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Making Predictions in a Changing World: The Benefits of Individual-Based Ecology
Richard A Stillman1, Steven F Railsback1, Jarl Giske1
1Richard A. Stillman is a professor in the Department of Life and Environmental Sciences at Bournemouth University, in Dorset, UK. Steven F. Railsback is an environmental scientist with Lang, Railsback, and Associates and an adjunct professor in the Department of Mathematics at Humboldt State University, in Arcata, California. Jarl Giske is a professor in the Department of Biology at the University of Bergen and at the Hjort Centre for Marine Ecosystem Dynamics, in Bergen, Norway. Uta Berger is a professor at the Institute of Forest Growth and Forest Computer Sciences at the Dresden University of Technology, in Tharandt, Germany. Volker Grimm is a researcher in the Department of Ecological Modelling at the Helmholtz Centre for Environmental Research, in Leipzig, Germany; is a professor at the Institute for Biochemistry and Biology at the University of Potsdam, Germany; and is a member of the German Centre for Integrative Biodiversity Research Halle-Jena-Leipzig, in Germany rstillman@bournemouth.ac.uk .
Abstract:
Ecologists urgently need a better ability to predict how environmental change affects biodiversity. We examine individual-based ecology (IBE), a research paradigm that promises better a predictive ability by using individual-based models (IBMs) to represent ecological dynamics as arising from how individuals interact with their environment and with each other. A key advantage of IBMs is that the basis for predictions-fitness maximization by individual organisms-is more general and reliable than the empirical relationships that other models depend on. Case studies illustrate the usefulness and predictive success of long-term IBE programs. The pioneering programs had three phases: conceptualization, implementation, and diversification. Continued validation of models runs throughout these phases. The breakthroughs that make IBE more productive include standards for describing and validating IBMs, improved and standardized theory for individual traits and behavior, software tools, and generalized instead of system-specific IBMs. We provide guidelines for pursuing IBE and a vision for future IBE research.
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