per farm and per use for those farms where the use was mentioned

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CHAPTER 5 Table 5.2. Diversity characteristics of the 12 most frequent use-groups based on various group definitions (see methods) Use

Statistic

Group definition Urec

Firewood Stot Savg d-1 d-1csa

Uadj

d-1bs Stot Savg d-1 d-1csa

Umain

d-1bs Stot Savg d-1 d-1csa

Uall

d-1bs Stot Savg d-1 d-1csa d-1bs

Gamma pool

Use-groups

(lcl) (ucl) (lcl) (ucl) (lcl) (ucl)

(lcl) (ucl) (lcl) (ucl) (lcl) (ucl)

(lcl) (ucl) (lcl) (ucl) (lcl) (ucl)

(lcl) (ucl) (lcl) (ucl) (lcl) (ucl)

156 14.1 13.3 14.9 5.3 4.4 6.5 2.9 2.6 3.3 27.7 156 15.2 14.4 16.0 5.6 4.7 6.8 3.1 2.8 3.5 27.7 70 3.0 2.6 3.3 3.9 3.0 5.4 2.9 2.2 4.1 14.5 156 15.7 14.9 16.6 5.9 5.0 7.3 2.9 2.6 3.3 27.7 A

Fruit

Timber

Shade

25 4.6

49 3.1

84 2.8

1.4 3.2 6.6 25 4.7 1.4 3.3 6.6 25 4.5 1.4 3.1 6.6 25 4.7 1.4 3.2 6.6 C

4.4 4.8 1.3 1.5 2.9 3.7

4.5 4.9 1.3 1.5 2.9 3.8

4.3 4.7 1.3 1.5 2.7 3.6

4.5 5.0 1.3 1.5 2.9 3.7

1.7 1.9

2.8 3.4 1.5 2.0

1.7 2.2 10.9 49 3.9 3.5 4.2 1.6 1.4 1.8 1.6 1.5 1.7 10.9 40 1.4 1.2 1.7 2.4 1.7 3.8 5.2 2.9 21.3 9.3 49 8.1 7.6 8.6 2.0 1.8 2.3 1.9 1.7 2.2 10.9 B

7.3

2.3 3.2

4.0 42.6 6.8 5.1 10.3 16.8 84 3.0 2.6 3.4 7.7 4.2 44.8 7.3 5.4 11.0 16.8 47 0.9 0.7 1.0 3.2 1.4 n.a. 4.8 2.6 29.7 10.6 84 13.9 13.2 14.6 3.8 3.3 4.5 6.8 5.1 10.3 16.8 A

BounConMedi- OrnaSoil CharBeve- Fodder dary struccinal mental fertility coal rage demartion improcation vement 34 20 58 53 27 27 4 7 2.4 1.8 1.2 1.0 0.8 0.6 0.4 0.2 2.1 1.6 1.0 0.7 0.6 0.5 0.3 0.2 2.6 1.9 1.4 1.2 0.9 0.8 0.5 0.3 3.0 1.2 1.5 2.0 1.8 2.0 1.2 2.2 2.5 1.2 1.1 1.4 1.1 1.3 1.0 1.5 3.9 1.4 2.1 3.0 4.3 4.0 1.5 4.5 4.1 1.3 7.6 6.0 3.9 2.9 1.3 1.6 2.9 1.2 3.6 3.2 2.6 1.9 1.2 1.1 7.3 1.4 n.a. 48.5 8.2 5.3 1.5 2.9 8.3 5.6 12.5 11.6 6.9 6.9 1.9 2.7 34 20 58 53 27 27 4 7 2.8 1.9 1.2 1.0 1.0 0.6 0.4 0.3 2.6 1.8 1.0 0.8 0.8 0.5 0.3 0.2 3.1 2.0 1.5 1.3 1.1 0.8 0.5 0.3 3.2 1.3 1.5 2.0 1.9 2.0 1.2 2.4 2.6 1.2 1.2 1.5 1.2 1.3 1.0 1.6 4.2 1.4 2.1 3.0 4.7 4.0 1.5 4.9 2.8 1.3 7.7 6.4 3.0 2.9 1.3 1.7 2.2 1.3 3.7 3.4 2.1 1.9 1.2 1.1 3.8 1.4 n.a. 56.5 5.4 5.3 1.5 3.5 8.3 5.6 12.5 11.6 6.9 6.9 1.9 2.7 22 6 49 30 12 10 4 3 2.0 1.4 0.8 0.4 0.4 0.1 0.4 0.1 1.9 1.3 0.6 0.3 0.4 0.0 0.3 0.1 2.2 1.5 1.0 0.5 0.5 0.1 0.5 0.2 2.9 1.2 1.4 2.0 1.7 1.1 1.3 2.8 2.4 1.1 1.0 1.2 1.2 1.0 1.0 1.4 3.7 1.3 2.7 8.9 3.2 1.3 1.9 173.7 3.8 1.2 4.5 3.2 1.9 1.3 1.3 1.7 2.5 1.2 1.8 1.4 1.3 1.0 1.2 1.2 7.9 1.3 n.a. n.a. 3.5 2.0 1.5 3.0 6.0 2.4 10.9 7.5 3.9 3.4 1.9 1.6 34 20 58 53 27 27 4 7 9.2 7.0 6.8 8.0 5.8 7.5 0.4 2.3 8.8 6.7 6.4 7.6 5.5 7.1 0.3 2.1 9.6 7.4 7.3 8.5 6.1 7.8 0.5 2.4 4.4 2.6 2.0 4.7 2.4 2.3 1.3 1.5 3.8 2.2 1.8 3.7 1.7 2.0 1.0 1.3 5.3 3.0 2.3 6.7 3.8 2.7 1.9 1.7 4.1 1.3 7.6 6.0 3.9 2.9 1.3 1.6 2.9 1.2 3.6 3.2 2.6 1.9 1.2 1.1 7.3 1.4 n.a. 48.5 8.2 5.3 1.5 2.9 8.3 5.6 12.5 11.6 6.9 6.9 1.9 2.7 C C B B C C D D

Stot: total number of species in the survey; Savg: average number of species per farm (including farms where use was not mentioned); d-1: inverse Berger-Parker index based on species abundance; d-1csa: inverse Berger-Parker index based on species cross sectional area; d-1bs: inverse Berger Parker index based on the broken stick distribution; lcl: lower confidence limit (p=0.95); ucl: upper confidence limit (p=0.95); gamma pool: pooling of use-groups based on Stot

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AGROECOSYSTEM DIVERSIFICATION Table 5.3. Density and abundance of the 12 most frequent use-groups based on various group definitions (see methods) Use

Statistic

Group Definition Urec

Firewood Navg Davg

Uadj

(lcl) (ucl)

(lcl) (ucl) (lcl) (ucl)

SDtot SDtot-d Navg Davg

Uall

(lcl) (ucl)

SDtot SDtot-d Navg Davg

Umain

Use-groups

(lcl) (ucl)

(lcl) (ucl)

SDtot SDtot-d Navg Davg

(lcl) (ucl)

(lcl) (ucl)

SDtot SDtot-d

448 389 506 1090 932 1248 7.0 5.8 477 416 537 1153 990 1316 7.4 6.2 32 25 40 58 44 71 0.8 0.7 503 441 564 1217 1051 1383 7.8 6.6

Fruit

Timber

Shade

46

101

23

96

37 55

74 118 3.9 1.4 46 37 55 97 75 119 3.9 1.4 45 35 54 94 73 116 3.8 1.4 46 37 55 97 75 119 3.9 1.4

81 122 245 177 314 5.0 2.3 139 117 161 356 268 443 7.3 3.7 12 8 16 22 14 30 0.6 0.4 171 148 195 418 329 508 8.5 4.7

59 0.7 0.6 24 61 0.7 0.6 3.9 8.9

14 31 34 83

15 33 36 85

1.2 6.5

2.7 15.1 0.2 0.1 319 281 358 752 631 874 9.0 6.7

BounConMedi- OrnaSoil CharBeve- Fodder dary struccinal mental fertility coal rage demartion improcation vement 201 106 13 26 57 17 95 2.1 176 87 5 16 17 8 48 0.9 225 125 21 36 96 26 143 3.4 583 225 25 100 91 33 211 8.5 467 179 11 50 39 17 118 0.3 699 270 39 149 143 49 303 16.7 17.2 11.2 0.4 1.9 3.4 1.2 52.6 1.2 14.6 2.4 0.2 0.9 2.3 0.6 20.0 0.9 213 109 13 26 59 17 95 2.3 188 90 5 16 19 8 48 1.0 238 128 21 36 99 26 143 3.5 603 231 25 100 97 33 211 8.7 488 185 11 50 45 17 118 0.5 718 276 40 150 150 49 303 16.8 17.7 11.5 0.4 1.9 3.6 1.2 52.6 1.2 13.7 2.4 0.2 0.9 2.5 0.6 20.0 1.0 191 99 6.9 1.9 11 2.1 95 0.7 166 80 -0.1 0.5 5 0.0 48 0.2 215 117 13.9 3.3 17 4.1 143 1.2 561 214 14 6.6 22.6 3.1 211 2.2 446 168 2 0.5 9.9 0.6 118 0.7 676 259 26 12.6 35.3 5.6 303 3.8 25.5 35.6 0.3 0.2 1.9 0.3 52.6 0.7 22.4 8.2 0.1 0.1 1.0 0.03 20.0 0.7 379 220 174 184 174 199 95 47 345 190 147 161 125 173 48 38 412 249 201 207 223 225 143 56 948 504 360 485 383 465 211 115 816 406 299 388 286 371 118 86 1080 601 422 582 479 559 303 144 27.9 25.2 6.2 9.1 14.2 17.2 52.6 16.4 22.9 16.4 2.9 6.8 8.8 10.5 20.0 6.6

Navg: average number of trees per farm; Davg: average density of trees (ha-1); SDtot: average species density (ha-1); SDtot-d: average species density when the dominant species is removed (ha-1); lcl: lower confidence limit (p=0.95); ucl: upper confidence limit (p=0.95)

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CHAPTER 5

45

25

20

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35 30

15

25 20

10 15 10

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40

5

5 0

0 0

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7

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Number of uses per species Figure 5.1. Frequency distribution of species with a specific total number of uses

regression including firewood

Average number of uses

4

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0

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Total number of uses Figure 5.2. The average number of uses per species versus the total number of uses per species. Sizes of circles correspond to the number of species with the same characteristics (the biggest circle corresponds to 35 species). The lines correspond to robust linear regression fits and 95% confidence intervals (8% of variation explained including firewood; 9% excluding firewood)

87

Total number of uses per species

AGROECOSYSTEM DIVERSIFICATION

14

14

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13

12

12

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Species frequency (number of households)

Average number of uses

Figure 5.3. The total number of uses per species over species frequency. The size of the circle corresponds to the number of species with the same use and frequency characteristics (largest circle corresponds to 30 individual species). The lines correspond to robust linear regression fits and 95% confidence intervals (36% of variation explained)

4

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Species frequency (number of households) Figure 5.4. Average number of uses per species per household over species frequency. The size of the circle corresponds to the number of species with the same use and frequency characteristics (largest circle corresponds to 30 individual species). The lines correspond to robust linear regression fits and 95% confidence intervals (3% of variation explained)

88

Number of uses per household

CHAPTER 5

30

30

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0 0

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Number of species per household

Average number of species per use

Figure 5.5. The number of uses per household versus the number of species per household. Circle size corresponds to the number of households with the same characteristics (largest circle corresponds to 7 households). The lines correspond to robust linear regression fits and 95% confidence intervals (39% of variation explained)

3

3

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Number of species per household Figure 5.6. The average number of species per use per household compared to household species richness. Circle size corresponds to the number of households with the same characteristics (largest circle corresponds to 7 households). The lines correspond to robust linear regression fits and 95% confidence intervals (48% of variation explained)

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AGROECOSYSTEM DIVERSIFICATION

20

25

15

Not frequent use groups

20

Frequent use groups

10 15

10 5

Percentage of use groups

Number of use groups

30

201

197

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Number of households where use group was recorded Figure 5.7. Frequency of use-groups expressed as number of use-groups recorded at a specific number of farms

35

Number of households

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0 0

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90

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30

CHAPTER 6 THE STUDY OF RANDOM AND PROXIMITY-BASED TREE SPECIES DIVERSITY ON FARMS IN WESTERN KENYA USING EXACT SPECIES ACCUMULATION CURVES R KINDT, P VAN DAMME & AJ SIMONS SUBMITTED TO JOURNAL OF APPLIED ECOLOGY

With the objective of promoting diversification of on-farm tree species composition through domestication, a survey was conducted in western Kenya involving a complete tree census, tree measurement and collection of ethnobotanical information in 201 smallscale farms (0.1-2.2 ha). Exact values of average expected species richness at each scale from farm to complete survey were calculated for random and proximity-based (randomly adding farms within the same village first) species accumulation based on the hypergeometric distribution. Alpha and beta diversity statistics were calculated for the 12 most frequent use-groups of trees using the ŜN=cNz(N) model. Monte-Carlo analysis involving 100,000 random site sequences determined ranges in species richness obtained for subsamples, and proved that random distribution of species over farms would result in higher proximity-based species richness at intermediate scales. Important differences detected between the use-groups in alpha and beta diversity can help in targeting agroecosystem diversification efforts in an often tree-rich but species-poor landscape.

SPECIES ACCUMULATION CURVES 6.1. Introduction One of the purposes of agroforestry tree domestication is enhancement of stability and productivity of agro-ecosystems by diversifying on-farm tree species composition (presence and abundance). Diversification and intensification of land use through domestication of agroforestry trees is one of the three pillars of ICRAF’s research (ICRAF 1997; ICRAF 2000). In this chapter, a methodology of studying tree species diversity at various scales (ranging from the individual farm to the complete survey) is presented and discussed. Ecological experiments and models have been able to show that there is a positive but conditional relationship between species diversity, and ecosystem stability and productivity (for example Naeem et al. 1994; Tilman 1996; Tilman et al. 1997a, 1997c; Hector et al. 1999; Yachi & Loreau 1999; Ives et al. 2000; Nijs & Roy 2000). Findings of models and experiments involving crop mixtures in agroecosystems showed the same positive relationship (Dover & Talbot 1987; Van Noordwijk et al. 1994; Swift et al. 1996; Trenbath 1999; Van Noordwijk & Ong 1999). However, whether higher productivity associated with higher diversity resulted from overyielding (a synergistic relationship among species so that productivity of the mixture is higher than any species growing in isolation), or from greater chances of sampling more productive species in more species-rich mixtures (a phenomenon described as the sampling effect), remains controversial (Huston et al. 2000; Kaiser 2000a, 2000b; Purvis & Hector 2000). In the latter case, each combination of species that includes the most productive species would have the highest productivity, including monocultures. McCann (2000) mentioned that the condition of generally weak bioenergetic consumer-resource interactions needed to be added so that diversity could enhance stability. The models mentioned above (Tilman et al. 1997b; Yachi & Loreau 1999; Ives et al. 2000; Nijs & Roy 2000) have shown that diversity in traits of species and environmental heterogeneity are explanatory factors for diversity-productivity and diversity-stability relationships. Heterogeneity in species characteristics results in saturating effects of increased diversity because randomly added species will each differ less from species already present. The studies (e.g. Tilman 1996 for stability, Hector et al. 1999 for productivity) demonstrated that diversity only explains a small amount of variation in the data so that some species-poor ecosystems can still have relatively high stability or productivity and vice versa. This variation probably also results from differences in heterogeneity in species traits at fixed species richness. Because of saturating effects of diversity on productivity and stability, the approach taken in this chapter was identification of use-groups with low diversity, since, on average, more important effects may be expected from increasing diversity within groups with lower diversity levels. Most species richness data refer to natural or semi-natural ecosystems. We focus on farm-level tree diversity in landscapes where farmer management dominates the presence of trees, but spontaneous regeneration of trees still occurs. Diversity enhancement is considered at all levels from farm (alpha diversity) to complete survey level as, on average, stability and productivity enhancement by diversification can be expected at each level where heterogeneity of species traits and environmental characteristics occurs. To accurately describe changes in species richness (S) with increasing farm number (N), the expected average species (ŜN) accumulation curves were calculated from the hypergeometric distribution. S obtained from random additions of farms was compared to proximity-based (randomly adding farms within the same village first) S to explore ways of enhancing richness at intermediate scales.

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CHAPTER 6 6.2. Material and methods Formulation of use-groups (4.1), species accumulation curves (4.2.2), randomization tests on the influence of sample size on species richness (4.2.3)

6.3. Results 6.3.1. Form of Species Accumulation Curves and Values of Associated Parameters Species accumulation curves for all species and the 12 most common use-groups are presented in figure 6.1. In figure 6.1, five different pools of species groups can be characterised in terms of Stot = Ŝ201 (i) highest richness, including firewood and all species (pool A); (ii) high richness, only including shade (pool B); (iii) medium richness, including medicine, ornamental and timber (pool C); (iv) moderate richness, including fruit, construction, soil fertility, boundary demarcation and charcoal (pool D); and (v) low richness, including fodder and beverage (pool E). Figure 6.2 illustrates that random beta diversity (zN) has decreasing values at increasing scale (N 2→201), except for construction where values were increasing. Within the five pools, individual groups show distinctive differences in random alpha (c, table 6.1) and beta diversity (table 6.1, figure 6.2). Random beta diversity of groups of the same pool had the same rank order (smaller or larger values) for all scales, except for construction and fruit in pool D. Within pool A, firewood had smallest alpha and beta diversity. In pools C, D, and E (again with the exception of construction), groups with smaller alpha diversity had larger beta diversity. Figure 6.2 shows that the decrease of zN is non-linear compared to ln(N), whereas comparing zN to N (figure not provided) showed stronger non-linear trends. Alpha diversity of many use-groups is quite low. Only five (six when also including construction with c=1.935) use-groups have buffering (redundancy) against loss of one species on the average farm by having at least one alternative species. Proximity-based ŜN (figure 6.1) and beta diversity (table 6.1, figure 6.2) followed a more complex pattern than random ŜN and beta diversity, since few new species were added when only a few sites could be sampled from the same village and vice versa. Obviously, alpha diversity and Stot were the same for random and proximity-based sampling. At intermediate scale, proximity-based beta diversity was lower than random beta diversity, even for beverage at N=50 (not clearly shown in the table and figures) with proximity-based beta diversity = 2.9950229 < 2.9950248 = random beta diversity. Generally, values of proximity-based beta diversity decreased with increasing scale. However, shade, charcoal, fruit, fodder and beverage were groups where z50 < z100. 6.3.2. Effects of Sample Size on Species Richness Table 6.2 shows that 100,000 randomizations yielded random Ŝ100 and Ŝ50 that were very close to the exact calculations based on the hypergeometric distribution. Table 6.2 further shows the large ranges obtained for random S – even when investigating the 95% confidence intervals for subsamples of 100 sites. Confidence intervals for 100 site subsamples, however, allow discriminating the same pools determined above from Stot, with only the intervals of boundary demarcation of pool D overlapping with those of groups belonging to pool C. Ŝ50 was similar for random and randomly-clustered sampling – which was expected for the average of 50 randomly selected farms and the average for four random subsamples of 50 farms. Intervals for proximity-based sampling were however substantially smaller, as the ranges of randomly-clustered richness were within the confidence interval of random richness. The only

93

SPECIES ACCUMULATION CURVES overlaps in ranges of randomly-clustered richness between pools occurred between boundary demarcation and two groups of pool C. The ranges obtained for randomly-clustered richness were in general above the proximity-based richness. The lowest ratio of proximity-based Ŝ50 to randomly-clustered Ŝ50, 0.77, corresponded to boundary demarcation, the highest ratio, 0.96, to construction. Proximity-based Ŝ50 was higher than the minimum values of randomly-clustered richness for medicine, charcoal, fruit, construction, and beverage. The former value only occurred within the confidence interval of charcoal and construction, although for charcoal the value may not belong to the actual interval since we calculated the interval in a conservative manner. Not observing the actual proximitybased richness in 100,000 random sequences is not surprising as we calculated that approximately 7.7 E+117 arrangements of 4 × 50 sites could be created from 201 samples. For total S of the use-groups (Stot= Ŝ201), no estimations of ranges are available as only one sample of 201 farms was taken. Therefore, the effect of sampling 201 farms on the accumulation curve could not be estimated.

6.4. Discussion Crawley & Harral (2001) and Rosenzweig (2001) cite several reasons why species accumulate in number with increasing sample size, including sampling effects for rarer species, spatial clumping and segregation, habitat effects, higher speciation and lower extinction rates, and lower chances of area-wide anomalies. Ritchie & Olff (1999) describe that species-area patterns will emerge if larger species only detect resources in larger patches within their habitat, but tolerate lower concentrations of resources than smaller species. Hanski & Gyllenberg (1997) demonstrated that interspecific differences in abundance and metapopulation dynamics result in species-area and distribution-abundance curves. Using the hypergeometric distribution to calculate the results from random sampling, we calculated species-area curves that occur when not every species is present on each site. The species accumulation patterns (and distribution-abundance patterns, Kindt, unpublished results) were also the result from anthropogenic effects, especially through decisions of individual farming households to obtain germplasm or to protect species. Accumulation curves presented in this article show how ŜN increases (or declines when investigating for N 201→1) when farms are randomly added (removed), when addition (removal) is not based on their proximity, or when all farms belonging to the same village are sampled first. The random site accumulation approach used here, however, also included an element of area contiguity because each site was a contiguous area. Hanski (1998) indicates that habitat destruction is likely to be a combination of loosing a contiguous region of the landscape, loosing a complete habitat fragment or reduction in area of an individual fragment. For each fragmentation pattern, an accumulation curve could be constructed, therefore many accumulation curves could be constructed for the same area. However, every accumulation curve will converge towards the same Ŝ1 and Stot. Using the hypergeometric distribution to calculate random ŜN at intermediate scales yielded exact results, what contrasts to calculating these values by random additions of sites (e.g. the MonteCarlo approach in this chapter; Colwell 1997; Rennolls & Laumonier 1999). Since the calculation by means of the hypergeometic distribution only used information on the farm/site-frequency of each species, it means that clustering of species within sites has no influence on the average species accumulation. This contradicts the statement by Gotelli & Colwell (2001) that samplebased accumulation curves can not be obtained theoretically as they also depended on the spatial distribution of individuals. The spatial distribution will, however, influence the range of values observed for a particular sample size (e.g. 50 farms), as some random sequences will obtain 50

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CHAPTER 6 farms that contain many different species, while some other random sequences will obtain 50 farms that share many species. Using the hypergeometric distribution to calculate proximity-based ŜN also contrasts to the practice of calculating the average richness of consecutive bisections of an area (e.g. Plotkin et al. 2000). Calculating ŜN by the second approach does not consider other contiguous subsamples of the same size that could be formed from combinations of the smallest sample size that overlap bisections. However, since we did not consider farm proximity within villages, most additions within villages were not expected to result in contiguous areas – because of the small farm and village sizes, we did not expect distance among farms within the same village to have a strong effect on beta diversity. An approach that would first determine combinations of sites resulting in contiguous areas, followed by calculating ŜN, could generate figures that are more accurate. These methods could either determine all combinations resulting in contiguous areas of the same size, followed by calculating ŜN through the hypergeometric distribution, or approximate ŜN through randomisations. Calculation of randomly-clustered SN as presented in this paper could subsequently investigate how frequently the observed proximity-based richness was observed. After equalling parameter c of the ŜN= cN z N model to Ŝ1, we demonstrated that a constant parameter zN could not accurately describe species accumulation in the groups we differentiated, which implies that beta diversity was not constant with scale. Moreover, we observed non-linear relationships between zN and N or ln(N). Plotkin et al. (2000) observed that log-log species-area curves for five 50 ha tropical forest plots did not have a constant slope, and therefore expanded the S=cAz model with a polynomial term. Other studies also showed overestimation of slopes for large areas, and underestimation for small areas (May & Stumpf 2000, Crawley & Harral 2001) with the classical power function model. Lomolino (2000) argued that the species-area relationship could be sigmoidal as the species richness of relatively small islands species seemed to be independent of island size, while an asymptote of species richness was observed for the largest islands. Losos & Schluter (2000) showed that the slope increased significantly beyond a threshold island size. Harte et al. (1999) demonstrated that self-similarity of species distributions within an area corresponded to a constant z value, but also mentioned that it is extremely unlikely that the species-area relationship would have constant z values across scales. Calculations (Richard Coe, ICRAF, unpublished results) demonstrated that the two-parameter species accumulation model actually represents rare cases of random or proximity-based species accumulation associated with very even species abundance distribution. The ŜN= cN z N (N: accumulated number of sites) and ŜA= cAz A (A: accumulated area) models are conceptually similar. By calculating the average size of one site (A1), ŜA for any size of area AN = N A1 can be calculated by N-based models by determining N. A possible criticism on calculation of species accumulation models in this paper could be that farm sizes vary, and that therefore random runs of site additions involved random additions of various farm sizes. A similar criticism could be that farm shapes differ, while patch shape influences S per unit area (Condit et al. 1996; Harte et al. 1999; Maddux et al. 1999). Differences in diversity of various on-farm niches such as external boundaries, woodlots and homestead areas and varying proportions of these niches associated with shape differences (Kindt, unpublished data) could amplify shape effects on diversity. Interpretation of accumulation curves should however not pose any problems, as long as one realises that accumulation curves resulted from double averaging. The horizontal axis represents accumulation of the average farm (size and shape), whereas the vertical axis shows associated ŜN. Accumulation curves thus still present the expected average species richness for N farms. In analogy to Fonseca & Ganade (2001) who calculated how functional groups are lost when species become randomly extinct, our methodology could be used to calculate how functional groups are lost when an area fragmentises at random. Calculating species accumulation patterns

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SPECIES ACCUMULATION CURVES from the hypergeometric distribution can also prove useful to separate effects of area reduction on alpha and beta diversity. Gonzalez (2000) for example differentiated between species loss through fragmentation by random sampling from the formerly continuous landscape, and subsequent species loss through fragmentation from community relaxation. The second process is a longer-term dynamical process where species that were included in fragmented patches by random sampling, disappear through time as they are sink species (species that do not reproduce enough to prevent their extinction after fragmentation) or because rare area-wide species extinctions occur (Rosenzweig 1999, 2001). The second process could be described by reductions in alpha diversity with time (c(t), t: time), since relaxation can be studied on areas of the same size (i.e. ideally not reflected in changes of the parameter that describes beta diversity). For the first process, we recommend calculating the expected loss in richness from random sampling by the hypergeometric distribution, rather than calculating the expected loss by the classical model with constant parameter z. Equalling average diversity at one site to alpha diversity may seem arbitrary, while beta diversity is usually not expressed as the exponent z of power function models describing species accumulation. Stot values provide indications of gamma diversity differences of use-groups, although these values are almost certainly underestimates. However, as Loreau (2000) pointed out, there is no precise ecological prescription on the division between scales. This author differentiated between multiplicative (β=γ/α) and additive (β=γ-α) approaches of defining beta diversity. Describing beta diversity as exponent z of species accumulation models anticipates the problems described in Loreau (2000) associated with the scale at which alpha diversity is measured. This description of beta diversity also conforms to the concept of intrabiogeographical-province species accumulation curves (Rosenzweig 1999, 2001), and observations that often much of the structure of local assemblages can be modelled as random draws from regional species pools (Griffiths 1999; Gaston 2000). Predicting S for a larger area than the sample area (“abundifaction”, in the context of this chapter means extrapolation of accumulation curves) is difficult (Hayek & Buzas 1997 pp. 344-345). Rennols & Laumonier (1999) advocated studying random subsamples of the total and the sample area to be able to extrapolate species-area curves. Plotkin et al. (2000) concluded that their models provided the most accurate framework to predict species richness from small samples. However, they first calculated parameters describing the complete form of species accumulation curves, and then calculated differences in calculations of Stot based on the variation in S for subsets of the area. The equivalent analysis for the method described here would be to first determine zN parameters, and then analyze the influence of parameter c estimation from subsets of data. Plotkin et al. (2000) observed accurate predictions because the five forests they studied mainly differed in parameter c (May & Stumpf 2000), thus mainly differed in alpha diversity. Large ranges in S observed for subsets of 100 and 50 sites as observed in this article caution against extrapolation of accumulation curves based on parameter zi estimations. However, in case Stot of a survey approximates the average that would be obtained by taking various samples of the same survey area size (Ŝtot), then extrapolation of parameters zN (N>Ntot) could allow for more reliable estimations of ŜN>Ntot. Species accumulation curves showed differences between use-groups, which allows targeting interventions towards use-groups of lower ŜN. If stability of production, or productivity, of individual farms would be linked to S at the individual farm level (conditions such as heterogeneity in environmental and species trait characteristics were discussed in the introduction), one target of domestication research and interventions could be to increase alpha diversity of use-groups. Interventions could focus on groups with low alpha diversity (for example, construction, medicine, soil fertility, charcoal, and fodder). Interventions may focus on other groups based on other criteria, but calculated alpha diversity values will still provide benchmark values of diversification.

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CHAPTER 6 Gamma and beta diversity could provide some answers on how alpha diversity could be improved. For groups with high gamma diversity, diffusion between farmers of germplasm and information on species usage could be promoted. Reasons to promote species that are already present in the landscape could be related to species conservation through use (Kindt & Lengkeek 1999), biosafety precautions, or ecological suitability considerations. For groups with low gamma diversity, interventions can attempt to introduce additional species, or to discover or promote alternative uses of species present. Improving gamma diversity could contribute to improving the stability (or productivity) of agroecosystems at the survey level. Using the same line of thought, approaches that would result in higher beta, but similar alpha and gamma diversity, could also improve ecosystem functioning, as ŜN for subsets of sites would be larger. Our results show that the average richness observed in the four villages (proximity-based ŜN) was substantially lower than the richness of random subsamples of the same size (randomly-clustered SN). This finding implies that a more random distribution of existing species over farms within the area would result in higher proximity-based beta diversity and ŜN, and potentially better agroecosystem functioning. Whereas this work identifies where to intervene in a tree-rich but often species-poor landscape, diversification should be farmer driven and not merely an exercise in curve shifting. Probably the most important factor to consider is that alpha diversity of individual farms may be quite high for use-groups with low average alpha diversity, and vice versa, so that relevance of diversification may be farm- and use-group dependent. Warner (1993) stated that farmers, confronted with deforestation, were most likely to establish trees for products for which non-tree alternatives were not available. Construction and timber could be such groups, whereas herbs could provide medicine, or residues from crops, firewood. Diversification of the former use-groups may therefore be more relevant. Botanical similarity (family, genus) or similarity in species’ characteristics (e.g. Pachepsky et al. 2001) could be another factor to consider when planning for diversification interventions. Even when targeting use-groups of higher alpha diversity, however, analysis of gamma and beta diversity as presented above can aid in designing interventions seeking to diversify agroecosystems.

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SPECIES ACCUMULATION CURVES Table 6.1. Parameter values for N=2, 50, 100, 150 and 201 for the ŜN= cN z N model for species accumulation curves for all species and species belonging to use-groups. Species group All Firewood Shade Medicine Ornamental Timber Boundary demarcation Charcoal Soil fertility Fruit Construction Fodder Beverage

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0.444 0.439 0.630 0.724 0.742 0.479 0.468 0.713 0.626 0.315 0.440 0.618 0.450

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Random accumulation z50 z100 0.483 0.460 0.476 0.454 0.706 0.665 0.773 0.746 0.804 0.774 0.516 0.494 0.510 0.489 0.774 0.745 0.665 0.646 0.332 0.321 0.402 0.431 0.731 0.659 0.491 0.455

z150 0.450 0.445 0.644 0.734 0.756 0.485 0.477 0.728 0.634 0.317 0.439 0.631 0.449

Proximity-based accumulation z2 z50 z100 0.581 0.442 0.448 0.573 0.442 0.447 0.884 0.670 0.656 0.916 0.738 0.741 0.915 0.753 0.754 0.625 0.485 0.485 0.617 0.441 0.463 0.889 0.753 0.738 0.700 0.607 0.620 0.398 0.317 0.314 0.261 0.392 0.413 0.908 0.678 0.639 0.680 0.463 0.455

z150 0.445 0.440 0.641 0.730 0.748 0.482 0.467 0.724 0.628 0.313 0.433 0.623 0.449

Table 6.2. Averages (ŜN), ranges (minimum: Min., maximum: Max.), and lower and upper 95% confidence interval limits (2.5%, 97.5%) for species richness obtained for random (r) and randomly-clustered (rc) subsamples (N: subsample size) obtained from random site sequences. Exact expected averages calculated for random (r) and proximity-based (p) accumulation (Accum.). Species group All Firewood Shade Medicine Ornamental Timber Boundary demarcation Charcoal Soil fertility Fruit Construction Fodder Beverage

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N 100 50 50 100 50 50 100 50 50 100 50 50 100 50 50 100 50 50 100 50 50 100 50 50 100 50 50 100 50 50 100 50 50 100 50 50 100 50 50

Exact calculations Accum. ŜN r 137.67 r 109.25 p 93.32 r 122.82 r 97.63 p 85.59 r 63.38 r 46.73 p 40.86 r 38.83 r 25.58 p 22.41 r 36.49 r 23.93 p 19.68 r 37.57 r 28.89 p 25.71 r 27.03 r 20.86 p 15.97 r 19.10 r 12.72 p 11.72 r 19.07 r 13.12 p 10.47 r 20.63 r 17.20 p 16.23 r 14.10 r 9.36 p 8.98 r 5.49 r 4.57 p 3.74 r 3.00 r 2.50 p 2.25

Results from 100,000 randomizations Subsamples ŜN Min. r 137.67 111.0 r 109.24 81.0 rc 109.25 104.4 r 122.83 98.0 r 97.63 72.0 rc 97.63 92.7 r 63.38 47.0 r 46.72 27.0 rc 46.73 42.7 r 38.84 18.0 r 25.57 9.0 rc 25.58 22.3 r 36.50 20.0 r 23.93 8.0 rc 23.93 20.6 r 37.57 23.0 r 28.89 17.0 rc 28.89 26.3 r 27.03 18.0 r 20.86 10.0 rc 20.86 18.3 r 19.09 7.0 r 12.72 2.0 rc 12.72 10.0 r 19.09 8.0 r 13.13 3.0 rc 13.12 10.8 r 20.62 14.0 r 17.19 10.0 rc 17.20 15.5 r 14.10 4.0 r 9.36 2.0 rc 9.36 7.0 r 5.49 3.0 r 4.57 1.0 rc 4.57 3.8 r 2.99 1.0 r 2.50 1.0 rc 2.50 2.0

Max. 162.0 143.0 113.8 145.0 129.0 101.6 78.0 66.0 50.0 57.0 47.0 28.1 49.0 41.0 26.5 48.0 43.0 31.3 34.0 32.0 22.8 27.0 25.0 14.3 27.0 24.0 14.8 25.0 24.0 18.3 20.0 18.0 10.8 7.0 7.0 4.8 4.0 4.0 2.6

2.5% 124.0 94.0 106.9 110.0 84.0 95.5 55.0 36.0 44.9 28.0 15.0 24.2 28.0 15.0 22.5 31.0 22.0 27.7 22.0 15.0 19.7 13.0 6.0 11.7 13.0 7.0 12.2 16.0 13.0 16.4 9.0 4.0 8.4 3.0 2.0 4.2 2.0 1.0 2.4

97.5% 150.0 124.0 111.5 134.0 111.0 99.6 71.0 56.0 48.3 48.0 36.0 26.8 44.0 33.0 25.3 43.0 35.0 30.0 31.0 26.0 22.0 24.0 19.0 13.8 24.0 19.0 14.0 24.0 21.0 18.0 18.0 14.0 10.3 7.0 6.0 4.8 4.0 4.0 2.6

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Figure 6.2. Changes in parameter zN of the S N = cN z N model for random species accumulation curves for all species and for the 12 most frequent use-groups in the landscape (horizontal axis: number of farms on ln-scale, vertical axis: parameter zN). Symbols (not all values included) and dotted lines correspond to random species richness, full line to proximity-based species richness, dashed lines connect the extreme values of z2 and z201. a: species’ pools A and B, b: pool C, c: pool D (symbols: see Fig. 1), d: pool E (see text)

CHAPTER 7 STUDYING ON-FARM TREE SPECIES RICHNESS AND EVENNESS IN WESTERN KENYA WITH DIVERSITY PROFILES R KINDT, P VAN DAMME & AJ SIMONS SUBMITTED STATISTICS

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Species diversity is a function of the number of species and the evenness in the abundance of the component species. Diversification, therefore, can involve adding new species, or making species more evenly distributed. We derived evenness profiles from the Rényi diversity series, which allowed ranking systems in richness and ranking them in evenness. We applied the methodology to investigate differences in diversity among the main functions of trees on western Kenyan farms. No group had perfectly even distributions. Evenness could especially be enhanced for construction materials, fruit, ornamental, firewood, timber and medicine, which included some of the most speciesrich groups of the investigated landscape. When considering only the evenness in the distribution of the dominant species, timber, medicine, fruit and beverage ranked lowest (> 60% of trees belonged to the dominant species of these groups). Interestingly, these were also the groups that are mainly grown by farmers to provide cash through sales. For our data, evenness profiles allowed for easier discrimination of differences in evenness than provided by the alternative method of the Lorenz order. The influence of sample size on Rényi diversity profiles was investigated, which is an expansion of species accumulation curves as also the sample size – evenness relationship can be investigated. Accumulation patterns indicated that most groups had abundance distributions most similar to the log series model. Shade and fodder had an abundance distribution more typical of the log normal model. Since the calculation of (accumulation patterns of) diversity and evenness profiles pose no computational difficulties, we advocate to use these methods to fully study diversity and evenness of systems of interest.

DIVERSITY PROFILES 7.1. Introduction Diversity means different things to different people. Most often in natural or agricultural systems, species counts are provided as the measure of diversity. Continuing this logic, diversification means adding more species. Species diversity, however, is a function of the number of species, and the evenness in distribution of species’ abundances (Magurran 1988 pp. 7-8; Purvis & Hector 2000). Options for diversification can therefore be dissociated into interventions that target species richness, and those that target species’ evenness. In the realm of agroforestry, underpinning the need for diversification is the desire to enhance the stability and productivity of agroecosystems (ICRAF 1997 pp. 6-7; ICRAF 2000 pp. 24-25). Field experiments and ecological models have shown the positive but conditional relationships between species diversity, and (agro)ecosystem stability and productivity (Kindt et al. – Chapter 2, 4 & 5). The conditions for the relationships are heterogeneity in environmental and species’ characteristics. Adding few individuals of a new species to a system will, therefore, have a smaller effect than adding more individuals that result in a more even abundance distribution of all species composing the system. Similarly, making the abundances of species already present in a system more evenly distributed may have larger effect than adding new species. Diversification with the aim of enhancing ecosystem stability and productivity should therefore seek a balance in increment of species richness and evenness. We investigated the diversity of various groups of species that contributed to similar service or production functions on farms in western Kenya, as our target was diversification within these functions, and not of overall species diversity. Since heterogeneity in characteristics of species results in saturating effects of increased diversity on ecosystem stability (Holmes 1998), we expect larger effects from increasing diversity within functions (“use-groups”) with lower diversity. Ranking of use-groups in diversity, therefore, allows prioritising their scope for diversification measured as the average expected effect of adding one species. Diversity ordering techniques were designed to differentiate more diverse from less diverse systems. Tóthmérész (1995) was able to show that the Rényi profile, which we use in this chapter to rank use-groups, is one of the most useful ordering techniques. We expanded the use of the Rényi series to ranking of evenness by dissociating the contributions of species richness and species’ evenness to diversity profile form. Doing so, we could prioritise use-groups for species richness and for species evenness increment. In addition, such approaches provide the opportunity to model effects of replacement, substitution and expansion at fixed and varying tree densities.

7.2. Material and methods Formulation of use-groups (4.1), diversity profiles (4.2.4), accumulation patterns of diversity profiles (4.2.5) 7.3. Results 7.3.1. Diversity Ordering and Examination of Richness and Evenness Contributions Figure 7.1 shows the Rényi diversity ordering of all species and the twelve most frequent usegroups. By examining the values at scales 0, 1, 2, and ∞, species richness and values of Shannon, Simpson, and Berger-Parker diversity indices can be inferred. The many intersections in the figure show the difficulties to order most groups in diversity. Table 7.1 provides parameters describing the pattern of the various accumulation curves. Figure 7.2 shows the Rényi evenness

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CHAPTER 7 ordering. This figure shows that many groups have intersecting profiles, so that many groups can not be ranked in evenness. We differentiated between five pools of species groups based on their total species richness (H0): (i) pool A - very high richness, including all species and firewood; (ii) pool B - high richness, including shade; (iii) pool C - medium richness, including medicine, ornamental and timber; (iv) pool D - moderate richness, including fruit, construction, soil fertility, boundary demarcation and charcoal; and (iv) pool E - low richness, including fodder and beverage. Some differences and similarities in diversity and evenness within and between pools can subsequently be detected (figures 7.1 and 7.2). Groups of pool A and B were more diverse than the other groups. The evenness profile shows that shade (pool B) has a more even distribution than groups of pool A. Diversity profiles of groups of pool C intersected all groups of pool D and E, with the exception of fruit and construction (pool D) and beverage (pool E). The evenness profiles, however, show that ornamental and timber had lower evenness than all groups of pool D, with exception of construction. The evenness profile of medicine intersected the other groups of pool C. All groups of pool C had intersecting evenness profiles, therefore none could be classified as more evenly distributed within the group. Most groups within pool D could be ranked in diversity, since their diversity profiles did not intersect. Boundary demarcation is the most diverse and evenly distributed group within the pool. Construction is the least diverse group of the pool and fruit the second least diverse. Their evenness profiles intersect however. Charcoal and soil fertility had intersecting diversity and evenness profiles. As their species richness is the same, charcoal can be categorised as the group where the dominant species is more evenly distributed, but where the other species are less evenly distributed. Within pool E, fodder is more diverse and more evenly distributed. This group has the most even distribution of all groups, resulting in intersections with diversity profiles of all groups of pool C and D, with the exception of boundary demarcation. Values of H∞ (figure 7.1 and table 7.1) lower than 0.5 show that the dominant species of timber, medicine, fruit, and beverage contains more than 60 percent of all trees of these use-groups. Values of H∞ between 0.5 and 1.5 indicate that the dominant species contains between 22 and 60 percent of all trees for soil fertility, charcoal, ornamental, fodder and boundary demarcation. The dominant species contains a smaller percentage of trees for the other groups. However, values of E∞ (figure 7.2) show that the dominant species for all species and firewood are not very evenly distributed. Medicine had the least evenly distributed dominant species. Values of (H0 - H1)( H0 -H∞)-1 provide an insight in evenness distribution of species within groups when excluding the dominant species. Fodder has, besides the most evenly distributed dominant species, also the most evenly distributed other species. Shade ranks second in distribution of other species. Construction, timber, and ornamental rank last in the distribution of other species. 7.3.2. Effects of Sample Size Figure 7.3 shows the accumulation patterns for diversity profiles of the various use-groups we distinguished. Several accumulation patterns can be observed. Diversity profile values increased with increasing numbers of sites for all scales for shade and fodder. Diversity profiles reached stable values from 50-100 farms onwards, except for small scales (α1) were decreasing for subsamples larger than 100 farms.

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DIVERSITY PROFILES Figure 7.4 shows the average profile values and associated 95 percent confidence intervals and ranges for 100 farm subsamples. The figure indicates that, on average, similar profiles are obtained as for the full sample. However, profiles, especially medicine, fodder, and shade, had large confidence intervals. The confidence intervals still allow classifying use-groups in the richness pools that we differentiated above. The only overlapping confidence intervals were for the groups of pool C and boundary demarcation (pool D). Within pools, most confidence intervals of diversity profile values overlap. However, within pool D, boundary demarcation has the most even distribution of other species, while the opposite phenomenon occurs for construction. Fodder is more evenly distributed than beverage in pool E. Figure 7.3 suggests that extrapolating profile values should model a non-linear change of Hα with increasing farm number as for species richness, except where stable values were obtained. Table 7.1 (H∞,l and H∞,u) indicates the ranges that are expected for the average value of H∞ when all farms would be sampled in the survey area. Relatively large ranges were even obtained for groups where stable values were obtained at the respective scale (timber, fruit, construction, and beverage). However, these ranges were among the smallest. Large ranges of H∞ for the complete sample (table 7.1) corresponded to large ranges of the statistic for 100 farm subsamples (figure 7.4).

7.4. Discussion 7.4.1. Interpretation of Diversity and Evenness Profiles We calculated the evenness profile as Hα-H0 = ln(Eα). This is an expansion of the decomposition of the Shannon diversity index by Hayek & Buzas (1997 eq. 14.1) as: H = ln(S) + ln(E) = ln(S) + ln(eH/S). Rousseau et al. (1999) used the Lorenz order to determine differences in evenness between systems. The Lorenz curve is calculated by connecting points representing the cumulative proportion of species against the cumulative proportion of abundances, where species abundances are arranged in ascending order. If some values of the Lorenz curve of system a are greater and none are smaller than those of system b, then system a is more evenly distributed. If the Lorenz curves intersect however, then systems are non-comparable. We provided the Lorenz curves for all groups in figure 7.5. Comparing the curves of figure 7.2 and 7.5 shows that a similar ordering of groups in terms of evenness is obtained: fodder is the most diverse, while most other groups have intersecting profiles (for instance boundary demarcation and shade or medicine). It can be demonstrated numerically that systems with the same Lorenz curve will have the same evenness profile, and that this phenomenon can only occur for systems with nxS (nx=1,2,3... and S fixed) species each. We interpreted ln(E∞) = H∞ - H0 as the contribution the dominant species to evenness. By substituting H∞ - H0 = ln(p-1) - ln(S) = -ln(Sp), it can be demonstrated that for two systems with the same E∞, Sa pa = Sb pb. The same condition can be deducted for the last sections of Lorenz curves of two systems (connecting the cumulative proportion of Stot-1 species to (1,1)) to reflect the same evenness. Where Sapa > Sbpb, or E∞ of system a is smaller, one of the conditions for system a to be less even

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CHAPTER 7 according to the Lorenz or Rényi evenness order is fulfilled. For instance, figures 7.2 and 7.5 show that beverage has a less evenly distributed dominant species than fodder. Rousseau et al. (1999) recommended using the k-dominance plot ordering method, a method of which a modified version also performed well in the analyses conducted by Tóthmérész (1995), but they did not consider the Rényi series. An advantage of the Rényi profiles over (modified) kdominance curves is that they allow for discrimination between species richness (scale 0), evenness (decline of the profile from H0 with increasing scale, ln(Eα)) and the influence of the dominant species on evenness (value at scale ∞) of various systems by comparing values on the same Hα axis. The logarithm in the formula makes visual comparisons of systems with many species easier. For instance, intersections can be distinguished easier in figure 7.2 than in figure 7.5. Another advantage of the Rényi profiles is that the values of the widely used Shannon and Simpson diversity indices are provided, what facilitates comparisons with other studies that used these indices. Because diversity is represented on a single axis versus fixed scales, it is also easier to show the effects of sample size on diversity by plotting sample size on a third axis as in figure 7.3. We therefore recommend using the Rényi series to compare richness and evenness of various systems, especially if the analysis includes investigation of sample size effects. 7.4.2. Using Only One Diversity Index The many intersections in diversity profiles have the result that ranking of groups based on a single diversity index will often be influenced by the relative contributions of evenness and richness in the calculation of the index. For example, using the Shannon index will rank medicine as more diverse, and ornamental as less diverse, while usage of the Simpson index would result in the reverse order. Fodder and medicine have a similar Shannon index value, but fodder has fewer species but larger reciprocal Simpson and Berger-Parker indices. Where diversity profiles intersect, it is not possible to rank one system as more diverse, and using a single diversity index will therefore result in incorrect conclusions. Because evenness profiles may intersect, calculating evenness based on the Shannon diversity index as J=H/ln(S) (Magurran 1988 eq. 2.22; Hayek & Buzas 1997 eq. 13.9) or as E = eH/S (Hayek & Buzas 1997 eq. 13.10) and ranking groups on J or E values will result in similar inaccuracies as ranking these groups in diversity based on a single diversity index. The interested reader may, for example, investigate evenness profiles for one system with 4 species of abundance 10, 6, 4 and 1 with another system with 8 species of abundance 50, 13, 12, 12, 12, 12, 11 and 9, which have similar Shannon evenness, but intersecting evenness profiles. For practical purposes (see below), when systems with lowest comparative evenness need to be selected, the decomposition of the Shannon index may prove sufficient. However, since the calculation of evenness profiles can be achieved easily (moreover, since software was developed that directly provides this information from farms × species matrices) we would still advocate to use the evenness profile for reasons of complete characterisation of evenness. Information on the evenness of the dominant species, as provided by an evenness profile, also offers an interesting statistic. The decomposition of the diversity profile into Hα = H0 + ln(Eα) explains that Shannon and Simpson indices should be perfectly correlated for systems that have the same evenness profile. Magnussen and Boyle (1995) found strong linear correlations between both indices (0.98) based on mathematical modeling of species’ distributions. As the models they used actually described the evenness in species distribution, this high correlation was to be expected.

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DIVERSITY PROFILES 7.4.3. Effects of Sample Size Magurran (1988, p. 72) indicates that a Rényi series value with larger scale parameter value has reduced sensitivity to sample size. Gimaret-Carpentier et al. (1998) observed that the Simpson index (α=2) reached stable values at lower sample sizes compared to the Shannon index (α=1). We observed these patterns for those groups where stable values were obtained at all larger scales (timber, fruit, construction, and beverage). Shade and fodder, however, showed the other extreme with increasing profile values with increasing sample size, and not asymptotic values. Hayek & Buzas (1997 pp. 378-390), based on May (1975), proposed to investigate accumulation patterns of H (figure 7.6), ln(E) and ln(E)/ln(S) (figure 7.7) to choose the best abundance distribution model (“SHE analysis”) as distributions often converge. Reaching asymptotic values for these statistics with increasing sample size would indicate that the species abundance distribution corresponds to respectively a log series, a broken stick (ln(E)≈-0.42 for S > 30) or log normal distribution. We could, therefore, conclude that most groups (and especially timber, fruit, construction and beverage) have abundance distributions more typical of the log series distribution (constant H), while the distributions of shade and fodder corresponded more to a log normal distribution (constant ln(E)/ln(S)). Possibly, some log series patterns resulted from not sampling all species. May (1975) describes that less complete samples often conform to the log series distribution, while samples with a large number of species are expected to be log-normally distributed (as a result from the Central Limit Theorem). Therefore, we can not predict whether larger sample sizes would have resulted in a smaller number of groups with patterns typical of the log series distribution than we observed here. As Rényi profile accumulation patterns can be calculated for various abundance distribution models, we suggest to expand the SHE analysis of Hayek & Buzas (1997 pp. 378-390) to the complete diversity profile, as in figure 7.3. For example, for a log series (and a geometric series), the Simpson and Berger-Parker indices are also expected to reach constant values (May 1975). This was a phenomenon that we could observe most clearly (specific figures not included, but see figure 7.3) for the groups with the clearest asymptotic H pattern. Analysis with simulated datasets showed an asymptotic pattern of Hα≥1 for a log series distribution that was not observed for a lognormal distribution (Kindt unpublished data). Since asymptotic H1 and H2 are only expected for the log series distribution, the findings of Gimaret-Carpentier et al. (1998) most likely correspond to studies of systems more conform to this distribution. 4.4. Designing Interventions Based on Diversity Profiles Analysis of diversity profiles provided insights in diversity structure of species groups. The value of using diversity profiles lays not only in determining which groups are more diverse. Diversity profiles also allow discrimination between richness and evenness contributions to diversity. If the intervention would attempt to increase diversity for two groups with intersecting profiles, then for one group improvement of richness could be attempted, while improvement of evenness would be the target for the other group. The lack of evenness in the distribution of the dominant species and the many steep decreases in profiles with increasing scale parameter value showed that diversity could be increased substantially in many use-groups by targeting evenness. No group had perfectly evenly distributed species. The log series distribution represents a substantially less even distribution than associated with the log normal distribution (May 1975). Evenness increment could be achieved by encouraging farmers to establish trees in more even numbers or by more species-even germplasm distribution. In combination with efforts to improve alpha diversity (the average species richness on an individual farm) in the landscape, this suggests for promotion of less frequent species both in terms of farm frequency as in terms of total abundance. The analysis shows use-groups with

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CHAPTER 7 steep diversity and evenness profiles where such diversity improvements would be most useful: i.e. the construction, fruit, ornamental (although this group will probably not constitute a priority to farmers), firewood, timber and medicine groups. When considering the frequency of the dominant species only (not how evenly this species is distributed), timber, medicine, fruit and beverage are the groups with frequencies larger than 60 percent. Interestingly, these are also the use-groups that could be categorised as providing more cash income to farmers (‘high value trees’ that could be selected to be of higher priority for domestication for this reason). Returning to our objective of diversification to stabilise or increase productivity, it may not be necessary to seek perfectly even distribution in species’ abundances. Since species diversity effects result from spatio-temporal environmental heterogeneity and differences in species characteristics relating to their performance within the environmental matrix, the most stable or productive system will result from optimal matching of richness and evenness in species characteristics with richness and evenness of environmental characteristics. However, including features such as potential diseases or market collapses into the environment in which tree species are grown on farms shows the risks associated with growing the best performing species in case a single species would be most suited to the major part of the spatio-temporal physical environment. One of the factors that we did not include in our analysis was difference in farm abundance among use-groups and among farms. Some species may be more farm size dependent in their abundance, for instance species used to improve soil fertility, while others may be less dependent, for instance species used to shade the homestead area. For use-groups with larger farm size dependence, recommendations for diversification could include farm size differences as small farms may not offer great scope for enhancement of evenness.

7.5. Conclusions The analysis of diversity did not include all aspects that could influence decisions on alterations in tree species composition on farms. Kindt et al. (Chapter 4) listed some potential aspects. These included potential differences in importance attributed by farmers to different groups so that diversification of a more important but more diverse group could be given priority over that of a less diverse but also less important group. Another aspect was possible differentiation among farms in alpha diversity, so that farms with low diversity for a specific use-group could be targeted, rather than only targeting groups with lower diversity at the survey level. However, the methods presented here provide meaningful insights in relationships between richness, evenness, and sample size of use-groups. They thus provide accurate guidance for attempts in alterations of these characteristics. Obviously, they allow also for detailed monitoring of the impact of interventions of these characteristics by providing a baseline to compare diversity before and after interventions.

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DIVERSITY PROFILES Table 7.1. Parameters of the diversity profiles of the most frequent use-groups. H1 was calculated directly as the Shannon diversity index. Confidence interval for H∞ (H∞,l : lower limit; H∞,u : upper limit) is based on the confidence interval for the dominant species. Use-group All Firewood Shade Medicine Ornamental Timber Boundary Charcoal Soil fertility Fruit Construction Fodder Beverage

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H0 5.16 5.05 4.43 4.06 3.97 3.89 3.53 3.30 3.30 3.22 3.00 1.95 1.39

H∞ 1.78 1.71 2.04 0.41 0.68 0.48 1.17 0.70 0.64 0.32 0.24 0.87 0.22

H0 - H∞ 3.39 3.33 2.39 3.65 3.29 3.41 2.35 2.60 2.66 2.90 2.75 1.08 1.17

(H0 – H1)/( H0 – H∞) 0.70 0.71 0.60 0.68 0.80 0.82 0.67 0.77 0.70 0.74 0.85 0.31 0.76

H∞;l 1.61 1.55 1.43 0.15 0.38 0.36 0.97 0.30 0.17 0.24 0.17 0.45 0.04

H∞;u 1.98 1.92 3.80 0.76 1.11 0.61 1.43 1.39 1.56 0.41 0.32 1.59 0.43

CHAPTER 7 Table 7.2. Abundance distribution of all species and the most frequent use-groups. Cells indicate the number of species with a given abundance in the group. ‡ Abundance Class 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20 21 22 23 24 25 26 28 29 30 31 32 33 36 40 41 42 43 47 48 49 50 51 52 53 55 56 60 65 68 69 70 72 74 75 77 79 81 83 86 88 91 94 96 100 101 102 103 108 110 111 113 119

All

Firewood 44 18 8 6 5 5 3 1 1 5 2 1 1 2 0 2 0 1 1 1 2 0 0 4 1 2 0 1 0 1 1 1 0 1 1 1 1 0 2 2 1 1 0 1 0 0 0 0 0 1 1 2 1 1 1 1 0 1 1 0 0 1 0 0 1 0 0 1 0 1 0

36 18 7 7 6 2 3 2 0 3 3 1 0 1 0 2 1 2 0 1 1 0 0 4 0 2 0 1 0 1 1 1 0 1 1 1 1 0 2 2 1 1 0 0 0 0 0 0 0 1 0 2 1 1 1 2 0 1 1 0 0 0 0 1 0 0 0 1 0 1 0

Shade 23 10 8 3 1 2 0 2 2 2 0 2 0 1 2 0 1 0 0 1 2 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0

Medicine 18 10 3 6 0 2 2 0 0 2 0 1 0 1 0 0 0 1 2 0 0 1 0 0 0 0 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 1 0 0 0 0 0 0 0 0

Ornamental 17 8 6 2 1 2 1 1 0 0 2 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

Timber 14 3 1 3 3 3 2 0 1 0 1 0 1 0 0 0 1 0 0 0 0 1 0 1 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

Boundary

Charcoal 5 1 1 1 0 2 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 2 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1

6 5 0 2 1 0 0 0 1 0 1 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

Soil fertility

Fruit 7 2 0 0 3 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

ConFodder Beverage struction 5 3 1 2 0 0 1 0 0 1 1 0 0 0 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

5 2 1 0 1 0 1 0 0 0 3 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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DIVERSITY PROFILES Table 2 (cont.) Abundance Class 122 145 150 161 162 165 188 189 191 201 203 225 246 248 250 269 277 296 318 320 335 364 370 404 413 454 465 476 495 505 608 617 618 621 849 1016 1030 1119 1276 1693 1733 1861 1869 1896 2019 2523 2686 2702 3783 4019 4458 5930 6261 6300 6729 7788 13258 15397 17244

All

Firewood 0 1 0 0 1 1 0 1 0 1 0 0 0 1 0 0 1 1 0 0 1 1 1 0 0 1 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1

Shade 0 1 0 0 1 1 0 0 0 1 0 0 0 1 0 0 1 1 0 0 1 1 0 0 0 1 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 1 1 0 1 1 1 1 0 1 0 1 1 1 1 1

Medicine 1 1 0 1 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Ornamental

Timber 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1

Boundary

Charcoal 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 1 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Soil fertility

Fruit 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0

ConFodder Beverage struction 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0

‡ This table was included in the submitted article to Environemtal and Ecological Statistics, which requires that raw data are provided, and was not referred to specifically in this section (as description of study area and data collection methods were combined in Chapter 3). The table allows for rapid calculation of diversity and evenness profiles (figures 7.1 and 7.2).

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all firewood shade medicine ornamental timber boundary charcoal soil fertility fruit construction fodder beverage

5

H

4

3

403.4 244.7 148.4 90.0 54.6 33.1 20.1 12.2

2

EXP(H)

6

7.4 4.5

1

2.7 1.6

0

1.0 0

0.1

0.25

0.5

0.75

1

1.25

1.5

1.75

2

2.5

3

10

100

200

inf

alpha Figure 7.1. Diversity profiles based on the Rényi series Hα for all species and species belonging to particular use-groups

0

all firewood shade medicine ornamental timber boundary charcoal soil fertility fruit construction fodder beverage

1.00

0.61

-1

0.37

ln(E)

0.22

-2

0.14

0.08

0.05

E

-3

0.03

-4

0.02 0

0.1 0.25 0.5 0.75

1

1.25 1.5 1.75

2

2.5

3

10 100 200 inf

alpha Figure 7.2. Evenness profiles based on the Rényi series Hα for all species and species belonging to particular use-groups

111

a

b

c

d

e

f

Figure 7.3. Accumulation curves for diversity profiles for various use-groups, represented as three-dimensional contours at full-sample profile values. Alpha values of 4, 4.5 and 5 correspond to alpha values of 10, 100 and ∞ respectively. Use-groups: a: firewood, b: shade, c: medicine, d: ornamental, e: timber, f: boundary demarcation

g

h

i

j

k

l

Figure 7.3 (cont.d). Accumulation curves for diversity profiles for various use-groups, represented as three-dimensional contours at full-sample profile values. Alpha values of 4, 4.5 and 5 correspond to alpha values of 10, 100 and ∞ respectively. Use-groups: g: charcoal, h: soil fertility, i: fruit, j: construction, k: fodder, l: beverage

DIVERSITY PROFILES

6

4

3

244.7 148.4 90.0 54.6 33.1 20.1 12.2

2

EXP(H)

5

H

403.4

all firewood shade medicine ornamental timber boundary charcoal fruit construction soil fertility fodder beverage

7.4 4.5

1

2.7 1.6

0

1.0 0

0.1

0.25

0.5

0.75

1

1.25

1.5

1.75

2

2.5

3

10

100

inf.

alpha Figure 7.4. Average profile values, 95 percent confidence intervals (rectangles) and ranges (lines) for 100 farm subsamples based on 100,000 random site sequences. All profile values were calculated for the same scales, but groups were presented at different alpha/scale values for better discrimination of group intervals and ranges. (inf.: ∞)

all firewood shade medicine timber ornamental boundary charcoal fruit construction fertility beverage fodder

Cumulative proportion of abundance

1.0 0.9 0.8 0.7 0.6 0.5

1.0 0.9 0.8 0.7 0.6 0.5

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1

0.0

0.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Cumulative proportion of species Figure 7.5. Lorenz curves for all species and species belonging to particular use-groups

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3

all firewood shade medicine ornamental timber boundary charcoal soil fertility fruit construction fodder beverage

Shannon

2

1

0 0

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200

Number of farms Figure 7.6. Accumulation curves for the Shannon diversity index for all species and species belonging to particular use-groups. Horizontal reference lines indicate full-sample values for 11 accumulation curves.

-0.1

all firewood shade medicine ornamental timber boundary charcoal soil fertility fruit construction fodder beverage

ln (E) / ln (S)

-0.2

-0.3

-0.4

-0.5

-0.6

-0.7

-0.8 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200

Number of farms Figure 7.7. Accumulation curves for ln(E)/ln(S) for all species and species belonging to particular use-groups. Horizontal reference lines indicate full-sample values for two accumulation curves.

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CHAPTER 8 DO FARM CHARACTERISTICS EXPLAIN DIFFERENCES IN TREE SPECIES DIVERSITY AMONG WESTERN KENYAN FARMS? R KINDT, AJ SIMONS & P VAN DAMME SUBMITTED TO AGROFORESTRY SYSTEMS

With the objective of planning diversification of on-farm tree species composition, a survey was conducted in western Kenya involving a complete tree census and collection of ethnobotanical information on 201 farms. Differences between farms in diversity of the 12 most frequent use-groups were analysed by species richness, Shannon, Simpson and Berger-Parker diversity indices, and Shannon evenness and equitability. A large range of values was detected among farms and use-groups. Multiple linear regression of diversity statistics on household characteristics indicated significant relationships. However, these relationships generally explained low percentages of variation (ranging 244%). The connection between household characteristics and use-group diversity allows targeting specific household types with lower diversity. Farm size had a positive relationship with diversity of most use-groups. However, accumulation curves revealed that the same area carried a larger abundance and diversity when it was composed of a greater number of smaller farms. If the pattern of further subdivision of farmland in the survey area continues and the same differences between smaller and larger farms prevail, then larger diversity per unit area can be expected. Because smaller farms contain smaller diversity, however, diversification with the aim of enhancing or stabilising productivity of individual farms may become an important priority in the survey area. The results presented allow for the identification of individual farms, use-groups, and household types for which diversification is more relevant, and at the same time allow for impact monitoring.

EXPLANATORY FACTORS OF FARM DIVERSITY 8.1. Introduction The objective of tree domestication research in western Kenya is the diversification of the tree species composition present in agroecosystems (Kindt & Lengkeek 1999). Making agroecosystems ecologically more stable and economically more rewarding through diversification and intensification of land use by the domestication of agroforestry trees is one of the three pillars of ICRAF’s research (ICRAF 1997; ICRAF 2000). This chapter provides an analysis of differences in tree species diversity among farms. Diversity will contribute to ecosystem productivity and stability under conditions of heterogeneity in species traits and environmental characteristics (Kindt et al. – Chapter 2, 4 & 5). Since the relationship between diversity and ecosystem functioning is saturating, diversification is expected to contribute more to ecosystem functioning if it increases the diversity of functional groups with the lowest diversity (Kindt et al. – Chapter 2, 4 & 5). The analysis of differences in species diversity had two objectives: to identify whether and where there exist substantial differences in species diversity among farms, and to identify whether potential differences could be linked to farm characteristics. The first objective deals with the possibility of identifying and targeting farms of low diversity. The second objective relates to the possibility of targeting specific types of households. Because of population growth and subsequent farm divisions in the area (Bradley 1991; Holmgren et al. 1994), special attention was given in a final set of analyses to the relationship between farm size and tree species diversity.

8.2. Material and methods Formulation of use-groups (4.1), alpha and gamma diversity (4.2.1), diversity and evenness statistics (4.2.6), regression, correlation and non-parametric analysis (4.4)

8.3. Results 8.3.1. Range of Diversity Parameters. Figures 8.1 to 8.8 show box plots for the distribution of the diversity characteristics of the various use-groups for H, ln (S), J, E, ln (d-1), ln (N), ln (S+1) and ln (N+1) respectively. These figures show the large range in diversity among farms, where some farms have relatively low diversity and other farms relatively high diversity. At the same time, the figures show that although some use-groups are more diverse on average, some farms may still have low diversity for these groups. Most important, the figures show that there is already high diversity in the agroecosystems studied. If we set the value of ln(2) as a benchmark value for H (this value corresponds to two equally distributed species, or a higher number of less equally distributed species), then we can see from Figure 8.1 that a large number of farms already had larger values, whereas the majority of farms are below this level for the majority of use-groups. Firewood and fruit were the only use-groups where more than half of the farms had values above the benchmark. Beverage and fodder were the only use-groups were all farms had lower diversity than the benchmark. No single group had values above the benchmark for all farms. When studying figure 8.2 with information on ln (S) and taking the same benchmark value of ln(2) (corresponding to 2 species – equally distributed species have the same value of ln (S) at all scales of the Rényi series, Kindt et al. - Chapter 6), then similar conclusions can be drawn as for

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CHAPTER 8 H. Because ln (S) can only take discrete values, box plots are probably not the ideal representation for the distribution of this statistic. They were maintained, however, for consistency with representation of other diversity statistics. Firewood (and fruit, ignoring outliers) is the only use-group where all farms had values above the benchmark, whereas other use-groups had values above and below the benchmark. Compared with H, values were higher which points to the fact that species were not equally distributed within use-groups. Figure 8.3 (J) presents results that confirm that most use-groups were dominated by a limited number of species, although some farms had almost equally (J≈1) distributed species. Again, a large range in values of farms can be observed. Figure 8.4 (E) provides a similar picture as for J. In some groups (fodder and medicine), more than half of the farms showed higher diversity than the one associated with the broken stick distribution. However, Figures 8.3 and 8.4 did not include farms where ln (S) equalled zero for the use-group. Figure 8.5 shows that most farms had values below the ln(2) value for ln (d-1). The benchmark value corresponds here to the most dominant species having half of the total abundance within groups. Firewood was the only group where more than half of the farms had higher values than the benchmark value. Figure 8.6 shows the large range in abundance of use-groups over farms. Most use-groups showed a large range in abundance, while large differences among use-group means were also observed. Figure 8.7 demonstrates that half of the farms had no trees contributing to the beverage, charcoal, fertility, fodder, and ornamental uses. These were the same use-groups with the lowest values in figure 8.2, with exception of the ornamental group. Boundary demarcation, firewood, fruit, and timber were groups where 75% of farms had two or more species. Only including farms where the use was present (figure 8.2) shows that shade joined the use-groups with this characteristic. Figure 8.8 highlights those use-groups that could be observed in figure 8.7 where particular uses are not common. The figure also shows the range in abundance that could be observed in figure 8.6. 8.3.2. Differences among Use-groups Table 8.1 provides the results of non-parametric significance tests on coefficients calculated by multiple linear regression. Table 8.1 shows that average values of diversity statistics of most usegroups differed significantly from the overall mean, as shown above in the box plot diagrammes. There were significant differences among the means of use-groups, but at the same time large differences within groups obscured the group mean differences (R2 values were not very high). Kruskal-Wallis Rank Sum tests and MRPP for each diversity statistic confirmed the differences between groups. Some use-groups were less diverse on average and could be targeted by domestication projects. For instance, fertility, beverage, charcoal, fodder and ornamental had lowest average H and ln(S) values. 8.3.3. Correlations of Diversity Statistics Spearman rank correlation coefficients calculated among diversity indices belonging to the same farms and the same groups are provided in table 8.2. Table 8.2 illustrates that correlations among diversity statistics are generally strong. Strength of correlation followed similarity in Rényi series scale parameter value. Because of weaker correlations between parameters at different scales in the Rényi series, absolute diversity ordering proves complicated. Absolute ordering implies that

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EXPLANATORY FACTORS OF FARM DIVERSITY all values in the series of one system are higher or lower than all the values of another system. Figure 8.9 confirms the complications with diversity ordering. Perfect ordering would result in monotonically increasing ln (d-1) with increasing ln (S). Farms that are more diverse in absolute terms would thus appear to the top and to the right of less diverse farms in figure 8.9. The figure shows that many farms will have intersecting diversity profiles. This implies that some farms have larger species richness but smaller equality in species proportions. Figure 8.9 also shows that H offers a reasonable compromise to order farms in diversity as values at the upper and right sides of the graph have higher H values in general. J and E parameters were strongly correlated (table 8.2), while correlations with other diversity statistics increased with the Rényi scale parameter (which could be expected as indices higher in the series are more strongly linked to evenness of species distribution). Because of the weaker correlations of evenness parameters with diversity statistics, ln (S) or H together with J or E present more information on variation in the data. Correlations of ln (N) and the diversity statistics show that increasing abundance is more strongly related to species richness, than the evenness of their distribution. Correlations throughout the Rényi series are positive, so increasing tree abundance on an individual farm is associated with an increase in diversity. However, negative correlation with J and E show that the farms with greater abundance have less equally distributed species than less abundant farms. Correlations however are such that their predictive power is weak; more abundant farms are not necessarily more diverse and less evenly distributed than less abundant farms. 8.3.4. Correlations among Shannon Indices of the Same Farm Table 8.3 provides information on the correlation of the Shannon index for different use-groups belonging to the same farms. The general picture is that correlations were very weak: most are not significantly different from zero. It is therefore difficult to classify farms as more or less diverse for all use-groups based on the information of one use-group. Diversity parameters should be calculated for all groups. Correlations mainly result from the same species used for several purposes, for example boundary markers that also produce fruit, construction, or firewood, or have ornamental value. These correlations also point to various purposes related to on-farm niches, e.g. trees in homestead areas used for shade, fruit, medicine and ornamental purposes. 8.3.5. Influences of Household Characteristics Table 8.4 indicates the coefficients calculated by multiple linear regression analysis for spatial and household characteristics as explanatory variables for diversity statistics. The results show that although 33 regressions proved significant at the 0.1% probability level (this corresponds with a Bonferonni correction for 50 tests for a significance level of 5%), their explanatory power (R2) was low. In other words, there were significant differences between the mean values of the diversity statistics, but the explanatory variables were not very effective in predicting the values of diversity statistics at individual farms. Partial linear regression results indicated that for many use-groups and diversity statistics, household characteristics explained more variation than spatial characteristics. S and N for all trees, construction, firewood, fruit, timber, and soil fertility are examples. For other use-groups however, spatial characteristics explained more variation; examples of this are beverage, boundary markers, and ornamental species. A third pattern occurred where both types of variables explained similar fractions of diversity (for example H for construction; H and S for medicine and shade). In the last four cases, the sum of exclusive variation explained by spatial and

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CHAPTER 8 household characteristics exceeded 100%. This means that the combination of both types of characteristics explained the variation better than these characteristics separately (Legendre & Legendre 1998 p. 533). Detection of trends in village diversity based on the gradient towards the Kakamega Forest National Reserve proved difficult. Shimutu, which is located closest to the forest, was lowest in overall N, and N for construction, fodder, timber, medicine, shade and soil fertility, and lowest H and S for fruit and shade. Largest values at the other end of the gradient (Ebuchiebe) were only observed for N of soil fertility. Cases where none of the intermediate villages (Madidi and Mutambi) had higher values included N for construction and timber, and H and S for fruit and shade. The results for other use-groups and diversity characteristics did not relate to forest distance, but showed that characteristics other than farm location relative to the forest would be better in explaining differences in diversity. However, the only case where Shimutu had the largest diversity statistic was observed for J of construction, which was a regression coefficient very close to the one for Ebuchiebe. Analysis of household and farm characteristics showed, among others, the following trends. Male-headed households had highest N for all trees, firewood, medicine, and soil fertility, highest H for timber and highest S for fruit, shade and soil fertility. For many use-groups, de jure femaleheaded households had similar diversity characteristics as male-headed households, but they contrasted with de facto female-headed households. Examples are S for all trees, construction, firewood, timber and ornamental trees and H for shade trees and N for construction, fodder, and timber. De jure female-headed households had lowest H for boundary demarcation and soil fertility, lowest N for shade trees and highest N for boundary demarcation. Farm size was positively related to diversity statistics of many use-groups, and relationships were very strong as shown by the coefficients and their significance. Exceptions were decreasing fruit H and J despite increasing S and N for this group, and decreasing ornamental S and shade J. The area-species relationship predicts increasing S with increasing farm size, therefore these results are not surprising. The importance of the area effect is more interesting, however. Whether larger farms are more or less diverse than predicted by the area-species relationship is the subject of the next section. Coefficients for indicators of wealth (housing type and number of cattle) seem to indicate that wealthier households have lower overall H, S and J, greater N for all trees, beverage, boundary demarcation, firewood, fodder, timber, ornamental, and shade, but smaller N for construction, fruit and medicine. Contradictory results were obtained for charcoal. Number of years the head of household was in charge of the farm was associated with higher H for construction and fruit, higher S for fruit and construction, higher N for medicine, and lower N for boundary demarcation. The maximum age of household head or partner was positively associated with H of medicine and shade, and overall and fruit, medicine and shade S. This household characteristic was negatively associated with fertility H and S, and construction S. The latter result contrasts with the finding of the number of years that a head was in charge of the farm. Number of children was positively correlated with H and S of all trees, boundary, fruit and medicine, H of timber, and N of construction. The only negative correlation was with S of the charcoal. Where schooling was retained after stepwise regression, its coefficient was negative, e.g. H for all trees, boundary demarcation, firewood, and soil fertility.

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EXPLANATORY FACTORS OF FARM DIVERSITY 8.3.6. Influences of Farm Size The relationship between area and diversity is normally not expressed by a linear relationship as discussed above, but through a power function model. For species richness and Shannon diversity, the fitted power function models were S = 12.423 N 0.186 (rse = residual standard error = 6.04) and H = 1.297 N 0.115 (rse=0.468). Respective models where parameters a were calculated beforehand were S = 12.375 N 0.188 (rse=6.03) and H = 1.239 N 0.138 (rse=0.467). Parameters a were closely approximated in models where this parameter was estimated. Figures 8.10 and 8.11 show the fitted models with prefixed parameters a (these models had slightly smaller residual standard error) and the range of observed values. As observed in the previous section, there remained considerable unexplained variation. Some farms with a small size had relatively high diversity, while some farms with larger sizes had relatively low diversity. The average diversity for a specific farm size can be predicted quite precisely, as 95% confidence intervals were small. The figures further demonstrated that a linear model would provide a similar fit to the data. Table 8.5 demonstrates that random addition of several farms resulted in most cases in higher H, S, and N than for single or fewer farms of the same combined size. This table also points to the area-species relationship – larger farms have, on average, higher diversity. The results indicate that when several farms are combined, diversity increases more than expected from a pure farm size increment effect.

8.4. Discussion and Conclusions The analyses show the large range in diversity characteristics among farms and use-groups. Some use-groups were more diverse on average throughout the survey but there were exceptions – some diverse groups included some farms with low diversity and some less diverse groups included some farms with high diversity. This means that use-groups with low average alpha diversity can be detected at the survey scale, but that diversification may not be appropriate for all farms since the diversity of some farms was already quite high. Correlations were high among diversity indices calculated for the same farms and same usegroups, but low among different uses for the same farms. Therefore, diversity indices should be calculated separately for use-groups, while a reasonable ranking of diversity of farms and usegroups could be obtained from calculation of a single diversity index. Magnussen & Boyle (1995) also found strong linear correlations between H and D-1 (0.98) based on mathematical modeling of species distributions. Because correlations become weaker at larger scale differences in the Rényi series, it is impossible to rank use-groups absolutely in diversity. Absolute ranking only occurs when all values of the Rényi diversity series rank in the same order, otherwise one system is richer and the other system possibly more evenly distributed (Kindt et al. - Chapter 6). Our recommendation is to compare farms based on a diversity index related to species richness (S or H) in combination with an evenness indicator (J or E). Despite the fact that J and E decreased for more diverse groups, richer groups still had more evenly distributed species according to the inverse Berger-Parker index. Regression analysis showed that various household and farm characteristics had significant relations with diversity characteristics of use-groups. Many of the relationships could point to causal effects of on-farm management of tree species diversity. One example is the relationship on a farm between number of children and diversity of fruit and medicine trees. Another example is the relationship between wealth and smaller abundance of fruit, construction, and medicine trees as this relationship could indicate that wealthier households prefer to purchase

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CHAPTER 8 these products rather than to produce them on their farm. A third example is the higher number of trees for boundary demarcation for de jure female headed households. Maybe legally femaleheaded households have greater problems with recognition by male neighbours of farm boundaries. An example of potentially spatially related causes for diversity is the lower abundance of construction, and timber trees in the village closest to Kakamega Forest. Some relationships were consistent with results reported by David & Swinkels (1994): wealthier households having more trees. However, the results contrasted with findings by David & Swinkels (1994) that male-headed households sold more poles and timber: our results showed a contrast between de facto female-headed households and the other two household types. Simple regressions confirmed the findings of David & Swinkels (1994): the abundance of construction and timber was positively associated with male-headed households and not significantly related to de jure female headed households (P(F) for de jure and de facto female heads was respectively 0.74 and 0.97). The difference between the multiple and single regression results show the value of the first type of analysis in isolating effects by removing possible effects from other variables. Differences among male-headed, de jure female-headed and de facto female-headed households showed a complex pattern. Male-headed farmers differed from female-headed households in some cases, whereas for other groups and diversity statistics male-headed and de jure female heads were more similar. Den Biggelaar (1995) found that female household heads in southern Rwanda were permitted to take decisions to plant trees, which is normally forbidden for women in Rwanda. Female household heads, however, did not plant more trees than wives of male heads, but their reasons for planting trees were more similar to reasons provided by male heads. Analysis of our dataset led to the contrasting finding that female heads established more trees than wives of male heads (averaging 115 trees per farm versus 4), but fewer than male heads (430). This finding could indicate a further evolution in tree planting practices, where permissions to plant trees also resulted in more planted trees by female heads of households. Jarvis et al. (2000) mention that gender, age, wealth or social status affect farmers’ knowledge, actions and access to resources regarding the maintenance of crop diversity. Long et al. (2000) provide some examples of the influence of wealth, age and gender on crop diversity. Citing examples of multiple regression analyses, Jarvis et al. (2000) also listed farm size, family size, and years of education as significant explanatory factors for farmer variety choices. The results from our study show that variables that influence crop diversity (wealth, age, gender, farm size, family size, years of education) also influence on-farm tree diversity. Whereas the relationships revealed by multiple regression were significant, their explanatory power was low. Low predictive values are common in diversity studies. For example, functional group and species richness explained only 17.7% of variation in the productivity of grasslands (Hector et al. 1999). Similar research on the relationship between species richness and grassland plant cover provided R2 values of 0.18 (experiment) and 0.16 (native grassland) (Tilman et al. 1996). Other examples are a R2 value of 0.203 of relative resource use versus functional group richness reported by Hooper & Vitousek (1998), and R2 values of 0.12 – 0.18 between species richness and the size of the largest individual exotic invader described by Levine (2000). Cromwell & van Oosterhout (2000) used a multiple linear regression approach to investigate crop diversity in two districts in Zimbabwe. They found that farm/household characteristics explained 86% of the variation in the number of crops grown, 80% of the variation in the number of crop varieties, and 50% of the variation in the proportion of the farm devoted to small grains. Considerably more variation was explained than in our study. However, some of the explanatory variables they used had an evident link to observed diversity, such as the fact whether small grains were valued by the household and whether respondents grew all varieties that they liked. Low R2 values indicated differences between average values with large percentages of unexplained variation (i.e. many exceptional cases against the overall trend). Data available here

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EXPLANATORY FACTORS OF FARM DIVERSITY only allow for speculation about the reason for the unexplained variation. One possibility could be that various types of farmers do have different strategies, but that many farmers are currently not able to establish the preferred diversity because of germplasm availability problems. Following this hypothesis, correlations among household characteristics and diversity would increase when more germplasm would become available to farmers. Another possibility is that some traditional practices that limited certain types of households establishing trees (such as taboos against women planting trees as described by Bradley, 1991 and David & Swinkels, 1994) are currently disappearing. Correlations among household characteristics and diversity would decrease when new customs develop following the latter hypothesis. One method by which future trends in use-group diversity versus household characteristics can be tested is by participatory research on desired levels of diversity. Such a survey is currently underway in the area. Combining several smaller farms resulted in larger average diversity and abundance for the same area than observed in undivided farms. Larger farms still had higher diversity than smaller farms, however. The higher diversity of combined farms indicates that beta diversity effects (differences in species composition among farms) caused higher diversity in farm combinations. The analysis of the relationship between (combined) farm size, and diversity and abundance, was repeated for separate villages to control for beta diversity between villages. Results of these analyses (not provided here) showed the same pattern as observed for random combinations of farms. Results that differences in farm area alone can not explain all differences in diversity, as observed in our survey, suggest that households manage tree species diversity on their farms. The differences in diversity between combinations of farms and undivided farms also allow speculation about potential effects on diversity from further subdivision of farms. If the same trends remain valid, then the expectation is that alpha diversity would further decrease for individual farms, but that beta diversity would more than compensate for this decrease so that diversity of the same area of farm size would increase. With decreasing diversity of individual farms, tree diversification may become more relevant for individual farms. Higher diversity per unit area could, however, imply that higher stability would already be present at the agro-ecosystem level. Higher species richness and abundance per unit area offer larger opportunities for tree species conservation through use in the landscape (Kindt & Lengkeek 1999). However, additional information is needed to check whether farmers would be interested in maintaining rare species on smaller farms, and for which species sustainable genetic management is possible and practical in such landscape (Kindt et al. – Chapter 11). The findings of higher tree abundance per unit area on smaller farms confirmed the “more people, less forests, more trees” postulation as formulated by Simons et al. (pers. comm.) citing examples from China, Kenya, Sri Lanka, Thailand and Vietnam. The literature cited by these authors included findings from Bradley et al. (1985) (based on data from Kakamega and Vihiga districts, to which our survey area belonged) and from Holmgren et al. (1994) who confirmed the relationship for the total of highland areas of Kenya. Such model of a U-shaped curve of tree biomass over population density and time was also described for Sahelo-Sudanian shrubgrasslands of north-east Nigeria (Mortimore et al. 1999), indicating that increased tree biomass with population density may be a global phenomenon for areas with high population densities.

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CHAPTER 8 Table 8.1. Mean, standard deviation, and range for six diversity statistics together with differences between use-groups calculated by randomisation tests on coefficients calculated by multiple linear regression on dummy variable coding all groups except fertility. The group mean for fertility therefore corresponds to the intercept, while means for the other groups correspond to the addition of the intercept and the coefficient. The probability of the Kruskal-Wallis rank sum test and Multi-Response Permutation Procedure probability with chance-corrected withingroup agreement statistic A describe the significance of differences among groups. Parameter Mean St. dev. / Range Regression R2 / P. Variable Intercept / Fertility Beverage Boundary Charcoal Construction Firewood Fodder Fruit Timber Medicine Ornamental Shade Kruskal-Wallis P. MRPP A / P.

H

ln (S)

0.608 0.586 0.495 Coeff. 0.146 -0.106 0.437 0.036 0.199 1.328 -0.077 0.844 0.417 0.299 0.125 0.589 0 0.327

1.022 2.431 0.877 0 0.666 P. Coeff. 0.260 0.097 -0.130 1e-5 0.661 0.535 0.036 3e-5 0.355 1e-5 2.387 0.271 -0.171 1e-5 1.217 1e-5 0.956 1e-5 0.333 0.024 0.299 1e-5 0.745 0 0 0.469

J 0.600 3.761 0.271 0 0.121 P. Coeff. 0.567 0.093 -0.252 1e-5 0.051 0.609 0.023 1e-5 0.003 1e-5 -0.01 0.046 0.213 1e-5 0.100 1e-5 -0.101 1e-5 0.20 1e-5 -0.064 1e-5 0.182 0 0 0.091

E 0.740 0.984 0.330 0 0.125 P. Coeff. 0.696 0.003 -0.308 0.250 0.056 0.719 0.026 0.938 0.006 0.797 -0.053 0.055 0.266 0.021 0.106 0.022 -0.132 8e-5 0.242 0.234 -0.082 7e-5 0.211 0 0 0.095

ln (d-1) 1.213 0 P. 0.003 0.301 0.739 0.910 0.315 0.049 0.044 0.013 1e-4 0.208 1e-4 0

0.332 0.371 0.330 coeff. 0.086 -0.071 0.263 0.018 0.107 0.668 -0.04 0.448 0.178 0.201 0.061 0.359 0 0.222

ln (N) 1.791 0 P. 0.127 1e-5 0.668 0.001 1e-5 0.361 1e-5 1e-5 1e-5 0.133 1e-5 0

3.522 1.969 0.469 coeff. 2.352 1.596 2.511 0.121 1.675 3.486 -1.056 0.849 1.898 -0.811 -0.107 -0.290 0 0.313

8.183 0 P. 1e-5 1e-5 0.551 1e-5 1e-5 1e-5 1e-5 1e-5 1e-5 0.576 0.082 0

Table 8.2. Spearman rank correlations between various diversity statistics calculated for the same farm and use-group (upper half) and probability of these correlations equalling zero (lower half) α Ln (S) H Ln (D-1) Ln (d-1) J E Ln (N)

ln (S) 0 1.000 0.000 0.000 0.000 0.006 0.000 0.000

H 1 0.896 1.000 0.000 0.000 0.000 0.000 0.000

ln (D-1) 2 0.847 0.990 1.000 0.000 0.000 0.000 0.000

ln (d-1) ∞ 0.812 0.973 0.993 1.000 0.000 0.000 0.000

J -0.078 0.576 0.702 0.766 1.000 0.000 0.000

E -0.120 0.541 0.670 0.737 0.998 1.000 0.000

ln (N) 0.582 0.387 0.346 0.324 -0.529 -0.547 1.000

α: Rényi scale parameter value (see methods)

Table 8.3. Spearman rank correlations of Shannon diversity indices between use-groups belonging to the same farms (upper half) and significance of these correlations (lower half). Correlations with probability lower than 10% are presented in bold. Beverage Boundary Charcoal Construction Firewood Fodder Fruit Timber Medicine Ornamental Shade Fertility

Beverage 1.000 0.669 0.279 0.267 0.293 n/a 0.979 0.813 0.121 0.144 0.221 0.542

Boundary -0.055 1.000 0.826 0.081 0.000 0.411 0.001 0.501 0.619 0.027 0.966 0.655

CharConcoal struction -0.204 -0.143 -0.024 -0.125 1.000 -0.064 0.564 1.000 0.287 0.001 0.262 0.966 0.556 0.937 0.359 0.000 0.198 0.992 0.805 0.024 0.404 0.789 0.131 0.304

FireFodder wood -0.134 n/a 0.387 0.121 0.119 -0.270 0.264 0.006 1.000 0.332 0.024 1.000 0.343 0.722 0.000 0.020 0.291 0.150 0.584 0.110 0.436 0.142 0.344 0.674

Fruit -0.003 -0.239 -0.066 -0.005 -0.067 -0.052 1.000 0.070 0.020 0.202 0.011 0.215

Timber -0.030 0.048 0.103 0.421 0.371 0.342 0.129 1.000 0.722 0.270 0.368 0.451

MediOrnacine mental 0.228 0.246 0.047 0.227 -0.182 0.039 -0.001 -0.231 0.100 0.056 0.247 0.296 0.219 0.130 -0.034 0.113 1.000 0.146 0.271 1.000 0.000 0.001 0.434 0.229

Shade 0.163 -0.003 -0.099 -0.020 -0.060 0.234 0.198 0.070 0.460 0.358 1.000 0.190

Fertitlity 0.094 0.038 -0.205 0.089 0.081 0.066 0.106 -0.060 -0.080 -0.146 0.122 1.000

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Table 8.4. Coefficients of household characteristics as explanatory variables on diversity characteristics (Shannon, ln (species richness), Shannon evenness and ln (group abundance+1) for various use-groups of species after stepwise linear regression (n: number of observations). The significance of coefficients for explanatory variables (shown below characteristics between brackets) as provided by multiple linear regression where residuals were normally distributed (Kolmogorov-Smirnov test), otherwise calculated by permutation tests (indicated by *). Total explained variation is expressed by the multiple correlation coefficient R2, with probability P(F). Results of partial linear regression are presented for spatial (village) and non-spatial (household and farm) characteristics (with percentage of total explained variation below). Group

Statistic (sample size)

All

H (n=183) ln (S) (n=183) J (n=183) ln (N+1) (n=183) H (n=59) ln (S) (n=59) ln (N+1) (n=183) H (n=179) ln (S) (n=179) J (n=146) ln (N+1) (n=183) H (n=74) ln (S) (n=74) J (n=25) ln (N+1) (n=183)

Beverage ‡

Boundary

Charcoal

St.dev. R2 Range (P(F))

0.49 2.35 0.40 2.44 0.16 0.87 0.84 6.23 0.09 0.56 0.28 1.09 2.23 8.03 0.45 1.67 0.55 2.07 0.27 0.97 1.41 6.94 0.36 1.42 0.48 1.94 0.33 0.98 1.57 6.42

0.20 (6e-7) 0.24 (4e-8) 0.15 (5e-6) 0.20 (2e-7) 0.05 (0.079) 0.08 (0.071) 0.48 (0) 0.28 (5e-10) 0.28 (2e-11) 0.08 (0.001) 0.07 (0.014) 0.06 (0.035) 0.06 (0.034) 0.44 (2e-4) 0.11 (0.001)

Perma- Thatch Number Number Years Shimutu Mutambi Male Female Farm nent roofed of cross- of local being village village headed headed size house house bred cattle head (closest (de jure) (acres) (least cattle (wealth) to (no (1 acre = (most forest) husband) 0.4005 wealthy) wealthy) (wealth) ha) -0.35 -0.22 -0.28 0.79 -0.75 (3e-4) (0.045) (0.003) (3e-4) (0.008) -0.15 -0.27 0.19 0.16 0.86 -0.32 (0.020)* (6e-4)* (0.007)* (0.079)* (1e-5)* (0.139)* -0.07 -0.09 -0.16 (0.004)* (0.001)* (0.062)* -0.26 -0.31 0.24 0.29 1.53 -0.27 (0.092)* (0.080)* (0.139)* (0.015)* (3e-5)* (0.042)* 0.12 (0.062)* -0.21 0.30 (0.032)* (0.093)* -0.67 3.24 1.23 0.86 (0.023)* (1e-5)* (0.062)* (0.034)* -0.62 -0.28 -0.22 -0.14 0.36 (0) (0.003) (0.008) (0.090) (0.036) -0.79 -0.38 -0.35 0.62 (1e-5)* (7e-4)* (5e-4)* (0.003)* -0.17 -0.12 (0.001)* (0.047)* -0.52 -0.56 0.47 -0.45 -0.97 (0.034)* (0.026)* (0.105)* (0.056)* (0.047)* -0.20 (0.032)*

ExEx- Intercept Ebuchieclusive clusive be village spatial non(farthest variation spatial from variation forest) 0.06 (0.33) 0.05 (0.23) 0.06 (0.44) 0.06 (0.28) n/a

0.11 (0.55) 0.23 (0.94) 0.05 (0.38) 0.16 (0.78) n/a

0.07 (0.84) 0.44 (0.92) 0.24 (0.88) 0.25 (0.90) 0.06 (0.72) 0.03 (0.48) n/a

0.04 (0.49) 0.03 (0.06) 0.06 (0.21) 0.04 (0.16) 0.02 (0.28) 0.04 (0.60) n/a

n/a

n/a

n/a

n/a

0.04 (0.39)

0.07 (0.66)

1.62 (0) 2.27 (0) 0.65 (0) 5.55 (0) 0.02 (0.065) 0.09 (0.071) 0.32 (0.171) 0.73 (0) 1.05 (0) 0.66 (0) 5.33 (0) 0.24 (0) 0.49 (0) 0.71 (0) 0.47 (0.154)

-0.81 -0.60 (0.004)* (0.072)*

-0.55 (7e-4)*

Maxi- Number Highest mum age of level of head or resident schoopartner children ling of head

0.23 (0.097)*

0.25 (0.121) 0.34 (0.010)*

-0.10 (0.007)*

0.34 (0.016) 0.28 (0.094)*

-0.42 (0.033)* 1.89 0.63 (0.011)* (0.117)*

-1.85 (0.059)*

-0.28 (0.015)

0.85 (0.136)*

-0.16 (0.137)

Table 8.4 (cont.d) Group

Statistic St.dev. R2 Spatial Non- Intercept (sample Range (P(F)) variation spatial size) variation Construction H 0.28 0.25 0.13 0.12 0.15 (n=179) 1.25 (2e-10) (0.53) (0.51) (3e-4) ln (S) 0.36 0.25 0.10 0.16 0.33 (n=144) 1.79 (4e-9) (0.41) (0.66) (0) J 0.30 0.16 0.09 0.07 0.59 (n=179) 0.98 (1e-4) (0.60) (0.43) (0) ln (N+1) 1.36 0.21 0.02 0.20 2.6314 (n=183) 6.95 (1e-7) (0.10) (0.97) (0) Firewood H 0.48 0.20 0.04 0.11 1.61 (n=183) 2.22 (4e-7) (0.24) (0.58) (0) ln (S) 0.40 0.22 0.05 0.21 2.29 (n=183) 2.37 (2e-8) (0.25) (0.93) (0) J 0.16 0.15 0.05 0.07 0.64 (n=183) 0.87 (6e-6) (0.36) (0.46) (0) ln (N+1) 0.85 0.20 0.05 0.17 5.32 (n=183) 6.23 (2e-8) (0.25) (0.82) (0) Fodder ‡ H 0.19 0.11 n/a n/a 0.12 (n=43) 0.67 (0.095) (0) ln (S) 0.24 0.11 n/a n/a 0.16 (n=43) 0.69 (0.083) (0) ln (N+1) 0.83 0.18 0.13 0.06 0.39 (n=183) 4.20 (6e-6) (0.71) (0.34) (0.034) Fruit H 0.47 0.14 0.03 0.05 0.94 (n=183) 1.90 (1e-5) (0.23) (0.39) (0) ln (S) 0.39 0.18 0.07 0.14 1.14 (n=183) 2.30 (2e-5) (0.41) (0.78) (0) J 0.25 0.17 0.03 0.10 0.72 (n=182) 0.95 (1e-7) (0.21) (0.60) (0) ln (N+1) 1.02 0.23 0.03 0.15 3.07 (n=183) 5.26 (6e-9) (0.14) (0.69) (0) Timber H 0.39 0.09 0.04 0.04 0.51 (n=179) 1.78 (1e-4) (0.46) (0.46) (0) ln (S) 0.59 0.14 0.04 0.10 0.82 (n=179) 2.56 (1e-5) (0.28) (0.69) (0) J 0.23 0.02 n/a n/a 0.47 (n=160) 0.95 (0.042) (0) ln (N+1) 1.49 0.13 0.01 0.13 3.43 (n=183) 6.95 (7e-5) (0.11) (0.99) (0)

Ebuchie- Shimutu Mutambi Male be village village village headed 0.19 -0.08 (4e-5)* (0.068)* 0.27 (1e-5)* 0.12 0.14 -0.10 (0.067)* (0.044)* (0.142)* -0.55 (0.029) -0.28 -0.18 -0.23 (0.002) (0.091) (0.014) -0.16 -0.25 (0.013)* (9e-4)* -0.07 -0.08 (0.011)* (0.003)* 0.46 (7e-4)* -0.12 -0.12 (0.098)* (0.059)* -0.16 -0.16 (0.091)* (0.053)* -0.70 -0.78 -0.52 (2e-5)* (1e-5)* (0.001)* -0.22 (0.009) -0.28 -0.15 (2e-4)* (0.021)* 0.11 0.09 (0.008)* (0.040)* -0.49 -0.36 -0.42 (0.009) (0.090) (0.028) -0.19 (0.004) -0.28 (0.004)* -0.51 (0.069)*

Female headed (de jure)

0.18 0.13 (0.005)* (0.103)* -0.15 (0.002)* 0.67 0.61 (0.004) (0.048) 0.22 0.22 (0.001)* (0.012)* 0.30 (0.013)*

Farm size (acres) 0.41 (4e-4)* 0.50 (1e-4)* 3.14 (0) 0.78 (3e-4) 0.82 (1e-5)*

Permanent house

Thatch roofed house

0.11 (0.152)*

1.33 (4e-5)*

-0.31 (0.017)*

0.85 (0.057) 0.28 (0.028)*

0.29 (0.051) 0.20 0.27 (0.143)* (0.074)*

2.25 (5e-4)*

-0.44 (0.069)*

-0.27 (0.042)

-0.30 (0.009) -0.11 (0.002)*

0.87 (0.048)*

0.35 (0.012)*

0.67 0.56 (0.012)* (0.108)*

Years Maxi- Number Schoobeing mum age of ling of head children head 0.24 (0.006)* 0.39 -0.26 (0.001)* (0.053)*

-0.16 (0.054)*

-0.38 (0.051) 0.37 -0.20 (0.026)* (0.026)* -0.44 (2e-5)* 2.44 -0.58 (0) (0.009)

0.10 (0.071)*

Number of local cattle

-1.19 (0.111) -0.68 (0.012)

0.30 0.42 (0.037)* (0.030)*

0.17 (0.004) 0.47 (2e-5)*

Crossbred cattle

0.58 (0.038)* 0.37 (0.018) 0.35 (0.007)*

0.32 (0.106)*

Table 8.4 (cont.d) Group

Statistic St.dev. R2 Spatial Non- Intercept Ebuchie- Shimutu Mutambi (sample Range (P(F)) variation spatial be village village village size) variation Medicine H 0.47 0.29 0.17 0.17 0.04 -0.22 -0.56 (n=104) 1.60 (1e-6) (0.58) (0.57) (0.702) (0.034)* (1e-5)* ln (S) 0.62 0.37 0.25 0.21 0.09 -0.46 -0.86 (n=104) 2.39 (4e-9) (0.67) (0.57) (0.55) (4e-4) (0) J 0.24 0.15 0.09 0.08 0.71 0.19 (n=60) 0.86 (0.023) (0.64) (0.56) (0) (0.012)* ln (N+1) 1.30 0.25 0.21 0.09 0.40 -0.46 -1.07 0.87 (n=183) 6.57 (7e-9) (0.86) (0.35) (0.086) (0.053)* (1e-4)* (5e-4) Ornamental H 0.37 0.08 n/a n/a 0.41 -0.28 -0.21 -0.16 (n=90) 1.38 (0.049) (0) (0.010)* (0.062)* (0.103)* ln (S) 0.60 0.32 0.23 0.07 0.78 -0.82 -0.56 -0.43 (n=90) 2.07 (2e-5) (0.73) (0.22) (0) (1e-5)* (0.002)* (0.002)* J 0.33 0.21 0.16 0.06 0.40 0.43 0.25 (n=48) 0.97 (0.012) (0.76) (0.30) (0) (0.008)* (0.085)* ln (N+1) 1.72 0.29 0.25 0.02 2.50 -2.09 -2.11 -1.21 (n=183) 5.99 (6e-12) (0.87) (0.08) (0) (1e-5)* (1e-5)* (1e-4)* Shade H 0.60 0.13 0.08 0.07 0.43 -0.44 (n=151) 2.10 (2e-4) (0.59) (0.54) (8e-4) (2e-4)* ln (S) 0.74 0.14 0.08 0.09 0.61 -0.55 (n=151) 3.13 (9e-5) (0.53) (0.62) (1e-4) (2e-4)* J 0.23 0.14 0.01 0.12 0.75 0.07 (n=115) 0.95 (0.004) (0.12) (0.89) (0) (0.134)* ln (N+1) 1.41 0.18 0.11 0.09 1.67 -0.80 -1.47 -0.51 (n=183) 5.83 (8e-6) (0.60) (0.53) (0) (0.004)* (1e-5)* (0.067)* Soil Fertility H 0.28 0.10 0.01 0.09 0.31 0.09 (n=123) 1.40 (0.027) (0.17) (0.96) (9e-4) (0.131)* ln (S) 0.42 0.08 0.05 0.05 0.40 0.23 (n=123) 1.79 (0.025) (0.58) (0.58) (0.002) (0.011)* J 0.32 0.13 n/a n/a 0.75 (n=123) 0.98 (0.180) (1e-4) ln (N+1) 1.61 0.07 0.03 0.05 0.94 0.42 -0.53 (n=183) 7.24 (0.009) (0.53) (0.72) (0) (0.13)* (0.11)* ‡ Regression analysis not performed for J because of small sample size (beverage: n=10; fodder: n=6).

Male headed

Female headed (de jure)

0.27 (0.133)*

Farm size (acres) 0.62 (0.006)* 1.11 (1e-4) 1.98 (4e-4)*

Permanent house

Thatch roofed house

-0.15 0.13 (0.115)* (0.117)*

Crossbred cattle

Number of local cattle

-1.65 (0.021)*

Years being head

Maxi- Number Schoomum age of ling of children head 0.42 0.55 (0.025)* (0.002)* 0.52 0.71 (0.023) (0.001)

0.64 (0.108)*

0.21 0.42 -0.52 0.40 (0.136)* (0.026)* (0.124)* (0.018)* 0.24 (0.059)* -0.44 1.31 (0.081)* (0.117)* 0.27 0.24 (0.017)* (0.133)* 0.30 (0.013)*

0.13 (0.082)* 0.08 (0.475) 0.45 (0.069)*

0.26 (0.116)* -0.19 -0.09 (0.067)* (0.115)* -0.53 1.17 0.58 (0.056)* (0.055)* (0.074)* -0.11 0.11 (0.116)* (0.060)*

1.62 (0.020)*

-0.26 (0.033)*

0.39 (0.076)* 0.57 (0.027)* 0.15 (0.114)* 1.01 (0.041)* -0.18 (0.146)* -0.38 (0.038)* -0.22 (0.359)

-0.26 (0.008)* -0.24 (0.103)* -0.41 (0.032)

CHAPTER 8 Table 8.5. Average values for species richness (S), tree abundance (N) and Shannon diversity (H) based on 5000 random additions of farms of the same original size and provided by the models with prefixed parameter a (see text). Figures between brackets are the exact average species richness (see text). Statistic S

N

H

Original farm size 0.25 (n=16) 0.5

(n=32)

0.75

(n=20)

1

(n=37)

1.5

(n=25)

2

(n=26)

2.5

(n=12)

3

(n=10)

4

(n=4)

5

(n=2)

Model 0.25 0.5 0.75 1 1.5 2 2.5 3 4 5 0.25 0.5 0.75 1 1.5 2 2.5 3 4 5 Model

(n=16) (n=32) (n=20) (n=37) (n=25) (n=26) (n=12) (n=10) (n=4) (n=2) (n=16) (n=32) (n=20) (n=37) (n=25) (n=26) (n=12) (n=10) (n=4) (n=2)

Average diversity for (combined) farm size 0.25 0.5 0.75 1 12.38 21.04 26.05 29.68 (12.37) (19.94) (25.50) (29.98) 14.75 n/a 22.83 (14.75) (22.68) 14.60 n/a (14.60) 15.46 (15.46)

1.5 36.46 (37.10) 28.61 (28.39) 22.44 (22.22) n/a 18.60 (18.16)

2 41.72 (42.82) 33.00 (32.98) n/a

2.5 46.83 (47.68) 36.49 (36.88) n/a

23.73 (23.61) n/a

n/a

15.93 (17.31)

n/a

n/a

18.25 (18.17)

3 51.38 (51.92) 39.94 (40.30) 31.89 (27.54) 29.48 (29.55) 27.20 (27.46) n/a n/a 19.91 (19.91)

4 59.00 (59.00) 45.45 (46.18) n/a

51.19 (51.22) n/a

34.28 (34.36) n/a

38.51 (38.43) n/a

26.96 (26.72) n/a

n/a

n/a 21.75 (21.75)

12.38 280

14.10 590 330

15.21 840 n/a 440

16.06 1081 660 n/a 490

17.33 1632 1014 879 n/a 600

18.29 2196 1384 n/a 983 n/a 500

19.07 2786 1672 n/a n/a n/a n/a 510

19.74 3387 1991 1720 1459 1186 n/a n/a 706

20.83 4549 2581 n/a 1953 n/a 1000 n/a n/a 680

1.24

1.69 1.60

1.90 n/a 1.28

2.02 1.93 n/a 1.42

2.18 2.05 1.57 n/a 1.68

2.28 2.13 n/a 1.72 n/a 1.67

2.34 2.20 n/a n/a n/a n/a 2.09

2.37 2.26 1.87 1.88 1.91 n/a n/a 1.85

2.43 2.32 n/a 1.99 n/a 1.95 n/a n/a 1.77

1.24

1.36

1.44

1.50

1.59

1.65

1.70

1.75

1.82

5 n/a

26.06 (26.76) n/a n/a 20.67 (20.67) 21.72 n/a 3298 n/a 2472 n/a n/a 956 n/a n/a 1658 n/a 2.36 n/a 2.08 n/a n/a 2.13 n/a n/a 1.45 1.87

129

4

54.598

3

20.086

2

7.389

1

2.718

0

1.000

Figure 8.1. Range of the Shannon diversity index over farms for different use-groups. The reference line is drawn at ln(2).

S

timber

shade

medicine

ornamental

fruit

fodder

fertility

Use groups

Figure 8.2. Range of species richness over farms for different use-groups. The reference line is drawn at ln(2).

1 1

1

0

0

Use groups

Figure 8.3. Range of the Shannon evenness (based on equal distribution) over farms for different use-groups. The reference line is drawn at 0.5

timber

shade

ornamental

medicine

fruit

fodder

firewood

fertility

construction

charcoal

boundary

timber

shade

ornamental

medicine

fruit

fodder

firewood

fertility

construction

charcoal

boundary

0 beverage

0

beverage

J

E

1

firewood

charcoal

beverage

timber

shade

ornamental

medicine

fruit

fodder

firewood

fertility

charcoal

boundary

beverage

construction

Use groups

construction

0

0

boundary

H

1

1

ln(S)

2

2

use groups

Figure 8.4. Range of the Shannon equality (based on the broken stick distribution) over farms for different use-groups. The reference lines are drawn at 0.5 and 1

2.7

0

1.0 timber

use groups

use groups

Figure 8.5. Range of the inverse Berger-Parker index over farms for different use-groups. The reference line is drawn at ln(2).

Figure 8.6. Range of tree abundance over farms for different use-groups

Use groups

Figure 8.7. Range of species richness for different use-groups, including those farms where the group does not occur. The reference line is drawn at ln(3) (S=2 as in the other figures).

1

2.7

0

1.0

timber

7.4

shade

2

ornamental

20.1

fruits

54.6

3

medicine

4

fodder

148.4

firewood

403.4

5

fertility

6

charcoal

timber

shade

medicine

ornamental

fruits

fodder

firewood

fertility

construction

1.000

charcoal

0

boundary

2.718

beverage

1

1,096.6

construction

7.389

2,981.0

7

beverage

2

ln(N+1)

20.086

S+1

3

8

N+1

54.598

boundary

4

ln(S+1)

N

1

shade

7.4

ornamental

20.1

2

fruit

3

medicine

54.6

fodder

4

fertility

148.4

firewood

403.4

5

charcoal

1,096.6

6

beverage

7

timber

shade

ornamental

fruit

medicine

fodder

firewood

fertility

construction

charcoal

boundary

1.000

beverage

0

2,981.0

construction

2.718

8

boundary

1

ln(N)

7.389

1/d

ln(1/d)

2

use groups

Figure 8.8. Range of tree abundance for different use-groups, including those farms where the group does not occur).

2

1.9

2

2.2

1 .7

ln(1/d)

1.5

1 1.2

1.0

7 1. 5 1.

1.2

1 1.7 1. 7 5 1.

0.7 0.5 0.2

0 0

1

0 2

3

4

ln(S) Figure 8.9. Species richness versus inverse Berger-Parker index of individual farms and individual use-groups. The contour grid corresponds to the Shannon diversity index.

Species richness

CHAPTER 8

50

50

40

40

30

30

20

20

10

10

0

0 0

1

2

3

4

5

6

Farm size (acres)

Shannon diversity index (log e)

Figure 8.10. Relationship between farm size and species richness. Full line is the fitted species-area relationship model (see text), dotted lines the 95% confidence intervals. Symbol size corresponds to farm size (maximum 6).

3

3

2

2

1

1

0

0 0

1

2

3

4

5

6

Farm size (acres) Figure 8. 11. Relationship between farm size and Shannon diversity index. Full line is the fitted Shannon-area relationship model (see text), dotted lines the 95% confidence intervals.

133

CHAPTER 9 COMPARISON OF TREE SPECIES COMPOSITION OF FARMS IN WESTERN KENYA USING CONSTRAINED ORDINATION I. ANALYSIS FOR SEPARATE USE-GROUPS R KINDT, P VAN DAMME, AJ SIMONS & H BEECKMAN SUBMITTED TO JOURNAL OF APPLIED ECOLOGY One of the objectives of tree domestication research is the diversification of the tree species composition of agroecosystems. We investigated whether substantial differences existed in the species composition of farms, so that diversification efforts could be guided by these differences – for example by introducing species that are dominant in one subsection of the landscape into subsections where these species are currently rare. Since we aimed to target diversification towards species that contributed to the 12 most important functions of trees on farms, we investigated various farms × species matrices that only included trees that provided the particular function on a particular farm. Ordinations of farms were obtained through Hellinger-distance-based linear and polynomial Redundancy Analysis (LRDA and PRDA), which all produced significant results expect LRDA for charcoal. Variance partitioning indicated significant influences of farm location, while socio-economic characteristics had significant influences on 7 LRDA and 9 PRDA ordinations. PRDA explained substantially more variation than LRDA (44% versus 18%). PRDA only explained 50% of the variation explained by unconstrained Principal Components Analysis on the first axis, believed to correspond to a “real-world” gradient. The relationship between farm location, farm characteristics and species composition were documented in various ordination diagrammes. These results were confirmed by multiple regression analyses using the abundance of dominant species as response variable, which implies that farms with a higher composition of a particular species also had higher abundance of this species. Despite the fact that unexplained variation implies that some farms differ in species composition with the majority of farms of the same type (same location or same socio-economic group), diversification can be guided for most farms by information on the dominant species of each type of farm. Diversification can either be achieved by introducing dominant species from other sections of the landscape, or by introducing rare species. The analysis would particularly be useful for groups of low average diversity that are dominated by only two species: construction, medicine, charcoal, beverage and fodder.

SPECIES COMPOSITION OF USE-GROUPS 9.1. Introduction One of the objectives of tree domestication research in general and more specific in western Kenya is the diversification of tree species composition in agroecosystems (Kindt & Lengkeek 1999). Making agroecosystems ecologically more stable and economically more rewarding through diversification and intensification of land use by domestication of agroforestry trees is one of the three pillars of ICRAF’s research (ICRAF 1997; ICRAF 2000). This chapter provides an analysis of on-farm tree species composition. The analysis of on-farm species composition had two objectives: to identify whether and where substantial differences in species composition exist between farms, and to test whether these differences are linked to characteristics of the respective farms. Where important differences in species composition exist, diversification could attempt to introduce new species in different sites. Where species composition is mainly related to characteristics of sites, diversification projects could be guided by these characteristics, allowing targeting specific types of households or areas.

9.2. Material and Methods Formulation of use-groups (4.1), analysis of species composition (4.5)

9.3. Results Figures 9.1 and 9.2 show ordinations of farms based on their species composition, while figures 9.3 - 9.14 provide ordinations for species belonging to particular use-groups. Table 9.1 provides a summary of the ordinations. All ordinations were significant, except the PRDA for the charcoal group. PRDA explained substantially more variance than LRDA (the respective averages being 44% and 18% of explained variance). For eight groups, this resulted in two axes of PRDA explaining more variance than all the canonical variance explained by LRDA. Both methods, however, only explained a fraction of the total variance, what resulted in the first axis explaining substantially less variance than PCA. The average variance explained on the first axis of PRDA was only 50% of that of the first PCA axis. Two axes of PRDA (represented in the figures) explain on average 23% of variance – two axes of PCA explain 50% of variance on average. For some PCA ordinations, the broken stick criterion revealed that many axes were significant. This points to data sets with a lot of variance that can only be represented on a large number of orthogonal axes. Whether this variance points to many ‘real world’ gradients, or whether these are distortion axes induced by the ordination method (see discussion) could not be resolved. The number of axes to be studied was related to the number of species differentiated in the diagrammes. Except for medicine, shade and soil fertility, the species number differentiated in the diagrammes equalled the number of broken stick axes plus 0-3. Because the site scores on the first RDA and PCA axes were strongly correlated, those sites were arranged on a similar gradient. PCA calculates the hypothetical gradient that expresses most variance on the first axis. RDA calculates a hypothetical gradient in the same way, with the restriction that this gradient is calculated as a combination of environmental characteristics (‘direct gradient analysis’).

136

CHAPTER 9 Table 9.2 provides a summary of the variance partitioning of the various use-groups. Differences between villages were significant for all use-groups. For the charcoal, fodder, medicine, and soil fertility use-groups, using environmental characteristics alone did not result in significant relationships with PRDA ordination. For the timber and ornamental groups, using PRDA instead of LRDA made the ordination significant. The reverse was the case for the medicine group. Polynomial versus classical RDA resulted in differences in Vj. Vj was only larger for PRDA for the beverage and boundary demarcation groups. Ordination for all species was the only case where Vj was smaller and positive. Vj of negative sign means that the two groups of environmental characteristics have effects of opposite signs, and that both environmental characteristics together explain the variance much better (Legendre & Legendre 1998 p. 533). Because of the higher amount of variance explained, the significance of the ordination, and the interaction terms between village and household characteristics that can occur in PRDA, all ordination diagrammes presented were the result of PRDA using all environmental characteristics – even where use of environmental characteristics alone was not significant. Another reason to present this type of ordination is that site scores that were presented were not fitted site scores based on environmental characteristics, but site scores that depended on species scores. The following discussion describes the interpretation of the ordination diagrammes based on vector lengths and positions. Strong influence of environmental characteristics on species composition was only expected in ordination diagrammes where vectors lengths were long and angles between vectors were small. Only vectors corresponding to linear correlation were analyzed. Regression coefficients from multiple regression confirming or rejecting trends observed in diagrammes were mentioned. The complete list of regression coefficients obtained from stepwise regression is provided in table 9.3. Because some species had the same abundance distribution for different uses, the regression coefficients were only mentioned once and reference was made to the respective use-group. Figures 9.1 and 9.2 show the ordination for all species and households. Figure 9.1 mainly highlights differences in species composition between Ebuchiebe, Shimutu, and Mutambi villages. Figure 9.2 features differences between Madidi and Mutambi on the vertical axis. Ebuchiebe is characterized by Euphorbia tirucalli (regression coefficients mentioned for the boundary demarcation use), Markhamia lutea (construction), Sesbania sesban (soil fertility), Grevillea robusta (timber) and Syzygium cuminii (fruit). Shimutu has more Psidium guajava (fruit), Tithonia diversifolia (boundary demarcation), Croton macrostachyus (firewood), Lantana camara (boundary demarcation), and Dracaena fragrans (boundary demarcation). Mutambi households contain more Coffea arabica (beverage), Buddleja davidii (boundary demarcation), and Camellia sinensis (beverage). Madidi features more Dracaena fragrans (like in Shimutu, see boundary demarcation), Buddleja davidii (like in Mutambi, see boundary demarcation) and Cupressus lusitanica (boundary demarcation). Not all correlations expected between species and household characteristics from the ordination could be confirmed. The relationship between farm size and Croton macrostachyus and Psidium guajava were confirmed (see discussions for firewood and fruit), but the one between farm size and Tithonia diversifolia not (firewood discussion). The positive link between the number of local cattle and Lantana camara observed in figure 9.2 could not be confirmed by regression analysis (ra=-0.46, P=0.60). Figure 9.3 reveals that the beverage group only consisted of two species, Camellia sinensis and Coffea arabica. Mutambi village differs from the other villages as it has higher presence of Camellia sinensis (rs=1.79, P=0.0001) and Coffea arabica (rs=1.97, P=0.0001). Ebuchiebe village had less Coffea arabica (rs=-0.37, P=0.0001). Farms with more Coffea arabica had more de jure female heads (rs=0.81, P=0.001), more permanent houses (rs=1.05, P=0.001), and heads with higher levels of schooling (rs=0.85, P=0.01) and higher age (rs=1.13, P=0.02). Regression analysis could not confirm that heads longer in charge of the farm was linked to abundance of this species (ra=-0.57, P=0.41). Camellia sinensis was mainly linked to the number of crossbred cattle (rs=1.98, P=0.05)

137

SPECIES COMPOSITION OF USE-GROUPS and farm size (rs=1.13, P=0.02). Farms with less Camellia sinensis had more de jure female heads (rs=-0.68, P=0.06). Figure 9.4 shows that species composition for the boundary demarcation use-group mainly differed for Buddleja davidii, Lantana camara, and Euphorbia tirucalli. The first species is mainly prominent in Mutambi and Madidi villages (negative correlations for the two other villages of rs=2.18, P=0.0001 and rs=-2.01, P=0.0001 respectively). The species is also negatively related to farms with permanent houses (rs=-0.73, P=0.04). The second species seems to dominate Shimutu, although its vector length is not very long. The regression analysis only highlighted higher absence of the species in Ebuchiebe (rs=-1.50, P=0.0001) and Mutambi (rs=-0.60, P=0.07), but the centroid position of Madidi indicated higher presence of the species in this village as well. The species is positively related to farm size (rs=2.82, P=0.0002). Euphorbia tirucalli dominates Ebuchiebe (rs=1.96, P=0.0001) and is more absent in Shimutu (ra=-1.22, P=0.0009). Regression did not confirm the positive relation to the number of children (rs=0.60, P=0.41). Dracaena fragrans, Cupressus lusitanica, and Tithonia diversifolia only contributed marginally to the ordination (shorter vector lengths) indicating their higher presence in Shimutu. Regression results were only explicit for Tithonia diversifolia (rs=1.62, P=0.0001). Directions of vectors and regression coefficients (table 9.3) for D. fragrans and Cupressus lusitanica indicated higher presence in Madidi as well. Regression coefficients for Cupressus lusitanica show that the species dominates Madidi and only to a lesser degree Shimutu – this pattern can be observed in figure 9.10 where the species is well represented. Figure 9.5 shows that farms mainly differ in charcoal group composition for Eucalyptus saligna and Markhamia lutea. Eucalyptus saligna is more dominant in Mutambi (rs=0.57, P=0.002). Markhamia lutea is more prominent in Madidi and less in Ebuchiebe (Ebuchiebe: rs=-0.80, P=0.002; Shimutu: rs=-0.56, P=0.06; Mutambi: rs=-0.50, P=0.06). Significance tests for this group (table 9.2) revealed that farm characteristics did not have a significant influence on ordinations. However, information on the diagramme for farm size, number of crossbred cattle, and age of household head agreed with partial regression results. Figure 9.6 indicates that the two species dominating charcoal also dominate the construction group. Markhamia lutea is more prominent in Ebuchiebe (rs=1.14, P=0.0001) and is positively associated with farm size (rs=2.91, P=0.0001), permanent houses (rs=0.79, P=0.02), and the number of years that the household head was in charge of the farm (rs=0.83, P=0.06). A positive correlation with age (ra=0.62, P=0.31) could not be confirmed, but the age vector was short and had a wider angle with the Markhamia lutea vector. No major village differences existed for Eucalyptus saligna (nor were significant coefficients detected), but de facto female households heads had fewer trees of the species (rs=0.86, P=0.001 and rs=0.76, P=0.032 for male and de jure female headed households respectively). Figure 9.7 shows the ordination of farms and species used for firewood. It is almost an identical ordination as for all species – most species have firewood as one of the given uses (Kindt et al. Chapter 5). Ordination mainly shows species typical for Ebuchiebe, Shimutu, and Mutambi. In Ebuchiebe, Euphorbia tirucalli (boundary demarcation), Sesbania sesban (soil fertility), Syzygium cumini (fruit), Markhamia lutea (construction) and Grevillea robusta (timber) dominate. Species characterising Shimutu are Croton macrostachyus (rs=1.76, P=0.0001), Tithonia diversifolia (boundary demarcation) and Lantana camara (boundary demarcation). In Mutambi, typical species are Buddleja davidii (boundary demarcation), Coffea arabica (beverage) and Camellia sinensis (beverage). Cupressus lusitanica and Harungana madagascariensis are not well represented in the diagramme. Their vectors indicate that they occur less in Ebuchiebe (respectively boundary demarcation and rs=1.03, P=0.0003). Table 9.3, however, indicates a low coefficient for Shimutu for Harungana madagascariensis. Apart from village characteristics, the only other environmental characteristic that features prominently in the diagramme is farm size (linked to Tithonia diversifolia, but ra=-0.09, P=0.89 and Croton macrostachyus, rs=1.11, P=0.01). Number of children (linked to Euphorbia tirucalli

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CHAPTER 9 but ra=0.60, P=0.41) is represented by a smaller vector. The link between Lantana camara and number of local cattle was discussed for Figs 1 & 2. The fodder group represented in figure 9.8 is another group with differences in composition for two species in first two RDA dimensions. The major difference detected was for Madidi village with more Sesbania sesban and Leucaena leucocephala. Table 9.3 confirms these differences with significant negative regression coefficients for all other villages. The ordination was not significant for farm characteristics. Number of crossbred cattle however had a position that was confirmed by regression (rs=1.07, P=0.01). Figure 9.9 mainly highlights differences in species composition for fruit between Ebuchiebe and Shimutu. Syzygium cumini and Mangifera indica are more prominent in Ebuchiebe, while Psidium guajava is more prominent in Shimutu. The respective coefficients for Ebuchiebe and Shimutu confirmed this trend (table 9.3). Carica papaya was another species contributing to the ordination diagramme. The negative association of this species with number of crossbred cattle could not be confirmed (ra=-0.27, P=0.12). Larger size farms have more Psidium guajava (rs=2.82, P=0). Figure 9.10 shows that differences in species composition in the timber group are mainly characterized by differences in Markhamia lutea, Cupressus lusitanica, and Zanthoxylum gillettii. Species that contributed less are Grevillea robusta, Syzygium cumini, and Eucalyptus saligna. Ebuchiebe is characterized by Markhamia lutea (rs=1.02, P=0.0001), Grevillea robusta (rs=0.40, P=0.003) and Syzygium cumini (fruit). Mutambi is characterized by Zanthoxylum gillettii (rs=0.39, P=0.001). Madidi features more Cupressus lusitanica (see table 9.3 where the other villages have negative coefficients). When considering household characteristics, the positive influence of permanent houses (rs=2.09, P=0.0001) and negative influence of thatch roofed houses (rs=-0.49, P=0.12) on Cupressus lusitanica is prominent. At the level of associations of the species with age of household head, farm size and number of local cattle, only the first relationship could be confirmed (respectively rs=-0.49, P=0.12, ra=1.11, P=0.21 and ra=0.37, P=0.69). The latter environmental characteristic, however, had a very short vector, while the first variable had the longest vector length. Figure 9.11 shows that species composition for medicine mainly differed for Harungana madagascariensis and Azadirachta indica. The ordination shows higher presence of the first species in Mutambi (rs=0.94, P=0.0001) and lower presence in Ebuchiebe (rs=-0.41, P=0.07). Household characteristics did not have a significant influence on ordination (table 9.2), while vectors were short. However, partial regression confirmed association of Azadirachta indica with age of household head (rs=0.30, P=0.06) and number of children (rs=0.31, P=0.04). The ornamental use-group is presented in figure 9.12. Only three species had significant contributions to the diagramme: Terminalia mantaly, Buddleja davidii and Cupressus lusitanica. The main differences highlighted in the diagramme are differences between Ebuchiebe, Shimutu, and Madidi. The first two villages feature less Buddleja davidii and Cupressus lusitanica, while the opposite phenomenon can be observed for Madidi. Regression coefficients (table 9.3) confirmed this observation. Positive correlation of Terminalia mantaly with permanent houses could not be confirmed (ra=0.14, P=0.53). The species was positively related to the number of crossbred cattle (rs=0.51, P=0.03), which is another indicator of wealthier households, and the highest level of schooling of the household head (rs=0.22, P=0.04). Figure 9.13 shows the ordination for the shade group. Three species feature prominently. Mangifera indica is positively correlated with Madidi (negative correlations for other villages, table 9.3). Croton macrostachyus is associated with Mutambi, although regressions could not confirm this observation (ra=0.14, P=0.34). Shimutu has more Bischofia javanica (rs=0.18, P=0.03). Of species that were not well represented, Madidi featured more Croton megalocarpus and Syzygium cumini (negative correlations for other villages, table 9.3), Mutambi and Madidi more Psidium guajava and Eriobotrya japonica (negative correlations for the other villages, table 9.3), while Mutambi had more Zanthoxylum gillettii (rs=0.21, P=0.0001). A positive relationship between Bischofia javanica and farm

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SPECIES COMPOSITION OF USE-GROUPS size could not be confirmed (ra=0.003, P=0.99), while the expected negative relationship with Mangifera indica had an opposite regression coefficient (rs=0.54, P=0.03). Figure 9.14 shows the ordination for the soil fertility enhancement group. The major gradient for villages can be detected for Sesbania sesban, with higher presence in Ebuchiebe (rs=0.55, P=0.04) and Madidi (other village coefficients), and lower presence in Mutambi (rs=-0.41, P=0.002) and Shinyalu (rs=-0.97, P=0.002). Species that were more dominant in Mutambi were Coffea arabica (rs=0.73, P=0.0001), Carica papaya (rs=0.22, P=0.003), Ricinus communis (not confirmed with ra=0.06, P=0.37) and Camellia sinensis (rs=0.55, P=0.001). Tephrosia vogelii featured more in Shimutu (rs=0.30, P=0.002). Farm characteristics were not significant (table 9.2). However, the negative relationship between years that the household head was in charge and Sesbania sesban was confirmed (rs=-0.80, P=0.08).

9.4. Discussion 9.4.1. Ordination Analysis Results Where species respond to the same gradients, relationships in the data can be represented adequately in a few dimensions (Økland 1996). The many axes that would need to be analyzed with unconstrained ordination for some groups (e.g. all species, firewood, and shade) could express that many gradients are present in these data. Økland (1999) however warned that (polynomial) distortion axes may occur because species never meet all assumptions of the species response models, and also pointed out that noise axes might occur as well. Therefore, not all axes necessarily express true gradients, and an ordination diagramme reflecting a small amount of variance may be relevant. Distortion and noise axes result in normal percentages of variance explained ranging 20-50% (ter Braak & Smilauer 1998 p. 121; Økland 1999). This makes it difficult to assess which fraction of remaining variance in the data could be explained by unmeasured environmental variables. However, if we hypothesise that the first axis calculated by PCA is a structure axis, then we could assess whether additional environmental variables could potentially explain more variance by the difference in variance explained on the first PCA (constrained) and RDA (unconstrained) axis. High correlations between RDA and PCA site scores indicate that the first axis describes the same gradient. Another method by which gradient differences were tested was by looking at species that contributed significantly to the first two PCA axes. As indicated in table 9.3, most species that contributed to the ordination by PRDA also contributed to the PCA ordination. Some species that did not contribute were highlighted in the tables. In general, more species contributed to the RDA than to the PCA ordination diagrammes. Only three species contributed to PCA ordinations on the first two axes and not to RDA ordinations: Bridelia micrantha for firewood, Eriobotrya japonica for fruit and Terminalia mantaly for shade. The first two species only contributed marginally to PCA ordinations. Because gradients expressed on first PCA and RDA axes were largely similar, additional environmental variables could allow RDA axes to reconstruct the PCA axes better. Despite the complexity caused by potential noise and distortion axes, it is thus still possible to assess potential additional amounts of variance explained by additional sets of explanatory variables by investigating how well the dominant gradient revealed by PCA is reconstructed by RDA. As indicated by Økland (1999), distortion and noise axes pose no problems to the variance partitioning method. Neither do these axes cause problems to compare the variance explained by LRDA versus PRDA. PRDA explained higher amounts of variance and the method was able to reconstruct the first PCA axes better, thus better explaining the dominant gradient in the data. Village differences alone were significant for all ordinations, while farm and household

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CHAPTER 9 characteristics alone were significant for the majority of ordinations. Because of negative Vj where only village differences were significant, ordination provided by using all environmental characteristics was still appropriate for all ordination analyses (more variance was explained). Multiple regression explained low percentages of variance (table 9.3). The highest amount of variance explained was for Coffea arabica for beverage (49%). Low amounts imply that single species were not strongly related to explanatory variables and that a lot of unexplained variance remained. This resulted in difficulties in reconstructing PCA axes in the dataset, even where an axis is mainly correlated with the same single species of which nearly all variance is explained (e.g. ordination for beverage, charcoal and soil fertility). The trends from ordination diagrammes were confirmed by multiple linear regression for most strong effects (where vector lengths were longer, or with centroids far from the origin). This result also means that the Hellinger transformation applied to the data did not effect the results very much, meaning that farms with higher abundance of the species also had higher frequency

nxi nx+ (see methods) of the species. Another consequence of this result is that the ordination diagrammes explained meaningful amounts of variance, although these were small if expressed in fractions of the total variance present. Jarvis et al. (2000) state that gender, age, wealth or social status affect farmers’ knowledge, actions and access to resources regarding the maintenance of crop diversity. Long et al. (2000) provide some examples of the influence of wealth, age and gender on crop diversity. Jarvis et al. (2000) also listed farm size, family size, and years of education as explanatory factors for farmer variety choices. Our results show that variables that influence crop choices (wealth, age, gender, farm size, family size, and education) also influenced on-farm tree composition. In many ordination diagrammes, the vector representing the multiple linear correlation had a different angle from the vector representing the linear correlation. One example is the difference between the schooling vectors in figure 9.1. These differences and the higher amount of variance explained by PRDA imply that significant second-order relationships between species composition and farm characteristics exist. However, considering these factors adds to the complexity of the analysis (see next section). 9.4.2. Ordination for Diversification As indicated above, environmental characteristics had a significant effect on differences in species composition on farms. Interpretation of ordination diagrammes and multiple regression analysis revealed environmental characteristics with large and significant effects. However, the amount of variance explained was still small. This was also true when investigating the first axis expected to be a structural gradient. These results are analogous with those from Kindt et al. (Chapter 8) investigating differences in diversity and abundance among use-groups. Environmental characteristics can be used as a guide to plan domestication activities based on differences in species composition. However, some farms will differ in composition from the majority of farms with the same characteristics. For instance, although for boundary demarcation most farms of Ebuchiebe were dominated by Euphorbia tirucalli, some farms had a composition that was more typical of Shimutu or Madidi. For a single species (e.g. Euphorbia tirucalli as a boundary marker), large amounts of unexplained variance remained as well. Exceptional cases, however, do not prevent targeting of interventions aiming at diversifying the agricultural landscape. For the example of Ebuchiebe, interventions can focus on diversifying from Euphorbia tirucalli, which could be relevant for the majority of, but not all, farms in the village.

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SPECIES COMPOSITION OF USE-GROUPS PRDA included second-order relationships between environmental characteristics and ordination axes. Considering second-order effects (for instance the relationship between Euphorbia tirucalli and the level of schooling) explains differences in species composition better, but adds to the complexity of the design of interventions. Diversification can either involve distributing dominant species found in one group of households to another one, or making rarer species more dominant. Both approaches increase alpha diversity (the average diversity of one farm) in a similar way, but have alternative effects on beta diversity (the differences between farms as observed by the changing slope of the speciesarea curve, Kindt et al. – Chapter 6). The result of distributing dominant species more widely on species accumulation curves pattern will be larger species richness values for few randomly accumulated farms, than the values of the curve corresponding to the original distribution. Making less frequent species more dominant will result in the two species accumulation curves to coincide at a larger number of farms (Kindt, unpublished data). The latter approach results in a larger number of accumulated farms showing a greater species richness. It is possible that the greater richness will influence the stability of the agroecosystem, due to the positive relationship between diversity and ecosystem stability under conditions of heterogeneity in species’ traits and environmental characteristics (Kindt et al. – Chapter 2, 5 & 6). However, a decision will need to be made by farmers on appropriateness of increasing more dominant or more rare species. The first approach could have the advantage that the species has better tested traits making it suitable for the particular use. It is also possible that a rare suitable species was overexploited. The two approaches reflect the two major pathways in landscape diversification as described by Van Noordwijk et al. (1997). The integration approach is mainly an approach of increasing alpha diversity, while the segregation approach focuses more on beta diversity. Analysis of species composition supplements analysis of alpha diversity of use-groups. The construction, medicine, charcoal, beverage, fodder, ornamental and soil fertility groups were those that were identified with alpha diversity lower than two (Kindt et al. – Chapter 5 & 6). Ordination diagrammes of the first five groups showed that only two species significantly contributed to the ordinations. The dominant species approach would involve wider distribution of both species. Farms with lower presence of either species can be identified in the diagrammes and could be targeted when distributing the other species. The rare species approach would involve selecting species that did not contribute to the ordination and distributing them more widely, or to introduce a new species. The same farms as identified for the dominant approach (farms dominated by a single species) could be targeted.

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CHAPTER 9 Table 9.1. Summary information on the ordination of all species and various use-groups. Variance is expressed as percentage of total variance. Ordination

Sp

Sp (2)

Var PRDA

Sign (99)

Var LRDA

Sign (9999)

Ax

All species Beverage Boundary demarcation Charcoal Construction Firewood Fodder Fruit Timber Medicine Ornamental Shade Soil fertility enhancement

70 2 27

15 2 6

42.23 50.33 48.44

0.01 0.01 0.01

21 18 68 7 13 31 26 31 50 22

2 2 13 2 4 6 2 3 8 6

44.27 42.49 43.21 48.41 36.01 41.27 43.51 47.60 42.21 45.84

0.21 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

12 0 5

Var PCA (1) 12.17 63.32 30.71

Var LRDA (1) 8.62 29.68 16.78

Var PRDA (1) 9.60 38.58 19.79

Cor. PCARDA 0.99 0.99 0.99

Var PCA (2) 20.64 100.0 47.74

Var PRDA (2) 15.74 50.33 28.13

23.52 34.26 30.30

0.0001 0.0001 0.0001

11.56 13.67 24.01 14.14 14.97 19.63 10.59 11.78 12.27 17.17

0.0108 0.0035 0.0001 0.0003 0.0001 0.0001 0.0040 0.0006 0.0001 0.0001

1 2 12 0 1 5 4 3 9 1

50.47 61.80 12.49 32.54 24.86 23.77 22.82 25.36 13.35 53.53

6.88 9.79 8.68 9.23 7.04 8.23 4.14 5.66 3.94 12.07

14.92 20.17 9.53 18.20 10.28 11.42 8.79 10.50 6.51 20.06

-0.99 0.99 0.98 0.85 -0.91 0.99 0.89 0.83 -0.93 0.99

61.23 81.14 21.81 60.50 46.62 37.96 42.54 43.29 23.55 61.99

21.05 29.52 16.07 30.66 16.92 18.36 15.57 19.04 10.78 24.41

(Sp: number of species in the site-species matrix analyzed; Sp (2): number of species included in the ordination diagramme with the first two axes; Var PRDA/LRDA/PCA (y): variance explained by polynomial RDA/linear RDA/PCA (for first y axes); Sign(99/9999): significance of ordination of lefthand column for 99/9999 permutations; Ax: number of axes to consider based on the broken stick distribution; Cor. PCA – RDA: correlation between the site scores on the first PCA and PRDA axis)

Table 9.2. Variance partitioning between village and household characteristics for all species and various use-groups. Variance is expressed as percentage of total variance. Ordination All species Beverage Boundary demarcation Charcoal Construction Firewood Fodder Fruit Timber Medicine Ornamental Shade Soil fertility enhancement

Vv LRDA/ PRDA 16.22 25.61 22.11 5.97 6.14 7.63 8.34 7.63 9.61 4.80 6.01 5.88 11.96

Sign (9999) 0.0001 0.0001 0.0001 0.0001 0.0007 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

Vh LRDA 10.20 13.19 12.81 5.99 8.00 10.46 5.22 7.53 5.69 10.39 6.49 7.70 5.36

Sign (9999) 0.0001 0.0014 0.0001 0.4723 0.1180 0.0001 0.7247 0.0674 0.6370 0.0001 0.2854 0.0059 0.6559

Vh PRDA 27.56 33.26 32.04 33.67 33.83 28.21 28.43 24.79 28.74 30.72 35.31 31.35 30.49

Sign (99) 0.01 0.01 0.01 0.27 0.02 0.01 0.48 0.06 0.01 0.21 0.06 0.01 0.20

Vve LRDA 13.32 21.07 17.49 5.57 5.67 13.55 8.92 7.44 13.94 0.2 5.29 4.57 11.81

Vve PRDA 14.67 17.07 16.40 10.60 8.66 15.00 19.98 11.22 12.53 12.79 12.29 10.86 15.35

Vhe LRDA 7.30 8.65 8.19 5.59 7.53 16.38 5.80 7.34 10.02 5.79 5.77 6.39 5.21

Vhe PRDA 26.01 24.72 26.33 38.30 36.35 35.58 40.07 28.38 31.66 38.71 41.59 36.33 33.88

(V v/h (e) LRDA/PRDA: (exclusive) linear/polynomial variance explained by village/household characteristics Sign(99/9999): significance of ordination of lefthand column for 99/9999 permutations)

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Table 9.3. Coefficients of village and household characteristics as explanatory variables on species abundance information for various use-groups of species after stepwise linear regression. The significance of coefficients for explanatory variables (shown below characteristics between brackets) as provided by multiple linear regression (indicated by *) where residuals were normally distributed, otherwise calculated by permutation tests. Total explained variance is expressed by the multiple correlation coefficient R2, with probability P(F). Ordination

Species (other use-groups where the species occurred with the same abundance pattern over households) Beverage Camellia sinensis (firewood, all) Coffea arabica (firewood, all) Boundary Buddleja davidii ‡ demarcation (firewood, all) Dracaena fragrans (all) Cupressus lusitanica (firewood, timber, all) Lantana camara (firewood, all) Tithonia diversifolia ‡ (firewood, all) Euphorbia tirucalli (firewood, all) Charcoal Eucalyptus saligna

R2 (P(F))

Intercept

Ebuchie- Shimutu Mutambi Male Female be village village village headed headed (farthest (closest (de jure) from to (no forest) forest) husband) 0.217 -0.04 1.79 -0.68 (6e-9) (0.870)* (0.0001) (0.0571) 0.491 -0.72 -0.37 1.97 0.81 (0) (0.041)* (0.0748) (0.0001) (0.0010) 0.394 2.33 -2.18 -2.01 0.44 (2e-16) (0)* (0.0001) (0.0001) (0.1546) 0.192 1.34 -1.88 -1.23 (3e-8) (0)* (0.0001) (0.0003) 0.275 1.51 -1.78 -0.74 -1.30 (1e-10) (0.001)* (0.0001) (0.0487) (0.0006) 0.242 2.78 -1.50 -0.60 (2e-8) (0)* (0.0001) (0.0737) 0.289 0.40 1.62 (3e-13) (0.029)* (0.0001) 0.266 1.95 1.96 -1.22 0.46 (5e-12) (0)* (0.0001) (0.0009) (0.1239) 0.124 -0.41 0.57 (3e-4) (0.192)* (0.0021) Markhamia lutea 0.128 0.44 -0.80 -0.56 -0.50 (0.001) (0.125)* (0.0019) (0.0576) (0.0530) Construction Markhamia lutea 0.284 0.85 1.14 (firewood, all) (2e-11) (1e-5)* (0.0001) Eucalyptus saligna 0.156 2.17 0.86 0.76 (timber) (4e-6) (0)* (0.001)* (0.032)* Firewood Croton macrostachyus 0.475 -0.10 1.76 0.42 (all) (0) (0.742) (0.0001) (0.0216) Harungana madagascariensis 0.301 0.23 -1.03 -0.82 0.72 0.28 (all) (2e-11) (0.328)* (0.0003) (0.0071) (0.0077) (0.1565) Fodder Sesbania sesban 0.100 0.39 -0.29 -0.39 -0.33 (8e-4) (4e-4)* (0.0223) (0.0023) (0.0128) Leucaena leucocephala 0.150 0.32 -0.33 -0.40 -0.35 (7e-6) (0)* (0.0001) (0.0001) (0.0001) ‡: Species that did not contribute significantly to the first two PCA axes

Farm size Perma- Thatch Number Number (acres) nent roofed of cross- of local (1 acre = house house bred cattle 0.4005 (most (least cattle (~wealth) ha) wealthy) wealthy) (~wealth) 1.42 1.98 (0.0449) (0.0547) 1.05 -1.57 (0.0014) (0.0355) -0.73 -0.46 (0.0446) (0.0776)

2.82 (0.0002)

2.09 -0.49 (0.0001) (0.1181) -1.06 -0.52 (0.0152) (0.0908) -0.50 (0.0189)

0.80 (0.0573) 0.89 0.79 (0.1344) (0.0146) 2.91 0.52 (0.0001) (0.1217) 2.02 (4e-4)* 1.11 (0.0112) 3.57 (0.0001) 0.28 (0.0756)

-1.72 (0.0300) -1.88 (0.0182)

Years being head

Maxi- Number Level of mum age of schoohead or resident ling of partner children head

1.13 (0.0162) -0.87 1.03 (0.1135) (0.0492) 1.77 (0.0049) 1.91 (0.0038) -1.33 -1.11 (0.0431) (0.1183)

0.83 (0.0580)

0.69 -0.77 (0.0975) (0.0335) 0.89 (0.0572)

1.26 (0.0018) 0.45 (0.0422)

-1.64 (0.0335) 1.07 (0.0143)

0.95 (0.06)* -0.57 (0.0942)

0.85 (0.0135)

-0.47 (0.1281) 0.59 (0.0357)

0.58 (0.0332)

Table 9.3 (cont.d) Ordination

Species

Fruit

Carica papaya

R2 (P(F))

Intercept

Ebuchie- Shimutu Mutambe bi

0.084 0.53 0.28 (0.001) (0.003)* (0.0449) Syzygium cumini ‡ 0.287 0.42 -0.93 (firewood ‡, timber ‡, all ‡) (1e-11) (0)* (0.0001) Mangifera indica 0.191 0.96 -0.63 (3e-7) (0)* (0.0001) Psidium guajava 0.216 2.06 -0.71 (all ‡) (2e-9) (0)* (0.004)* Timber Grevillea robusta 0.165 -0.32 0.40 (firewood, all) (1e-5) (0.177)* (0.0033) Markhamia lutea 0.195 -0.07 1.02 0.55 (8e-7) (0.765)* (0.0001) (0.0071) Zanthoxylum gillettii ‡ 0.191 -0.08 -0.12 (1e-7) (0.438)* (0.1461) Medicine Harungana madagascariensis 0.210 0.16 -0.41 -0.61 (6e-8) (0.349)* (0.0682) (0.0201) Azadirachta indica 0.041 -0.05 (0.023) (0.596)* Ornamental Terminalia mantaly 0.053 0.10 (0.007) (0.066)* Buddleja davidii 0.151 0.92 -1.20 -1.13 (1e-5) (1e-4)* (0.0001) (0.0001) Cupressus lusitanica ‡ 0.183 1.35 -1.27 -1.20 (9e-7) (0)* (0.0001) (0.0001) ‡: Species that did not contribute significantly to the first two PCA axes

-0.51 (0.0001) -0.36 (0.0011) -0.64 (0.011)* -0.27 (0.0485) 0.39 (0.0001) 0.94 (0.0001)

Male headed

Female Farm size Permaheaded nent (de jure) house

Thatch roofed house

-0.64 (0.0016)

0.18 (0.0434)

-0.57 (0.0174)

1.06 (0.0005) 1.09 -0.24 (0.0001) (0.0961) 2.82 (0)* 0.52 (0.0791)

Number Number of cross- of local bred cattle cattle -0.68 (0.0567)

Maxi- Number Level of mum age of schoohead or resident ling of partner children head 0.67 (0.0256) -0.24 (0.1112)

0.37 (0.0042)

0.44 (0.0196) 1.74 (0.0025)

-1.05 (0.0350)

0.84 (0.0378)

0.68 (0.0263)

0.69 (0.0921)

0.40 (0.0469)

0.29 (0.1033) -1.13 (0.0834) 0.51 (0.0308)

-0.45 (0.0852) -0.96 (0.0003)

Years being head

0.30 (0.0571)

0.31 (0.0407) 0.70 (0.1083)

-0.39 (0.0736)

1.37 (0.0494)

0.22 (0.0413)

Table 9.3 (cont.d) Ordination

Species

Shade

Mangifera indica

R2 (P(F))

Intercept

Ebuchie- Shimutu Mutambe bi

0.231 0.91 -0.46 (2e-8) (0)* (0.0003) Croton megalocarpus 0.116 0.46 -0.44 (0.001) (6e-5)* (0.0009) Syzygium cumini ‡ 0.180 0.34 -0.31 (2e-5) (4e-4)* (0.0005) Psidium guajava ‡ 0.090 0.06 -0.11 (0.005) (0.028)* (0.0038) Eriobotrya japonica ‡ 0.148 -0.17 -0.23 (3e-5) (0.259)* (0.0008) Zanthoxylum gillettii 0.173 -0.03 (7e-7) (0.610)* Croton macrostachyus 0.097 -0.34 (0.001) (0.194)* Bischofia javanica 0.115 0.166 (6e-4) (0.087)* Soil fertility Ricinus communis 0.045 0.21 (0.04) (0.011)* Carica papaya ‡ 0.082 0.23 (0.004) (0.032)* Coffea arabica ‡ 0.143 -0.03 (1e-6) (0.637)* Camellia sinensis ‡ 0.276 0.01 (4e-11) (0.977)* Tephrosia vogelii ‡ 0.082 0.02 (0.002) (0.8450)* Sesbania sesban 0.195 1.32 0.55 (firewood, all) (2e-6) (2e-5)* (0.0364) ‡: Species that did not contribute significantly to the first two PCA axes

Male headed

-0.87 -0.53 (0.0001) (0.0001) -0.57 -0.29 0.23 (0.0001) (0.0404) (0.0217) -0.51 -0.35 0.14 (0.0001) (0.0002) (0.0386) -0.16 (0.0021) -0.34 (0.0014) 0.21 0.10 (0.0001) (0.0181) 0.18 -0.16 (0.0265) (0.0609)

Female Farm size Permaheaded nent (de jure) house -0.30 (0.0114)

0.54 (0.0278) 0.32 (0.1042) 0.23 (0.0375) 0.42 (0.0501) -0.31 (0.0114)

Number Number of cross- of local bred cattle cattle -0.64 (0.1121) -0.51 (0.0671) -0.31 (0.0413)

0.52 (0.0413)

0.85 (0.0329)

0.29 (0.1455)

1.46 (0.0091)

Years being head

0.37 (0.0573) 0.35 (0.1407)

2.61 (0.0023)

Maxi- Number Level of mum age of schoohead or resident ling of partner children head -0.32 (0.0309) -0.24 (0.1152)

0.27 (0.0077)

0.15 (0.0244)

0.22 0.11 (0.0028) (0.0654) 0.73 (0.0001) 0.55 (0.0015)

0.30 (0.0016) -0.97 -0.41 (0.0022) (0.1218)

0.26 (0.1319) 0.20 (0.0706) 0.11 (0.0642)

Thatch roofed house

0.47 (0.0178)

0.83 (0.0387)

0.96 (0.0080) -0.25

0.24 (0.0676)

-0.20 (0.0951) -0.37 (0.0190)

-0.29 (0.1022) -0.80 (0.0762)

-0.57 (0.0843)

0.30 (0.0238) -0.45 (0.1577)

0.65 (0.0069) -0.18 (0.0240) -0.21 (0.0626)

0.20 (0.1297) -0.60 (0.0780)

Axis II (6.1 % tot. var., 14.7 % can. var., spec-env. corr. 0.88)

CHAPTER 9

0.6

buddav

0.5

cofrob

0.4

ncros camsin

0.3

Mut

0.2 0.1

school

school1

Mad age harmad fedjfedf age1 heady1 tha heady nloc1 cuplus drafra psigua lancam Shi titdiv cromac nloc

0.0 -0.1 -0.2 -0.3

euptir

nchil nchil1 syzcum sesses

Ebu

grerob marlut

-0.4

Ebuchiebe Shimutu Mutambi Madidi

-0.5 -0.6

size1

-0.7 -0.8

size -0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Axis I (9.6 % tot. var., 22.8 % can. var., spec-env. corr. 0.89)

Axis III (4.3 % tot. var., 10.1 % can. var., spec-env. corr. 0.82)

Figure 9.1. Ordination plot for all species. % tot. var.: percentage of total variance explained on the axis; % can. var.: percentage of canonical variance explained on the axis; spec-env. cor.: species-environment correlation.

0.5

0.4

Ebuchiebe Shimutu Mutambi Madidi

buddav 0.3

nloc

cuplus drafra

nchil sesses nchil1 syzcum

Mad

0.2

0.1

nloc1 lancam

school

fedf school1

fedjtha

0.0

age1 age

Shi

-0.1

titdiv

Ebu grerob marlut

Mut

euptir

heady1 heady harmad brimic

-0.2

size1 size -0.3

cromac

camsin ncros

-0.4

cofrob

-0.5 -0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Axis I (9.6 % tot. var., 22.8 % can. var., spec-env. corr. 0.89)

Figure 9.2. Ordination plot for all species. Abbreviations as in figure 9.1.

147

Axis II (11.7 % tot. var., 23.3 % can. var., spec-env. corr. 0.56)

SPECIES COMPOSITION OF USE-GROUPS

Ebuchiebe Shimutu Mutambi Madidi

camsin

1.0 0.9 0.8

ncros

0.7 0.6

size

ncros1

0.5 0.4

size1

0.3 0.2

age

Mut

0.1

fedf male iron Mad tha Ebu nchil1 Shi

0.0 -0.1

age1

perm school1 school

fedjnloc1 nloc

-0.2

cofspp

-0.3

heady1 heady

nchil

-0.4 -0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

Axis I (38.6 % tot. var., 76.7 % can. var., spec-env. corr. 0.78)

Axis II (8.3 % tot. var., 17.2 % can. var., spec-env. corr. 0.76)

Figure 9.3. Ordination plot for the beverage use-group. Largest symbol size (Ebuchiebe village) corresponds to 47 farms and origin before species centring. Abbreviations as in figure 9.1.

1.0 0.9

buddav

0.8 0.7 0.6 0.5

nloc

0.4 0.3

school

0.2

Mut Mad fedj nloc1 fedf age1 drafraage perm cuplus Shi titdiv lancam

nchil nchil1

0.1 0.0 -0.1

Ebu

euptir

-0.2 -0.3

Ebuchiebe Shimutu Mutambi Madidi

-0.4 -0.5 -0.6 -0.7

size1 size

-0.8 -0.9 -0.9

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Axis I (19.8 % tot. var., 40.9 % can. var., spec-env. corr. 0.82)

Figure 9.4. Ordination plot for the boundary demarcation use-group. Largest symbol size (Ebuchiebe village) corresponds to 18 farms. Abbreviations as in figure 9.1.

148

Axis II (6.1 % tot. var., 13.8 % can. var., spec-env. corr. 0.75)

CHAPTER 9

1.0

eucsal

0.9

Ebuchiebe Shimutu Mutambi Madidi

0.8 0.7 0.6 0.5 0.4

size

heady

ncros

0.3

size1

0.2

ncros1

age age1

Mut

0.1 0.0

Ebu

-0.1

perm

nloc1nloc

Mad

marlut

-0.2 -0.3

school

-0.4

nchil

-0.5 -0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

Axis I (14.9 % tot. var., 33.7 % can. var., spec-env. corr. 0.54)

Axis II (9.4 % tot. var., 22.0 % can. var., spec-env. corr. 0.64)

Figure 9.5. Ordination plot for the charcoal use-group. Largest symbol size (Ebuchiebe village) corresponds to 33 farms and origin before species centring. Abbreviations as in figure 9.1.

1.0

Ebuchiebe Shimutu Mutambi Madidi

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

school

0.0

fedf Mut Mad perm Ebu fedj

school1

-0.1 -0.2

size1

nloc heady1 heady

ncros

-0.3

age1

-0.4

size

age

-0.5

marlut

-0.6 -0.7 -0.8

eucsal

-0.9 -1.0 -0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Axis I (20.2 % tot. var., 47.5 % can. var., spec-env. corr. 0.59)

Figure 9.6. Ordination plot for the construction use-group. Largest symbol size (Mutambi and Madidi village) corresponds to 11 farms. Abbreviations as in figure 9.1.

149

Axis II (6.5 % tot. var., 15.1 % can. var., spec-env. corr. 0.85)

SPECIES COMPOSITION OF USE-GROUPS

0.7 0.6

buddav

0.5

cofrob

0.4 0.3

ncros

school

camsin

Mut

0.2

harmad age1 ageheady1 heady cuplus nloc1

-0.1

lancam

-0.2 -0.3

nloccromac

euptir

nchil nchil1

Mad fedjfedf tha

0.1 0.0

school1

syzcum sesses

Ebu

grerob marlut

Shi titdiv

-0.4

Ebuchiebe Shimutu Mutambi Madidi

-0.5 -0.6

size1

-0.7 -0.8

size

-0.9 -0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Axis I (9.5 % tot. var., 22.1 % can. var., spec-env. corr. 0.88)

Axis II (12.5 % tot. var., 25.7 % can. var., spec-env. corr. 0.68)

Figure 9.7. Ordination plot for the firewood use-group. Abbreviations as in figure 9.1.

0.8

sesses

Ebuchiebe Shimutu Mutambi Madidi

0.7 0.6 0.5

ncros

0.4 0.3

ncros1

0.2 0.1

Mut

0.0

Mad

-0.1 -0.2 -0.3 -0.4 -0.5

size

-0.6

leuleu

-0.7 -0.8 -0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Axis I (18.2 % tot. var., 37.6 % can. var., spec-env. corr. 0.73)

Figure 9.9. Ordination plot for the fodder use-group. Largest symbol size (Ebuchiebe village) corresponds to 43 farms and origin before species centring. Abbreviations as in figure 9.1.

150

Axis II (6.6 % tot. var., 18.4 % can. var., spec-env. corr. 0.57)

CHAPTER 9

0.8

carpap

Ebuchiebe Shimutu Mutambi Madidi

0.7 0.6 0.5

heady

0.4 0.3 0.2

nchil

0.1

Shi

0.0

Ebu

-0.1 -0.2

syzcum

nloc

psigua

-0.3

size size1

-0.4

ncros1 ncros

-0.5

manind

-0.6 -0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Axis I (10.3 % tot. var., 28.6 % can. var., spec-env. corr. 0.68)

Axis II (6.9 % tot. var., 16.8 % can. var., spec-env. corr. 0.74)

Figure 9.9. Ordination plot for the fruit use-group. Largest symbol size (Shimutu village) corresponds to 2 farms. Abbreviations as in figure 9.1.

marlut

0.6

Ebuchiebe Shimutu Mutambi Madidi

0.5 0.4 0.3

size nchil nchil1 ncros

cuplus

grerob syzcum

size1

0.2 0.1

Ebu

ncros1 perm

0.0

age1 nloc1 nloc

-0.1 -0.2

school

Shi Mad fedj

tha

Mut

age

eucsal heady

-0.3 -0.4

zangil

-0.5 -1.0

-0.9

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

Axis I (11.4 % tot. var., 27.7 % can. var., spec-env. corr. 0.70)

Figure 9.10. Ordination plot for the timber use-group. Largest symbol size (Mutambi village) corresponds to 5 farms. Abbreviations as in figure 9.1.

151

Axis II (6.8 % tot. var., 15.6 % can. var., spec-env. corr. 0.58)

SPECIES COMPOSITION OF USE-GROUPS

0.3

school ncros

0.2

heady heady1

fedftha Mad perm Ebu

0.1 0.0 -0.1

Mut

harmad

-0.2 -0.3

age1age

nloc1 nchil1 nloc

-0.4

size

-0.5

nchil -0.6

Ebuchiebe Shimutu Mutambi Madidi

-0.7 -0.8 -0.9

azaind -1.0 -0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Axis I (8.8 % tot. var., 20.2 % can. var., spec-env. corr. 0.64)

Axis II (8.5 % tot. var., 17.9 % can. var., spec-env. corr. 0.65)

Figure 9.11. Ordination plot for the medicine use-group. Largest symbol size (Shimutu village) corresponds to 26 farms and origin before species centring. Abbreviations as in figure 9.1.

0.3 0.2

size

heady

Shi

nloc

0.1

size1

Ebu

0.0 -0.1

perm

Mad

-0.2

cuplus

-0.3

nchil

-0.4

school ncros

buddav

-0.5

ncros1 school1

-0.6

Ebuchiebe Shimutu Mutambi Madidi

-0.7 -0.8

terman

-0.9 -0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Axis I (10.5 % tot. var., 22.1 % can. var., spec-env. corr. 0.65)

Figure 9.12. Ordination plot for the ornamental use-group. Largest symbol size (Ebuchiebe and Shimutu village) corresponds to 32 farms and origin before species centring. Abbreviations as in figure 9.1.

152

Axis II (4.3 % tot. var., 10.1 % can. var., spec-env. corr. 0.66)

CHAPTER 9

bisjav

0.5 0.4

manind 0.3 0.2

cromeg syzcum Mad

sizesize1

0.1

Shi

0.0 -0.1 -0.2

tha perm fedj Mut

fedf

psigua

erijap

zangil age1 age

-0.3 -0.4

Ebuchiebe Shimutu Mutambi Madidi

nloc

-0.5 -0.6

cromac

-0.7 -0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Axis I (6.5 % tot. var., 15.4 % can. var., spec-env. corr. 0.70)

Axis II (4.4 % tot. var., 9.5 % can. var., spec-env. corr. 0.75)

Figure 9.13. Ordination plot for the shade use-group. Largest symbol size (Shimutu village) corresponds to 17 farms and origin before species centring. Abbreviations as in figure 9.1.

0.7

cofrob

Ebuchiebe Shimutu Mutambi Madidi

0.6 0.5

riccom

0.4

camsin carpap

0.3

ncros nloc

0.2

Mut

sesses

0.1

fedf perm EbuMad

0.0 -0.1

ncros1

heady1 heady

Shi

-0.2 -0.3

tepvog

-0.4 -1.1

-1.0

-0.9

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Axis I (20.1 % tot. var., 43.8 % can. var., spec-env. corr. 0.61)

Figure 9.14. Ordination plot for the soil fertility group. Largest symbol size (Ebuchiebe village) corresponds to 29 farms. Abbreviations as in figure 9.1.

153

CHAPTER 10 COMPARISON OF TREE SPECIES COMPOSITION OF FARMS IN WESTERN KENYA USING CONSTRAINED ORDINATION II. ANALYSIS FOR SEPARATE NICHES R KINDT, P VAN DAMME, AJ SIMONS & H BEECKMAN SUBMITTED TO AMBIO One of the objectives of tree domestication research is the diversification of the tree species composition of agroecosystems. We investigated whether substantial differences existed in the species composition of farms, so that diversification efforts could be guided by these differences – for example by introducing species that are dominant in one subsection of the landscape into subsections where these species are currently rare. Since we aimed to target diversification at specific on-farm niches, we investigated farm × species matrices that only listed trees that occurred either in homesteads, mixed in cropland, woodlots, fallows, external boundaries, internal boundaries, or on contours in cropland. Ordinations of farms were obtained through Hellinger-distance-based linear and polynomial Redundancy Analysis (LRDA and PRDA) were all significant except for crop contours. Variance partitioning indicated significant influences of farm location, while socio-economic characteristics had significant influences on 6 LRDA and 4 PRDA ordinations. PRDA explained substantially more variation than LRDA (47% versus 14%). PRDA only explained 49% of the variation explained by unconstrained Principal Components Analysis on the first axis, believed to correspond to a “real-world” gradient. The ordination results were confirmed by multiple regression analyses using the abundance of dominant species as response variable. The relationship between location and species composition differed with those of previous surveys. Differences in sampling intensity and the fact that locations and time of sampling differed in each survey could have caused the differences. Compositional differences among surveys reflected more differences in species proportional abundance, rather than presence or absence of dominant species, thus changes in abundance of component species would easily lead to changes in the species composition type.

SPECIES COMPOSITION OF NICHES 10.1. Introduction One of the objectives of tree domestication research in general and more specific in western Kenya is the diversification of tree species composition in agroecosystems (Kindt & Lengkeek 1999). Making agroecosystems ecologically more stable and economically more rewarding through diversification and intensification of land use by domestication of agroforestry trees is one of the three pillars of research of the International Centre for Research in Agroforestry (ICRAF 1997; ICRAF 2000). Addressing questions on species distribution in the agricultural landscape, species composition was investigated for various on-farm niches. On-farm niches for trees refer to the location on the farm and the establishment pattern of trees at the location. The niches that were distinguished for western Kenya were trees in the homestead area, trees mixed in cropland, trees on contours in cropland, trees on external boundaries of the farm, trees on internal boundaries on the farm, trees in woodlots, and trees in fallows. The distribution of trees in on-farm niches has been recorded in several surveys of agroforestry species (e.g. Ponce et al. 1991; Thijssen et al 1993), including previous surveys in the same area (Franzel pers. comm.; Kindt 1997). Categories of onfarm niches should be largely self-explanatory, easily identifiable in the field and from aerial photographs, and correspond to local experience with tree establishment (Bradley 1991 p. 166). The analysis provided in this chapter supplements analysis of various types of functions of tree species in the studied western Kenyan agroecosystem.

10.2. Material and Methods Formulation of niche matrices (4.1), analysis of species composition (4.5)

10.3. Results 10.3.1. Comparison with Previous Surveys Table 10.1 mentions farm frequency of species in three niches as recorded by Bradley (1991) and during the survey. The clearest difference between the trends observed by Bradley (1991) are the higher percentage of farms with Euphorbia tirucalli in Ebuchiebe than Mutambi. However, the confidence intervals for the percentages of the species just overlapped. The higher frequency of Persea americana and Psidium guajava in cropland in Mutambi and lower frequencies of Sesbania sesban and Markhamia lutea in this village and niche were confirmed (but confidence intervals just overlapped for Markhamia lutea). However, all frequencies were higher in the later survey, a pattern that was also observed for many other species and niches (e.g. Markhamia lutea in hedges, Psidium guajava in other niches). The higher percentages introduced differences between Ebuchiebe and Mutambi for Mangifera indica with a higher frequency in the first village. Results from both surveys indicate that most species belong to several niches. Table 10.2 lists all species mentioned by Lauriks (1996) and Lauriks et al. (1999). The species that also contributed to the ordination diagramme for the external boundary (Buddleja davidii and Cupressus lusitanica) were added to the table. The major differences of the results of our survey with those obtained by Lauriks et al. (1999) and Lauriks (1996) were the lower frequency of Euphorbia tirucalli in Mutambi and Madidi villages, and the higher frequency of the species in Ebuchiebe. In the first two villages, the effect is caused by the higher proportion of Cupressus lusitanica, Buddleja davidii and Dracaena steudneri. Ebuchiebe had a lower proportion of Lantana

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CHAPTER 10 camara. The difference for Ebuchiebe could have resulted from the sampling frame of 25 km2 cells used by Lauriks et al. (1999). The cells east and west to the Ebuchiebe cell belonged to hedge type 1, which is a hedge type that describes the Ebuchiebe species composition very well. The coordinates of the farm sampled by Lauriks (1996) (677.25 E, 12.71 N) indicates that the position was about 3 km north of Ebuchiebe. The sampling from this author therefore does not exclude the boundaries of the hedge type 1 subregion extending further west or east than the 25 km2 grid boundaries of the Ebuchiebe cell. The hedge types that corresponded most to the composition recorded in Mutambi, Madidi and Shimutu villages was hedge type 3 containing a mixture of Dracaena steudneri, Euphorbia tirucalli, Lantana camara and Tithonia diversifolia. Cells north-west (for the first village) and south of the cells to which these villages belonged had this species composition. The results from table 10.2 also indicate that differences between hedges were mainly differences in frequencies of species, not between occurrence of these species. Hedge types 1 and 2 shared six of the seven species used to categorize differences between these types, while at least five of these species were encountered in each village of the recent survey. 10.3.2. Results of Constrained Ordination Table 10.3 provides information on the ordinations by niches. Figures 10.1 to 10.7 present the ordination diagrammes. The complete list of regression coefficients obtained from stepwise regression is provided in table 10.5. All ordinations were significant, except LRDA and PRDA for crop contours. PRDA explained substantially more variation than LRDA (respective averages are 47 and 14% of explained variation). Both methods however only explained a fraction of total variation, what resulted in the first axis explaining substantially less variation than PCA. The average variation explained by the first axis of PRDA was only 49% of that of the first PCA axis. Two axes of PRDA (corresponding to the diagrammes included) explain on average 17% of variation – two axes of PCA explain 35% of variation on average. Except for crop contours, the broken stick criterion revealed that many axes (minimum five) were significant. This points to data sets with a lot of variation that can only be represented on several orthogonal axes. The number of significant axes corresponded to the number of species differentiated in the diagrammes. The site scores on the first RDA and PCA axes were strongly correlated, except for homesteads and crop contours. These two groups were also the groups where variation explained on first and second PCA axes was almost similar. This indicates that PCA did not identify a dominant first gradient in the data, and that any two orthogonal axes belonging to the same plane would have expressed a similar amount of variation. RDA expressed another first axis belonging to a similar plane, as species that significantly contributed to the ordination were similar. Table 10.4 provides a summary of the variance partitioning of the various niches. Differences between villages were significant for all niches. LRDA had significant ordination for all niches except for crop contours. PRDA was not significant for crop contours and not highly significant (P=0.09) for woodlots and tree fallows. LRDA and PRDA had different joint explained variation (Vj). Vj was always smaller for PRDA. Ordination for external boundaries was the only case where Vj was positive. A negative sign for Vj means that both environmental characteristics together explain the variance better than the sum of their individual effects (Legendre & Legendre 1998 p. 533). Figure 10.1 shows the ordination for species occurring mixed in cropland. Mutambi is characterized by Coffea arabica (rs=2.06, P=0.0001) and Camellia sinensis (rs=1.71, P=0.0001).

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SPECIES COMPOSITION OF NICHES Madidi and Ebuchiebe contain more Sesbania sesban, Madidi and Mutambi more Psidium guajava (negative coefficients for the other villages, table 10.5). Ebuchiebe contains more Markhamia lutea (rs=0.38, P=0.04). The higher abundance for Persea americana, Carica papaya, Mangifera indica and Cajanus cajan for Madidi could only be confirmed for the latter species (table 10.5). For Persea americana and Mangifera indica, only the lower abundance in Ebuchiebe was confirmed (rs=-0.60, P=0 and rs=-0.15, P=0.08, respectively). Permanent houses have more Coffea arabica (rs=1.16, P=0.0005) and Markhamia lutea. (rs=0.45, P=0.08) and less Carica papaya (rs=-0.60, P=0.0004). The relationship between Coffea arabica and the number of years that the head was in charge of the farm could not be confirmed (this pattern also occurred for the beverage group), but the relationship with de jure female household heads was (rs=0.79, P=0.001). Farm size was positively correlated with the presence of Markhamia lutea (rs=0.81, P=0.05). Camellia sinensis presence and age of the household head correlation could not be confirmed (ra=0.40, P=0.60), but the vector length for this species was short. Figure 10.2 shows the species composition for the homestead area. Only three species are well represented. Croton macrostachyus is typical for Shimutu (rs=1.00, P=0.0001). Mangifera indica is more typical of Ebuchiebe and Madidi (table 10.5). Lower presence of Psidium guajava in farms with thatch-roofed houses was confirmed (rs=-0.42, P=0.009), but not higher presence in permanent houses (rs=-0.33, P=0.15). The species also occurred more in households with de jure female heads (rs=0.33, P=0.09). Croton macrostachyus occurred more on farms with heads being longer in charge (rs=0.53, P=0.06), with more cattle of local race (rs=1.08, P=0.003) and less on farms with thatch-roofed houses (rs=-0.33, P=0.02). The relationship with farm size could not be confirmed (ra=0.12, P=0.73), but the angle between both vectors was larger. Of species that were not well represented, Markhamia lutea was associated with permanent houses (rs=0.33, P=0.03) and the time the household head was in charge of the farm (rs=0.76, P=0.001). Syzygium cumini was more abundant in Madidi (table 10.5). Figure 10.3 provides the ordination of species occurring in woodlots. Mutambi had higher proportion of Harungana madagascariensis (rs=0.60, P=0.02) and Bridelia micrantha (rs=0.23, P=0.02). Farm size was associated with Eucalyptus saligna (rs=2.69, P=0.002) and fewer de facto household heads had the species (positive coefficients for the other head categories, table 10.5). The link between the species and the number of cattle of local race could not be confirmed (ra=0.31, P=0.77). Croton macrostachyus was associated with the age of the household head (rs=0.80, P=0.02), Mangifera indica was not (ra=-0.13, P=0.34), but both species vectors were short. Harungana madagascariensis was associated with the level of schooling of the household head (rs=0.76, P=0.02) and negatively linked with permanent houses (rs=-0.52, P=0.09). The positive links with the number of crossbred cattle and the number of children could not be confirmed (respectively ra=0.56, P=0.49 and ra=0.65, P=0.14). Figure 10.4 shows the ordination of species and farms for tree fallows. Differences between villages were not evident. More Senna didyomobotrya (rs=0.06, P=0.09) and Bridelia micrantha (rs=0.23, P=0.01) occurred in Mutambi. The village was also characterized by less Psidium guajava (rs=-0.41, P=0.09). The clearest gradient is present for farms with larger sizes. These farms have more, Psidium guajava (rs=1.57, P=0.004), Croton macrostachyus (rs=1.40, P=0.0001), Harungana madagascariensis (rs=1.62, P=0.0001), Markhamia lutea (rs=0.87, P=0.004) and Bridelia micrantha (rs=1.15, P=0.0001). Senna didyomobotrya was positively linked with the number of years a head was in charge (rs=0.15, P=0.03). Farms with more P. guajava had fewer crossbred cattle (rs=-1.15, P=0.10). Figure 10.5 shows the ordination for the external boundary. The main differences in species composition are those revealed among villages. Ebuchiebe contains more Euphorbia tirucalli (rs=1.94, P=0.0001) and Markhamia lutea (rs=0.71, P=0.003). Shimutu contains more Lantana camara (rs=0.73, P=0.05), Tithonia diversifolia (rs=1.6, P=0.0001) and, Psidium guajava (rs=0.49, P=0.04). Mutambi and Madidi contain more Buddleja davidii (negative coefficients for the other

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CHAPTER 10 villages, table 10.5). A similar pattern occurring for Cupressus lusitanica was not confirmed (the coefficient for Mutambi was also negative, table 10.5). Dracaena fragrans is more dominant in Shimutu and Madidi (negative coefficients for the other villages). The relationship between farm size and Lantana camara could not be confirmed (ra=0.94, P=0.31). The age of the household head was positively related to Cupressus lusitanica (rs=2.01, P=0.002), as was a permanent house (rs=2.20, P=0.0001). The latter variable had the opposite relationship with Buddleja davidii than apparent from the ordination (rs=-0.68, P=0.05). The positive relationship between this species and number of crossbred cattle could not be confirmed (ra=0.54, P=0.60), but the relationship with number of children (a longer vector but with a wider angle) was (rs=0.88, P=0.09). Figure 10.6 shows that the ordination for the internal boundary only distinguishes two species. Buddleja davidii is mainly associated with farms occurring in Madidi (negative coefficients for the other villages, see table 10.5) and with more children (rs=0.79, P=0.002). Cupressus lusitanica was associated with farms with permanent houses (rs=0.89, P=0.0009) and more cattle of local race (rs=0.91, P=0.02). The association between the species and the number of crossbred cattle could not be confirmed (ra=-0.26, P=0.69). Figure 10.7 shows the ordination for species occurring on contours. The main ordination is based on differences for Sesbania sesban and Psidium guajava. Ebuchiebe contains more Sesbania sesban (rs=-0.14, P=0.03), while Madidi contains more Psidium guajava (negative coefficients for the other villages, table 10.5).

10.4. Discussion 10.4.1. Comparison with Previous Surveys The results from the survey differed in various aspects with the stratification obtained from two previous studies, while other patterns were confirmed. The specific locations where samples were taken were different in all surveys, while the samples were also taken at different times and at different intensity (only one sample per cell in one case versus minimum 50 in the other surveys). Lauriks et al. (1999) mentioned that some of their findings contrasted with those of Bradley (1991). They found other hedge types in some parts of Vihiga than the type dominated by E. tirucalli. When comparing the results from Bradley (1991) and Lauriks et al. (1985), it is also apparent that the more intense sampling by the first survey revealed farms that did not contain the dominant hedge species. One example is the occurrence of Lantana camara on approximately a quarter of farms of the A1 stratum described by Bradley (1991), a species that dominated the hedge type for the same area in the results of Lauriks et al. (1999). These results agree with the finding that some farms appeared in the ordination diagrammes with composition more typical of other villages (see discussion below). With farms belonging to the same village differing much in species composition, the sampling method of Lauriks et al. (1999) could have sampled farms with hedge types that were less frequent in the village or cell. The regional distribution of hedge types in Vihiga could thus also have been an artefact created by particular selections of farms. For instance, hedge type 3 was only sampled three times and types 4 and 5 twice – their distribution could be more an indication of being less frequent in the whole area than their occurrence in the specific location. An indication for potential sampling effects are also farms encountered by Lauriks (1996) with two hedge types: the farm in the cell north of Madidi was sampled twice and contained a hedge more similar to type 1 and a hedge more typical of type 2. The results thus indicated that the strata as obtained by various surveys did not agree completely with each other. The results however confirmed a spatial partitioning of species composition since typical species compositions for each village could be determined for most use-groups and niches. The spatial pattern of species composition over the survey area is therefore expected to

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SPECIES COMPOSITION OF NICHES either be more patchy than described by the previous surveys (i.e. consisting of a smaller-scale mosaic of various types of composition), or still consisting of large areas with dominant hedge types but with more irregular boundaries. Lauriks et al. (1999) concluded that their method was not detailed enough to reveal intra-district differences. To investigate which pattern of species composition provides a better description, a more detailed spatially distributed sampling scheme should be used (e.g. 1 km2 cells or transect surveys). Because some farms were found with exceptional species composition in this and the Bradley surveys, the sampling scheme should include various samples per village. The method used by Lauriks et al. (1999) was based on the squared difference in species profiles. As indicated by Legendre & Gallagher (2001), the distance between species profiles should not be used to analyse ecological gradients. However, if Lauriks et al. (1999) would have used a distance measure such as the Hellinger distance used in the ordinations presented here, differences in species composition would still have prevailed. 10.4.2. Ordination Results As we observed for ordinations for use-groups (Kindt et al. - Chapter 9), ordinations and multiple regressions did not explain all variation in the data, while PRDA and PCA site scores on the first axis were strongly correlated (with the exception of homesteads and crop contours). These results indicate that single species were, but not strongly, related to the explanatory variables that we used. The ordination diagrammes and regression results showed that various exceptional cases occurred where sites with similar environmental characteristics had dissimilar abundances and species compositions. Although not all variation of Hellinger-distances among sites could be explained by RDA, the consistency between RDA and multiple regression results and the significance of the relationships between species and environmental characteristics allow to consider environmental characteristics to guide diversification. As we discussed for results of use-groups (Kindt et al. Chapter 9), exceptional cases do not prevent targeting of interventions. For example, the ordination diagrammes show typical species compositions of various villages. Species typical of one village could be introduced in another, but in some exceptional farms, the species would already occur. The analyses in this article show differences in species composition of separate on-farm niches. These analyses therefore allow for the design of interventions for separate niches. In the previous paper, we mentioned that diversification could be targeted towards use-groups with lower diversity of the average farm (alpha diversity). Average species diversity on crop contours is 0.2 (1.9 when only including those farms where the niche is represented), internal boundaries 0.9 (2.4), fallows 1.3 (6.3), woodlots 2.9 (3.9), external boundaries 4.5 (4.8), homesteads 5.0 (5.6), and cropland 6.5 (6.6). Crop contours (figure 10.7) and internal boundaries (figure 10.6) could therefore be the focal niches for diversification. The ordination diagrammes show that both groups are dominated by two species only. However, when we analyze niche diversity by the frequency of the dominant species by summing up species abundances over all farms (the Berger-Parker diversity index, Magurran 1988 eq. 2.31), internal boundaries and crop contours are the most diverse with relative frequencies of 0.16 and 0.19, respectively. Frequencies of dominant species in homesteads are 0.22, in external boundaries 0.30, tree fallows 0.41, cropland 0.56, and woodlots 0.65. It is obvious that the choice of the niche with the lowest diversity depends on the criterion used. Choice of the niche to diversify will in reality also depend on importance attributed to its diversification by farmers, while it has to be kept in mind that diversity for the same niche varies among farms so that diversification of the niche may not be relevant for all farms. However, the analysis presented

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CHAPTER 10 here enables to select niches where most farms have low diversity and provides information on species composition of each farm that can be used to guide interventions that seek farm diversification. Studies of species-rich ecosystems often attempt to reduce complexity by investigating functional groups rather than individual species. Functional groups can be defined as clusters of species that play the same role in maintaining and regulating ecosystem processes (Gitay et al. 1996). Schulze & Mooney (1994) point out that functional groups will be differently defined depending on the processes studied. Norberg et al. (2001) define functional groups as clusters of species that share similar resources and predators. Colasanti et al. (2001) differentiate between functional groups and functional types, where one species may be simultaneously a member of several functional groups, but not of several functional types. When one species is removed from its functional group, other species belonging to the group may increase in abundance (number of individuals) taking over its role. As De Leo & Levin (1997) pointed out, macro-level functional integrity (such as productivity) of ecosystems may be preserved by maintaining functional groups while reducing diversity within groups, but their structural integrity (such as resilience – the time of the ecosystem to recover after disturbance) may be weakened. A similar analysis as performed above for on-farm niches could be applied to functional ecological groups: identification of groups with lowest diversity in combination with investigation how various sites differ in species composition, which leads to suggestions for interventions seeking to increase site diversity. However, the concept of using functional groups to plan conservation has been a topic of controversy. Walker (1995) is in favour arguing that conservation efforts should focus on important species. Gitay et al. (1996) and Chapin et al. (2000) pointed out the possibility that not all functions of an individual species within an ecosystem are known, so that effects of species removals cannot be predicted. The same reasons to target or not to target conservation apply to targeting of diversification.

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SPECIES COMPOSITION OF NICHES Table 10.1. Farm frequency (%) of species mentioned in Bradley et al. (1985) and Bradley (1991) (B.) and in our survey (95% confidence interval). Species Euphorbia tirucalli Persea americana Cupressus lusitanica Mangifera indica Markhamia lutea Sesbania sesban Lantana camara Eucalyptus saligna Psidium guajava

B. 45 24 15 27 21 18

A1 / Ebusikhale / Ebuchiebe Hedge Homestead Cropland Survey B. Survey B. Survey 92 (80-99) 0 0 10 (1-19) 21 54 (40-68) 15 60 (50-74) 20 (9-31) 12 (3-21) 22 6 (1-13) 6 (1-13) 28 64 (50-78) 32 (20-45) 58 (40-72) 18 (7-29) 27 58 (40-72) 0 16 (5-27) 27 66 (50-80) 14 (4-24) 0 0 26 (10-39) 24 34 (20-48) 23 44 (30-58) 44 (30-58) 0 42 (30-56) 0 24 (10-36)

B. 95 50 0 0 22 0

A2 / Kegoye / Mutambi Hedge Homestead Cropland Survey B. Survey B. Survey 68 (50-81) 0 0 4 (0-10) 38 46 (30-60) 65 88 (80-97) 32 (20-45) 6 (1-13) 22 0 0 30 24 (10-36) 44 (30-58) 20 (9-31) 14 (4-24) 3 28 (20-41) 2 (0-6) 4 (0-10) 0 34 (20-48) 30 (20-43) 4 (0-10) 2 (0-6) 10 (1-19) 0 22 (10-34) 25 36 (20-50) 26 (10-39) 30 56 (40-70) 35 58 (40-72)

Table 10.2. The species composition in hedges provided by Lauriks et al. (1999) for hedge types and Lauriks (1996) for hedge composition of individual 25 km2 cells (L. village) or cells south or southeast (L. S or SE) from the villages we surveyed, and as obtained from our survey (with 95% confidence interval). Species L. Euphorbia tirucalli Dracaena steudneri

64.3

Lantana camara

13.8

Markhamia lutea

6.0

Tithonia diversifolia Aloe sp. Psidium guajava

2.6 0.0 3.0

Buddleja davidii

-

Cupressus lusitanica Other species

-

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2.3

8.1

Composition of hedge types in percent (village) Hedge type 1 (Mutambi, Madidi) Hedge type 2 (Ebuchiebe) L. L. Mutambi Madidi L. L. Ebuchiebe Mu. Ma. Eb. 62.7 91.5 18.0 17.0 5.2 2.1 74.0 (12.0-25.4) (11.0-23.6) (63.0-84.9) 19.6 2.5 13.0 18.0 0.8 0.0 0.3 (3.2-22.1) (10.0-25.2) (0.0-0.8) 0.0 0.0 9.0 13.0 52.9 84.1 5.6 (2.6-15.4) (7.2-17.8) (0.6-10.5) 0.0 0.0 0.7 3.9 5.1 0.0 7.4 (0.2-1.1) (0.1-7.7) (3.0-11.8) 0.0 0.0 0.7 0.0 6.1 0.0 0.0 (0.0-1.8) 0.0 0.0 0.0 0.0 3.3 0.0 0.0 0.0 4.5 0.8 1.6 2.1 4.8 3.7 (0.3-1.2) (0.6-2.5) (1.4-6.0) 27.5 14.6 0.0 (14.0-40.8) (7.5-21.8)) 0.0 0.0 26.5 26.8 0.0 4.4 (12.0-41.4) (17.0-36.7) (0.0-11.4) 17.7 1.5 3.8 23.1 24.4 11.2 4.6

L. S 32.7 40.4 1.9 0.0 0.0 0.0 15.4 -

Shimutu L. Shimutu SE 0.0 8.9 (3.8-14.0) 0.0 24.0 (16.0-32.2) 17.9 21.9 (13.9-29.9) 0.7 2.4 (1.0-3.9) 68.9 20.0 (12.0-28.9) 0.0 0.0 0.0 6.3 (3.5-9.1) 0.0

0.0

0.0

9.6

12.5

6.9 (2.3-11.5) 9.6

CHAPTER 10 Table 10.3. Summary information on the ordination of various on-farm niches. Variance is expressed as percentage of total variance. Ordination

Sp

Sp (2)

Var PRDA

Sign (99)

Var LRDA

Sign (9999)

Ax

Cropland Homestead Woodlot Tree fallow External boundary Internal boundary Crop contours

61 62 54 51 50

10 7 5 7 8

39.70 40.41 37.88 53.87 42.70

0.01 0.01 0.05 0.02 0.01

34

2

46.70

19

2

66.18

11 9 5 5 11

Var PCA (1) 12.68 11.63 33.78 31.98 20.63

Var LRDA (1) 6.48 4.01 4.12 5.61 10.28

Var PRDA (1) 7.45 5.63 10.21 13.43 13.31

Cor. PCARDA 0.94 -0.12 -0.99 -0.99 0.99

Var PCA (2) 22.98 22.00 46.96 42.32 33.44

Var PRDA (2) 12.49 9.38 14.82 19.23 20.54

17.12 13.79 12.10 10.36 22.01

0.0001 0.0001 0.0006 0.0184 0.0001

0.06

12.97

0.0002

5

24.59

5.48

10.36

0.99

38.35

17.55

0.44

8.76

0.1637

2

22.98

3.84

13.37

-0.10

42.34

22.53

(Sp: number of species in the site-species matrix analyzed; Sp (2): number of species included in the ordination diagramme with the first two axes; Var PRDA/LRDA/PCA (y): variance explained by polynomial RDA/linear RDA/PCA (for first y axes); Sign(99/9999): significance of ordination of lefthand column for 99/9999 permutations; Ax: number of axes to consider based on the broken stick distribution; Cor. PCA – PRDA: correlation between the site scores on the first PCA and PRDA axis)

Table 10.4. Variance partitioning between village and household characteristics for various onfarm niches. Variance is expressed as percentage of total variance. Ordination Cropland Homestead Woodlot Tree fallow External boundary Internal boundary Crop contours

Vv LRDA/ PRDA 10.3 6.12 3.79 2.97 14.98 4.63 2.52

Sign (9999) 0.0001 0.0001 0.0027 0.0137 0.0001 0.0001 0.0250

Vh LRDA 8.62 8.10 8.78 8.48 10.60 9.41 6.44

Sign (9999) 0.0002 0.0006 0.0078 0.0193 0.0001 0.0009 0.3135

Vh PRDA 27.11 28.66 28.66 40.44 29.88 35.81 51.10

Sign (99) 0.01 0.04 0.09 0.09 0.01 0.06 0.31

Vve LRDA 8.50 5.69 3.32 1.88 11.41 3.56 2.32

Vve PRDA 12.59 11.75 9.22 13.43 12.82 10.89 15.08

Vhe LRDA 6.82 7.67 8.31 7.39 7.03 8.34 6.24

Vhe PRDA 29.40 34.29 34.09 50.90 27.72 42.07 63.66

(V v/h (e) LRDA/PRDA: (exclusive) linear/polynomial variance explained by village/household characteristics; Sign(99/9999): significance of ordination of lefthand column for 99/9999 permutations)

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Table 10.5. Coefficients of village and household characteristics as explanatory variables on species abundance information for various on-farm niches after stepwise linear regression. The significance of coefficients for explanatory variables (shown below characteristics between brackets) as provided by multiple linear regression (indicated by *) where residuals were normally distributed, otherwise calculated by permutation tests. Total explained variance is expressed by the multiple correlation coefficient R2, with probability P(F). Ordination

Mixed In Cropland

Species (other niches where the species occurred with the same abundance pattern over households) Coffea arabica Camellia sinensis Psidium guajava ‡ Mangifera indica Persea americana Carica papaya Cajanus cajan ‡ Sesbania sesban Markhamia lutea Eucalyptus saligna

Homesteads Syzygium cumini ‡ Mangifera indica Azadirachta indica ‡ Psidium guajava Markhamia lutea ‡ Eucalyptus saligna Croton macrostachyus

R2 (P(F))

0.514 (0) 0.214 (1e-8) 0.120 (4e-5) 0.016 (0.085) 0.175 (2e-6) 0.090 (2e-4) 0.171 (9e-7) 0.137 (3e-5) 0.11 (8e-4) 0.025 (0.034) 0.172 (8e-6) 0.202 (1e-7) 0.026 (0.028) 0.150 (2e-5) 0.088 (3e-4) 0.091 (6e-4) 0.318 (2e-13)

Female Farm size Perma- Thatch Ebuchie- Shimutu Mutambi Male roofed nent (acres) village headed headed be village village house (de jure) (1 acre = house (farthest (closest (least (most 0.4005 (no to from wealthy) wealthy) ha) husband) forest) forest) -0.45 -0.33 2.06 0.79 1.16 (0.176)* (0.0979) (0.0001) (0.0012) (0.0005) -0.08 1.71 -0.55 1.18 (0.708)* (0.0001) (0.0880) (0.0622) 0.568 -0.22 -0.38 0.55 (0)* (0.0927) (0.0044) (0.0003) 0.44 -0.15 (0)* (0.0838) 1.60 -0.60 -0.22 (0) (0)* (0.151)* 0.38 -0.60 (0.008)* (0.0004) 0.43 -0.51 -0.49 -0.49 (3e-5)* (0.0001) (0.0001) (0.0001) 1.11 -0.93 -0.54 1.40 (0)* (0.0001) (0.0064) (0.0021) 0.49 0.38 -0.34 0.38 0.81 0.45 (0.002)* (0.0448) (0.0909) (0.0847) (0.0536) (0.0768) 1.03 (0)* 0.10 0.21 -0.13 -0.15 -0.15 (0.064)* (0.0032) (0.0608) (0.0367) (0.1039) 0.43 0.18 -0.43 -0.26 0.86 (4e-5) (0.0968) (0.0007) (0.0193) (0.0003) 0.06 (0.067) 0.26 0.33 1.43 -0.33 -0.42 (0.135)* (0.0881) (0.0002) (0.1471) (0.0086) 0.04 0.33 (0.641) (0.0281) 0.32 0.62 0.40 -0.45 (0.002) (0.0002) (0.0203) (0.046) -0.19 1.00 -0.33 (0.247) (0.0001) (0.0163) Intercept

Number Number of cross- of local cattle bred cattle (~wealth) (~wealth) -1.51 (0.0312) 1.41 (0.1232)

-0.67 (0.093)*

Years being head

Maxi- Number Level of mum age of schoohead or resident ling of partner children head 0.76 (0.0925)

-0.82 (0.009)*

0.48 (0.1435)

-0.53 (0.006)*

0.65 (0.0111)

0.20 (0.1276)

-0.71 (0.0559)

0.37 (0.0813)

-0.82 (0.0339)

0.36 (0.0183)

-0.22 (0.0886) 0.32 (0.0309) 0.76 (0.0011) 1.08 (0.0028)

0.53 (0.0565)

0.63 (0.0574)

0.55 (0.0092)

Table 10.5 (cont.d) Ordination

Species

R2 (P(F))

Intercept

Woodlots

Eucalyptus saligna

0.134 (3e-4) 0.036 (0.085) 0.109 (3e-5) 0.077 (0.006) 0.206 (3e-7) 0.069 (0.005) 0.201 (5e-7) 0.100 (8e-4) 0.226 (4e-8) 0.167 (1e-5) 0.099 (9e-4) 0.045 (0.016)

1.32 (0.003)* 0.18 (6e-4)* -0.10 (0.606) -0.13 (0.191) 0.13 (0.586)* -0.09 (0.016) 0.09 (0.329)* -0.23 (0.104) 0.27 (0.027)* 0.123 (0.258)* 0.32 (0.084) -0.01 (0.783)

Mangifera indica Croton macrostachyus Bridelia micrantha ‡ Harungana madagascariensis Fallows

Senna didymobotrya ‡ Bridelia micrantha Markhamia lutea Harungana madagascariensis Croton macrostachyus Psidium guajava Sesbania sesban ‡

Ebuchie- Shimutu Mutambe bi

Male headed

Female Farm size Permaheaded nent (de jure) house

-0.73 (0.0407)

0.98 (0.0094)

0.88 (0.0758)

-0.66 (0.0078)

-0.20 (0.0814) -0.23 (0.0356) -0.34 (0.1211) 0.16 (0.0558)

-0.62 (0.1243) -0.09 (0.07) 0.61 (0.0005)

0.23 (0.0147) -0.78 0.60 (0.0051) (0.0163) 0.06 (0.0935) 0.23 (0.0104)

-0.22 (0.1049)

-0.41 (0.0861)

Number Number of cross- of local bred cattle cattle

2.69 (0.0020)

0.35 (0.1137) 1.67 (0.0044) -0.19 (0.0565) -0.21 (0.1378) -0.23 (0.0955) -0.22 (0.0743)

Thatch roofed house

1.15 (0.0001) 0.87 (0.0041) 1.62 (0.0001) 1.40 (0.0001) 1.57 (0.0036)

0.47 (0.0526) -0.52 (0.0863)

0.18 (0.0339)

-0.48 (0.0928) -0.70 (0.0714) -0.64 (0.0907) -0.70 (0.0566) -1.15 (0.0981)

-0.57 (0.0121) -0.61 (0.0434)

Years being head

Maxi- Number Level of mum age of schoohead or resident ling of partner children head 1.67 (0.0209) -0.15 -0.11 (0.13) (0.12) 0.80 (0.0196) 0.23 (0.0960) 0.76 (0.0154) 0.15 0.14 (0.0335) (0.0056) -0.41 (0.0183) 0.54 (0.0354) -0.62 (0.0121) -0.31 (0.1544)

Table 10.5 (cont.d) Ordination

Species

R2 (P(F))

Intercept

Ebuchie- Shimutu Mutambe bi

External Boundaries

Markhamia lutea

0.117 (2e-4) 0.257 (3e-12) 0.309 (7e-13) 0.310 (3e-12) 0.198 (5e-8) 0.162 (2e-6) 0.274 (2e-12) 0.163 (2e-6) 0.191 (1e-6) 0.141 (2e-5) 0.022 (0.043) 0.161 (6e-5)

0.77 (0)* 2.15 (0)* 1.65 (0)* 0.73 (0.102)* 1.29 (0)* 1.94 (0)* 0.22 (0.059) 1.02 (3e-5)* 0.95 (3e-4)* 0.30 (0.066) 0.031 (0.365)* 0.35 (2e-4)*

0.71 -0.36 (0.0035) (0.1405) 1.94 -1.12 (0.0001) (0.0013) -1.75 -1.70 (0.0001) (0.0001) -2.01 -1.12 -1.19 (0.0001) (0.0023) (0.0014) -1.79 -1.05 (0.0001) (0.0013) -1.20 0.73 -0.76 (0.0009) (0.0508) (0.0435) 1.56 (0.0001) 0.49 -0.34 (0.0375) (0.1050) -1.04 -0.90 -0.41 (0.0001) (0.0001) (0.0439)

Euphorbia tirucalli Buddleja davidii Cupressus lusitanica Dracaena fragrans Lantana camara Tithonia diversifolia ‡ Psidium guajava

Internal Boundaries

Buddleja davidii

Crop Contours

Sesbania sesban

Cupressus lusitanica

Psidium guajava

0.14 (0.0344) -0.23 (0.0033)

‡: Species that did not contribute significantly to the first two PCA axes

-0.20 (0.0282)

-0.15 (0.0527)

Male headed

Female Farm size Permaheaded nent (de jure) house

Thatch roofed house

0.57 (0.0981)

1.51 (0.0050) 0.35 (0.0819)

-0.19 (0.0006)

-0.68 (0.0538) 2.20 (0.0001) -0.79 (0.0533)

-0.53 (0.0396)

-0.53 (0.0571)

-0.50 (0.0172)

Number Number of cross- of local bred cattle cattle -1.66 (0.0378)

Maxi- Number Level of mum age of schoohead or resident ling of partner children head

2.01 (0.0021) -1.13 (0.0629)

0.89 (0.0009) 0.28 (0.1094)

Years being head

0.91 (0.0205) 0.65 (0.0232)

-0.48 (0.1174)

-1.01 (0.0217) -0.61 (0.0880)

-0.22 (0.1267)

0.88 (0.0945) 1.29 (0.0313) 1.74 (0.0054)

0.79 (0.0224) -0.46 (0.1500)

Axis II (5.0 % tot. var., 12.7 % can. var., spec-env. corr. 0.70)

CHAPTER 10

0.7 0.6

marlut

0.5 0.4

cofrob

0.3

nloc

eucsal size

0.2

perm school

size1

heady ncros heady1

0.1

Shi fedj tha iron fedf

Ebu

0.0 -0.1

sesses

Mut camsin

psigua

Mad

-0.2

nloc1 ncros1

age age1

cajcaj manind

Ebuchiebe Shimutu Mutambi Madidi

-0.3

carpap -0.4

perame

-0.5 -0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Axis I (7.5 % tot. var., 18.8 % can. var., spec-env. corr. 0.80)

Axis II (3.8 % tot. var., 9.3 % can. var., spec-env. corr. 0.63)

Figure 10.1. Ordination plot for species occurring mixed in cropland. % tot. var.: percentage of total variance explained on the axis; % can. var.: percentage of canonical variance explained on the axis; spec-env. cor.: species-environment correlation. Largest symbol size (Ebuchiebe village) corresponds to 2 farms and origin before species centring.

0.4 0.3

school school1

0.2 0.1

cromac eucsal Shi

0.0

marlut heady1 heady

-0.1 -0.2

size

size1

age1 age nchil

tha fedfEbu permiron Mad syzcum Mut fedj azaind ncros1 nchil1

ncros manind

nloc1 nloc

-0.3 -0.4 -0.5 -0.6

Ebuchiebe Shimutu Mutambi Madidi

-0.7 -0.8

psigua

-0.9 -0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

Axis I (5.6 % tot. var., 13.9 % can. var., spec-env. corr. 0.74)

Figure 10.2. Ordination plot for species occurring in homesteads. Largest symbol size (Mutambi village) corresponds to 6 farms and origin before species centring. Abbreviations as in figure 10.1.

167

Axis II (4.6 % tot. var., 12.1 % can. var., spec-env. corr. 0.73)

SPECIES COMPOSITION OF NICHES

0.9

harmad 0.8

Ebuchiebe Shimutu Mutambi Madidi

0.7

school1

0.6 0.5

ncros

0.4

school

nchil1 nchil

ncros1

0.3

brimic

0.2

Mut

0.1

nloc nloc1

Mad fedf perm fedj Ebu Shi

0.0 -0.1

heady

eucsal

size

-0.2

size1 age cromac manind

-0.3

age1

-0.4 -0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

Axis I (10.2 % tot. var., 27.0 % can. var., spec-env. corr. 0.56)

Axis II (5.8 % tot. var., 10.8 % can. var., spec-env. corr. 0.69)

Figure 10.3. Ordination plot for species occurring in woodlots. Largest symbol size (Mutambi village) corresponds to 15 farms and origin before species centring. Abbreviations as in figure 10.1.

0.7

psigua

0.6

Ebuchiebe Shimutu Mutambi Madidi

0.5 0.4 0.3

nloc nloc1

0.2

sesses

0.1

tha Ebu fedf Shi perm Mut

0.0

age1 age cromac

size1

-0.1

size

ncros1 school1

-0.2

marlut

heady1 brimic

-0.4

nchil school

ncros

harmad -0.3

nchil1

heady

sendid

-0.5 -1.0

-0.9

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Axis I (13.4 % tot. var., 24.9 % can. var., spec-env. corr. 0.75)

Figure 10.4. Ordination plot for species occurring in fallows. Largest symbol size (Ebuchiebe village) corresponds to 42 farms and origin before species centring. Abbreviations as in figure 10.1.

168

Axis II (7.2 % tot. var., 16.9 % can. var., spec-env. corr. 0.76)

CHAPTER 10

0.6 0.5

size size1

0.4

psigua

0.3

titdiv

lancam

Shi

0.2

tha

0.1

marlut

Ebu

drafra

0.0

heady

-0.1 -0.2

nloc1

-0.3

school

ncros1 ncros

nloc

-0.4

euptir

fedjfedf Mad perm Mut

age1 age

nchil1 nchil

Ebuchiebe Shimutu Mutambi Madidi

-0.5

cuplus buddav

-0.6 -0.7 -0.8 -0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Axis I (13.3 % tot. var., 31.2 % can. var., spec-env. corr. 0.80)

Axis II (7.2 % tot. var., 14.4 % can. var., spec-env. corr. 0.70)

Figure 10.5. Ordination plot for species occurring on external boundaries. Largest symbol size (Mutambi village) corresponds to five farms and origin before species centring. Abbreviations as in figure 10.1.

0.2 0.1

Ebu Shi

0.0

heady

fedj fedf Mut Mad

nchil1 nchil

-0.1

age1 -0.2

age

perm

-0.4

buddav

school1 school

-0.3

size1 size nloc

-0.5

ncros1 ncros

nloc1

-0.6

Ebuchiebe Shimutu Mutambi Madidi

-0.7 -0.8 -0.9

cuplus -1.0 -0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Axis I (10.4 % tot. var., 22.2 % can. var., spec-env. corr. 0.67)

Figure 10.6. Ordination plot for species occurring on internal boundaries. Largest symbol size (Ebuchiebe village) corresponds to 37 farms and origin before species centring. Abbreviations as in figure 10.1.

169

Axis II (9.2 % tot. var., 13.8 % can. var., spec-env. corr. 0.64)

SPECIES COMPOSITION OF NICHES

0.3 0.2

ncros

0.1 0.0

fedf Mad

psigua

Ebu

-0.1 -0.2

school1

-0.3

school

Ebuchiebe Shimutu Mutambi Madidi

-0.4 -0.5 -0.6 -0.7 -0.8 -0.9 -1.0

sesses

-1.1 -1.0

-0.9

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

Axis I (13.4 % tot. var., 20.2 % can. var., spec-env. corr. 0.83)

Figure 10.7. Ordination plot for species occurring on crop contours. Largest symbol size (Ebuchiebe village) corresponds to 43 farms and origin before species centring. Abbreviations as in figure 10.1.

170

CHAPTER 11 FARMERS’ DECISION-MAKING TREE DIVERSITY

IN

MANAGING ON-FARM

R KINDT, P VAN MELE & P VAN DAMME IN PREPARATION Tree domestication research and agroforestry are aimed at diversifying and sustaining agricultural landscapes for increased social, economic and environmental benefits. A vast body of ecological literature has demonstrated the function of species diversity in enhancing the stability and the productivity of ecosystems. In this chapter, we focused on local reasons for management of species diversity as documented through on-farm surveys. Farmers wanted more species on their farms, and the species accumulation curve for the species wanted was above that for species currently present on their farms. Such pattern contradicts the hypothesis that much diversity would be spontaneously regenerating tree species that are not actually desired. However, since we based our investigations on participatory maps of diversity that was currently present, we could not investigate what the consequence would be if many species would not regenerate spontaneously any longer, since this represents a hypothetical situation. There was a strong relationship between the richness present on a farm and the richness desired, which potentially indicated limitations in knowledge on alternative species that limits levels of desired diversity. For only one product, medicine, all farmers expected that enough would be produced on their farm at the desired abundance. This finding indicates that farmers prefer to obtain several products from their farm, rather than maximizing production for one product. For the same product or service, most farmers preferred to have several species. The most common reasons for diversity that were mentioned were complementarity among species for a particular use, or continuous supply. Risk avoidance also featured frequently. For timber, many farmers explained to still be experimenting with several species as they did not know their production characteristics. The situation that farmers already appreciate diversity offers an ideal framework for diversification. The fact that many farmers tested new species by evaluation one tree that would then become the seed source for more trees on the farm if the species tested positively suggests that some species may suffer genetic erosion. For some species, the introduction of more diverse germplasm may therefore be necessary.

MANAGEMENT OF ON-FARM DIVERSITY 11.1. Introduction The objective of tree domestication research in western Kenya is the diversification of the tree species composition present in agroecosystems (Kindt & Lengkeek 1999). Making agroecosystems ecologically more stable and economically more rewarding through the diversification and intensification of land use by the domestication of agroforestry trees is one of the three pillars of ICRAF’s research (ICRAF 1997; ICRAF 2000). Ecological surveys, experiments and models have demonstrated the positive, but conditional and saturating, relationship between diversity and ecosystem productivity and stability that is based on heterogeneity in environment and species traits (Kindt et al. – Chapter 2, 5 & 6). In this article, we focus on the reasons that farmers provided for maintaining tree diversity on their farms as communicated during semi-formal interviews. The open-ended nature of the questions that we asked farmers did not limit responses to the role of diversity in ecosystem behaviour, and therefore enabled capturing some other reasons to maintain various species on the same farm. The results are presented according to some of the topics that were discussed with farmers.

11.2. Results 11.2.1. Changes in Diversity and Abundance Based on the diversity that was encountered on their farms in the first survey, a subset of farmers were interviewed in a second survey about the changes that they wanted in species composition on their farms. They were asked whether they wanted to add some new species, whether some species had to be removed, and what the changes in abundance of each species would be. The discussions were conducted based on participatory mapping of the species diversity within each on-farm niche. Figure 11.1 shows the average species accumulation by randomly adding farms for the species that are present and for the species that are desired. Figure 11.1 indicates that farmers want more species on their farms at all scales from 1 to 78 farms. The hypothesis can, therefore, be rejected that the diversity that was encountered during the first survey was merely a short-term transient that resulted from spontaneous regeneration of many species that were not wanted, but only tolerated, by farmers. Table 11.1 reveals that differences among the curves could be explained almost entirely from differences in alpha diversity of farms – between the differences in the average richness of a single farm that was present and that was wanted. Beta diversity, expressed by the exponent in the Sˆ N = aN z N model (Kindt et al. – Chapter 6), was almost similar (the maximal difference was 0.00835 for z2, calculated from the data used to draw figure 11.1). This finding implies that farmers want on average one extra species on their farm, while the differences among farms in species composition remained similar. Investigations were done for the most frequent use-groups, all trees that provided a certain product or service that was provided on more than 25 farms. Table 11.1 shows that the average increase of species richness was mainly reflected in average increases for fruit, timber, and medicine, while fewer species would be used mainly for firewood, on average. Figure 11.2 shows the diversity profile for all species occurring on farm versus the species wanted (Hα against α; a more diverse system has larger Hα than a less diverse system for all α; Kindt et al. – Chapter 7). No major differences were observed (two-sample Kolmogorov-Smirnov test: P=0.9996). Figure 11.2 also shows the higher desired species composition for the total survey that can be observed in figure 11.1 (S; H0 = ln(S)). However, the desired trees were less evenly

172

CHAPTER 11 distributed since the respective evenness profile (Hα - H0, figure not provided) was lower. The difference between the proportion of the dominant species (pdom; H∞ = ln(1/pdom); pdom is the Berger-Parker diversity index) was very small (29% versus 30%, calculated from the data used to create figure 11.1). This pattern indicates that the higher richness of figure 11.1 was caused by additional species of low abundance, which did not alter the other facet of diversity, evenness, very much. Table 11.1 provides information on average changes in evenness of individual farms through the Shannon diversity index (H1) which combines information on richness and evenness, and the Berger-Parker index which only reflects the proportion of the dominant species. The similar Shannon diversity index for two of the three groups with largest increments in richness, timber and medicine, shows that the richness increment was accompanied by a less even distribution in species. Although pdom is lower for these groups, the dominant species is less evenly distributed for these groups, if H∞ - H0 is calculated. Fruit was the only group with relatively (compared to the other use-groups, absolute increments were small) large increments for richness and evenness. Figure 11.3 shows that there is a strong relationship between the richness present on a farm, and the richness desired for that farm. The two farms with highest richness at present would still be increased in diversity, whereas the farm with smallest richness at present also had the smallest desired richness. No saturation point for diversity could be observed (e.g. farmers desiring maximum 20 species). The strong relationship revealed in figure 11.3 between present and desired richness shows that the average increase of richness is not mainly caused by increases for farms of low richness. This figure confirms that high species richness on farm is desired by farmers, and rejects the hypothesis that high richness observed on a farm would be temporary as species that regenerated spontaneously had not yet been removed. Figure 11.4 shows that the relationship between the number of uses present and the number of uses desired on a farm is positive, as for the relationship for species richness, but not as strong as the latter relationship. The figure also indicates that, in general, fewer uses were desired than present. This relationship is probably related to the fact that the interview was not conducted for each use that was maintained for each farm, however, rather than reductions in the number of uses derived from the species that were maintained. When requested when new trees would be established, the majority of farmers communicated that they would be established in the same or next year. Farmers that could not convey a planting year (29% of cases) mentioned that establishments would be done in the near future 86% of the time. Only one farmer explained that some trees would be planted three year after the interviews, while no farmer mentioned planting 2 years after the interviews. Farmers were asked whether species that were not wanted any longer would be protected if they regenerated naturally. The answer was only positive for eight cases, in which case they would be cut and used as firewood after a few years. Again, this finding rejects the hypothesis that much diversity could be explained by temporary occurrence of spontaneous diversity without prior desire to maintain this diversity. The unexplained variation of the regression represented in figure 11.3 shows that many farmers wanted either fewer or more species than the actual number on their farms. We investigated whether differences at a fixed diversity level could be linked to characteristics of these households by multiple linear regression analyses that used diversity present and household characteristics as explanatory factors for desired diversity. Seventy-six percent of variation in wanted richness of all trees on a farm was explained, of which 70% could be explained by richness alone, and 18% explained by household characteristics exclusively. Considering richness present, farmers in the village close to Kakamega Forest, de jure female-headed households, and wealthier households wanted lower diversity levels (regression coefficients have not been

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MANAGEMENT OF ON-FARM DIVERSITY reported here). Farmers that were longer in charge of their farms wanted lower diversity, while older farmers still wanted more diversity. The major pattern that we observed was large variation for each type of household and farm, however. This finding indicates that individual farmer strategies regarding diversity exist that are not linked to household characteristics only (e.g. female-headed, number of children). Regression results (not represented) for species richness and abundance of the most frequent use-groups analyzed separately showed similar trends of small percentages of variation explained by household characteristics. Figure 11.5 indicates that there is only a very weak relationship between the logarithmic BergerParker indices. Some farmers wanted a higher proportion of the dominant species, while others wanted a smaller proportion, almost regardless of the actual proportion on their farms. The poor relationship in Berger-Parker indices could be related to the weak relationship between abundance present and abundance wanted, presented in figure 11.6. This figure further shows that almost all farmers wanted more trees on their farm. Information from table 11.1 for changes in average abundance of use-groups shows that abundance would be increased for each group. The larger average abundance was not reflected in substantial increments in diversity (S, H, and BP of table 11.1 are all diversity indices). Hence, establishment of greater number of trees on farms was not primarily focused on average increments of species richness or evenness of use-groups, despite the opportunities (adding new species, increasing the abundance of currently less-frequent species) offered for diversification at larger abundance. We expected that the desired abundance for each use-group as communicated by farmers would reflect the abundance needed to satisfy all household needs. When we asked farmers whether less, equal, or more than the household needs would be provided, we obtained a range of results, however. The respective percentages of farmers that would obtain less, equal, or more trees for use-groups that provided products were for charcoal (21, 32, 47), for construction (25, 29, 46), for firewood (42, 17, 42), for fruit (14, 26, 60), for timber (11, 13, 76) and for medicinal trees (0, 5, 95). Firewood, which is a use that is needed daily, was the group with the largest perceived lack in production. Medicine, which is not needed every day and only in small volumes, was the only group where all households mentioned that enough would be produced from the own farm. The percentages of farmers that would not obtain enough mainly indicate limitations in farm size, which, in combination with the management option of producing various tree products and services rather than concentrating on fewer use-groups, results in less production than needed to satisfy all needs of some farmers. Often when not enough was produced, the product would be bought. Some farmers with not enough firewood would collect it from land of neighbours that is left fallow. One farmer explained that he would select the standing tree at the neighbour’s farm to be bought for firewood. Some farmers indicated that not enough firewood could be produced because many trees were meant for another main purpose. Some farmers that would not obtain enough charcoal mentioned that they would use firewood instead. Farmers with not enough fruit would sometimes obtain it free from neighbours. One farmer mentioned to lack the knowledge of proper tree management to produce enough fruit. One farmer explained that he wanted to conserve many timber species for future generations, but that this resulted in not being able to produce enough, what would have happened if he had established only the best species. Asked what would be done with surplus production, most farmers answered that the surplus would be sold to neighbours, markets, or dealers for the specific product. Some farmers with a surplus of construction wood would convert it into firewood, timber, or charcoal, or materials for fencing. Farmers with surplus firewood stated that it would either be kept for later use, or sold, or given to neighbours. Farmers with more fruit production than needed by the family would sometimes give it to visitors or relatives. Some farmers mentioned that fruit would

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CHAPTER 11 otherwise rot or be eaten by birds. Farmers with more timber than needed, would sell it to furniture makers or timber dealers. Farmers with surplus medicine would often give if to neighbours, although many farmers sell medicine to neighbours. Medicine sold at markets would often be processed first. As we did not measure actual production, or actual household needs, it was impossible to investigate relationships between diversity, abundance, and productivity. We could therefore not conduct parallel analysis to those of ecological experiments that investigate the relationship between diversity and productivity. Finding linkages between diversity and productivity at fixed abundance as in ecological experiments, could be one rationale to promote diversity on farms. 11.2.2. Reasons for Changes in Abundance of Individual Species We could have asked farmers directly why there was a difference between the actual number of trees and species and their desired numbers. As we discovered that some species were desired in larger abundance than desired, and other species in lower abundance, we interviewed farmers about changes in individual species as we were interested in knowing why some species would be reduced in number and others increased. We felt that these questions better reflected the dynamics in diversity on farms – opposite to asking why there was a net increment or decrease –, and would focus answers more on actual reasons for these changes. Differentiating between species, 28 species (23%) were desired in lower abundance (including 8 species that were not wanted anymore). For 13 (11%) species, the same abundance was wanted. For 82 species (66%), more trees were wanted, which included 16 new species. The overall increase in abundance (section 11.2.1) was therefore not a general trend for each species. The high percentage of species that was wanted in higher abundance, and the explanations that farmers could provide to increase their abundance, confirm that diversity is desired on farms. Almost all farmers that wanted a species in higher abundance explained this by its good characteristics, either related to its productivity or suitability to the on-farm niche. The question was asked why these species were not established in the desired abundance immediately. The main reasons that farmers provided were: germplasm was not available yet (86% of cases), a change in opinion about abundance needed (22%), recent changes in land allocation or having recently moved (12%) and/or space that had to be created by removing other species on the farm first (15%). The germplasm restrictions were often related to waiting until more wildlings (naturally regenerated trees) had occurred on the farm. As species were often represented by only one individual on the farm, most wildlings will probably be descendants from this single mother tree. Some farmers mentioned that trees had to be mature before cuttings could be taken. The wish to obtain seedlings of good quality was a far less frequent explanation for delays in establishing species at their desired abundance. Changes in opinion on the desired abundance were often related to older trees that would not produce that much any longer, new insights in the potential of some species (we did not probe how these insights had emerged), or additional needs of the household such as family growth. In a few cases, new boundary conflicts with neighbours resulted in a higher abundance needed for trees that demarcate external boundaries. The main reasons why some species were wanted in lower abundance were that the species was not very suitable for the particular on-farm niche (45%), that the species did not perform very well (23%), or the related reason that other species performed better (23%). Most frequently mentioned for niche compatibility was that the species cast too much shade on crops in cropland or on the crops of the neighbour (for species that demarcated external boundaries). For the homestead area, reasons were that the species took too much space, produced excessive leaf litter, competed with other species, or had dangerously weak branches. Space requirements for other species was also mentioned for crop contours, which were reserved by some farmers for

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MANAGEMENT OF ON-FARM DIVERSITY Napier grass (Pennisetum purpureum) grown for fodder. Although one farmer explained that Spathodea campanulata had low importance, the farmer still wanted to keep one tree so that the species would not disappear from the farm. Bad production of a species was sometimes inferred from the performance of one individual tree (e.g. one farmer explained that Mangifera indica, a frequent species in the area, did not perform very well on the farm based on the performance of one tree). In two cases, farmers explained that their father knew the purpose of particular species, but that they did not, and that the species was therefore not wanted anymore on the farm. In four cases, the farmer explained that disease problems and not knowing how to properly manage the species were reasons not to want a particular species any more. In seven cases, the explanation was that fewer trees would produce enough. In analogy to asking why farmers did not establish trees in higher abundance immediately where this abundance was desired, farmers were also asked why they established some species in higher abundance than required later. The reasons that farmers provided were: the farmer had expected the species to perform better (23%), the decision was only later made which species to establish (21%), not enough germplasm was available yet of a better species (11%), or that cropland was expanded later (5%). Where the farmer had expected better performance, the farmers sometimes mentioned their surprise because the species performed better at the neigbhour’s farm. Farmers that mentioned that they had only made decisions later motivated their decision by stating that small trees did not harm crop yield very much or simply explained that they had not had the time to remove species of lower priority. Where farmers expanded cropland, sometimes also soil fertility improving or soil erosion controlling species were wanted in lower abundance. Respondents reasoned in these cases that the fertility had already been added or that erosion had been controlled, which indicates that not all farmers understand the underlying ecological principles for service functions of trees. In seven cases, species that were established by ancestors were not wanted any longer. In one case, the farmer had expected the species to produce less, but since the species was more productive, fewer trees were wanted now. Examples as cited above indicated that many farmers were experimenting what the best abundance of a particular species would be on their farms. Some farmers were testing the validity of local believes on the function of some indigenous tree species. Some farmers believe that Dracaena steudneri trees would prevent that thunderstorms would harm the farm. One farmer explained that he would not plant the species any longer, as it was not very effective for that purpose. Another farmer, however, explained that he would increase the abundance of the species from one to ten trees as he experienced that one tree was not very effective in preventing thunderstorms and, therefore, wanted to test whether more trees would provide the service. Information on changes in abundance was also collected during the inventory phase. Farmers were asked whether each tree encountered would be replaced later, and, if answered in a positive manner, whether replacement would be done by the same species. The answers provided did not correspond very well to the information collected on desired species and abundances in the later interview. Thirty-five species (33%) were not mentioned as a species that replaced a tree present on farm during the first interview, while only seven species were not wanted anymore according to the information of the second interview. The opposite situation, where a species was indicated of being replaced but was not wanted anymore on the farm according to the second interview, only occurred once. The fact that replacement of a tree during the first interview was specific to the niche in which the tree was growing was not responsible for the differences between both interviews. Species that were not recorded as replaced during the first interview would still be established in the same niche according to the second interview. It is still possible that the differences between the two interviews are related to different spaces within each niche where the species could be planted. We expect, however, that differences between the interviews were related to the fact that farmers often wanted to keep a species on their farm albeit by keeping

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CHAPTER 11 only one tree of the species as some farmers indicated, and that the second interview was better in revealing this strategy of farmers. 11.2.3. Reasons Provided by Farmers for Establishing and Maintaining Tree Diversity After the participatory mapping exercise, and questions that related to desired changes in abundance of particular species, discussions were held with farmers that focused on the composition of species of use-groups that were maintained on the farm. The first topic of the discussion formed the reasons of maintaining several or only one species within a particular use-group. Table 11.2 summarizes information for the most frequent usegroups, with post-classified reasons for their diversity (information in the following paragraphs for use-groups separately gives a better indication of the actual reasons that farmers provided). Differences in characteristics and continuous supply were the most common reasons for diversity. The reason of “differences in characteristics of species” indicates that farmers perceived that mixtures of species provided various characteristics to a use-group that were difficult to combine in a single species (e.g. fast growth and quality of timber). The explanation that each species had good characteristics was included as it occurred frequently, although this category is not an actual explanation why several species were desired. This category probably points to risk avoidance strategies in case one species in the use-group would not provide the use due to pests, diseases or other problems that would prevent that a particular species would provide the product or the service. Experimenting whether particular species would perform well indicates farmer willingness of diversification. The ‘testing’ category further shows that desired farm composition (section 11.2.1) may change over time – fewer species could be desired if species test negatively, or more species could be desired in case farmers test new species on their farms. Respondents that desired different species for firewood mentioned the different growth and maturity rates and the fact that most species had another main purpose. Therefore, many species were needed to produce enough daily-needed firewood. Some farmers mentioned that they wanted to avoid that species would become extinct on their farm, what would happen if requirements of firewood would result in all its trees to be cut. This type of response does not directly reflect a farmer strategy of diversification, since extinction of species by firewood usage could be prevented by establishing enough trees of the species on the farm. However, the fact that farmers opt to use several species in lower abundance for firewood (with higher perceived extinction risks) rather than few species in higher abundance, probably reflects risk avoidance strategies. Few farmers said that the diversity simply resulted from natural regeneration. As these species were still included in the maps that reflected the diversity desired on farms, spontaneous recruitment of species does not mean that these species are not wanted – maybe these farmers are just happy with the spontaneous diversity and do not perceive the need to actively plant additional trees. Potentially the reasons of spontaneous regeneration and avoidance of species extinction are related, reflecting a strategy of protecting the various species that regenerate spontaneously which results in the desired quantity and diversity of trees that provide firewood. Many farmers with different fruit species mentioned that they wanted to provide choice to family members and to customers, that the fruits had different tastes, or that some fruits were better for home consumption and others were better for sale. Some farmers explained that fruits differed in vitamin contents while a balanced supply was needed. Seasonality and differences in maturity were another important reason (of ensuring continuous supply) for diversity within this group. Sometimes the possibility of pest or disease attacks on particular species was mentioned (Citrus spp. often experience such problems), thus indicating risk avoidance strategies. When farmers wanted several timber species, they mentioned different qualities of timber provided by different species. Some species were for sale and others for direct use on the farm.

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MANAGEMENT OF ON-FARM DIVERSITY Many farmers wanted to provide choice to customers. Several farmers listed different growth rates, other uses besides timber, and the need for a continuous supply as reasons for diversity. Some farmers said that all the species were marketable, as they had hard wood and linear timber. Risk avoidance was an important reason in the timber group because stem deformations were often expected and could not be predicted, and farmers felt that growing a diversity of species would result in a greater percentage of trees of good form. Many farmers mentioned that they wanted to test the growth characteristics of various new species for timber. This finding indicated that farmers were not familiar with timber production characteristics (quantity and quality) of several species, which is probably related to the longer time needed to obtain the product and greater differences in price/quality for timber. Diversity within the construction wood was explained most often by needing two species for house construction: one species that was elastic and did not crack when nailed (characteristics typically provided by the local Markhamia lutea), and another species with fast growth and straight poles (most often provided by Eucalyptus spp.). One farmer said that it was a local taboo to use two species for house construction. Farmers that mentioned that several species had good characteristics explained that all species were strong, straight, termite resistant, long-lived, and with good sprouting after coppicing. An important reason for different shade species was differentiation in shade density, so that the family had the option to choose the most suitable species for the conditions of a specific day. One farmer explained that different species offered different features to look at when sitting below them, which indicates that also personal or sentimental values can play a role in decisionmaking on diversity. Differences in growth rates and the fact that some species had other functions were given as reasons for diversity as well. One farmer explained that although mango had the densest shade, it was not safe to sit beneath it when it had fruits so that an alternative species had to be available under these circumstances. Farmers that stated that the various species all had good characteristics mentioned that they occurred in the homestead area, that they were rarely removed for other products and had a long lifespan, had wide crowns and thick shades. One farmer mentioned the possibility that one species would shed its leaves. Another farmer explained that it is prestigious to have shade trees around the house. Some farmers with different species for boundary demarcation explained that some species were recognized by the governmental land surveyors and neighbours for land demarcation, while other species made the boundary more compact, were more beautiful, or produced timber besides demarcating the boundary. Some farmers mentioned the different growth rates of the species so that the fence could be compact shortly after tree establishments. Farmers that explained that each species had good characteristics said that they all were allowed on the boundary as they did not hinder the crops of the neighbours much, had small diameters, could be coppiced, and had a long lifespan. Some farmers made the differentiation between species that were better on external boundaries (between farms) and species that were better on internal boundaries (mainly around the homestead). On external boundaries, recognition of boundary demarcation by a particular species was more important, while on internal boundaries ornamental value had higher importance. The reason that different species were grown in different niches thus reflected differences in species characteristics – these reasons could have been classified in the first category for diversity (“species differ in characteristics”). Some farmers with different medicinal species mentioned that they treated different types of diseases. A frequent reason that was mentioned was that species mixtures would be more effective, especially if no precise diagnosis could be made. Difference in growth rates was provided as well as a reason to grow diversity. One farmer mentioned that medicinal species were often attacked by diseases, so that many species were needed in order to ensure production of medicine.

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CHAPTER 11 In the soil fertility group, neither difference in species’ characteristics nor continuous supply were mentioned. It could be concluded from table 11.2 that soil fertility improvement was only group where farmers did not perceive compatibility in characteristics of several species. This finding is maybe related to the short-term nature of service provision within this group through 3-6 month fallows and/or similarity among the nitrogen-fixing species. The finding could also reflect onfarm soil fertility research conducted by ICRAF on some farms that were included in the survey. As one of the objectives of the soil fertility research is selection of the best species for soil fertility improvement, farmers could be indicating to wait for results of this research and the concept of a superior species excludes consideration of compatibility among species. Some farmers with various species for charcoal mentioned the reason that maturity varies among species, whereas charcoal is needed continuously. One farmer described differences in maturity as a risk limitation strategy because it was not known which species would reach maturity first. For ornamental species, differences in appearance were mentioned as a reason for diversity within the group. The fact that several species had ornamental value was also provided as a reason for diversity within this group. When farmers were asked for reasons for having only one species in a use-group, the most commonly provided reason was that the one species was the only species with good characteristics. The single species wanted for timber had straight and fast growth, and hard wood. Farmers explained that Eucalyptus spp. provided all necessary woody house construction elements, had fast growth, resprouted rapidly after coppicing, had a long lifespan, tolerated competition in a woodlot well, had straight stems, and was not very susceptible to diseases. Mangifera indica was the only species desired for shade as some farmers explained that the species produced a dense shade, had a large crown, did not shed its leaves often, and had a long lifespan. For boundary demarcation, farmers often mentioned that the species should be recognized by the governmental land surveyors and by neighbours. Other reasons that some farmers gave were that the species should be compact and propagated easily by cuttings. One farmer mentioned that it should be possible to nail barbed wire on the stem of the species. Some farmers mentioned that Azadirachta indica was the only species with an effective medical treatment, often mentioning a particular ailment. Some farmers explained that Sesbania sesban had proven its soil improvement properties, had fast growth, produced firewood, and was compatible with crops. In some cases farmers mentioned that the species was recommended by researchers and stated that the species produced nodules. The desire for mono-specific ornamental use-groups was explained as Terminalia mantaly having systematic branching and leafing patterns and not shedding its leaves often. Table 11.3 gives information that touches on the other aspect of diversity besides richness: the evenness (or lack of evenness) in the abundance of the various species. The discussions were held with the same farmers that were interviewed about reasons to maintain more than one species in a use-group (their number is provided in table 11.2). The reasons that farmers provided were post-classified in the categories provided in table 11.3, whereas the discussion of the various usegroups provides the reasons that farmers gave more literally. Difference in species performance was the most common reason provided for differences in abundance. Difference in growth rate, the age of reaching maturity, and the quality of the product were most frequently mentioned. For firewood, differences in coppicing potential, niche compatibility (the suitability to grow the species in a woodlot, on boundaries or mixed in cropland), thorniness, and tree size were listed. For fruit, farmers listed differences in yield, fruit taste, fruit size, seasonality (fruiting during the dry season), marketability, and susceptibility to diseases. For timber, the likelihood of stem deformations, tree size, marketability, niche requirements, and susceptibility to diseases were mentioned. For construction, differences in straightness, lifespan, coppicing ability, competition in woodlot arrangements, and the number of

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MANAGEMENT OF ON-FARM DIVERSITY products used for construction were listed. For shade, differences in tree size, shade density, frequency of leaf shedding, and the strength of branches were mentioned. For boundary demarcation, differences in appearance, recognition as boundary markers, size, drought tolerance, and thorniness were mentioned. Farmers provided differences in marketability, effectiveness, tree size, and the number of useful parts were provided for the medicine group. The other important reason for differences in species abundance within the same groups that was provided was the other functions that some species had. One farmer for example explained to select the best Cupressus lusitanica individuals to grow into timber, while other trees were pruned within the fence to form a uniform surface. Reasons related to germplasm included difficulties to obtain germplasm, natural regeneration, easiness of germination, or the fact that cuttings of smaller sizes could be used. Small overall group abundance was a reason given for similarity in species’ abundances. Small group abundance was related to farm or family size, or niche requirements. New species are tested in small abundance. This practice probably often results in poor appreciation of a particular species as frequently only one tree is evaluated. The practice could also lead to severe inbreeding where farmers only test one tree and later use this tree as sole germplasm source for the farm. In some cases, the same reason was used by some farmers to explain establishment of fewer trees, and by other farmers to explain establishment of more trees for the same species. For example, some farmers explained that fewer Euphorbia tirucalli trees were needed because they were recognized for boundary demarcation and thus not removed, while other farmers provided the boundary demarcation property as the reason to establish more trees. The abundance needed probably relates to the relationship among neighbours. Another example is that some farmers explained a low abundance by natural regeneration, what was a reason for high abundance for other farmers. In the former case, farmers said that the species only regenerated naturally, while in the latter case, the species also regenerated naturally. A third example was one farmer not establishing many Eucalyptus spp. trees because of their high marketing potential (a reason for their high abundance according to other farmers) because this factor made them also more likely to be stolen from the farm. A final example is high medicinal efficiency, which was the reason for some farmers to establish many and for other farmers to establish few trees of Azadirachta indica. 11.2.4. The Relationship between Species Ranking and Diversity For each use-group and farm, information was collected on the preference ranking of each species. We asked farmers to rank species as this exercise is often conducted during Participatory Rural Appraisal to plan on-farm tree planting activities (e.g. Franzel et al. 1996). The hypothesis that we wanted to test was whether diversity within a group could be caused by differences among farmers. Since our results indicated that farmers desired diversity for the use-groups on their farms (see above), investigating the relationship between species ranking and diversity focused more on influences on the evenness within a group (i.e. that species with high priority would be established in much higher abundance). Table 11.4 shows that no use-group had a single species that ranked first, since two (construction) up to 16 species (timber, shade) got first ranking on some farms. Therefore, if each farmer only established the species with first priority, use-groups would contain at least two species. For most use-groups, trees with first ranking constituted less than 50% of all trees in the use-group, which shows that farmers wanted to maintain many species of lesser priority. The only exceptions were boundary demarcation, construction, and soil fertility improvement. For these groups, however, species with first priority were only 13% - 32% of species that would be maintained on farms, which shows, again, that many species of lesser priority would be maintained.

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CHAPTER 11 For some use-groups, the overall species of first priority could be determined quite easily based on the percentage of farmers that gave first priority to the particular species. The prime examples with high percentages of farmers that choose the same species are Eucalypus saligna for construction, Warburgia ugandensis for medicine and Sesbania sesban for soil fertility improvement, and, to a lesser extend, Euphorbia tirucalli for boundary demarcation. For these use-groups, the overall species of first priority also was dominant (having the largest abundance within the group). For the other use-groups, however, other species were more frequent, which was often linked to other purposes of the species. Examples are Markhamia lutea for charcoal (proportion 0.50, also used for construction), Euphorbia tirucalli for firewood (0.32, also used for boundary demarcation), or Cupressus lusitanica as ornamental species (0.91, also used for boundary demarcation). This finding shows that a use-group may be dominated by a certain species as it has several purposes, and not because the species had the best ranking. The various purposes of some species thus result in the fact that, for some use-groups, the best species does not have the largest abundance. For the other use-groups, it was also more difficult to determine an overall species of first priority, as the highest percentage of farmers that gave the same species first ranking was only just above 50% (for fruit and shade). The general pattern that we observed was, therefore, that although there was a relationship between priority and abundance of species (especially for boundary demarcation, construction, and soil fertility improvement), while farmers still opted to establish a range of species of lower priority. Although farmers could categorise some species as more important than other species, they were still interested in growing diversity.

11.3. Discussion We observed that most of the diversity that was encountered on farms during an inventory was desired by farmers. During participatory mapping, most species were maintained. Farmers could explain why some species would be increased in abundance, and why several species were wanted within most use-groups. Moreover, rather than finding that only a fraction of diversity was actually desired, while the other fraction was only transient diversity caused by natural regeneration, we found that farmers wanted more diversity than what was recorded in their fields. We should be careful with the interpretation of the results that we obtained, however. The exercise that collected information on diversity desired by farmers was based on a map of the diversity that was encountered on a farm. A part of ‘desired’ diversity could have been caused by this method, that could have simulated ‘protection’ of some species. The question remains whether many species would be actively planted if they would not regenerate naturally. It is a difficult question to investigate, however, since the situation of ‘no natural regeneration’ is hypothetical in the landscapes that we investigated. Discussions with farmers of hypothetical situations are not very useful in most cases as these only yields hypothetical answers (Fergus Sinclair, University of Bangor, pers. comm.). Changes in composition would be realized in a very short time span, so that the information on desired composition could have been influenced by the easiness by which this composition could be attained from the present composition, and only reflect short term planning objectives of farmers. Although we explained to farmers that germplasm problems should not influence the ‘ideal farm’, farmers may still have reflected on current germplasm problems, which may also have restricted farmers in ‘establishing’ more species on ‘ideal’ farms. Knowledge problems could be the reasons that some farmers only wanted few species (those farms that are plotted in the lower-left corner of figure 11.3), so that increased information on the uses of species could

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MANAGEMENT OF ON-FARM DIVERSITY influence farmers in establishing more species. Better knowledge on species’ uses could increase diversity on farms that have low diversity at present, especially since we could not observe any saturation point for diversity on farms, and since farmers were not only desiring to grow species of first priority on their farms. We found several indications that knowledge was a limiting factor for on-farm diversity. Some farmers explained that they did not know what their ancestors were using a species for. One farmer explained that neighbours were using bark from a species that occurred on his farm, but that they were not willing to explain the exact treatment. Knowledge problems are also reflected in indications of farmers that experimented with new species. Many farmers tried to enhance their knowledge by testing the performance of particular species on their farms. Since farmers were not only growing species of first priority, we expect that farmers were testing new species for diversifying the species composition of particular uses. A problem could be that farmers often based their judgment of the performance of a species based on the performance of a single tree, thus ignoring intraspecific variation. Some farmers expressed their surprise that the species was doing very well on their neighbour’s farm, but not on theirs. On the other hand, some farmers were testing species outside or at the limits of their ecological range – for example, one farmer was testing cocoa (Theobroma cacao), another farmer cashew (Anacardium occidentale) – so that testing one tree could be sufficient. Testing species with a single individual could result in reductions of genetic variation, as this single tree would often be the mother tree for the other trees to be established on the farm in case it performed well. As farmers were increasing the abundance three-fold on the ideal farms (table 11.1), one could question how realistic they had been as not enough space may be available on their farms to sustain the abundance. Maybe a more realistic exercise would have been to limit farmers to the actual abundance, and let them choose one species for each available ‘planting hole’. We wanted to let farmers free in changing abundance of each species, however, to investigate changes in diversity related to changes in abundance. In addition, our experience was that space was still available on many farms to increase tree abundance. As we expect that information on changes in diversity may be for the short term, and not realistic in all cases, the real test for changes in abundance and diversity will be by monitoring actual modifications that farmers make in on-farm composition. It is very likely that as farmers’ experience in existing and new species on their farms increases, that on-farm composition will progressively differ more from the present composition. Our results indicate, however, that future tree composition on farms, if different, will continue to harbor diversity. As described by Holmgren (2001), the planning process can be considered to consist of five steps, including inventory (collection of data), evaluation (organization of data), strategy (general direction for changes), design (particular direction of changes) and management (implementation), while feedback is necessary at every stage. Holmgren (2001) further states that only the landholder can effectively conduct whole-farm planning, as he or she is the only person that can consider all strategic, practical, and technical elements of the entire farm. As we found relatively small percentages of variation in abundance and richness explained by household characteristics, we also indicated that individual perceptions and management strategies differ among farmers of the same socio-economic characteristics. Under these conditions, the role for research or development seems to lay in providing farmers with information on consequences of possible management strategies (e.g. genetic erosion, increases of soil erosion) and information on other options (e.g. alternative species within a use-group, tree management). It is interesting to note that fruit, timber, and medicine were the use-groups with largest increments in abundance. These use-groups were identified by ICRAF and its partners as priority groups to increase farmers’ incomes through agroforestry, and trees belonging to these groups were described as ‘high-value trees’ (ICRAF 2000). It is likely that farmers made a similar analysis,

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CHAPTER 11 and were concentrating more on the marketable tree products. The analogous analysis suggests that activities that focus on these use-groups may be implemented easily. The fact that most tree species provided several uses may have created the artifact that diversity was richer in some use-groups than strictly desired by farmers. This may especially have happened for the diversity of firewood, as firewood is a secondary product of most species. Adding a new species for timber, for instance, also resulted in addition of a new firewood species. Some of our discussions on diversity within a use-group may therefore not have been entirely relevant, as diversity was a by-product by diversity in other groups. Some farmers realized this confusing effect by explaining that diversity in the firewood group was caused by diversity in other use-groups. We recorded some of the multiple criteria that farmers are using in decision-making regarding the trees that are maintained on a farm for the same purpose. Most criteria were based on the quantity and quality of the provision of products or services by trees. Growth rate, or growth after coppicing, reflect criteria of quantity. Taste, straightness, or density of the canopy are indicators of quality. Other criteria included personal values (“enjoyable to look at”, “preservation for future generations”), while other criteria were community values (taboos). At the same time, the fact that several use-groups were maintained on a farm shows that farmers are interested in growing diversity for the various products and services that can be provided by a diversity of species. Overall, our findings indicate that farmers are mainly interested in growing tree diversity for the variation in products or services that can be provided among and within usegroups. Within a use-group, tradeoffs in use characteristics were often listed as important criteria for diversity, for example beauty versus legality, dry season versus humid season production, or preference in the market versus preference by the family. Species within a use-group are therefore preferably compatible in characteristics. As, theoretically, for a new species with random characteristics, compatibility within a use-group can be achieved more easily when a use-group has few (at the extreme, only one) species, projects that aim at diversifying on-farm species composition could target use-groups that are currently of lower diversity. These groups are also buffered the least against production loss through non-performance of one species. Interventions could focus on introducing new species for the particular purpose, more widely distributing species that provide the purpose on few farms currently, or promoting new uses of species. Obviously, evenness should be considered besides richness, as groups of lower evenness will offer less compatibility and less buffering against risks. Some of the reasons that farmers provided for diversity in tree species composition were analogous to reasons provided for farmers growing a diversity of crop varieties. Jarvis & Hodgkin (2000) list five aspects of farmer decision making that seem likely to have most influence the extent and distribution of genetic diversity. These include decisions on which agro-morphological characteristics are important, farming practices, planting niche, population sizes, and seed sources, of which we recorded criteria that apply to the first three decisions (we did not investigate farmer decision-making regarding intraspecific diversity in this paper). Reasons cited for the interest of farmers in crop diversity include the exploitation of different microhabitats (e.g. Colfer 1991; Haugerud & Collison 1990), different uses (e.g. Bedigian & Harlan 1983; Shigeta 1990), different maturities (e.g. Clawson 1985; Richards 1993), risk avoidance and yield stability (e.g. Voss 1992; Merrick 1990), perceptual differences (e.g. Boster 1985) and cultural diversity (e.g. Cleveland 1993). Other examples that farmers manage different crop varieties are provided in van der Heide & Tripp (1996). Farmers thus seem to make similar decisions regarding tree diversity. Our results also agree with the information presented in Arnold & Dewees (1995) on the reasons that farmers grow trees on farms. In summarising the information from Arnold & Dewees (1995), Arnold (1995) conclude that farmers plant trees in pursuit of their livelihood goals of income generation, risk management, household food security and optimum use of available land, labour and capital. Tree species and varieties are thus planted to produce various products

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MANAGEMENT OF ON-FARM DIVERSITY and various services. Scherr (1995) for example reports on the results of a tree planting project in that trees are planted for building poles, fruit, green manure, shade, fencing, fuelwood, timber, ornamental, fodder and other uses. This researcher further stated that although most of the tree species provide several products and services, on-farm products and services are typically provided by several tree species. In conclusion, we documented that farmers desire on-farm tree diversity. Our results also indicated that some use-groups and some farmers of lower diversity could be targeted by projects that aim at diversifying agroecosystems. These projects could mainly focus on the provision of additional information on species’ uses and consequences of management options, as management strategies of farmers of the same socio-economic characteristics are likely to differ widely, even when tree information and germplasm are distributed more widely than at present.

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CHAPTER 11 Table 11.1. Average diversity (with standard deviation) that was present versus diversity wanted on a single farm for all species and for species that belonged to specific use-groups. Averages were calculated based on the farms where the use-group was present or wanted. S: species richness, H1: Shannon diversity index, BP: inverse Berger-Parker index (1/pdom), N: abundance. Group

All Firewood Fruit Construction Timber Boundary demarcation Shade Medicine Soil fertility improvement Charcoal Ornamental

Number Number S S of farms of farms present wanted where where present wanted 78 78 15.8 (5.1) 16.6 (5.0) 78 78 14.4 (4.6) 13.2 (4.2) 78 78 4.7 (1.6) 5.0 (1.7) 78 77 2.2 (0.7) 2.1 (0.7) 77 77 4.4 (1.9) 4.8 (2.1) 77 76 2.4 (1.2) 2.3 (1.1) 65 65 4.0 (2.1) 3.8 (1.9) 50 49 1.9 (1.1) 2.5 (1.6) 53 49 1.5 (0.9) 1.5 (0.8) 29 27 2.1 (1.4) 2.0 (1.3) 28 22 1.4 (0.6) 1.5 (0.7)

H1 present

H1 wanted

BP present

BP wanted

N present

N wanted

1.5 (0.5) 1.5 (0.5) 1.0 (0.5) 0.5 (0.3) 0.7 (0.4) 0.4 (0.4) 0.9 (0.5) 0.5 (0.5) 0.2 (0.3) 0.3 (0.4) 0.2 (0.3)

1.4 (0.5) 1.3 (0.4) 1.2 (0.4) 0.4 (0.3) 0.7 (0.5) 0.4 (0.4) 0.8 (0.5) 0.6 (0.5) 0.2 (0.3) 0.3 (0.4) 0.2 (0.3)

2.4 (1.1) 2.3 (1.0) 1.9 (0.8) 1.4 (0.3) 1.4 (0.4) 1.4 (0.5) 1.9 (0.9) 1.5 (0.7) 1.1 (0.4) 1.2 (0.4) 1.1 (0.3)

2.1 (0.8) 2.0 (0.7) 2.4 (0.9) 1.3 (0.3) 1.5 (0.6) 1.3 (0.4) 1.8 (0.8) 1.7 (0.8) 1.1 (0.3) 1.2 (0.3) 1.1 (0.2)

416 (281) 398 (886) 39 (83) 94 (530) 107 (659) 213 (615) 35 (428) 3 (29) 23 (274) 12 (27) 8 (118)

1360 (921) 1210 (886) 59 (83) 305 (530) 493 (659) 796 (615) 135 (428) 13 (29) 126 (274) 12 (27) 16 (118)

Table 11.2. Main categories of reasons provided by farmers to have several species in a usegroup. Figures indicate the number of farmers that listed these reasons, with figures between brackets reflecting the percentage of the farmers that desired diversity (>1 species) of the usegroup, except in the first column where the figure expresses the percentage of all farmers with the use-group on their farms. Use-group

Firewood Fruit Timber Construction Shade Boundary demarcation Medicine Soil fertility improvement Charcoal Ornamental

Households that desire minimum two species

Species differ in characteristics

Continuous supply

78 (100) 77 (99) 71 (92) 68 (88) 58 (89) 57 (75) 35 (71) 15 (31) 14 (52) 9 (41)

2 (3) 25 (32) 27 (38) 49 (72) 16 (28) 26 (46) 23 (66) 0 (0) 2 (14) 2 (22)

28 (36) 44 (57) 18 (25) 3 (4) 14 (24) 3 (5) 9 (26) 0 (0) 5 (36) 1 (11)

Main reasons for diversity Each species Species use has good different characniches teristics 23 (29) 0 (0) 14 (18) 1 (1) 15 (21) 1 (1) 14 (21) 0 (0) 27 (47) 0 (0) 16 (28) 8 (14) 9 (26) 0 (0) 10 (67) 0 (0) 3 (21) 0 (0) 2 (22) 0 (0)

Risk avoidance

Testing

5 (6) 3 (4) 10 (14) 2 (3) 1 (2) 1 (2) 0 (0) 0 (0) 1 (7) 0 (0)

0 (0) 2 (3) 15 (21) 0 (0) 0 (0) 0 (0) 2 (6) 0 (0) 0 (0) 0 (0)

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MANAGEMENT OF ON-FARM DIVERSITY Table 11.3. Main categories of reasons provided by farmers for differences in desired tree number among various species that belong to the same use-group. Figures indicate the number of farmers that listed these reasons, with figures between brackets reflecting the percentage of the farmers that desired diversity (>1 species) of the use-group Use-group Species differ in performance Firewood Fruit Timber Construction Shade Boundary demarcation Medicine Soil fertility improvement Charcoal Ornamental

30 (38) 56 (73) 28 (39) 53 (78) 26 (45) 42 (74) 19 (54) 7 (47) 6 (43) 3 (33)

Main reasons for differences in abundance Species have other Differences in Small total group purposes germplasm abundance availability 49 (63) 3 (4) 0 (0) 14 (18) 5 (6) 1 (1) 33 (46) 4 (6) 2 (3) 5 (7) 4 (6) 0 (0) 18 (31) 1 (2) 4 (7) 4 (7) 2 (4) 0 (0) 6 (17) 1 (3) 1 (3) 4 (27) 0 (0) 0 (0) 2 (14) 3 (21) 0 (0) 2 (22) 0 (0) 0 (0)

Some species are only tested 0 (0) 2 (3) 7 (10) 0 (0) 1 (2) 0 (0) 3 (9) 0 (0) 0 (0) 0 (0)

Table 11.4. Distribution of species of first priority within use-groups. The first two columns list the distribution of those trees that got first priority on a minimum of one farm. The other columns document the distribution of the overall species of first priority (the species that got the highest number of first rankings). Group

Firewood Fruit Timber Construction Shade Boundary demarcation Medicine Soil fertility improvement Charcoal Ornamental

Number of Percentage of species with trees with first priority 1 (% priority within a of all species) group 11 (11) 31 4 (21) 21 16 (42) 19 2 (13) 81 16 (28) 40 6 (32) 68 6 (16) 44 3 (14) 58 8 (33) 14 4 (17) 7

Dominant species with ranking 1 Number (% of Number (% of farms) of farms farms) with rank with rank 1 2 24 (36) 22 (33) Eucalyptus saligna ° 36 (52) 18 (26) Persea americana ° 22 (37) 11 (19) Grevillea robusta ° 52 (98) 1 (2) Eucalyptus saligna ° 23 (52) 11 (25) Mangifera indica ° 23 (56) 9 (22) Euphorbia tirucalli ° Warburgia ugandensis 20 (69) 2 (7) Sesbania sesban 8 (80) 0 (0) Bridelia micrantha 2 (22) 0 (0) 2 (40) 0 (0) Terminalia mantaly ° Identity

°: exotic species, *: the species has the largest abundance in the group

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Percentage of group abundance 20 16 3 81* 3 49* 28* 70* 4 3

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120

0.6

110

Number of species

90 80

richness present richness wanted parameter z present parameter z wanted

70 60

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50 40 30

Parameter z of S=aN^z

100

20 10 0

0.4 0

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10

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35

40

45

50

55

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65

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75

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Number of farms Figure 11.1. Species accumulation curve for the umber of species wanted and the number of species present (left axis), and the accumulation curve for parameter z of the model S=aNz(N) (right axis).

148.4

5

90.0

4

54.6

diversity wanted diversity present

33.1

H

12.2

EXP(H)

20.1

3

7.4

2

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3

10

100

inf.

alpha Figure 11.2. Diversity present versus diversity wanted represented by the Rényi diversity profile for fixed scales of the profile.

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Species richness desired

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35

35

30

30

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25

20

20

15

15

10

10

5

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0

0 0

5

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35

Species richness present

Number of uses desired

Figure 11.3. Number of species wanted versus number of species present. Full line represents the robust linear regression and dotted lines the 95% confidence intervals (54% variation explained)

15

15

10

10

5

5

0

0 5

10

15

Number of uses present Figure 11.4. Number of uses wanted versus number of uses present. Full line represents the robust linear regression and dotted lines the 95% confidence intervals (16% variation explained). The size of the symbol corresponds to the number of farms with the same number of uses present and desired (maximum = 8).

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ln(inverse Berger-Parker index desired)

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2.0

2.0

1.5

1.5

1.0

1.0

0.5

0.5

0.0

0.0 0.0

0.5

1.0

1.5

2.0

ln(inverse Berger-Parker index present)

Abundance wanted (log 10)

Figure 11.5. Inverse Berger-Parker index (1/pdom) of trees present versus trees wanted. Full line represents the robust linear regression and dotted lines the 95% confidence intervals (8% variation explained)

4.0

4.0

3.5

3.5

3.0

3.0

2.5

2.5

2.0

2.0

1.5

1.5 1.5

2.0

2.5

3.0

3.5

Abundance present (log 10) Figure 11.6. Relationship between abundance present and abundance wanted. Full line represents the robust linear regression and dotted lines the 95% confidence intervals (14% variation explained)

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CHAPTER 12 INTRASPECIFIC DIVERSITY OF TREES ON FARMS IN WESTERN KENYA – RESULTS FROM SIMULATIONS OF INDIVIDUAL-BASED METAPOPULATION DYNAMICS R KINDT, AJ SIMONS, P VAN DAMME & D REHEUL IN PREPARATION We investigated the distribution of species on farms in four villages in western Kenya, and what the influence of this distribution was on intraspecific diversity under the present farmer management using information on the preferred origins and forms of germplasm of each individual tree. In total, 160 species were investigated out of the 175 species encountered in the landscape surveys. Results showed that species were aggregated within farms and within villages. We therefore expected that the preferred origin of the own farm would have strong effects on the dynamics of intraspecific diversity. However, as information on reproductive ecology and intraspecific diversity currently present in the metapopulations (350 situations where a species occurred in one of the villages, with farms harbouring subpopulations) was very scarce, we could only make approximate investigations based on stratified-randomized geneflow. Since we assumed that selfing was not possible for most species, and that pollen had 25% chance of originating from outside the village – using some general data on reproductive ecology of tropical tree species – obtaining germplasm from the tree that was replaced resulted in a smaller percentage of homozygous trees than by obtaining the germplasm at random from the same farm or at random from the same village. The highest proportion of homozygous trees were obtained for species with an abundance of 2-10 trees, but the highest number of homozygous trees is expected for species with abundance > 10. The differences among the different scenarios of germplasm acquisition were small, however, which was related to comparing genetic diversity after 50 flowering seasons, assuming no inbreeding in the first season, and a relatively high percentage of long-distance pollen migration. Small population sizes for most species indicate, however, that present levels of genetic diversity may be very low, and that introduction of more diverse germplasm might increase genetic diversity substantially. Our metapopulation model could be used when more information becomes available. Simulations with the model showed that good approximations were obtained for theoretical metapopulations.

GENETIC DIVERSITY IN METAPOPULATIONS 12.1. Introduction One of the purposes of agroforestry tree domestication is the enhancement of the stability and productivity of agroecosystems by diversifying on-farm tree species composition (species richness and evenness of species abundances). Diversification and intensification of land use through the domestication of agroforestry trees (woody perennials) is one of the three pillars of ICRAF’s research agenda (ICRAF 1997; ICRAF 2000). In this chapter, analyses of genetic diversity of species that are grown on farms in western Kenya are conducted through individual-based metapopulation simulations based on the observed distribution of trees on farms, tree age distribution, and forms and origins of germplasm used by farmers. Although knowledge of reproductive ecology of most tropical tree species is very limited (Boshier 2000), we believe that using general assumptions of geneflow in tropical species still provides insights that can aid in formulating recommendations on intraspecific management of trees on farms.

12.2. Material and Methods Metapopulations (4.6)

12.3. Results 12.3.1. The Distribution of Species on Farms Figure 12.1 shows the rank-abundance graph of the 175 species encountered in the survey. Only for 47 species (27%), more than 50 trees were counted. Only 30 species (17%) occurred in densities larger than one tree ha-1. Of 44 species (25%), only a single individual was encountered, while for 18 species (10%) only two trees were counted. Investigating the density distribution of species showed that all species were aggregated within farms since the normalised average farm density was greater than one (above the reference line in figure 12.2). The general trend presented in figure 12.2 is that species with lower abundance have a more aggregated. This pattern obviously occurred for species with an abundance of 1 as they could only occur on one farm – the range of values observed for species of this abundance only indicates the range in farm sizes. Eucalyptus saligna occurring in the highest abundance had the lowest aggregation, with a value of 1.03 for the normalised within-farm density. However, the second most abundant species, Camellia sinensis, and some other species with abundances larger than 1000, clearly had an aggregated distribution. For many species, the trees were aggregated within villages as well, as can be seen from within-village values above the reference line. Figure 12.3 shows the relationship between the logit-transformed farm frequency and the logarithm of species density. In accordance to the Density-Abundance relationship, species that occurred on a larger number of farms had, on average, a higher abundance as indicated by the regression line. However, many species with high densities had lower farm frequencies (represented below the regression line), while few species had the opposite distribution. 12.3.2. Origins and Types of Germplasm Table 12.2 shows a cross-tabulation of the actual and farmer-preferred forms and origins of germplasm, averaged over all species and expressed in percentage of total abundance. Table 12.2 shows that cuttings were the most important germplasm source for trees. The percentages of seedlings, wildlings, and seed were each only about half of the percentage of cuttings. Fruit was

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CHAPTER 12 not a major germplasm source. The majority of trees were obtained from within the same village, with slightly more than half of the trees obtained from the own farm. Most cuttings were obtained from neighbours, most seedlings from outside the village, and most wildlings and seed from the own farm. Investigating the farmer-preferred origins showed that the own farm was preferred in a higher percentage than the actual number of trees originating from this origin. Farmers especially expressed to prefer more cuttings and seed from within the own farm. Seed had 10% larger preference than the actual number of trees established by this form of germplasm. The 10% difference corresponded to fewer seedlings that farmers preferred to obtain. The pattern that emerges is that farmers would produce their own seedlings from seed obtained from their own trees, rather than sourcing them from outside. Cuttings were preferred in a similar amount as for the actual preferences, but they would preferably not be obtained from the neighbours. The actual and preferred number of wildlings did not differ much, where the majority would be obtained from the own farm. Of the wildlings that were obtained on the farm, 95.5% were not transplanted. 12.3.3. Genetic Diversity in Hypothetical Metapopulations Figure 12.4 shows the Rényi profiles calculated on the allelic frequencies obtained after 50 seasons. Table 12.3 provides the averages and ranges of profile values at scales 0, 2 and ∞, and information on the evenness of the distribution of the dominant allele, H∞-H0. Random mating resulted in four alleles remaining in the metapopulation after 50 generations, with the dominant allele occupying about half of the loci (pdom=0.49). The age-structured metapopulation (scenario 1) was the most diverse, while the two metapopulations where 1% of pollen was obtained from the outside were also more diverse than the ideal population. However, on average, these populations did not contain many more alleles (respectively 6.8, 6.7, and 5.4) than the ideal population, while the dominant allele was less evenly distributed. Obtaining 50% wildlings and 50% cuttings resulted in diversity that was very similar to the diversity of the ideal population, with an average of 4.01 alleles. The metapopulation where trees only had 50% chance of flowering each season was the least diverse containing 2.1 alleles on average with pdom=0.70. The metapolation with a female/male ratio of 4 was the second least diverse (2.6 alleles, pdom=0.64). Although the dominant allele had a larger frequency than the dominant allele in the ideal population, these alleles were more evenly distributed. The metapopulations where trees had higher chances of obtaining pollen from the same tree or the same subpopulation were less diverse than the ideal population. Within these metapopulations, the metapopulation with the lowest chance of obtaining pollen from the same tree (Pm=0, resulting in a chance of 0.10) was the most diverse (3.9 alleles, pdom=0.505). The subpopulations with the highest chance of pollination from the same tree (Pt=Ps=20, resulting in a chance of 0.6452) had the lowest diversity. A higher chance of obtaining germplasm from the same subpopulation in the latter two metapopulations resulted in higher diversity. Avoidance of selfing (Pt=0) or only obtaining seedlings from the same subpopulation resulted in higher diversity than the ideal population, with alleles=4.05 and pdom=0.492, and 4.09 and 0.487, respectively. The differences with the ideal population, however, of the metapopulations with higher or lower selfing, or higher or lower chances of obtaining germplasm from the same subpopulation, were not that large, spanning average allelic richness of 2.6 – 4.09. Investigation of differences between the directly calculated Simpson index and 2-H2 revealed that direct calculation resulted in values that were on average 2.39 (1.12 - 3.47)% higher, with highest

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GENETIC DIVERSITY IN METAPOPULATIONS differences for lowest values. Calculation of Ne from 2-H2 would thus result in higher estimations. However, both values indicated the same trends. Observed homozygosity (table 12.3) differed most strongly from the expected heterozygosity (based on the allelic frequencies) for those metapopulations that had higher chances of obtaining pollen from the same tree or subpopulation. Calculating the ratio between Ne based on the observed heterozygosity, and Ne based on the Simpson index (table 12.3), resulted in ratios of 0.77 (Pt=20), 0.54 (Pt=Ps=20), 0.50 (Pt=Ps=Ws=20) and 0.92 (Pm=0). Increases in homozygosity thus followed the trend of lower diversity (see above), while obtaining more germplasm from the same metapopulation increased homozygosity. The mixed-scenario metapopulation, which was the third most diverse in allele frequencies, had the lowest heterozygosity (21.7) and ratio (0.27). Avoidance of selfing or obtaining germplasm only from the same subpopulation lowered homozygosity, however not to a large degree (respective ratios of 1.02 and 1.001). Ratios for the other metapopulations were also within the 0.99 (50% flowering) - 1.04 (age-structured) interval. 12.3.4. Changes in Genetic Diversity for Species Encountered on Farms in Western Kenya Figure 12.5 shows the average number of homozygous trees encountered for the various scenarios of germplasm forms and origins. Obviously, avoidance of selfing resulted in no homozygous trees encountered for species with abundance 1. The highest proportion of homozygous trees was encountered for species with abundance between 2 and 10. Values of the proportion of homozygous trees above the reference line (dot-dashed) representing the reciprocal of species abundance indicate a chance of obtaining, on average, at least 1 homozygous tree. Comparison with the reference therefore indicates that in most cases no homozygous trees are expected for species with abundance < 10, while more homozygous trees are expected for species with larger abundance. The fact that many metapopulations had values larger than 0 in figure 12.5 however indicates that, even for metapopulations where on average fewer than one homozygous individual occurred, in some cases homozygous individuals were obtained The reference line intersected the Supersmoother (S-Plus 2000) lines of species dominated by wildlings around abundance 22, and species dominated by seeds or seedlings around abundance 37 for scenario a, which used the farmer-preferred forms and origins. Only obtaining sexually generated germplasm from the replaced tree (scenario b) resulted in an intersection at abundance 200, while obtaining these forms of germplasm randomly from the farm (scenario c) resulted in an intersection at abundance 17. The scenario of selecting seeds at random from the village (scenario d) resulted in an intersection around 20. Scenario d resulted in the largest number of metapopulations with abundance < 10 above the reference line (5.5%), while scenario c resulted in the largest number of metapopulations of abundance ≥ 10 above the reference line (53.7%). Figure 12.6 shows the average expected number of homozygous trees encountered if alleles would be sampled at random from the metapopulations. For scenario a, intersections for sexually obtained germplasm were observed for abundance 4, and for cuttings at abundance 5. Intersections for sexually obtained forms of germplasm occurred at abundances 3 in scenarios b, c and d. Obviously, no intersection occurred for cuttings in scenario b. An intersection occurred for this form of germplasm in scenario c at abundance 14. Scenario c resulted in the largest number of metapopulations of sizes 25%) over distances of one kilometre – this figure inspired the parameter value of Po=0.75 in our calculations. Although individual insect pollinators may only fly in between near neigbours, pollen carryover (i.e. pollen transfer from flower A→B by pollinator X and subsequently from B→C by pollinator Y) may result in pollination of several hundred meters (Hamrick & Murawski 1990; Dawson et al. 1997). Chase et al. (1996) found that isolated trees could act as stepping stones for geneflow among populations. Young & Boyle (2000) indicate that pollen flow can be high in fragmented populations, provided vectors can pass non-forest habitat. Young & Merriam (1994) and White et al. (2002) showed that fragmentation could actually lead to an increase in pollination distances. Aldrich & Hamrick (1998) indicated further complexities in metapopulation dynamics as they found that 68% of seedlings in forest remnants originated from remnant adults in surrounding pastures. Stacy et al. (1996) reported the combined effects of subpopulation size and species aggregation, so that plants in small clusters received more pollen from outside than plants occurring in larger clusters or in more even distributions. These studies point towards potentially complex patterns in pollen flows. To investigate the influence of pollen flow on genetic diversity, pre- and postzygotic processes should be considered (e.g. self-incompatibility, Boshier 2000).

198

CHAPTER 12 More precise predictions of changes in genetic diversity should not only rely on measurements of the factors mentioned above, but also consider factors such as differences in flowering phenology or the influence of agroforestry practices on flowering and pollen and seed vectors. Tree removal could be specified for each tree and each season, possibly linked with productivity of the individual tree and farmer decision-making, while better information could be obtained on actual subpopulation sizes. The greatest weakness in our approach in estimating genetic diversity, however, was not the uncertainty in tree-specific parameter values, but calculating the probability in identity by descent from the present generations of trees, ignoring effects of past geneflow and population sizes, and assuming that each allele that originated from outside had a new identity. In case initial genetic diversity would have been quite low – which is likely if patterns of germplasm acquisition have a long history in the area (table 12.2) –, then actual heterozygosity would decline much faster. However, as we wanted to investigate the influence of various management options on genetic diversity, we were still provided with changes in genetic diversity that could be used for comparative purposes. In how far loss of genetic variation poses dangers to inbreeding depression remains to be investigated for each particular species and situation. In comparison with other plant species, trees contain high levels of molecular diversity (Hamrick & Murawski 1990; Hamrick et al. 1992). In contrast with other plants, genetic variation partitions within rather than among tree populations (Hamrick et al. 1992). Trees often carry a heavy genetic load of deleterious recessive alleles (Boshier 2000). Lande (1995) and Lynch et al. (1995) mention that populations with small effective sizes have reduced ability of genome purging, i.e. the removal of slightly deleterious alleles by natural selection, which could lead to their extinction through ‘mutational meltdown’. Byers & Waller (1999) conclude that purging is an inconsistent force within plant populations. Young & Boyle (2000) mention that to assess short-term population viability, examination of the interactions between inbreeding, heterozygosity, fitness, and demography is needed. Young & Boyle (2000) also indicate the potential danger of outbreeding depression where fragmentation leads to breakdown of local populations, while geneflow between populations is maintained. Kindt & Lengkeek (1999) described that for areas with increasing deforestation, conservation of tree species on-farm may offer the best option for some species. Where conditions in agroecosystems vary from those in forest ecosystems, natural selection may favour different gene complexes. Domestication may also select other genotypes than natural selection, e.g. trees with smaller competitive ability (shorter stems) but higher production (more fruit). Conservationthrough-use will therefore probably not lead to equivalent genetic conservation as by in situ conservation. Where natural habitats disappear, however, this may be the only feasible species conservation strategy. Kindt et al. (Chapter 4 - 9) have described methods of analysing species diversity to diversify agroecosystems, which could lead to larger stability and productivity of these systems (provided the ecological conditions for a positive link between diversity, and stability and productivity, are satisfied). Methods that focus on increasing the evenness in species abundance distributions could lead to larger population sizes of less frequent species – increasing the abundance of less frequent species would thus not only have an ecological-functioning, but also a genetic diversity (avoidance of inbreeding depression, conservation) advantage. Minimum population sizes for less frequent species versus tree abundances required by farmers further advocate for beta-diversity approaches (i.e. not to attempt to grow each species everywhere, but to segregate species compositions among villages and farms). These also conform to ecological considerations on the asymptotic relationship between diversity and species stability and productivity (Kindt et al. – Chapter 2, 4 & 5).

199

GENETIC DIVERSITY IN METAPOPULATIONS Table 12.1. Parameters for each tree used during randomizations Parameter Aget Agemax Agemat

Explanation

Randomisation ‡ Ages+1=Ages+1; Ages+1=0 if Ages+1 > Agemax Predetermined or Age0=INT(ran*(Agemax+1)) Predetermined

Pt

Age of the individual in season s Maximum age of the individual Maturity; age at which individual trees can flower or produce cuttings Chance of flowering (0-1) in each season Presence (1) or absence (0) of female (FF) or male flowers (MF) on the individual Number of other trees with female (FF) and male (MF) flowers in the same subpopulation (s) and in other subpopulations of the metapopulation (m) Chance of pollination from the same tree

Ps

Chance of pollination from the same subpopulation

Pm

Chance of pollination from other subpopulations

Po

Chance of pollination from outside the metapopulation or mutation (0-1) Chance that the wildling originates from the same subpopulation

Flow FFt and MFt FFs/m and MFs/m

Ws Wo FM

Chance that the wildling originates from outside the metapopulation or chance of mutation (0-1) Chance that a new tree is female, if the species is dioecious

Tree flowers if Flow ≥ Ran and Ages ≥ Agemat Calculated for each tree with Ages=0 in season s Calculated for each tree with Ages=0 in season s Pollen from the same mother tree if Ran < Po Ps Pt MFt / (Ps (Pt MFt + MFs) + Pm MFm) Pollen from the same subpopulation if Ran < Po (Ps (Pt MFt + MFs) / (Ps (Pt MFt + MFs) + Pm MFm) Pollen from the metapopulation if Ran < Po (Ps (Pt MFt + MFs) + Pm MFm) / (Ps (Pt MFt + MFs) + Pm MFm) Pollen with new allele identity if Ran ≥ Po (Ps (Pt MFt + MFs) + Pm MFm) / (Ps (Pt MFt + MFs) + Pm MFm) Wildling from the same subpopulation if Ran < Wo Ws (FFt + FFs) / (Ws (FFt + FFs) + FFm) Wildling from the metapopulation if Ran < Wo (Ws (FFt + FFs) + FFm) / (Ws (FFt + FFs) + FFm) Tree is female if Ran > 1 / (1 + FM)

‡ Ran: random number Î[0,1[ that was calculated for each comparison, except for choice of pollen and wildling origins

Table 12.2. Actual and farmer-preferred (Pref.) forms and origins of germplasm expressed in percentages of the total number of trees. Some farmers did not have preference for particular forms or origins of particular species (No. pref.) Form Cutting Seedling Wildling Seedling or wildling Seed Fruit Total

200

Own farm Actual Pref. 7.3 34.3 2.5 7.8 19.1 16.7 0.0 2.1 10.4 24.9 0.1 0.2 39.3 86.0

Neighbours Actual Pref. 24.0 4.7 5.9 0.8 0.7 0.2 0.0 0.0 6.0 1.8 0.1 0.1 36.7 7.6

Origin Outside village Actual Pref. 7.1 0.1 15.5 4.6 0.1 0.0 0.0 0.0 1.1 0.9 0.2 0.0 23.9 5.6

No pref. 0.2 0.6 0.0 0.0 0.0 0.0 0.8

Actual 38.4 23.9 19.8 0.0 17.5 0.4 100

Total

Pref. 39.3 13.7 17.0 2.1 27.6 0.3 100

CHAPTER 12 Table 12.3. Results for random mating and eleven scenarios of non-random mating for metapopulations of five subpopulations of size 10 (100,000 permutations). Average values are provided, with the range of minimum-maximum values in between brackets. (Parameters: see text) Scenario 0

Parameters different from random mating None

1 2

Agemax=4, 5 generations of 10 trees with Age0 =0, 1, 2, 3 or 4 Flow=0.5

3

FM=4

4

Pt=20

5

Pt=Ps=20

6

Pt=Ps=Ws=20

7

Pm=0

8

Pt=0

9

Seedlings all from the same subpopulation Po=0.99

10 11 12

50% wildlings and 50% cuttings Combination 1+2+6+9

Ne

2H0

39.3 (0-100) 23.6 (0-100)

Simpson (%) 39.9 (13-100) 24.9 (10-100)

50.3 (1-178) 89.4 (1-238)

64.2 (8-100) 55.8 (2-100) 52.7 (12-100) 74.1 (30-100) 74.2 (30-100) 43 (2-100) 38.4 (0-100) 38.5 (2-100) 31.4 (0-100) 38.5 (2-100) 78.3 (40-100)

64.6 (18-100) 56.9 (9-100) 44.4 (15-100) 52.4 (16-100) 49.5 (15-100) 41.1 (14-100) 39.6 (13-100) 39.2 (14-100) 32.1 (10-100) 39.7 (13-100) 34.3 (11-100)

24.8 (1-127) 30.5 (1-279) 43.6 (1-159) 34.6 (1-143) 37.6 (1-153) 48.4 (1-171) 50.8 (1-181) 51.6 (1-171) 66.1 (1-244) 50.7 (1-180) 60.9 (1-223)

Hom (%)

4.0 (1-10) 6.8 (1-14)

2-H2 (%) 37.7 (13-100) 23.8 (10-100)

2-H∞ (%) 49.5 (18-100) 35.2 (13-100)

2.1 (1-6) 2.6 (1-34) 3.5 (1-9) 2.9 (1-8) 3.1 (1-9) 3.9 (1-10) 4.0 (1-9) 4.1 (1-10) 6.7 (1-18) 4.0 (1-10) 5.4 (1-14)

61.1 (18-100) 53.7 (9-100) 41.9 (15-100) 49.4 (16-100) 46.6 (15-100) 38.8 (14-100) 37.4 (13-100) 37 (14-100) 30.1 (10-100) 37.5 (13-100) 32.4 (11-100)

70.2 (24-100) 64.0 (16-100) 53.4 (19-100) 60.2 (18-100) 57.7 (19-100) 50.5 (17-100) 49.2 (16-100) 48.7 (17-100) 42.9 (13-100) 49.2 (17-100) 44.5 (16-100)

E∞ -0.99 -1.27 -0.57 -0.72 -0.91 -0.78 -0.82 -0.96 -0.99 -0.99 -1.53 -0.98 -1.26

201

GENETIC DIVERSITY IN METAPOPULATIONS Percentage of species 10

20

30

40

50

60

70

80

90

100 1.0

50

100,000

abundance (left axis) abundance (right axis) density

10,000

0.9 0.8

40

30

1,000

20

100

Abundance

Abundance

0.7 0.6 0.5 0.4 0.3 0.2

10

10

Density (trees/ha)

0

0.1 0

1 0

0.0

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170

Species rank Figure 12.1. Rank-abundance and rank-density curves for the 175 species encountered on farms.

Log10(abundance) 0

1

2

3

4

5

10,000

3

100

2

10

1

Normalized density +1

1,000

1

log10 (normalized density+1)

4

within farm outside farm and within village outside village

0 1

10

100

1,000

10,000

100,000

Abundance Figure 12.2. Normalized densities of for all species for distance categories of the same farm, the same village, and other villages. Normalized densities larger than 1 indicate aggregation within the distance category. The size of the symbol corresponds to the number of species with the same abundance and normalized density.

202

CHAPTER 12 Log10(density) -2

-1

0

1

2

3 2

10.000

1

1.000

0

0.100

-1

0.010

-2

0.001

-3

P/(1-P)

100.000

0.001

0.010

0.100

1.000

10.000

100.000

Log10(P/(1-P))

-3

1,000.000

Density (trees/ha) Figure 12.3. Density-abundance relationships for all species. The size of the symbol corresponds to the number of species with the same density and abundance. Robust linear regression (S-PLUS 2000) (full line, dotted lines 95% confidence intervals) explained 95% of variation.

8

2

4

1

2

0

1

0 1 2 3 4 5 6 7 8 9 10 11 12

2

H

H

3

0

0.1 0.25 0.5 0.75 1 1.25 1.5 1.75 2

2.5

3

10 100 inf.

alpha Figure 12.4. Rényi diversity profiles (H) for Wright-Fisher populations of size 50 (full lines), and various scenarios of nonrandom mating (dotted lines) calculated for various scale parameter values (alpha, inf.: ∞). The details of the various scenarios are detailed in table 12.3.

203

0.3

0.3 dominated by seeds or seedlings dominated by wildlings dominated by cuttings no dominant form

N9: 26.8

0.1

0.1

a

0.0 1

10

0.3 N9: 31.7

0.1

0.1

c

0.0

d

0.0 1

10

100

1,000

10,000

100,000

1

10

100

1,000

10,000

100,000

Figure 12.5. The average proportion of homozygous trees (vertical axis) encountered within each village during simulations of 50 seasons (1000 randomizations for species with abundance < 1000, 100 for other species) plotted against species abundance (horizontal axis). Lines are Friedman’s variable span smoother (S-PLUS) for species dominated by various forms of germplasm, except the dotteddashed reference line, which corresponds to the reciprocal of abundance. The arrows indicate the percentage of metapopulations of size < 10 and ≥ 10 above the reference line. a: scenario that uses the preferred forms and origins for every species and farm, b: scenario where germplasm is collected from the old tree, c: scenario where germplasm is collected from the same farm, d: scenario where germplasm is collected at random within the village.

0.5

dominated by seeds or seedlings dominated by wildlings dominated by cuttings no dominant form

N9: 58.5

0.2

0.2

0.1

0.1

a

0.0 1

10

0.5 N9: 90.2

N>9: 98.2

0.2

0.2

0.1

0.1

c

0.0 100

1,000

100,000

N