2014
DOI: 10.1111/mec.12794
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All models are wrong

Abstract: As the field of phylogeography has continued to move in the model-based direction, researchers continue struggling to construct useful models for inference. These models must be both simple enough to be tractable yet contain enough of the complexity of the natural world to make meaningful inference. Beyond constructing such models for inference, researchers explore model space and test competing models with the data on hand, with the goal of improving the understanding of the natural world and the processes un… Show more

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Cited by 10 publications
(10 citation statements)
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“…Approximate Bayesian computation (ABC; Beaumont, Zhang, & Balding, ) has been broadly applied in phylogeography (Bertorelle, Benazzo, & Mona, ; Hickerson, ), making it a suitable exemplar here. ABC was used to explore the circumstances under which a population with a history of lineage fusion carries signatures that are quantitatively different from long‐term panmixia within a single isolated population (Appendix S1).…”
Section: Approachmentioning
confidence: 99%
“…Approximate Bayesian computation (ABC; Beaumont, Zhang, & Balding, ) has been broadly applied in phylogeography (Bertorelle, Benazzo, & Mona, ; Hickerson, ), making it a suitable exemplar here. ABC was used to explore the circumstances under which a population with a history of lineage fusion carries signatures that are quantitatively different from long‐term panmixia within a single isolated population (Appendix S1).…”
Section: Approachmentioning
confidence: 99%
“…It should, however, be straightforward to extend our approach for dealing with other more complex scenarios. Indeed, the flexibility of ABC allows the relative classification of quite specific models of evolution and demography, but also allows comparisons between previously tested models with new and more complex ones (Hickerson ). Of course, the results of any model comparison need to be interpreted with caution, given that they are ultimately informative only about the models actually considered and require extensive validation using pseudo‐observed data sets (Marin et al .…”
Section: Discussionmentioning
confidence: 99%
“…Although this is not the same as traditional hypothesis testing in the sense that there is not a null hypothesis to reject or fail to reject, we demonstrate that model‐based approaches are useful for choosing among a priori hypotheses to make meaningful inferences. We recognize that there are shortcomings to this strategy, most important of which is that we may not have considered a model that could be better supported by the data (Hickerson ). We chose to evaluate a set of models designed to test specific hypotheses because it is more tractable than trying to compare all possible models and provides more useful insights than comparing several models that do not have a meaningful biological interpretation.…”
Section: Discussionmentioning
confidence: 99%