Stillman Richard A, Railsback Steven F, Giske Jarl, Berger Uta, Grimm Volker
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
Bioscience. 2015 Feb 1;65(2):140-150. doi: 10.1093/biosci/biu192. Epub 2014 Dec 12.
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.
生态学家迫切需要具备更强的能力,以预测环境变化如何影响生物多样性。我们研究基于个体的生态学(IBE),这是一种研究范式,它通过使用基于个体的模型(IBM)来将生态动态表示为个体如何与环境以及彼此相互作用而产生的,有望具备更强的预测能力。IBM的一个关键优势在于,预测的基础——个体生物的适应性最大化——比其他模型所依赖的经验关系更为普遍和可靠。案例研究说明了长期IBE项目的实用性和预测成功性。开创性项目有三个阶段:概念化、实施和多样化。在这些阶段中持续进行模型验证。使IBE更具成效的突破包括描述和验证IBM的标准、关于个体特征和行为的改进和标准化理论、软件工具,以及通用而非特定于系统的IBM。我们提供了开展IBE的指导方针以及对未来IBE研究的展望。