Assessing model adequacy leads to more robust phylogeographic inference

Phylogeographic studies base inferences on large data sets and complex demographic models, but these models are applied in ways that could mislead researchers and compromise their inference. Researchers face three challenges associated with the use of models: (i) ‘model selection’, or the identification of an appropriate model for analysis; (ii) ‘evaluation of analytical results’, or the interpretation of the biological significance of the resulting parameter estimates, delimitations, and topologies; and (iii) ‘model evaluation’, or the use of statistical approaches to assess the fit of the model to the data. The field collectively invests most of its energy in point (ii) without considering the other points; we argue that attention to points (i) and (iii) is essential to phylogeographic inference. Phylogeography makes inferences about the evolutionary history of species by using statistical models of historical demography to analyze genetic data. These models are increasingly complex and sometimes applied in ways that can compromise the quality of phylogeographic inference.Inferences are most often derived from estimates of evolutionary parameters made using these models. Parameter estimates are contextually dependent on the model used to estimate the parameters and are informative only if the model is a reasonable fit to the data.A variety of approaches can be used to assess model adequacy, from simple visual examinations to statistical goodness of fit tests. The increased power and interpretability of statistical approaches justify their increased complexity.A review of existing software packages demonstrates that, when tests for model adequacy are built into software packages by developers, users are more likely to conduct these analyses.