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

July 31, 2017

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.

Citation Information

Publication Year 2017
Title Model selection for the North American Breeding Bird Survey: A comparison of methods
DOI 10.1650/CONDOR-17-1.1
Authors William A. Link, John R. Sauer, Daniel Niven
Publication Type Article
Publication Subtype Journal Article
Series Title Condor
Series Number
Index ID 70189975
Record Source USGS Publications Warehouse
USGS Organization Patuxent Wildlife Research Center

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