Journal
ANNUAL REVIEW OF ECOLOGY, EVOLUTION, AND SYSTEMATICS, VOL 49
Volume 49, Issue -, Pages 95-114Publisher
ANNUAL REVIEWS
DOI: 10.1146/annurev-ecolsys-110617-062249
Keywords
model fit; phylogenetics; comparative methods; posterior prediction; parametric bootstrap; cross-validation
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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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