Biodiversity in Southern Africa

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Biodiversity is important for sustaining life on Earth yet it is threatened globally. The BIOTA Southern Africa project analysed the causes, trends, and processes of change in biodiversity in Namibia and western South Africa over nearly a full decade, from 2001 until 2010. This book, which is comprised of three volumes, offers a summary of the results 9 7 8 from 3 9 3 3the 1 91 many 77 84 35 98 3and 3 1 diverse 17458

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subprojects during this ﬁrst period of long-term observation and related research, at both local and regional scales, and with a focus on sustainable land management options for the region.

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2 Patterns and Processes at Regional Scale KLAUS HESS PUBLISHERS

ISBN-Namibia

Biodiversity in Southern Africa

Namibia ISBN Namibia ISBN

Biodiversity in Southern Africa Vol. 2

Patterns and Processes at Regional Scale

© University of Hamburg 2010 All rights reserved Klaus Hess Publishers www.k-hess-verlag.de

ISBN all volumes: 978-3-933117-44-1 (Germany), 978-99916-57-30-1 (Namibia) ISBN this volume: 978-3-933117-46-5 (Germany), 978-99916-57-32-5 (Namibia) Printed in Germany

Suggestion for citations: Volume: Schmiedel, U., Jürgens, N. (2010) (eds.): Biodiversity in southern Africa 2: Patterns and processes at regional scale. Göttingen & Windhoek: Klaus Hess Publishers. Article (example): Petersen, A., Gröngröft, A., Mills, A., Miehlich, G. (2010): Soils along the BIOTA transect. – In: Schmiedel, U., Jürgens, N. (eds.): Biodiversity in southern Africa 2: Patterns and processes at regional scale: 84–92. Göttingen & Windhoek: Klaus Hess Publishers. Corrections brought to our attention will be published at the following location: http://www.biota-africa.org/biotabook/

Cover photograph: Giraffes on the game farm Omatako Ranch (Observatory S04 Toggekry) in the Namibian Thornbush Savanna. Photo: Jürgen Deckert, Berlin/Germany. Cover Design: Ria Henning

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Article III.7.6 – Author’s copy – Please cite this article as follows: Blaum, N., Lohmann, D., Rossmanith, E., Schütze, S., Schwager, M., Steinhäuser, J., Tews, J., Wichmann, M., Jeltsch, F. (2010): Model-based simulation tools. – In: Schmiedel, U., Jürgens, N. [Eds.]: Biodiversity in southern Africa. Volume 2: Patterns and processes at regional scale: pp. 302–306, Klaus Hess Publishers, Göttingen & Windhoek.

Model-based simulation tools

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NIELS BLAUM*, DIRK LOHMANN, EVA ROSSMANITH, SHARON SCHÜTZE, MONIKA SCHWAGER, JUTTA STEINHÄUSER, JOERG TEWS, MATTHIAS WICHMANN & FLORIAN JELTSCH

Summary: This chapter will give a short introduction on how process-based simulation models can be further developed into management and educational tools and will present three BIOTA modelling tools, which address different key problems of landuse management and climate change impacts in African savannas: (1) the Devil’s Claw Simulation tool (DCSim), which simulates the population dynamics of a medicinal plant endemic to southern Africa, (2) the Ecological-Economic Savannah Rangeland Management tool (EESRaM), which simulates a livestock farm in a thornbush savanna in Namibia, and (3) the BIOTA Kalahari Biodiversity Simulator (KBioSim), which simulates population dynamics of four species indicative of a particular spatial scale and depend on large trees as vegetation structures for e.g. nesting and sheltering. The three modelling tools address different key problems of landuse management and climate change impacts in African savannas to communicate and visualise problem oriented research on ecosystem and population dynamics under e.g. different landuse and climate change scenarios.

Introduction Process-based simulation models are ideal tools to communicate and visualise problem oriented research on ecosystem and population dynamics under e.g. different landuse and climate change scenarios. A further didactic development of such simulation models would offer an excellent opportunity to develop educational and management tools that are equipped with a user-friendly interface. The current progress of technical opportunities enables the implementation of interactive computer tools that simulate complex ecosystem and population dynamics “on-demand”. The user will quickly understand animations or graphs showing the impacts of e.g. different management options on savanna vegetation dynamics. The advantages of such tools are that the user can systematically explore and change management parameters (e.g. stocking rates, harvesting cycles, etc.) at different time scales and evaluate the impacts of his or her own decisions immediately. This playful exploration allows for a quick understanding of ecosystem 302

dynamics under different landuse and climate change scenarios. Hence, such tools can aid communication of findings, ecosystem understanding, have the potential to support a decision making process, and enhance transdisciplinary communication. In particular, rule- and individualbased models (IBMs) developed within the BIOTA research framework, have also been developed as educational and management tools (e.g. the Kalahari Biodiversity simulator). Compared to other modelling approaches (algebraic or statistical models), the basic rules, model structure and output of rule-based IBMs are easy to understand and also comprehensible for non-expert target groups and stakeholders such as rangeland managers, farmers or politicians. Thus, this modelling approach allows direct communication with stakeholders and more importantly the inclusion of feedback from stakeholders for model tool improvement. In this chapter we will present three BIOTA modelling tools to address different key problems of landuse management and climate change impacts in African savannas. B IODIVERSITY

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First we will present the Devil’s Claw (Harpagophytum procumbens) Simulation tool (DCSim), which simulates the population dynamics of a medicinal plant endemic to southern Africa. This model allows impact testing of different harvesting strategies (under different grazing impacts) on population dynamics and yield of storage tubers, which are used for medicinal purposes (see Article III.7.2). The second tool, the EcologicalEconomic Savannah Rangeland Management tool (EESRaM), simulates a livestock farm in a thornbush savanna in Namibia and it is based on an agentbased, ecological-economic simulation model (see Chapter IV.2). Here, different management strategies can be tested while the user can virtually buy and sell animals exploring the economic impacts as well as the ecological state of the savanna vegetation. Third we will show the BIOTA Kalahari Biodiversity Simulator (KBioSim) (Blaum et al. 2008). This educational and management tool simulates population dynamics of four species indicative of a particular spatial scale and depend on large trees as vegetation structures for e.g. nesting and sheltering. Spatialtemporal changes in population size can be tested under various combinations of landuse (livestock production and wood cutting) and climate change scenarios.

The Devil’s Claw simulation tool (DCSim) The aim of DCSim is to identify sustainable harvesting strategies for the storage tubers of this medicinal plant, which are often an important additional income for the poorest people in communal areas (Strohbach & Cole 2007). DCSim is based on an individual- and rule-based simulation model developed within the BIOTA framework (Schütze 2009). DCSim is the

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first simulation tool, where the impact of different harvesting strategies and grazing intensities on population structure, dynamics and crop yield of this endemic plant can be systematically explored. The structure of the user-friendly computer program surface is similar to well designed webpages. The navigation bar includes background information on the Devil’s Claw, harvesting methods and strategies, while the user can choose between different harvesting methods and harvesting cycles in the simulation section (Fig. 1). In the simulation section, the user can develop his or her own harvesting strategy by choosing between three methods (from careful removal of some secondary tubers to harsh removal of all secondary tubers) and harvesting frequency (e.g. each year, every 5 years, etc.). After selecting a simulation time, the user can explore the impact of the chosen harvesting strategy on crop yield and population dynamics. Initial results will show changes of the spatial dynamics of juveniles, reproductive and non-reproductive individuals for a rangeland area of 20 ha. Further, time series of population size and storage tuber yield for one exemplary annual rainfall time series will be displayed. Averaged trends from repeated simulations for a specific harvesting strategy can also be explored. The navigation bar also provides a direct link to compare the impacts of all harvesting options (Figs. 2 & 3). This option allows the identification of the most sustainable harvesting strategy (i.e. a strategy with the highest yield and the least impact on the Devil’s Claw population size for a selected time horizon).

Fig. 1: User surface and structure of the Devil’s Claw Simulator tool (DCSim).

Fig. 2: Screen shot of DCSim showing the options for harvesting strategies.

The Ecological-Economic Savannah Rangeland management tool (EESRaM) EESRaM enables users (such as rangeland managers, farmers, etc.) to systematically explore the impacts of different rangeland management strategies (regarding temporal dynamics of number and type of cattle—i.e. in what situation to buy or sell a certain number of E XPANDING

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Fig. 3: Screen shot of the impacts of different harvesting methods with a harvesting frequency of three years on population size and yield.

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Fig. 5: Screen shot of EESRaMs, showing the simulated time series of rainfall, account balance, animal numbers, profit, animal conditions and vegetation cover.

livestock) on vegetation dynamics and economic costs and benefits for a single livestock farm in a semi-arid savanna. The tool is based on two dynamically linked simulation models, parameterised for the Omaheke region in eastern 304

Namibia with a mean annual precipitation of ~400 mm and a high inter-annual variation, including: 1) an ecological model, which simulates the dynamics of perennial and annual grasses, woody vegetation, and the corB IODIVERSITY

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responding biomass production (based on Tietjen et al. 2009), and 2) an economic farm model that simulates cattle production including herd dynamics, productivity, costs and income of a cattle farm (see Subchapter IV.2.6). At the start screen, the user can decide relevant farm management information such as the size of the farm, number of workers, number of animals to start the business, water infrastructure (number and type of water pumps) and number of simulation years. After entering database information, users can access the main tool interface (Fig. 4), including information on: (1) finances, (running costs, bank account balance), (2) livestock (number, type, age and condition of the animals), (3) mortality in the last season, (4) vegetation condition (shrub and perennial grass cover), and (5) current season rainfall totals . Based on this information, the user can decide upon the number and type of livestock (cow, oxen, male/female weaners, heifers) bought and sold season by season. In order to visualise current veld condition, pictures representing the given vegetation state will appear (note: representative pictures of the current veld and animal condition can be displayed on demand at any time). At the end of the simulation, a summary containing time series of animal numbers, account balance, animal condition, cover of perennial grasses and shrubs, and seasonal rainfall will appear (Fig. 5). This enables the user to evaluate the impacts of different management decisions on veld and animal conditions, economic performance, and to identify possible relationships between the given factors. For example, one can test for correlation between stocking rate, rainfall and vegetation state or costs, by simulating either stable herd sizes over time or adapt them to the precipitation. Note: Simulation results can only be interpreted as trends, and not as quantitative measures of landuse outcomes. Nevertheless, the real-time simulations reflect state of the art knowledge in the area and are useful for communicating systems’ dynamics, trade-offs and correlations. Recently, a similar version of the tool was used to conduct surveys with



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resettlement farmers in the Omaheke region. Hereby, discussion was stimulated and understanding was improved for both sides—researchers and farmers. This also enabled the inclusion of feedback from stakeholders for current tools’ improvement. In this sense, simulation tools have proven to encourage transdisciplinary communication for a better development between research and application.

The Kalahari Biodiversity Simulator (KBioSim) KBioSim is a non-profit educational and management tool that demonstrates and explores the complex responses of four indicative species on possible climate and landuse changes in the southern Kalahari rangelands. The four representative species (Tree Rat Thallomys nigricauda, Sociable Weaver Philetairus socius, Raisin Bush Grewia flawa and Tawny Eagle Aquila rapax) were selected based on their specific dependence on woody vegetation structures, influence of landuse activities—i.e. trees (wood harvesting) and shrubs (overgrazing)—and differences in their spatial scale of dispersal, home range and migration (Fig. 6). The main objective of KBioSim is to improve the understanding of ecological mechanisms and processes on different spatial scales that have to be considered when trying to predict the consequences of management decisions or environmental changes on the sensitivity of species. The tool dynamically links a savanna vegetation model (Jeltsch et al. 1997) to five dynamic single species population models Camelthorn Acacia erioloba (Jeltsch et al. 1999), Tree Rat Thallomys nigricauda (Steinhäuser 2004), Sociable Weaver Philetairus socius (Schwager et al. 2008), Raisin Bush Grewia flawa (Tews et al. 2004) and Tawny Eagle Aquila rapax (Wichmann 2002). At the start screen of KBioSim the user will receive brief guidelines (Fig. 7). First, the user can choose between exploring the impacts of landuse (high versus low wood cutting rate, and high versus moderade livestock grazing) and climate change scenarios (decrease in mean annual rainfall or increase in annual rainfall E XPANDING

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Fig. 6: Focal species of KbioSim depending on large Acacia-trees as vegetation structure: Tree Rat (Thallomys nigricauda), Sociable Weaver (Philetairus socius), Raisin Bush (Grewia flawa) and Tawny Eagle (Aquila rapax).

Fig. 7: User surface of Kalahari Biodiversity Simulator (KBioSim) showing user’s manual and landuse and climate scenarios options for two rainfall areas (Twee Rivieren and Kimberley, SA).

variability) on population dynamics of the focal species. Second, the impacts of the selected scenario can be explored for two rainfall regimes (high 350 mm versus low 175 mm annual rainfall). A simulation time can then be selected. After the real time simulation, basic information of the two focal regions is

displayed and the user can explore the impacts of the selected scenario on the spatial (animated) and temporal dynamics of the focal species for one time series (Fig. 8). In the final screen, a comparison across the focal species enables the user to evaluate the impacts of the selected scenarios (Fig. 9). 305

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Acknowledgements The authors’ general acknowledgements to the organisations and institutions, which supported this work are provided in Volume 1.

Fig. 8: Screen shot of the spatial dynamics of tawny eagle breeding pairs for a landuse scenario of ‘annual wood cutting rate 5% in Twee Rivireren’.

Fig. 9: Screen shot of the temporal dynamics of all indicative species for a landuse scenario of ‘annual wood cutting rate 5% in Twee Rivireren’.

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References Blaum, N., Schwager, M., Rossmanith, E., Tews, J., Wichmann, M.C., Jeltsch, F. (2008): Understanding mechanisms and processes of possible climate and landuse changes on species diversity: The BIOTA Kalahari Biodiversity Simulator KBioSim© – an educational and management tool. – Poster presentation at the congress “Biodiversity of Africa Observation and Sustainable Management for our Future”, Spier, 29 Sep to 3 Oct 2008. Jeltsch, F., Milton, S.J., Dean, W.R.J., Rooyen, N. van (1997): Analysing shrub encroachment in the southern Kalahari: a grid-based modelling approach. – Journal of Applied Ecology 34: 1497–1508. Jeltsch, F., Moloney, K., Milton, S.J. (1999): Detecting process from snapshot pattern: lessons from tree spacing in the southern Kalahari. – Oikos 85: 451–466. Schwager, M., Covas, R., Blaum, N., Jeltsch, F. (2008): Limitations of population models in predicting climate change effects: a simulation study of sociable weavers in southern Africa. – Oikos 117: 1417–1427. Steinhäuser, J. (2004): Strukturelle Diversität und Populationsdynamik: Modellierung und Freilanduntersuchung zur Baumratte Thallomy nigricauda in der südlichen Kalahari. – Diploma thesis in Ecology. Potsdam: University of Potsdam. Strohbach, M., Cole, D. (2007): Population dynamics and sustainable harvesting of the medicinal plant Harpagophytum procumbens DC. (Devil’s Claw) in Namibia – Results of the R+D Project 800 86 005. – BfN-Skripten 203. Bonn: BfN, Federal Agency for Nature Conservation. Schütze, S.J. (2009): Towards a sustainable use of the Devil’s Claw: results from a simulation model. – Diploma thesis in Geoecology. Potsdam: University of Potsdam Tews, J., Schurr, F., Jeltsch, F. (2004): Seed dispersal by cattle may cause shrub encroachment of Grewia flava on southern Kalahari rangelands. – Applied Vegetation Science 7: 89–102. Tietjen, B., Zehe, E., Classen, N., Groengroeft, A., Schiffers, K., Oldeland, J., Jeltsch, F. (2009): Effects of climate change on the coupled dynamics of water and vegetation in drylands. – Ecohydrology. DOI: 10.1002/eco.70. Wichmann, M.C., Dean, W.R.J., Jeltsch, F. (2004): Global change challenges the Tawny Eagle (Aquila rapax): modelling extinction risk with respect to predicted climate and land use changes. – Ostrich 75: 204–210.

