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A guide to ecosystem models and their environmental applications

期刊

NATURE ECOLOGY & EVOLUTION
卷 4, 期 11, 页码 1459-1471

出版社

NATURE PORTFOLIO
DOI: 10.1038/s41559-020-01298-8

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资金

  1. Department of Environment, Land, Water and Planning Victoria
  2. Parks Victoria
  3. Alfred Deakin Post-doctoral Research Fellowship
  4. Australian Research Council Discovery Early Career Researcher Award

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Applied ecology has traditionally approached management problems through a simplified, single-species lens. Repeated failures of single-species management have led us to a new paradigm - managing at the ecosystem level. Ecosystem management involves a complex array of interacting organisms, processes and scientific disciplines. Accounting for interactions, feedback loops and dependencies between ecosystem components is therefore fundamental to understanding and managing ecosystems. We provide an overview of the main types of ecosystem models and their uses, and discuss challenges related to modelling complex ecological systems. Existing modelling approaches typically attempt to do one or more of the following: describe and disentangle ecosystem components and interactions; make predictions about future ecosystem states; and inform decision making by comparing alternative strategies and identifying important uncertainties. Modelling ecosystems is challenging, particularly when balancing the desire to represent many components of an ecosystem with the limitations of available data and the modelling objective. Explicitly considering different forms of uncertainty is therefore a primary concern. We provide some recommended strategies (such as ensemble ecosystem models and multi-model approaches) to aid the explicit consideration of uncertainty while also meeting the challenges of modelling ecosystems. Ecological management strategies - from conservation to fisheries - require ecosystem-level thinking. This Review describes the main types of ecosystem model, how to select an appropriate model for a given application, and how to manage complexity and uncertainty.

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