Criteria and Indicators for Sustainable Forest Management: Assessment and Monitoring of Genetic Variation Forestry Department Food and Agriculture Organization of the United Nations Forest Genetic Resources Working Papers
Forest Resources Development Service Forest Resources Division Forestry Department
Working Paper FGR/37E FAO, Rome, Italy
By G. Namkoong (USA) T. Boyle (UNDP), Y. A. El-Kassaby (Canada), C. Palmberg-Lerche (FAO), G. Eriksson (Sweden), H-R. Gregorius (Germany), H. Joly (France), A. Kremer (France), O. Savolainen (Finland), R. Wickneswari (Malaysia), A. Young (New Zealand), M. Zeh-Nlo (UNDP) and R. Prabhu (CIFOR)
November 2002
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Table of Contents DISCLAIMER ABSTRACT INTRODUCTION EFFECTS OF FOREST PROCESSES ON GENETIC DYNAMICS DRIFT SELECTION MIGRATION MATING SYSTEM FOREST PRACTICES THAT AFFECT GENETIC DYNAMICS HARVESTING OF WOOD GRAZING FIRE HARVESTING NON-TIMBER FOREST PRODUCTS (NTFP) CRITERIA, INDICATORS AND VERIFIERS CRITERION: CONSERVATION OF THE PROCESSES THAT MAINTAIN GENETIC VARIATION INDICATOR 1: LEVELS OF VARIATION INDICATOR 2: DIRECTIONAL CHANGE IN ALLELE OR GENOTYPE FREQUENCIES INDICATOR 3: MIGRATION AMONG POPULATIONS INDICATOR 4: REPRODUCTIVE PROCESSES/MATING SYSTEM OPERATIONAL USE OF THE PROPOSED INDICATORS AND VERIFIERS USE AND APPLICATION OF INDICATOR SPECIES PREVENTIVE OR RESTORATIVE ACTIONS RESEARCH PRIORITIES ACKNOWLEDGEMENTS REFERENCES ANNEX 1. PRINCIPLES, CRITERIA, INDICATORS AND VERIFIERS ANNEX 2. CRITERIA AND INDICATORS: CONCEPTS AND DEFINITIONS ANNEX 3. INTERNATIONAL PROCESSES ON CRITERIA AND INDICATORS FOR SUSTAINABLE FOREST MANAGEMENT: INDICATORS RELATED TO GENETIC DIVERSITY ANNEX 4. GLOSSARY OF TERMS
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DISCLAIMER The Forest Genetic Resources Working Papers report on issues addressed in the work programme of FAO. These working papers do not reflect any official position of FAO. Please refer to the FAO website (www.fao.org/forestry) for official information. The purpose of these papers is to provide early information on on-going activities and programmes, and to stimulate discussion. Comments and feedback are welcome. For further information please contact: Mr. Pierre Sigaud, Forestry Officer (Forest Genetic Resources) Ms. Christel Palmberg-Lerche, Chief Forest Resources Development Service Forest Resources Division, Forestry Department FAO Viale delle Terme di Caracalla I-00100 Rome, Italy e-mail:
[email protected] [email protected] or: FAO Publications and Information Coordinator:
[email protected] The present paper is based on workshops and field tests undertaken by the Centre for International Forestry Research (CIFOR) from 1995 to 1998. CIFOR assembled an international team of forest geneticists to advise on the feasibility and design of criteria and indicators for field-based assessment of conservation of genetic variation as a component of sustainable forest management. Dr. Gene Namkoong was invited to lead the team, but sadly passed away in 2002, before publication of the results. This paper is therefore dedicated to the memory of Dr. Namkoong, recognizing his status as the world‟s pre-eminent forest geneticist and his lifetime of contributions to the field. FAO, in concurrence with CIFOR, and noting that four of the authors are standing or former Members of the FAO Panel of Experts on Forest Gene Resources, has undertaken to help in final editing of the publication, and will make the document available in printed form and on-line. For quotation:
FAO (2002) Criteria and Indicators for Assessing the Sustainability of Forest Management: Conservation of Biological Diversity and Genetic Variation. Document prepared by G. Namkoong, T. Boyle, Y. A. El-Kassaby, C. Palmberg-Lerche, G. Eriksson, H.-R. Gregorius, H. Joly, A. Kremer, O. Savolainen, R. Wickneswari, A. Young, M. Zeh-Nlo and R. Prabhu. Forest Genetic Resources Working Papers, Working Paper FGR/37E, Forest Resources Development Service, Forest Resources Division. FAO, Rome (unpublished).
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ABSTRACT
The present paper contains proposals for genetic criteria and indicators which can form part of a more general set of, economic, environmental and social criteria and indicators for the monitoring of forest sustainability at the scale of forest management units. These proposals are intended for use in guiding tropical forest management, but because of the complexity of tropical forests, it is not possible to prescribe a uniform set of indicators for all circumstances. The pattern of genetic diversity that has already evolved is due to a balance of several evolutionary forces that operate at different spatial and temporal scales and forest practices would therefore be expected to affect several genetic factors. To provide guidance on what genetic processes may be affected by forest practices, we therefore first describe the genetic processes, then the forest practices that are expected to affect genetic processes, and we provide a matrix of relationships between types of forest practices and indicators of genetic processes. Since the intention of criteria and indicators is to steer and gradually improve forest management actions, the report identifies two main concerns of sustainability: first, whether a previous or a proposed practice poses substantial risk to genetic adaptability and second, what conservation or enhancement measures can be effective to mitigate any adverse impacts. We propose one criterion for conservation of forest genetic resources and four indicators related to processes that maintain genetic diversity. For each indicator, sets of verifiers are suggested which differ in the biologically relevant features they measure, in their precision, and in technical facilities they require. Finally, the need for rapid assessment and precision under difficult field conditions requires research and development of efficient direct and surrogate measures of the genetic resource. We therefore include recommendations for short- and medium term research that would improve the scientific value, cost-effectiveness, ease of use, and further development of genetic criteria and indicators. KEYWORDS: Biological diversity; Forest genetic resources; Criteria and Indicators; Monitoring; Sustainable forest management.
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INTRODUCTION
Genetic variation[1] is a provision for species to have successfully met the challenges of the past and for them to survive and reproduce under current environmental conditions. Conservation of genetic variation is a necessary precondition for the future evolution and adaptability of local populations and of the entire species (see e.g. Newman and Pilson 1997). Thus, conservation of genetic variation is a necessary element in the maintenance of all other levels of biological diversity that we value for their existence and utility. However, genetic variation is difficult to measure directly and is often cryptic in its effects on population and ecosystem dynamics. Hence its loss is easy to ignore until it is too late to restore - and this can threaten the capacity of species and ecosystems to adapt to changing environments. Genetic erosion ultimately induces species extinctions and ecosystem loss, and eliminates the possibility of using genetic variation for ecological restoration and economic gain. To avoid extinctions and to maintain or enhance the levels of genetic variation and its distribution among individuals, populations, and species, requires conserving or managing the dynamic forces of evolution. These include mutation, selection, drift and migration, as mediated through the mating system, i.e. the pattern by which individuals mate with each other, including inbreeding and outcrossing. These forces are not entirely independent of ecological factors, but genetic dynamics are not identical with ecosystem dynamics and require separate consideration. Furthermore, these genetic forces are also not entirely independent in their effects on each other and the patterns of genetic variation found in any one species are the result of their joint effects. Nevertheless, these forces can be separated as factors, can be measured, and therefore can be useful for understanding and estimating the dynamics of change. We assert no priority for genetic versus ecological forces or measures, but confirm their differences and the necessity to estimate different threats and influences. Practical applications of indicators may require that one serve as a surrogate for the other but they should not be confused by forest managers as being identical. Criteria and indicators are tools which form part of a four-level hierarchy of information and knowledge (see Annex 1), from highly detailed, but narrowly specific information represented by “verifiers” to generic, but widely applicable knowledge represented by “principles”. Forming the middle two layers of this hierarchy, criteria and indicators are most useful to conceptualize, evaluate, monitor and support sustainable forest management (Annex 2). They may be identified and applied at various levels: global, regional, (and eco-regional) national, and local which, in the context of forest sustainability, would normally be the forest management unit level. Assessment (or evaluation) of sustainable forest management is the process by which information about forest management is collected with a view to establishing, within a defined framework of expectations, the current status and probable future direction of the interactions between human beings and forests, using certain criteria and indicators (Prabhu et al. 1996). Assessment can thus be seen as an important step in a process that Munda (1993) describes as cycling through initial disorientation, reorientation or choice, towards a solution or decision.
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Criteria and indicators, which are neutral assessment tools for monitoring trends, provide a means to measure, assess, monitor and demonstrate progress towards achieving the sustainability of forests in a given country or in a specified forest area, over a period of time. By contrast certification is a means to certify the achievement of certain, predefined standards of forest management in a given forest area, at a given point in time, agreed upon between producers and consumers. However, it may be possible to draw on criteria and indicators when developing standards or guidelines for performance at the management unit level, for forest certification, as has been done in many cases (see Annex 2 for relationship between the concepts). Probable users of criteria and indicators will include government institutions trying to design more sustainable policies pertaining to forestry and other related sectors, forest owners, or donors wanting to evaluate the sustainability of the activities undertaken in various natural resource management projects. Potentially the most relevant users would be those forest managers who want to improve the sustainability of their management at the forest management unit level through a process of continuous monitoring of the impacts of management interventions and modification of practices in response to those impacts - that is, applying the principles of “adaptive management”. The application of criteria and indicators for sustainable forest management is very attractive because it provides for continuing assessment and adaptation, rather than periodic assessment at which time the damage may have been already done. However, if criteria and indicators are to be applied by untrained or semi-trained teams in forest management units (FMU), the assessment process needs to be straight-forward and easily interpretable. These requirements have guided our design and assessment of criteria and indicators focusing on genetic conservation. When criteria and indicators at the forest management unit level are applied and made operational, they would ideally be incorporated into standard FMU management activities, such as inventory. Any additional effort would be limited to, perhaps, adding a small team of 3-5 people, over a period of 1-2 weeks. This is because criteria and indicators assessment will clearly be a costly exercise, and larger teams or longer periods will make the process too costly to be acceptable to those who bear the costs, be it governments, non-governmental organizations or industry. In the time available, these teams will need to assess indicators related to all criteria and thus to all aspects of sustainability: biophysical (environmental), social, and economic. These considerations dictate that the most important characteristic of an effective indicator or verifier will be its relevance, and the practicality of assessment in a short period. The need for practicality is a serious constraint for the assessment of “conservation”, which implies a need to consider temporal dynamics. It is not anticipated that criteria and indicators will be applied to situations where landuse change is planned, for example on areas scheduled for conversion to agriculture urban areas, or industrial/infrastructure development, or establishment of non-native forest tree plantations. The impacts of such land-use changes on native genetic resources fall outside the scope of the present paper. Furthermore, it is noted that it may not be necessary to include conservation of genetic variation as a major goal for every FMU. Various authors (e.g. Seip 1996), and documentation in the on-going criteria and indicators processes, stress that different forest units may be managed for different
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purposes, with varying emphasis on conservation, production, protection, recreation, etc.. Criteria and indicators are at times developed within a conceptual framework which recognizes “pressure”, “state”, and “response” indicators. In a complementary process to the present one, aimed at identifying criteria and indicators of biodiversity conservation, Stork et al. (1997) showed the equivalence of pressure-state-response indicators to different components of a system involving human interventions, mediating processes, processes that conserve biological diversity, and the response of biological diversity itself (Figure 1). Brown et al. (1997) pointed out that pressure, state, and process (equivalent to response) indicators have different information contents. For example, assessing verifiers of the mating process is potentially very valuable, but it is also rather difficult and expensive. Verifiers of population size may be used as surrogates of mating, but its information content is less. Conversely, individual genetic variation provides more information on the actual response to mating, but the effects of other developmental processes are confounded in verifiers of individual variation. Therefore, the choice of appropriate indicators and verifiers is always going to be a trade-off between information content, scale of monitoring, and costs. We are encouraged in our approach by the report of the CIFOR project on testing criteria and indicators for sustainable forest management (Prabhu et al. 1996), which documents the lack of criteria and indicators for assessing biological diversity at all levels of the biotic hierarchy. It suggests that a “tool box approach” would have the highest utility for potential users. The term “tool box” reflects the fact that prescriptive approaches to assess sustainability for such complex systems as forests, which are used for many diverse purposes, would be doomed to failure. Rather, providing forest managers with a tool box allows them to select and apply the most appropriate tools for their specific needs. It is important to assess genetic variation in order to determine sustainability because genetic variation and diversity provide the basis for adaptation to changing conditions. The loss of genetic variation within a species leaves that species vulnerable to local extinction. For the same reason, genetic variation and diversity are far better indicators of ecosystem stress than measures related to population sizes, or such-like. For example, if individuals within a species are obliged to inbreed at a high rate, due to changes in the environment, reproductive success may not initially be affected, so that population levels may remain constant while genetic variation is being eroded. By the time that reduced reproductive success results in detectable decreases in population size, the damage may have been done at the genetic level, and the species may already be doomed to extinction. However, as genes are not easily measurable, and their individual effects often are subtle, it is an extremely difficult task to design effective and practical ways to measure genetic diversity. The various processes which support the establishment of systems of national and regional level criteria and indicators around the world have to date not succeeded in defining realistic indicators for genetic diversity (see Annex 3).
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Figure 1. Conceptual model of the relationships between anthropogenic interventions under different forest management regimes, mediating processes, ecological processes which shape biodiversity, and biodiversity. Indicators that are relevant to the left-hand side of the figure are “Pressure” indicators, while those on the right are “State” or “Response” indicators, which are better surrogates for biological diversity than for genetic variation.
Adapted from Stork et al. (1997). In the present paper, we focus mainly on tree species and variation in them. Tree species provide the basic structure in forests within which other organisms find suitable habitats. However, disruptions to genetic processes may well occur in other forestrelated organisms even when the trees are not adversely affected. Therefore, a comprehensive assessment of conservation of genetic variation must not exclude other groups of species, such as forest shrubs and herbs, vertebrates and invertebrates.
Strictly speaking, it is not genetic variation per se that needs to be conserved, but the additive genetic coefficient of variation. See Houle (1992) for a discussion of this point. For simplicity we refer to “genetic variation” throughout this document. [1]
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EFFECTS OF FOREST PROCESSES ON GENETIC DYNAMICS
As a result of forest management practices or as the side effects of climate change, environmental disasters, or other unpredictable events, resultant changes in genetic variation are attributable to changes in the basic evolutionary processes. These processes are: 1. Random genetic drift, the non-directional changes in genotypic frequencies among generations due to random chance in small populations 2. (Natural) Selection, relative differences among genotypes in viability or reproductive success 3. Migration, the exchange of genes between populations that differ in genotypic frequencies 4. Mating, the process mediating the recombination and assortment of genes between generations. Mutation, a fifth evolutionary force, is excluded from consideration here, since we are primarily concerned with effects of human interventions and relatively short term processes, of less than ten generations; mutation rates are not likely to be significant in such cases. All the above evolutionary processes affect the levels and distribution of genetic variation and the present state of the genetic resources is a result of their joint effects over time. Variation within populations may be increased by migration if populations were originally distinct, but at the expense of variation between populations and possibly with decreased fitness. The size of the receiving population may be increased, but the adaptability of the resultant genetic material is dependent on the forms of selection and migration that also occur. It is also clear that the present-day condition is not necessarily an optimum state for adaptation to future conditions. However, regardless of any difficulties inherent in defining an optimum condition (and noting that so-called “undisturbed forests” may not any longer exist -), we assume that if the processes that maintain the levels and distribution of genetic variation remain intact, then the biological capabilities for adaptability are expected to be in at least as good a condition in the immediate future as they are now. The exact levels and distribution of genetic variation would be expected to have changed during evolutionary history, but our central concern is for the processes and the structures necessary to maintain those processes, and not for the present distribution of variation as an end in itself. In the absence of any other information, it is possible to use some general rules of thumb of population genetics that suggest that population sizes of several hundred or a few thousand are sufficient to indefinitely maintain quantitative genetic variation and to
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save even rare alleles (Franklin 1980). For species with random mating and without inter-population divergence, simple counts of reproductive adults may be sufficient for assessing genetic viability, but such rules are imprecise, and are of limited value for the complex structure of many forest tree populations. Local populations may normally be much smaller and transient, and genetic divergence among populations may be an important adaptive feature of the species to conserve. Especially in the species-rich tropics, many species are widely dispersed and contiguous populations of large size are an aberration. Therefore, it is necessary not only to measure static population size parameters, but also to monitor the effects of forest practices on adaptive genetic processes. The effects of various types of biotic and abiotic changes that are evident at the forest stand level usually affect, or are the result of, more than one of the above genetic processes. For example, changes in the abiotic environment can change relative genotypic viabilities, but it can also affect pollinator behaviour and other inter-species interactions. Because biotic interactions, such as in pollinator-plant relationships, are generally more complex in the tropics than in temperate ecosystems, the indirect effects of several, simultaneous environmental changes are likely to also be more complex in the former, and to operate on a variety of time scales.
DRIFT Populations may be established by only a few individuals, which results in a limited sample of the species‟ genetic variation in each population. Also, populations may be periodically reduced in size and consequently lose genes at random. Thus, levels of genetic variation are directly affected by processes of genetic drift, and forest practices affect drift. Forest operations or fire may effectively reduce population sizes, leaving few individuals to contribute seed or pollen. In addition, sources of migration may be cut off, and the effectiveness of pollinators may be reduced, all leading to a reduction in the effective population size (Ne) and to random losses of variation by drift. Frequently, the number of mating individuals and their reproductive output may be much smaller than the total number of adults or number of flowers. Furthermore, inequities in mating frequencies and non-random mating generally further reduce Ne. If the reproductive population remains small, the random drift of alleles will severely reduce genetic variation that may be significant for future adaptability for several generations.
SELECTION Directed selection, whether resulting from natural or human-induced pressures, favors certain genotypes over others and therefore acts to maintain population viability and reproductive success through adaptive responses. However, some genes would be lost at higher rates than expected by drift alone, and very strong selection may reduce population sizes below recuperation levels. Thus, selection can simultaneously reduce genetic variation, and change population averages. Both of these shifts may influence future adaptability. Events that lead to genetically significant selection may be unintentional, for example, a single event such as harvesting of wood, could result in changes in several traits, not only those considered desirable for harvest. Thus, traits such as juvenile growth rates, reproduction age, and pollinator behaviour, can be affected by logging as well as by the timing and design of harvests. Furthermore, selection on one trait may induce genetically correlated changes in other traits and in
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genetic processes. Hence, even simple natural and man-induced selection may have multiple effects on the distribution and dynamics of genetic variation, and may thus affect present and future viability and reproduction.
MIGRATION Changes in the degree of seed and pollen exchange between populations directly affect the divergence between populations and indirectly affects drift within populations. Conversely, high migration rates may also increase local levels of genetic variation. The mating system is also affected by both seed and pollen migration as the pool of potential mating partners is altered. Isolation between populations can be affected by the increase or decrease of barriers to exchange between populations, distance between them, or changes in pollen or seed vectors. The form and rates of migration mediate the genetic divergence between partially independent populations of a species (i.e. metapopulations) and affects the distribution of genetic variation. The creation of forest islands by fires or creation of patch openings within continuous forests, can change migration rates and the relative sizes and patterns of openings vs. forest cover can strongly influence genetic divergence. For species that are dependent on local extinctions and subsequent colonization, an additional factor is the proximity of an adequate recruitment pool of immigrants.
MATING SYSTEM The mating system that mediates the inter-generational process of evolution is also subject to change caused by forest level events. The mating system determines the composition of the male and female mating pools, the extent to which germplasm is exchanged between individuals (outcrossing rates), and the rates of immigration and emigration. Thus, the levels of heterozygosity, the relative importance of vegetative reproduction, and the exposure of mutants to selection, are all affected by changes in the mating system. Forest practices such as harvesting for wood, thinning, or conversion of some areas of forests to other uses, can cause changes in change population density, pollen and seed dispersal rates and dispersal mechanisms, and in timing of flowering; these, in turn, will cause changes in the mating system. Populations that are usually large but suddenly undergo severe reductions may also be exposed to inbreeding depression and loss of seed viability, reducing their immediate capacity for survival.
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FOREST PRACTICES THAT AFFECT GENETIC DYNAMICS
Forest level events caused by forest management and utilization practices, or due to inadvertent changes which result from climate change or environmental disasters, can be expected to induce certain kinds of changes to the genetic processes that in turn affect the evolution and sustainability of forests. If forest management has only a slight impact, or if environmental changes are small, these events may not have significant genetic consequences. However, the forest manager needs to consider the impact of forest practices on genetic aspects of sustainability. Some of these forest level events are described below, and displayed below as a matrix in Table 1. TABLE 1. Genetic consequences of different types of utilization Drift
Direct selection
Logging - commercial species
X
X
Logging - non-commercial species
X
Grazing
X
Fire
X
Gene flow
X
X X
NTFP harvesting - nonreproductive parts
X X
Mating system X
X
NTFP harvesting - reproductive parts
NTFP harvesting - whole individual
Indirect selection
X
X
X
HARVESTING OF WOOD The term “logging” is often used for widely divergent activities related to the harvesting of wood. In many countries, certain areas of forest are scheduled for conversion to other lands uses, usually agriculture, in national land use planning. Clearing of these “conversion forests” is by means of clear-cutting. Although the term logging is often applied to this form of harvesting, it is not considered relevant for the purposes of these criteria and indicators, as this is obviously a non-sustainable activity from a forestry point of view, and is designed as such. Harvesting of wood, or logging, directly impacts the genetic resources of the commercial species through changes in their population age and density distributions. Noncommercial species, both plant and animal, will also be affected, either due to intentional or accidental damage during the logging process, or due to the resulting changes in environmental conditions. Forest-dependent animal species will also be affected. For any animal species with small initial populations, the effects of drift would be enhanced. Directional selection would also be likely to affect at least the logged species, but the
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changed growing conditions would also affect the conditions required for survival and reproduction of other species. For isolated populations of any type of species, the response to selection can be strong, and for plant species with specialized pollen or seed dispersal mechanisms, logging would also be expected to change the mating system.
GRAZING Grazing of livestock in forests is a very common practice, especially in tropical seasonal forests. Grazing will have a thinning effect on regenerating vegetation and changes in the population density of grazed species could affect levels of genetic drift. Since grazing may also compact soils and alter stand structure, it may induce selectively significant environmental changes. It could also affect populations of forest-dwelling herbivores.
FIRE Intense fire that inflicts heavy mortality and reduces population sizes, would increase drift. Migration may increase if the migration vectors are abiotic, but may decrease if fire adversely affects biotic dispersal agents. Direct selection may be felt on traits that affect fire resistance. Very strong fires would however indiscriminately remove all genotypes.
HARVESTING NON-TIMBER FOREST PRODUCTS (NTFP) Harvesting parts of trees or shrubs may involve removing reproductive structures such as fruit or seed, and directly reduce the effective size of the pool of reproducing parents. Fruit and seed are often harvested very intensively, resulting in severe genetic impacts (see e.g. Ganeshaiah et al. 1998). Direct selection will affect reproductive traits, but the strongest genetic effects would be felt on the mating system and gene migration dependent on the harvested parts. If non-reproductive parts are harvested, such as leaves, then only indirect selection will affect the viability or fecundity of affected individuals, and this could be genetically significant. When whole plants are harvested the effects of reduced population size will be genetically significant. Harvesting of animals (such as hunting and fishing), may have as severe impacts as harvesting of whole plants.
CRITERIA, INDICATORS AND VERIFIERS
In developing criteria, indicators, and verifiers, we emphasize that the conservation of evolutionary processes is a necessary biological criterion by which the sustainability of forests can be judged and we use genetic indicators of those processes to judge the sustainability of those evolutionary processes. To conserve these evolutionary processes, we define four genetic indicators that are necessary for sustainability. For each indicator, we list several ways to measure threats to the processes. These options conform with the “tool kit approach”, described above, which offers a forest manager a choice of verifying devices. Two types of verifiers are described, those which can be obtained by field assessment of demographic or morphological parameters and those
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which require laboratory analysis of genetic parameters. Both types contain several verifiers, and these vary in their precision and ease of use. The proposed criterion for the conservation of genetic variation is:
CRITERION: CONSERVATION OF THE PROCESSES THAT MAINTAIN GENETIC VARIATION Different types of forest use are likely to affect several of the processes that maintain genetic variation as indicated in Table 1. The processes of genetic change have effects that can be measured in the level and structure of genetic variation and in the processes themselves. These measures can serve as indicators of whether genetic variation and the processes of evolution are being maintained. We propose 4 indicators of the genetic processes: 1. Levels of genetic variation 2. Directional change in gene or genotypic frequencies 3. Gene migration between populations 4. Reproductive processes/mating system As shown in Table 2, one or more indicators need to be assessed for each of the genetic processes. TABLE 2. Relationship between genetic processes and indicators Processes
Indicators Levels of genetic variation are maintained
There is no directional change in genic/genotypic frequencies
Drift
X
Direct selection
X
X
Indirect selection
X
X
Migration Mating
There are no changes in gene flow/migration
There are no changes in the mating system
X
X X
To be most useful to forest managers, we can combine the effects of forest level events on genetic processes and the indicators of those processes, as given in Tables 1 and 2, to provide managers with tools to warn of possible changes in genetic processes which arise from particular management practices or by accident. This is shown in Table 3, where indicators are directly linked to forest practices and other forest level events. Table 3 allows forest managers to determine which genetic indicators need to be
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assessed based on their knowledge of forest practices in the FMU. Thus, the need for genetic expertise is minimized in the assessment of sustainability of genetic variation. For example, if harvesting of wood is taking place, the following indicators need to be assessed:
Overall levels of genetic variation Possible directional change in genic/genotypic frequencies Possible changes in reproductive processes/mating system.
TABLE 3. Indicators relevant to some forest operations Modes of harvesting or use
Indicators Levels of There is no There are no genetic directional change changes in gene variation are in genic/genotypic flow/migration maintained frequencies
There are no changes in the reproductive processes/mating system
Logging commercial species
X
X
X
Logging/wood harvesting of non-commercial species
X
X
X
Grazing
X
Fire
X
NTFP reproductive
X
X
NTFP - nonreproductive
X
X
NTFP - whole individual
X
X
X
X
X
X
The reason for this, as shown in Figure 2, is that selective harvesting of wood primarily affects the genetic processes of drift and mating, and the indicators listed above are indicators of those processes. Using Table 3, the forest manager does not necessarily need to understand the genetic theory behind this. For each of the indicators, the verifiers may be parameters of the genetic system that directly reflect genetic processes or other factors of the reproductive system. For example, genetic parameters such as gene frequencies can serve as measures of the level of genetic variation, and differences in frequencies can be used to estimate population differentiation. These data can also be analyzed to estimate parameters of the mating system, such as the outcrossing rate, selection effects, and migration rates. Alternatively, verifiers may be estimates of demographic statistics from which we can infer genetic processes. For example, changes in population size and age class structure may reflect changes in the levels and distribution of genetic variation as well as the mating system. The distinction between genetic and demographic verifiers reflect the
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point made by Brown et al. (1997) that the choice of verifier will always be a trade-off between high information content (in our case, generally genetic verifiers) and ease of measurement (in our case, generally demographic verifiers). Thus the different verifiers that can be measured and used for any one indicator differ widely in the precision with which they estimate the genetic parameters, and in the costs and facilities required to sample and measure them. We list several verifiers for each indicator, divided into two groups. The first group of verifiers uses demographic information and is intended to serve as the first step in assessing risk of local extinction many of the demographic verifiers can be estimated from standard forest inventory information. If additional precision is desirable, then at least one of the second set can be used. Within each group, the manager is expected to use the most informative verifier that can be feasibly estimated. The more costly and precise measures would generally be reserved for use only on the most critically threatened processes where decisions require high precision. For each indicator, verifiers within each group (demographic and genetic) are listed in the approximate order of precision with the least precise but easiest verifiers to measure, last. The risk we seek to assess is that of local species extinction as a response to previous or proposed forest practices, and critical parameter levels are given that are expected to provide a reasonable margin of safety against that risk. The parameter levels for the two types of verifiers are expressed as deviations from some reference populations. Reference populations will ideally be areas of forest ecologically similar to the test site, where there have been no management interventions. Such conditions will often be difficult to find, so in practice the reference populations will often be forest areas which have been subject to interventions, but sufficiently long ago and/or of sufficiently benign influence that the genetic processes of the reference population can be considered to be uncompromised. Since variation exists at many gene loci and these loci are partially independent in their evolutionary behaviour, the use of multiple verifiers can provide more complete information about the genetic processes than can single verifiers. For example mating system parameters are best measured by verifiers that are not subject to selection on vegetative vigor such as isozymes or RFLP‟s, whereas parameters of adaptability to environmental conditions are best measured by verifiers of traits such as growth or phenology that may be heritable. Thus, multiple genetic verifiers are preferred over single verifiers for complex events. Physiological traits would be particularly useful as estimators of adaptability to general environmental conditions as well as to stress conditions. However, to demonstrate their heritability, existing field trials must be evaluated; and, in the absence of such trials, they must be established. This is expensive and time consuming. The development of accurate, short-cut procedures would be very valuable. Many random DNA markers based on PCR techniques such as RAPD‟s, AFLP‟s, and SSR‟s (see Annex 4 for definitions of these and other technical terms) can provide a multitude of data on non-selective processes since they mostly have no physiological effect. Some markers may be associated with physiological effects but finding these markers requires
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Figure 2. Relationship between Forest Events and Genetic Processes and Their Indicators
special experimental methods such as used in QTL analyses. Random markers are not likely to show associations in large, predominantly out-crossed species and may be useful only if very large numbers of markers are well dispersed throughout the genome (Strauss et al. 1992, FAO 2001). Some markers, such as isozymes and those derived from c-DNA probes have functional meaning. However, currently available molecular methods are either too difficult to use in most field conditions, or require such large initial development time, that they would not often be useful in the present context. More research is needed to develop procedures, facilities, and training, before the molecular methods can be useful in all but the most critical and valuable cases. Ideally, a broad mixture of phenotypic, field measures, and multiple locus molecular markers that would sample the whole genome would be available to provide direct genetic process information.
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The above indicators consider the genetic processes as if they were independent of the ecosystem on which they have an impact and which impacts them. The effects of genetic management on important target species and the ecosystems in which they occur must be considered. Forest species have evolved different natural histories and are not equally easy or valuable to sustain in the future. Indicators of the capacity of species for genetic management are therefore needed to judge the likelihood that recommended actions could successfully sustain the genetic resource. A genetic “triage” (Koshy et al. 2001) can be useful in this context to assess the potential for restoration and future sustainable management. The genetic “triage” builds on long-established principles of medical triage, through which species are assigned to one of three possible groups - those for which conservation is secure, those for which local extinction is inevitable, and those for which modifications to management interventions can ensure conservation where otherwise local extinction would occur. It is only this third group which is of immediate concern to the manager.
INDICATOR 1: LEVELS OF VARIATION Levels of variation within a population result from a balance among all the genetic processes that have previously been outlined (Table 1). Effective population size (Ne) is generally recognized as an indicator of the changes that alter the level of variation within a population. However Ne is extremely difficult to assess in aged structured and heterogeneous populations, whereas measurement of population size is more realistic and its estimate, in addition to the assessment of actual level of variation, may have equal prediction for the maintenance of genetic variation. Indicator 1 therefore combines two different concepts, population size and levels of variation, for which demographic and genetic verifiers are proposed. Comparisons of the verifiers between the target population and the reference population should be made on comparable and nonimpacted cohorts, distributed on areas of similar sizes. Demographic verifiers 1.D.1 Census number of sexually mature individuals This number accounts for all individuals expected to be sexually mature at present time, according to their age or size. It also includes those individuals that will reach maturity before the next human intervention in the forest. 1.D.2 Census number of reproducing individuals If fecundity and fertility data can be obtained (based on inventory data in surrogate populations or in the study population), then this number corresponds to the subset of 1D1 that is expected to contribute to the next generation. This verifier is easier to apply to plant than to animal species. 1.D.3 Coefficient of phenotypic variation Assessments of in situ phenotypic variation of important adaptive traits will provide some crude indication of genetic variation of the same traits. Adaptive traits include such traits as for example, timing of flowering, sex ratio in dioecious species, colour of resin (which reflects chemical variation in the resin). An assessment of the variability in such traits
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within a population can provide information of genetic variation. For example, assuming low heritability for these traits (h2 =0.04), as may be the case in extremely heterogeneous environments, a coefficient of phenotypic variation of 12% would indicate that the genetic coefficient of variation is about 2.4%. Genetic verifiers The genetic verifiers that are proposed correspond to genetic parameters commonly used in population or quantitative genetics, and for which confidence intervals have been developed. They are ranked according to the resources needed for their assessments. Their applicability depends on the molecular or biochemical techniques available for the target species.
1.G.1 Number of alleles The structural and functional unit of inheritance being transmitted during the reproductive process is the gene. Genes influence hereditary traits among the offspring. These genes can exist in different forms or states, known as alleles. Allelic richness is strongly affected by changes in population sizes, and could be assessed on a reduced number of loci that are polymorphic in the reference population. Rapid estimates of the number of alleles could be obtained by bulking enzyme or DNA extracts of several samples for multi-allelic and co-dominant markers (isozymes, microsatellites). We would recommend that the target population comprises all alleles present in the reference population at a frequency higher than 0.05. The sample size necessary to detect, with a probability of 95%, at least one copy of an allele present with a frequency of 0.05, is 120 alleles (60 genotypes). Assessments should be made on at least 5 loci which have been shown to be polymorphic in the reference population. 1.G.2 Gene diversity If multi-allelic and co-dominant markers are not available, then levels of variation could be assessed at anonymous and randomly distributed markers (RAPDs or AFLPs) by using measures that account for allele frequencies (Nei‟s gene diversity or expected heterozygosity, H). Phenotypic frequencies (corresponding to the presence/absence of a fragment for dominant markers) could be used for calculating these parameters. To be realistic, we propose to assess H with the same sampled size as allelic richness (60 genotypes). This will represent a 95% confidence interval, varying between 0.07 and 0.10 (depending if the species is preferentially outcrossing or not). Comparisons between the reference and target populations should be based on at least 10 loci. 1.G.3 Genetic variation Additive variance of important adaptive traits could be estimated in common garden (field) experiments. Narrow sense heritability values or coefficient of genotypic or additive variance, could be used as verifiers for levels of genetic variation. We would recommend a sampling strategy that permits comparison of genetic variation between the target and the reference populations for traits exhibiting heritability values even as
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low as 0.04. This could be achieved by collecting 50 open pollinated progenies, each represented by 50 seedlings. Critical levels If census numbers in the target population are above critical absolute values (1.D.1 > 50 or 1.D.2 > 30) and the coefficient of phenotypic variation in the target population is higher or not significantly different from the reference population, then Indicator (1) can be considered as indicating sustainability. Decisions based on demographic verifiers rely therefore on two parameters: the census number and the level of phenotypic variation. However, if either one of these is not known, additional assessments with genetic verifiers have to be made before reaching a conclusion. If demographic verifiers fail to indicate sustainability for Indicator (1), genetic verifiers need to be assessed. The genetic verifiers proposed are largely complementary. This means that it is not necessary to assess all such verifiers - it is sufficient to select one that fits the resources available and techniques mastered, though assessment of more than one verifier may provide additional useful data. If the selected verifier in the target population is no more than 25%, or 1 standard error, lower than the corresponding value in the reference population, then this indicator is acceptable.
INDICATOR 2: DIRECTIONAL CHANGE IN ALLELE OR GENOTYPE FREQUENCIES Natural or human induced selection is the main force that will change gene and genotypic frequencies in a directed fashion. They operate on phenotypic traits in individuals, and directionally affect genotypic frequencies to the extent that those traits are heritable. For example, selective removal of trees with good stem form may leave a residual population with changed genotypic frequencies, leading to a form of genetic high-grading. In systems using selective harvesting or involving breeding, conscious selection may be assumed, but in many cases, indirect selection effects may also change gene and genotypic frequencies. These may be due to the complex processes such as logging which may selectively remove understorey individuals of a particular size or form, or they may be due to complexities of the genetic system where genetic changes in a given trait, such as size, may lead to changes in a correlated trait, such as age of reproduction. Directional change may also be due to non-selective causes: a history of divergent selection may result in differences of adaptive significance. Demographic verifiers 2.D.1 Phenotypic Shifts. If phenotypic mean differences between a reference population and the affected population are evident, then a genotypic change may have occurred that may cause a change in the economic or ecological sustainability of the population. These differences would be expected to be most strongly evident in traits with high heritability and those under direct selection pressure. Other traits that may be indirectly affected but which at the same time are of potential significance for future adaptations, may be less obviously affected, but should also be observed and monitored. They - can best be measured in common garden (field) experiments, but inferences can be drawn also on the basis of
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paired stands or cohorts. Only very large differences would be expected to significantly affect allele frequencies and hence differences of greater than 2 standard errors from the reference population mean would be of any concern. 2.D.2. Age/Size Class Shifts. If differences occur in the age or size class distribution exist between a reference population and the affected population, genic level changes may be caused by age related selection. Under conditions of minimal disturbance or in steady state environments, an age class distribution which is in equilibrium may be attained and its disturbance is equivalent to an environmental disturbance. Differences from a reference cohort, or the absence of some age classes, may portend directional selection. In nonequilibrium populations, differences in age classes are expected and may not imply threats, but any large change is still worth noting and monitoring. As in the case of verifier 2.D.1, only very large differences would affect allele frequencies, and hence only statistically highly significant differences in mean values should be considered, unless simultaneous changes in other parameters, such as population size, are noted. 2.D.3. Environmental Shifts. If differences in the environment that affect either viability or reproduction are caused by forest level events, then selection may be so strong as to affect population viability. For example, shade requiring species may be exposed to full sunlight or species requiring full light may be exposed only to shade, and neither may survive without strong frequency shifts. If survival or reproduction is less than 50% of that estimated for a reference population, then selective differences can be significant. Genetic verifiers 2.G.1. Genotypic Frequency Shifts. Differences in genotypic frequency distributions among cohorts may be measured as changes in heterozygosity, for example in cases where inbred individuals are selectively eliminated. However, generational changes can seldom be estimated in the management framework and hence, changes would have to be observed indirectly, among cohort populations. A simple assessment can be made on the basis of heritability estimates made on surrogate or reference populations, coupled with estimates of the selection differential resulting from the forest intervention. 2.G.2 Marker Frequency Shifts. In some cases, selection will result in directional changes in genotypic frequencies at some marker loci among generations or from a reference population. This is the case when there is close linkage and stochastic association between the marker and the selected loci. Such effects may be estimated using as few as a dozen loci with more than 30 sample individuals per population. However, the lack of detectable changes in gene frequencies does not prove an absence of effects of selection. These changes are the most technically challenging ones to detect, and they provide only weak tests of selection; however, their value lies in the fact that they are direct measures of directional genetic change.
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2.G.3 Genetic Mean Shifts. The most direct assessment, which at the same time is the most costly one in terms of time and effort, is the establishment of a genetic estimation experiment aimed at analyzing differences in traits of adaptive significance. The establishment of such trials would generally however require more time than is available before management decisions have to be made. Critical levels Differences in the genetic verifiers between the reference or other comparison population and the affected population must be fairly marked for concern to be raised. If the difference is less than a 25%, or if observed changes are less than 1.5 standard errors, it is usually considered that there is little cause for concern regarding sustainability.
INDICATOR 3: MIGRATION AMONG POPULATIONS Migration is a strong force that reduces genetic differences among populations, but which may increase variation within populations. Migration among populations which may be adapted to different environments may cause a reduction in fitness in the receiving populations. However, if local populations have earlier been greatly reduced in size, then migration, especially through seed, can increase the mating pool and lead to increased fitness. Within any one zone in which the environment is similar, the exchange of migrants among meta-populations will generally not reduce the fitness of receiving populations. The demographic verifiers listed below are of significance to sustainability in those cases in which isolation barriers are broken between populations with different adaptations. The impact will be greatest when the adaptive differential is large and the resident population sizes are small. Demographic verifiers 3.D.1. Physical Isolation. Reductions in degree of physical isolation by loss of neighbors or by easing of physical or biotic barriers to pollen or seed migration. 3.D.2. Mating Isolation. Breaking mating isolation by changes in age class distribution and by loss of neighbors that may increase the frequency of mating among distant parents. 3.D.3. Seed Dispersal. Changes in seed dispersal distances due to physical or biotic factors affecting seed dispersal vectors. (This verifier can only be used when the seed vector is known).
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3.D.4. Pollen Dispersal. For plant species, the effective distance of pollen dispersal may be increased by factors which lead to increased pollen vector influences, caused either by changes in the species of pollen vector, or in variation in their inter-individual distances. (This verifier can only be used when the pollen vector is known). Genetic verifiers 3.G.1 Gene Flow. Gene flow can best be estimated by tracing neutral marker loci and estimating statistics [2] such as Fst, Gst, d, Dj, and theta statistics. The use of cytoplasmic gene markers can discriminate between male and female migrants. Low levels of differentiation suggest a large gene flow among populations. The allocation of samples within and among populations should include at least 30 individuals within populations and should emphasize between population sampling when estimation of population differences require precise estimation. The reference populations to which such statistics refer would also have to represent a range of populations. Critical levels Changes in the verifiers of less than 50% in the demographic verifiers, or of less than 10% in the genetic parameters, would indicate sustainability in regard to this indicator.
INDICATOR 4: REPRODUCTIVE PROCESSES/MATING SYSTEM The reproductive processes is the bridge between generations and hence mediates evolution. The mating system involves the levels of fecundity and fertility, propensities for inbreeding versus outbreeding, and unequal or non-random mating associations. It is the primary determinant of how genetic variation is partitioned among individuals (levels of heterozygosity) and hence, of how much inbreeding depression will be expressed in the next generation. The mating process is affected by sex ratio, phenology, and specific compatibility relationships between potential mates; and is subject to selection. Measures of pollen flight and numbers of reproductive organs have proven to be difficult to obtain in the field and have failed to accurately predict the genotypic structure of the resulting seedling pool. Demographic verifiers 4.D.1. Parental pool size Modification of the density of potential parents indicates possible changes in future mating success. The threshold for this indicator will depend on the distance of pollen dispersal. The density will have to be assessed on sexually mature individuals. Modification of flowering phenology within the population may also portend changes in the potential parental pool.
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4.D.2. Seed Germination Modification of the percentage of seed germination (or of filled seed count) relative to the reference population, can reflect changes in mating success. It does not give information on the number nor the distribution of the mates. The percentage of germination could be assessed for 10 individuals (4 replicates of 50 seeds per tree; seeds should not be taken from a single branch on any tree) in standard conditions; and the necessary pretreatment used if it is known. Low percentages may be signs of increasing selfing, leading to inbreeding depression. Large variations between individuals will characterise unbalanced contributions to reproduction from the different maternal parents. 4.D.3. Pollinator Abundance Modification of pollinator abundance or composition during the mating season indicates potential changes in the male gamete pool. The procedure for estimating this factor will be different according to the types of pollinators. If the tree species is bird or bat pollinated, an animal population census should be carried out; if this is not possible then environmental modifications that can affect population size of the pollinator should be evaluated. If the species is insect pollinated, flower visitors should be captured and the pollen they carry should be identify to include only true pollinators in the estimate (pollinators should be captured from 5 individuals; if the composition is very variable, more individuals will have to be included). Comparison will have to be carried out relative to a reference population. 4.D.4. Ratio between Male and Female Flowers Modification of the ratio of male and female functions portends changes in mating success. Particular attention should be paid to dioecous species, for which sex ratio modifications will reduce the potential mating pool. The sex ratio should be assessed on at least 30 sexually mature individuals and compared with that of a reference population. Genetic verifiers 4.G.1. Outcrossing rate Changes in the outcrossing rate will point to changes in potential inbreeding, and possible increases in inbreeding depression. The multilocus outcrossing rate can be estimated from open-pollinated families; seeds should be collected on 20 individuals in the population, 10 progeny per tree have to be assessed for at least 4 polymorphic loci (isozymes could be used or microsatellites). The value obtained for the outcrossing rate will have to be compared with estimations from “undisturbed” reference populations to estimate how significantly it may affect inbreeding. 4.G.2. Correlated mating Even if the outcrossing rate is not changed, modification of gene flow within a given population can strongly affect the genetic makeup of the next generation. Comparison of multi-locus outcrossing rates with the average of single locus estimates will give some indication of the frequency of mating among relatives. Furthermore, based on this, one
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can estimate how many fathers have sired a given progeny. This should be sampled on at least 30 parents and for at least 6 loci with microsatellites or with 20 loci for isozymes. Critical levels Changes in these verifiers which are greater than 50% for the demographic verifiers, and greater than 10% for the genetic verifiers, would be considered to be critical.
[2]
See annex 4 for glossary of terms.
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OPERATIONAL USE OF THE PROPOSED INDICATORS AND VERIFIERS
To use the above indicators effectively in forest management, we recommend using: 1. A species selection process to provide a preliminary screening of vulnerable species, followed by, 2. Risk assessment based on the indicators. Based on the information generated, and assessment of the probability that management actions will reduce risks to sustainability (Indicator 5), it is recommended to apply a decision making system to determine if the targeted species can be deemed to be managed sustainable or, at least, cannot be deemed to be threatened under the prevailing or proposed forest management system. As the species selection process is intended to identify those species which are highly susceptible to loss of genetic variation, it can be concluded that, if these species are found to be sustainable in the management system applied, the other species (which by definition are less vulnerable and less susceptible), are also in the same condition. It is expected that the indicator species will be examined first using at least one of the demographic verifiers and, subsequently, if added genetic precision is desired, using at least one of the genetic verifiers. To provide information for comparison, since no common standard exist the verifiers must be assessed both in a reference population and in the affected population. The value of using the above indicators stems from their regular assessment over time, to reveal trends. Without the benefit of baseline data, the value of initial assessments of verifiers will be limited. This drawback can be overcome by sampling populations which are thought to represent different levels of sustainability. By taking such an approach, the initial values of verifiers can be used to provide a preliminary understanding of the state of forest management in relation to sustainability, without awaiting true time series data derived form repeated measurements. However, the use of such a “pseudochronosequence” confounds variation in verifiers due to environmental heterogeneity, and comparisons must therefore be treated with caution. It is only through second and subsequent assessments, of the same site, that direct estimates of temporal dynamics are possible. If the forest management unit is large, the distribution of samples at the appropriate scales must ensure that micro- and macro-scale influences are captured and measured. The time scale becomes very important when changes and rates of change are to be assessed. However, it is rarely possible to directly assess changes that occur in evolutionary time scales and apply them to forest management decisions which have to be made in time scales of months or a few years. Therefore, important differences will often be estimable only by assessing, at regular intervals, differences among contemporary reference populations with differences in age or environmental factors, and not by following cohorts over several years or generations. While applying such
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compromise solutions, research should be carried out in parallel to validate the assumptions on which the substitutions of variables are made. Species selection The use of criteria and indicators to assess sustainability will usually be undertaken when an intervention is planned or, alternatively, following a management intervention (harvesting of wood and NTFP, etc.). Naturally occurring, spatial and temporal variation in genetic variation will make it impossible to assess historical trends in genetic variation, and thus to establish baseline data prior to forest management interventions. Only through a series of subsequent assessments will such information become available. To establish baseline information from other comparable locations, or “reference populations”, must generally be used. Such data can provide information used to assess the changes due to the management interventions. The indicators of genetic processes cannot be applied to all species in the forest and hence a system for selecting genetic “indicator species” is needed. As noted by Brown et al. (1997), ideally taxa monitored should be selected from all major groups of organisms. However, the amount of information that can be derived from verifiers partly depends on the amount of prior knowledge of the species and its genetics. Especially in tropical forests, little is known about forest related species in general, however, trees are often the best studied organisms, and are also critical in providing habitats for other organisms. Standard forest inventories provide information from which many verifiers can be derived, however, usually only information on forest tree species is included in them. We present below an example of a tree species selection process, noting however that, ideally, other species of plants, vertebrates and invertebrates should also be included in the exercise, resources permitting. Since the assessment of any target species requires a certain amount of prior research and documentation of their status in reference populations as well as in the test sites, there are obvious opportunities for reductions in costs if the same species are selected in all FMU‟s within a given region in a country. The accumulation of data from reference populations over time will then make the process of assessing and monitoring genetic indicators progressively cheaper. Species selected should be those which are thought to be most susceptible to adverse affects from the forest management interventions under consideration. The species will vary according to the location and the type of intervention. If several different types of management intervention are practiced and different types of product harvested, a number of different target species will likely need to be identified. As Brown et al. (1997) noted, indicator species, or indicator taxa, should be biologically, ecologically and taxonomically representative of the types of species found in the area. Thus, although trees may be the major initial species monitored, opportunities to incorporate information on other organisms, such as birds, mammals, and invertebrates, should be sought when possible. Criteria for selection of indicator tree species for different types of interventions are proposed below.
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A. Selective harvesting of wood, selective logging In the context of this paper, “logging” refers to the planned and regulated harvesting of wood by means of selective removal of individual large trees. The intensity of harvesting can vary from less than one tree per hectare in parts of central Africa to more than 10 trees per hectare in parts of southeast Asia. As noted above, “logging” is not used here in reference to the process of converting forests to other lands uses, as this is obviously a non-sustainable activity as concerns forestry, and is designed as such. Similarly, the unregulated cutting of any and all mature individuals of some species due to their high value is usually undertaken with little consideration for sustainability, and is therefore not considered in this context. An example of this latter situation is mahogany (Swietenia mahagoni), which over the past decades has reportedly undergone extreme genetic erosion due to the harvesting of superior phenotypes in its natural area of occurrence, in the Caribbean and Central America (Rodan et al., 1992). The proposed characteristics of indicator species used for assessing the impact of logging can be sub-divided into two categories. The first category includes those characteristics for which information will normally be easily available, probably without the need for additional field visits. The second category includes those characteristics in which little information is available, and which may require additional field work and other investigations. The proposed characteristics are generally listed in order of importance, but it is not essential that a selected species meet all characteristics, and indeed it is desirable that a mixture of species is included. For example, while the first characteristic refers to the value of the species for logging, some non-commercial species should also be included. a) Characteristics for which information is readily available:
The species is harvested, and has high value (influences drift, selection) The species is rare (drift, mating system) The species has a spatial distribution which makes it susceptible to adverse impacts of logging - see note below The reproductive niche of the species requires shade (mating system) The species is dioecious (drift, mating system).
b) Characteristics for which information is generally less readily available:
The species is ecologically valuable - it is a “keystone” species[3] The species flowers only after attaining a large size or advanced age (influences mating system, drift) The pollinators/seed dispersers of the species are highly specific (mating system).
Most of the above characteristics are exclusive: species either meet them or do not meet them. This allows species to be screened out, leaving relatively few candidate indicator species. The remaining candidate species can then be prioritized based on those characteristics which are scaleable, such as largest maximum size, degree of aggregation and, possibly, economic value. In this way, a number of indicator species, perhaps up to 10, can be prioritized in the final list.
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As mentioned above, the characteristics can be applied in any area to select those species which are the most appropriate ones to use as indicator species in a given ecological region. It is envisaged that this would be done in consultation with an ecologist with detailed local knowledge. As examples of this process, species which may be selected as indicator species in southern Cameroon, and in Kalimantan, are shown in Table 4. Table 4. Species potentially susceptible to loss of genetic variation from logging in Southern Cameroon and Kalimantan. Rank Southern Cameroon Kalimantan “Sapelli”: Entandrophragma cylindricum - “Meranti merkuyung”: Shorea johorensis 1 Meliaceae Dipterocarpaceae “Azobé”: Lophira alata - Ochnaceae “Meranti ketuko”: S. pauciflora 2 Dipterocarpaceae “Fraké”: Terminalia superba - Combretaceae “Meranti merah”: Shorea macroptera 3 Dipterocarpaceae “Iroko”: Millicia excelsa - Moraceae “Bangkirai”: Shorea laevis 4 Dipterocarpaceae “Sipo”: Entandrophragma utile - Meliaceae “Ulin”: Eusideroxylon zwageri - Lauraceae 5 “Moabi”: Baillonella toxisperma - Sapotaceae “Mersawa”: Anisoptera costata 6 Dipterocarpaceae “Movingui”: Distemonanthus benthamianus - “Keruing”: Dipterocarpus gracilis 7 Cesalpiniaceae Dipterocarpaceae “Ngollon”: Khaya ivorensis - Meliaceae “Bembueng”: Agathis borneensis 8 Araucariaceae “Tali”: Erythrophleum ivirense - “Perupok”: Lophopetalum javanicum 9 Cesalpiniaceae Celastraceae “Bilinga”: Nauclea diderrichii - Rubiaceae “Jelutung”: Dyera costulata - Apocynaceae 10
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B. NTFP Harvesting Selecting indicator species for effects of the harvesting of non-timber forest products is complicated by the fact that so many different products are harvested, involving a large range of different harvesting procedures. The likely susceptibility of species can be ranked according to the impact of the harvesting method on the individuals or populations from which such products are collected. For example, collection of reproductive structures (flowers, fruits or seeds) can have a potentially large impact on genetic variation. Harvesting of complete individuals to derive products from them, which includes reproductive structures, would be expected to have an even larger impact, as it not only removes the possibility of reproduction in the current year, but also future reproduction. Also the harvesting of immature individuals will have a large potential impact on genetic variation. The proposed characteristics for indicators species for NTFP collection are: a) Characteristics for which information is readily available:
Number of high value individuals being harvested in whole or in part (influences drift, selection)
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Level of species rareness (drift, mating system) Distribution of the species: grows in clumps and has a predictable distribution (e.g. always on ridge top) making it easy to find and to harvest (drift, migration) Complete individuals are harvested, often when immature, or (if no species in this category), reproductive structures are harvested (drift, selection) The species is not amenable to cultivation (drift) The species is dioecious (drift, mating system).
b) Characteristics for which information is generally less readily available:
The species is ecologically valuable - it is a “keystone” species (influence is direct and indirect) The species flowers only after attaining a large size (drift).
As examples of this process, species which could be selected as indicator species in southern Cameroon, and in Kalimantan are shown in Table 5. Table 5. Species potentially susceptible to loss of genetic variation from collection of non-timber forest products in Southern Cameroon and Kalimantan. Rank 1 2 3 4 5 6 7 8 9 10
Southern Cameroon Coula edulis - Olacaceae Carcinia kola - Gutiferes Scorodophloeus zenkerii - Cesalpiniaceae Alstonia boonei - Apocynnaceae Ricinodendron heudelotii - Euphorbiaceae Tricoscypha acuminata - Anacardiaceae Irvingia gabonensis - Irvingiaceae Tetrapleura tetraptera - Mimosaceae Poga oleosa - Rhizophoraceae Cola acuminata - Sterculiaceae
Kalimantan Aquillaria malacensis - Thymelaeaceae Local rattans (mainly Calamus spp.) Eurycoma longifolia - Simaroubaceae Baccaurea macrocarpa - Euphorbiaceae Durio kutejensis - Bombacaceae Parkia speciosa - Leguminaceae Shorea pinanga - Dipterocarpaceae Artocarpus anisophyllus - Moraceae Pentaspadon motleyi - Anacardiaceae Pinanga spp. - Arecaceae
C. Fire In natural circumstances, fire is an unusual event in humid forests. However, in drier forest ecosystems species are frequently ecologically adapted to fire. Human activities often greatly increase the incidence of fire, and wildfires may result in considerable economic and ecological damage. Proposed characteristics of indicator species used for assessing effects of fire are:
The species does not have fire-adapted characteristics such as thick bark or underground dormant structures (influences drift) The reproductive niche of the species requires organic matter and/or shade (drift, migration) Fruit/seed of the species is mature at the beginning of, or during, the dry season (drift)
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D. Additional considerations In addition to identifying indicator species for different types of intervention or effects, additional considerations may help identify species of special interest. Such considerations may include:
known state of threat or endangerment degree of endemism (influences drift, migration)
USE AND APPLICATION OF INDICATOR SPECIES The complexities and interactions among indicators makes it difficult to ensure sustainability over time, even if these are regularly monitored. It is much easier to identify conditions of non-sustainability, though still with some considerable margins of error. If management interventions do not have effects on given species which render the management clearly unsustainable from a genetic point of view, it can be assumed that the levels of risk are acceptable. However, there can never be a full guarantee that given species do not go extinct, or are not impoverished genetically due to natural or manmade influences. It is necessary that the standards of acceptability are sufficiently rigorous to ensure that even moderate threats to sustainability will flag a warning or lead to recommendations for changes in forest management practices. Thus, in a simplified manner, one could say that forest practices can either:
lead to non-sustainability, or do not pose unacceptable risk of causing species extinction or severe genetic degradation.
A third scenario is that, although likely non-sustainable under present conditions, if modified forest practices can help alleviate negative impacts in the future - “conditionally acceptable” (see discussion under Indicator 5, above). If the forest practices planned or in effect, pose no unacceptable risk to the most sensitive processes acting on the most sensitive species, we then infer that there is also likely to be no unacceptable risks for less susceptible species. We propose the following process to determine the sustainability of management in terms of genetic conservation: Step 1. The types of interventions within the FMU are recorded and mapped. Using Table 3 as a guide, the specific indicators that should be assessed are determined. Step 2. With advice from forest ecologists knowledgeable of local conditions, those species which are likely to be most susceptible to negative impacts of the forest management interventions planned, or taking place, in the FMU are identified. These species should be identified on a regional basis within each country, to promote ease of comparison among FMUs.
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Step 3. The most susceptible species for each intervention is assessed, initially using demographic verifiers, subsequently supplemented by genetic verifiers if greater precision is required. Step 4. Information derived from verifiers of Indicators (1) through (4) is combined to obtain an overall assessment of sustainability. The most meaningful way in which this information can be used should be further researched. However, general principles can be established for a first evaluation based on combining information from multiple indicators. For example, if management philosophy calls for a rigorous assessment of sustainability, it follows if verifiers of any one of the four indicators fall below the critical level, this indicates overall non-sustainability. A more conservative approach may recognize that, for example, if the critical values for indicator 2 (directional change) are exceeded, this may not indicate non-sustainability if the values for indicators 3 (migration) and 4 (reproductive system) are very good. Such an approach recognizes the possibility of compromises. Irrespective of the rigour of evaluation adopted, two possible conclusions can be reached:
IF the most susceptible species are found not to face unacceptable risks, it is concluded that the management interventions undertaken can be considered to support sustainability. IF the effects of management interventions on the most susceptible species are found to lead to non-sustainability of the resource, then a system is used to establish the critical points in the species lists. This may be a bracketing strategy, in which the second species tested may be the 5th most susceptible, and if it fails the 15th, etc. Then, if resources permit, a species halfway between the positions of failure and lack of failure can be tested until the point of non-sustainability is established and action taken to gradually improve the situation (see below).
Step 5. Any species for which the conclusion in Step 4 was “non-sustainability” is then assessed in order to establish what measures must be taken to improve the situation. If such action is feasible, and implementable in the immediate future, the status of “unsustainable” is modified to “conditionally acceptable”.
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PREVENTIVE OR RESTORATIVE ACTIONS The potential of the genetic system to recover would indicate that sustainable forestry is in fact biologically possible. For the targeted species, various management options may be available, and some of them would be feasible depending on the state of the system as determined by Indicators (1) through (4). For each of these four indicators, critical levels of the verifiers can be determined for developing a genetic “triage” system, as described above (Koshy et al. 2001). For example, if levels of genetic variation are so low that demographic recovery is unlikely, if prospects for adaptation are so poor that future adaptation will not be possible, if migration is so scarce that colonization is unlikely, or if the mating system cannot generate acceptable levels of genetic variation, then little can be done to conserve the system, and the methods used can be considered to have led to non-sustainable management. If assessment of the indicators shows acceptable levels of risk, the methods of management can be considered to promote sustainability. Finally, if some of the indicators are below critical levels, a modification of the management practices may improve overall sustainability. If such modifications in management interventions help improve the status of those indicators which have been below critical levels, then conditional acceptability of management methodologies could be declared. The form that corrective measures take will depend on the degree to which improvement in sustainability is required. Some examples of interventions in a FMU in which harvesting of wood is the prime activity, in a rough order, from the most drastic to the more benign, include:
planting a genetically variable population for species that have become genetically impoverished (“artificial recolonization”), changing species to be harvested to more “robust” ones, reducing harvest levels or modifying cutting regimes to leave sufficient residual populations for regeneration, shifting the wood harvesting operations to environmentally less sensitive areas, releasing potential parents from competitive suppression through silvicultural interventions, carrying out site amelioration to favor regeneration of target species, increasing the rotation age to allow more time for recovery, raising the cutting size limits, or implementing other measures to moderate the indirect effects of logging.
It may then be possible to array the forest management practices recommended for sets of species occurring in a given area, and that set would constitute a recommended forest management system from the point of view of genetic conservation as an integral part of management. It is expected that a finite set of practices will be found to be satisfactory for the threats facing many or all species in a given area and hence that a single management prescription for an area can be derived. Such management prescriptions must be continuously monitored, and must be flexible enough to allow the introduction of adjustments in accordance with new know-how and experiences.
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The verifiers for the success of these actions are the same as those for Indicators (1) through (4). Added to these is the existence of ex situ resources, and the operational feasibility of restoration techniques. Further development of decision support systems are needed to assist forest managers in assessing risks and in judging when the political value of additional documentation and justifications is worth its cost.
[3]
The species is ecologically valuable (see Paine 1966).
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RESEARCH PRIORITIES
Since genetic processes are usually not directly observable, and methods for assessing those parameters that are most precise and informative are often expensive to develop and to use, two problems exist that can be relieved by further research. One problem is the relationships between demographic and genetic processes, and the other is on critical levels of the parameters. Research to better predict the relationships between ecological or demographic factors and genetic processes would allow us to predict what types and intensities of forest level events portend significant changes in genetic parameters. Better understanding of those relationships would also allow us to predict the ecological effects that changes in the genetic system would be reasonable to infer. The relationships described by the matrices in Tables 1, 2, and 3, are very crude and their refinement to include levels of intensity would be very useful in a decision support system. Furthermore, with more precise information on the ecological-genetic interface, more precise surrogate measures could be identified and more efficient measures of the characteristics to be monitored could be developed. Opportunities for incorporating additional elements of genetic research should be sought in areas where ecological assessments are already planned. This would provide the forest manager with more options for choosing measures that are appropriate and available for specific forest conditions. A third type of problem requiring research is the general lack of understanding of the joint effects that simultaneous changes in all of the parameters has on the structure of genetic variation and on the evolution of adaptability in affected populations. We have had to assume independence among the factors influencing genetic variation and hence have had to set conservative, independent critical levels for acceptability of changes in indicator species. It is perceivable that factors interact, and that high levels of acceptability in one variable can compensate for low levels of another; on the other hand, there is also a possibility that acceptable levels of change in two variables may render a population more susceptible to extinction than either one alone. The joint factor evaluation of risk and interpretation of information from multiple indicators need to be developed. Specific research areas in need of attention 1. Effect of environmental, biotic, demographic changes on genetic processes and parameters, and their effects on the reliability of different types of gene markers. 2. Effect of increasing distance between mating pairs to map pollination dynamics. 3. Seed migration distances and dynamics. 4. Effects of genetic variables on demographic and ecological processes. 5. Predictive indicators for species that are vulnerable to genetic change.
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6. Defining critical levels of genetic verifiers that call for active interventions to counter negative effects. 7. Methods for estimating phenotypic genetic variation in situ. 8. Determining utility of surrogate measures, such as changes in pollinator guilds that in effect change outcrossing rates. 9. Develop indirect assessment tools for genetic parameters that are more easily measured in the field. 10.Generate generally applicable base-line genetic data on population structure and levels of variation and genetic processes for tropical tree species that exist in different successional or dispersal states. 11. Develop a joint risk function for simultaneous evaluation of critical levels of all indicators.
ACKNOWLEDGEMENTS The authors would like to thank the staff of the Tropenbos-Cameroon project in Kribi, especially Mr. Oscar Eyog Matig, for their assistance in conducting the field test of genetic criteria and indicators. Drs. Jim Jarvie and Plinio Sist provided the information for Kalimantan in Tables 4 and 5, and Mr. Oscar Eyog-Matig provided the information for Cameroon. Their assistance is gratefully acknowledged.
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REFERENCES
Brown, A.H.D., Young, A.G., Burdon, J.J., Christidis, L., Clarke, G., Coates, D. and Sherwin, W. (1997) Genetic indicators for State of the Environment Reporting. Dept. of Environment, Sports and Territories Technical Report, Canberra (in press). Bruford, M.W. and Wayne, R.K. 1993. Microsatellites and their application to population genetic studies. Current Opinion in Genetics and Development 3: 939-943. FAO. 2000. Criteria and Indicators for Sustainable Forest Management and Implications for Certification and Trade in Africa. Secretariat Note FO:AFWC/2000/5. Twelfth Session, African Forestry and Wildlife Commission. Lusaka, Zambia 27-30 March 2000. FAO, Rome: FAO. 2001. Forest genomics for conserving adaptive genetic diversity. Paper prepared by Konstantin V. Krutovskii and David B. Neale. Forest Genetic Resources Working Papers, Working Paper FGR/3 (July 2001). Forest Resources Development Service, Forest Resources Division. FAO, Rome (unpublished). FAO. 2001a. Criteria and indicators of sustainable forest management of all types of forests and implications for certification and trade. Secretariat Note COFO-2001/3. Fifteenth Session of the Committee on Forestry. Rome Italy 12-16 March 2001. FAO, Rome. FAO. 2001b. Criteria and Indicators for Sustainable Forest Management: A Compendium. Paper compiled by Froylán Castañeda, Christel Palmberg-Lerche and Petteri Vuorinen, May 2001. Forest Management Working Papers, Working Paper 5. Forest Resources Development Service, Forest Resources Division. FAO, Rome Franklin, I.R. 1980. Evolutionary change in small populations. Pages 135-149 In: Soulé, M.E. and Wilcox, B.A. (eds.). Conservation Biology: An evolutionary-Ecological Perspective. Sinauer Assocs., Sunderland, Mass., USA. Ganeshaiah, K N, R. Uma Shaanker, K. S. Murali, Uma Shankar, and K. S. Bawa.1998. Extraction of non-timber forest products in the forests of Biligiri Rangan Hills, India. 5. Influence of dispersal mode on species response to NTFP extraction. Economic Botany, 52(3): 316-319. Gregorius, H.-R. 1984. Measurement of genetic differentiation in plant populations. Pp. 276-285 In: Gregorius, H.-R. (ed.). Population Genetics in Forestry. Springer - Verlag, Berlin, Heidelberg, New York, Tokyo. Houle, D. 1992. Comparing evolvability and variability of quantitative traits. Genetics, 130: 195-204. Koshy, M.P., Namkoong, G., Kageyama, P., Stella, A., Gandara, F., and Neves do Amaral, W.A. 2001. Decision-making strategies for conservation and use of forest
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genetic resources. Chapter 25 In: Engels, J. Ramanatha Rao, V. Brown, A.H.D. and Jackson, M. (eds.). Managing Plant Genetic Diversity. CABI Publishing (ISBN: 0851995225). Mullis, K.B. 1990. The unusual origin of the polymerase chain reaction. Scientific American, 262:56-65. Munda, G., 1993. Fuzzy Information in Multicriteria Environmental Evaluation models. Free University of Amsterdam, the Netherlands, Ph.D. Thesis. Nei M. 1972. Genetic distance between populations. American Naturalist 106:283-292. Newman, D. and Pilson, D. 1997. Increased probability of extinction due to decreased genetic effective population size: experimental populations of Clarkia pulchella. Evolution, 51: 354-362 Paine, R.T. 1966. Food web complexity and species diversity. American Naturalist 100: 65-75. Prabhu, R.; Colfer, C.J.P.; Venkateswarlu, P.; Tan, L-C.; Soekmadi, R. and Wollenberg, E. 1996. Testing Criteria and Indicators for Sustainable Management of Forests: Final Report of Phase I. CIFOR Special Publication, Bogor, Indonesia. Rodan, B.D., Newton A.C. and Verissímo, A. 1992. Mahogany conservation: status and policy initiatives. Environmental Conservation. 19: 331-338. Seip, H.K. 1996. Forestry for human development, a global imperative. Scandinavian University Press, Norway. Stork, N.E., Boyle, T.J.B., Dale, V., Eeley, H., Finegan, B., Lawes, M., Manokaran, N., Prabhu, R. and Soberon, J. (1997) Criteria and Indicators for Assessing the Sustainability of Forest Management: Conservation of Biodiversity. CIFOR Working Paper (in press). Strauss, S.H., R. Lande and G. Namkoong. 1992. Limitations of molecular marker aided selection in forest tree breeding. Can. J. For. Res. 22: 1050-1061. Vos, P., Hogers, R., Bleeker, M., Reijans, M., van der Lee, T., Hornes, M., Frijters, A., Pot, J., Peleman, J., Kuiper, M. and Zabeau M. 1995. AFLP: A new technique for DNA fingerprinting. Nucleic Acids Research 18: 7213-7218. Williams, S.G.K„ Kubelik, A.R., Livak, K.J, Rafalski, J.A. and Tingey, S.V. 1990. DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucleic Acids Res. 18: 6531-6535.
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ANNEX 1. PRINCIPLES, CRITERIA, INDICATORS AND VERIFIERS [4]
Criteria and Indicators form part of a hierarchy of assessment tools. The four levels of this hierarchy are Principles, Criteria, Indicators, and Verifiers. Each level in the hierarchy is defined as follows Principle: A fundamental truth or law as the basis of reasoning or action. A principle is commonly formulated around a core concept based on societal ethics, values, and tradition as well as on scientific knowledge. Usually principles can be expressed concisely and crisply, for example, sustainable development principle, sustained yield principle, sovereignty principle, polluter pays principle, and a set of forest principles negotiated at the Earth Summit (CSCE Seminar and the “Montreal Process”, 1993). In the context of sustainable forest management, principles are seen as providing the primary framework for managing forests in a sustainable fashion. They provide the justification for criteria, indicators and verifiers. Consider that principles embody human wisdom, where wisdom is defined as: a small increment in knowledge created by a person’s (group’s) deductive ability after attaining a sufficient level of understanding of a knowledge area. Wisdom therefore depends on knowledge. E.g. “Ecosystem Integrity is maintained or enhanced” or “Human well-being is assured”. Criterion[5]: A standard that a thing is judged by. In order to be able to monitor the implementation of the principle of sustainability in forestry, it is necessary to define what it truly means in practice. It is essential to define what conditions forest management must fulfil in order to be sustainable. A criterion describes the different sides of sustainability on a conceptual level. A criterion is a distinguishing element which a thing is judged by (Ministerial Conference on the Protection of European Forests and the “Helsinki Process”, 1993). It is a distinguishing element or set of conditions or processes by which a forest characteristic or management measure is judged (Ministerial Conference on the Protection of European Forests and the “Helsinki Process”, Geneva Sept 1994). A criterion can therefore be seen as a „second order‟ principle, one that adds meaning and operationability to a principle without itself being a direct measure of performance. Criteria are the intermediate points to which the information provided by indicators can be integrated and where an interpretable assessment crystallises. Principles form the final point of integration. In addition, criteria should be treated as reflections of knowledge. Knowledge is the accumulation of related information over a long period of time. It can be viewed as a large-scale selective combination or union of related pieces of information. E.g. “Principal functions and processes of the forest ecosystem are maintained” or “Processes that maintain genetic variation are maintained” Indicator[6] An indicator is any variable or component of the forest ecosystem or the relevant management systems used to infer attributes of the sustainability of the
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resource and its utilisation. Indicators should convey a „single meaningful message‟. This „single message‟ is termed “information”. It represents an aggregate of one or more data elements with certain established relationships. A quantitative or qualitative variable which can be measured or described and which when observed periodically demonstrates trends (Sixth Meeting of the Working Group on Criteria and Indicators for the Conservation and Sustainable Management of Boreal and Temperate Forests, Santiago de Chile Febr.1995 - the “Montreal Process”). A change indicator does not, in itself, tell whether the change is favourable or unfavourable. The indicators have to be judged on the scale of acceptable standards of performance which may vary widely from region to region and from time to time. By repeatedly measuring the fulfilment of the criteria, we can evaluate whether forest management is moving towards or away from sustainability (Ministerial Conference on the Protection of European Forests and the “Helsinki Process”, 1993). E.g. “Directional change in allele or genotype frequencies” Verifier: Data or information that enhances the specificity or the ease of assessment of an indicator. At the fourth level of specificity, verifiers provide specific details that would indicate or reflect a desired condition of an indicator. They add meaning, precision and usually also site-specificity to an indicator. They may define the limits of a hypothetical zone from which recovery can still safely take place (performance threshold/target). On the other hand, they may also be defined as procedures needed to determine satisfaction of the conditions postulated in the indicator concerned (means of verification). E.g. “Number of alleles”
[4]
Adapted from Prabhu et al. (1996) - complemented with additional sources, as cited in the text. See also Annex 2 [6] See annex 4 for glossary of terms. [5]
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ANNEX 2. CRITERIA AND INDICATORS: CONCEPTS AND DEFINITIONS [7]
National-level criteria define the range of forest values to be addressed and the essential elements or principles of forest management against which the sustainability of forests may be assessed. Each criterion relates to a key element of sustainability. Indicators measure specific quantitative and qualitative attributes (and reflect forest values as seen by the interest group defining each criterion) and help monitor trends in the sustainability of forest management over time. Changes in national-level indicators between periods indicate whether a country is moving towards, or away from, sustainability. Criteria and indicators as applied at the forest management unit level provide similar information for individual FMU‟s. Viewed this way criteria and indicators are similar, for example, to economic indicators such as interest rates and inflation rates that governments use to assess the health of an economy. If these indicators suggest that an economy is moving away from the desired direction, the government is able to adjust its management policies to achieve the preferred outcome. Change in the indicators of SFM provides similar information to policy makers and practicing foresters to allow them to intervene appropriately to correct any undesirable trends (FAO 2000). National level indicators will contribute towards the development and regular updating of policy instruments (laws, policies, regulations), while trends in the indicators at the forest management unit level will help adjust forest management prescriptions over time to meet established national goals The overall aim of the development of both national and forest management unit level criteria and indicators is to achieve better forest management over time. While criteria and indicators developed at these two levels differ in concept and substance, forest management unit level criteria and indicators should be linked to the national level, and the two levels must be mutually compatible (FAO 2001a). Criteria and indicators, at both national and forest management unit level, are neutral assessment tools for monitoring trends, they cannot be used as substitutes for minimum agreed-upon forest management standards which underpin certification. On the other hand, forest management unit level criteria and indicators can be used to guide the development of minimum standards of performance at the management unit level, thus indirectly linking criteria and indicators at this level with forest products certification. (FAO 2001a). Certification is one of a number of market-based instruments that may contribute to improved management of forests and improved forestry sector development. The goal is to link trade in forest products to the sustainable management of the forest resource, by providing buyers with information on the management standards of the forests from which the timber came. While assessment results from criteria and indicators work cannot be compared between countries, the specified performance standards used for certification purposes can, by definition, be compared. (FAO 2001a).
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Certification processes need to be closely linked to national and forest management unit level criteria and indicators because a number of macro-level criteria for certification, such as the legal and policy frameworks, should be based on the national level data provided within national criteria and indicators processes. A similar dependence of national level criteria and indicators on certification, however, need not exist. In other words, certification, though helpful, is not a necessary condition for achieving sustainable forest management (FAO 2001a).
[7]
Source: Forest Resources Division, Forestry Department, FAO/Rome. Unpublished File Note, 1 April 2001.
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ANNEX 3. INTERNATIONAL PROCESSES ON CRITERIA AND INDICATORS FOR SUSTAINABLE FOREST MANAGEMENT: INDICATORS RELATED TO GENETIC DIVERSITY [8]
Criterion 3 of the Dry forests - Asia Process is “Maintenance and Enhancement of Bio-diversity”. Indicator 3.6 Existence of mechanisms for the conservation of genetic resources Criterion 2 of the Dry-Zone Africa Process is “Conservation and Enhancement of Biological Diversity in Forest Ecosystems”. Indicators 2c are entitled “Genetic Indicators (fauna, flora)”. In fact, some of the proposed indicators measure diversity at the species level. For example, indicator 10 is “Number of forest dependent species with reduced range”, and indicator 11 is “Population levels of key species across their range”. Indicator 12 is a true genetic indicator, “Management of genetic resources”, Criterion 5 of the ITTO process (national and forest management unit level) is “Biological Diversity”. This Criterion relates to the conservation and maintenance of biological diversity, including ecosystem, species and genetic diversity. However, the genetic diversity indicators is 5.6: “Existence and implementation of a strategy for in situ and/or ex situ conservation of the genetic variation within commercial, endangered, rare and threatened species of forest flora and fauna” The Central America/Lepaterique Process is largely silent on genetic diversity, with national level indicators under Criterion 2, “Conservation and Maintenance of Environmental Services Provided by Forest Ecosystems” referring only to “Number of forest species conserved ex-situ (e.g. in seed banks)”, Forest Management Unit Level indicators under Criterion 4, “Contribution of Forest Ecosystems to Environmental Services” also propose the same indicator as the only measure of genetic diversity. Criterion 1 of the Montreal Process (national level) is “Conservation of biological diversity”. Two indicators of genetic diversity are proposed: “Number of forest dependent species that occupy a small portion of their former range”, and “Population levels of representative species from diverse habitats monitored across their range” Criterion 4 of the Pan-European (Helsinki) Process (national level) is “Maintenance, Conservation and Appropriate Enhancement of Biological Diversity in Forest Ecosystems”. The various “concept areas” under this criterion offer few details for genetic diversity. Concept Area “General Conditions” states that “Existence and capacity of an institutional framework to [inter alia] - maintain, conserve and appropriately enhance biological diversity at the ecosystem, species and genetic levels”. Concept Area “Biological Diversity in Production Forests” includes a quantitative indicator 4.3, “Changes in the proportions of stands managed for the conservation and utilisation of forest genetic resources (gene reserve forests, seed collection stands, etc.); differentiation between indigenous and introduced species” Criterion 4 of the Tarapoto Proposals (Amazon Cooperation Treaty countries), at the National Level, is “Conservation of Forest Cover and of Biological Diversity.
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[8]
One indicator within this criterion is “Measures for the conservation of genetic resources”. Criterion 2 of the Near East Process is “Conservation of Biological Diversity in Forest Areas”. Proposed genetic indicators are “Existence of the number of seed provenance”, “Number of forest-dependent species with reduced range”, and “Population levels of key species across their range”.
Source: FAO (2001b).
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ANNEX 4. GLOSSARY OF TERMS AFLP
“Amplified Fragment Length Polymorphism” (Vos et al. 1995) involves the use of “restriction enzymes” to create DNA fragments, which are then amplified using PCR (qv) techniques and visualized on polyacrylamide gels either through radiographic or fluorescence methodologies.
c-DNA
DNA synthesized from an RNA template (as happens during normal cell division)
d; Dj ; statistics Effective population (Ne)
theta Statistics used in the estimation of genetic distance (Gregorius 1984)
Size of an idealized population that would have the same genetic properties as size those observed for the real population.
FST; GST
Measures of population sub-division, expressing the reduction of heterozygosity within sub-populations, relative to expected herterozygosity in a population without sub-divisions (Nei 1978).
Heritability
The proportion of phenotypic variance attributable to genetic variance, or the extent to which genetic individual differences contribute to individual differences in observed behavior
Isozymes
Any form of an enzyme encoded by a single genetic locus
Microsatellites
Microsatellites, alternatively known as simple sequence repeats (SSRs), short tandem repeats (STRs) or simple sequence length polymorphisms (SSLPs), are tandem repeats of DNA units generally less than 5 base pairs in length (Bruford and Wayne, 1993). Variation results from differences in the number of repeat units. Microsatellites are amplified using PCR techniques and visualized on polyacrylamide gels either through radiographic or fluorescence methodologies
Ne
See “Effective population size”
PCR
“Polymerase Chain Reaction” - a method for generating unlimited copies of any fragment of DNA (Mullis, 1990)
QTL
“Quantitative Trait Locus” - a polymorphic locus which contains alleles that differentially affect the expression of a phenotypic trait
RAPD
“Randomly Amplified Polymorphic DNA” (Williams et al. 1990).
SSR
See “Microsatellites”
Theta statistics
See “d; Dj”
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