Model selection for the North American Breeding Bird Survey: A comparison of methods

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The North American Breeding Bird Survey (BBS) provides data for >420 bird species at multiple geographic scales over 5 decades. Modern computational methods have facilitated the fitting of complex hierarchical models to these data. It is easy to propose and fit new models, but little attention has been given to model selection. Here, we discuss and illustrate model selection using leave-one-out cross validation, and the Bayesian Predictive Information Criterion (BPIC). Cross-validation is enormously computationally intensive; we thus evaluate the performance of the Watanabe-Akaike Information Criterion (WAIC) as a computationally efficient approximation to the BPIC. Our evaluation is based on analyses of 4 models as applied to 20 species covered by the BBS. Model selection based on BPIC provided no strong evidence of one model being consistently superior to the others; for 14/20 species, none of the models emerged as superior. For the remaining 6 species, a first-difference model of population trajectory was always among the best fitting. Our results show that WAIC is not reliable as a surrogate for BPIC. Development of appropriate model sets and their evaluation using BPIC is an important innovation for the analysis of BBS data.

Publication type Article
Publication Subtype Journal Article
Title Model selection for the North American Breeding Bird Survey: A comparison of methods
Series title Condor
DOI 10.1650/CONDOR-17-1.1
Volume 119
Issue 3
Year Published 2017
Language English
Publisher American Ornithological Society
Contributing office(s) Patuxent Wildlife Research Center
Description 11 p.
First page 546
Last page 556
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