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Evaluating Model Performance in Evolutionary Biology

Authors
  • Brown, Jeremy M.
  • Thomson, Robert C.
Type
Published Article
Journal
Annual Review of Ecology, Evolution, and Systematics
Publisher
Annual Reviews
Publication Date
Nov 02, 2018
Volume
49
Pages
95–114
Identifiers
DOI: 10.1146/annurev-ecolsys-110617-062249
Source
Annual Reviews
Keywords
License
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Abstract

Many fields of evolutionary biology now depend on stochastic mathematical models. These models are valuable for their ability to formalize predictions in the face of uncertainty and provide a quantitative framework for testing hypotheses. However, no mathematical model will fully capture biological complexity. Instead, these models attempt to capture the important features of biological systems using relatively simple mathematical principles. These simplifications can allow us to focus on differences that are meaningful, while ignoring those that are not. However, simplification also requires assumptions, and to the extent that these are wrong, so is our ability to predict or compare. Here, we discuss approaches for evaluating the performance of evolutionary models in light of their assumptions by comparing them against reality. We highlight general approaches, how they are applied, and remaining opportunities. Absolute tests of fit, even when not explicitly framed as such, are fundamental to progress in understanding evolution.

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