Predicting the ecological consequences of environmental change: a review of the methods.

Published online
11 Oct 2006
Content type
Journal article
Journal title
Journal of Applied Ecology
DOI
10.1111/j.1365-2664.2006.01182.x

Author(s)
Sutherland, W. J.
Contact email(s)
w.sutherland@uea.ac.uk

Publication language
English

Abstract

There is a clear need to increase our ability to predict the consequences of environmental change. The seven main approaches that are currently used are: extrapolation, experiments, phenomenological models, game-theory population models, expert opinion, outcome-driven modelling and scenarios. Each approach has different strengths and weaknesses. In practice, several approaches are often combined. Adaptive management aimed at testing hypotheses is excellent in principle and widely advocated. In reality, however, it is almost never carried out because the changes in management usually have to be severe in order to bring about detectable changes in a reasonable time, and the political risks of such management are usually considered too high. Game-theory population models are used to determine population-level phenomena based upon the decisions individuals make in response to resource depletion, interference, territoriality or rank. This allows predictions to be made regarding responses to novel conditions. The main drawback is that for some models considerable information is required. Much of conservation practice is not based upon evidence. Evidence-based conservation is the practice of accumulating, reviewing and disseminating evidence with the aim of formulating appropriate management strategies. Evidence-based medicine revolutionized medical practice and similar opportunities exist to improve conservation practice. Synthesis and applications. The conventional approach of making assumptions and deriving models to make predictions about the consequences of environmental change is often unsatisfactory for complex problems, with considerable uncertainty. Tackling such problems is likely to require greater exploration of techniques such as expert opinion, output-driven modelling and scenarios.

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