Informative priors can account for location uncertainty in stop-level analyses of the North American Breeding Bird Survey (BBS), allowing fine-scale ecological analyses

Abstract/Summary

Ecologists can learn a lot about species by studying the precise locations in which they do (and do not) occur, but the location information associated with many species records is imprecise. A prominent example of this is the North American Breeding Bird Survey (BBS), in which volunteer observers have surveyed birds at points along consistent routes across the United States for over fifty-five years. As the BBS was designed for large-scale analyses, detailed location information for each bird count is not recorded. We estimate location uncertainty, and the resulting uncertainty in land cover covariates, for the BBS data and present a modeling method that accounts for this uncertainty in a way that opens new possibilities for fine-scale uses of this extensive dataset, unlocking its potential to advance the study of the relationships between birds and their immediate habitat. More broadly, our methods and modeling framework could be used in a variety of situations in which covariate or location uncertainty is a challenge.