Model selection in ecology and evolution

Abstract

Recently, researchers in several areas of ecology and evolution have begun to change the way in which they analyze data and make biological inferences. Rather than the traditional null hypothesis testing approach, they have adopted an approach called model selection, in which several competing hypotheses are simultaneously confronted with data. Model selection can be used to identify a single best model, thus lending support to one particular hypothesis, or it can be used to make inferences based on weighted support from a complete set of competing models. Model selection is widely accepted and well developed in certain fields, most notably in molecular systematics and markrecapture analysis. However, it is now gaining support in several other areas, from molecular evolution to landscape ecology. Here, we outline the steps of model selection and highlight several ways that it is now being implemented. By adopting this approach, researchers in ecology and evolution will find a valuable alternative to traditional null hypothesis testing, especially when more than one hypothesis is plausible.Science is a process for learning about nature in which competing ideas about how the world works are evaluated against observations [1]. These ideas are usually expressed first as verbal hypotheses, and then as mathematical equations, or models. Models depict biological processes in simplified and general ways that provide insight into factors that are responsible for observed patterns. Hence, the degree to which observed data support a model also reflects the relative support for the associated hypothesis.Two basic approaches have been used to draw biological inferences. The dominant paradigm is to generate a null hypothesis (typically one with little biological meaning [2]) and ask whether the hypothesis can be rejected in light of observed data. Rejection occurs when a test statistic generated from observed data falls beyond an arbitrary probability threshold (usually P , 0.05), which is interpreted as tacit support for a biologically more meaningful alternative hypothesis. Hence, the actual hypothesis of interest (the alternative hypothesis) is accepted only in the sense that the null hypothesis is rejected. By contrast, model selection offers a way to draw inferences from a set of multiple competing hypotheses. Model selection is grounded in likelihood theory, a robust framework that supports most modern statistical approaches. Moreover, this approach is rapidly gaining support across several fields in ecology and evolution as a preferred alternative to null hypothesis testing [1,3,4]. Advocates of model selection argue that it has three primary advantages. First, practitioners are not restricted to evaluating a single model where significance is measured against some arbitrary probability threshold. Instead, competing models are compared to one another by evaluating the relative support in the observed data for each model. Second, models can be ranked and weighted, thereby providing a quantitative measure of relative sup...

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