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      Making Predictions in a Changing World: The Benefits of Individual-Based Ecology

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          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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          Most cited references 56

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          Individual-based Modeling and Ecology

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            Do simple models lead to generality in ecology?

            Modellers of biological, ecological, and environmental systems cannot take for granted the maxim 'simple means general means good'. We argue here that viewing simple models as the main way to achieve generality may be an obstacle to the progress of ecological research. We show how complex models can be both desirable and general, and how simple and complex models can be linked together to produce broad-scale and predictive understanding of biological systems. Copyright © 2013 Elsevier Ltd. All rights reserved.
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              Modelling ecological systems in a changing world.

               Peter Evans (2012)
              The world is changing at an unprecedented rate. In such a situation, we need to understand the nature of the change and to make predictions about the way in which it might affect systems of interest; often we may also wish to understand what might be done to mitigate the predicted effects. In ecology, we usually make such predictions (or forecasts) by making use of mathematical models that describe the system and projecting them into the future, under changed conditions. Approaches emphasizing the desirability of simple models with analytical tractability and those that use assumed causal relationships derived statistically from data currently dominate ecological modelling. Although such models are excellent at describing the way in which a system has behaved, they are poor at predicting its future state, especially in novel conditions. In order to address questions about the impact of environmental change, and to understand what, if any, action might be taken to ameliorate it, ecologists need to develop the ability to project models into novel, future conditions. This will require the development of models based on understanding the processes that result in a system behaving the way it does, rather than relying on a description of the system, as a whole, remaining valid indefinitely.
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                Author and article information

                Journal
                Bioscience
                Bioscience
                bioscience
                bioscience
                Bioscience
                Oxford University Press
                0006-3568
                1525-3244
                12 December 2014
                01 February 2015
                12 December 2014
                : 65
                : 2
                : 140-150
                Affiliations
                Richard 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@ 123456bournemouth.ac.uk .
                Article
                10.1093/biosci/biu192
                4778170
                26955076
                © The Author(s) 2014. Published by Oxford University Press on behalf of the American Institute of Biological Sciences.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com .

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                Pages: 11
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                February 2015

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