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Data from: Using artificial intelligence classification of videos to examine the environmental, evolutionary and physiological constraints on provisioning behavior

Citation

Williams, Heather M.; DeLeon, Robert L. (2020), Data from: Using artificial intelligence classification of videos to examine the environmental, evolutionary and physiological constraints on provisioning behavior, Dryad, Dataset, https://doi.org/10.5061/dryad.sqv9s4n1v

Abstract

The use of artificial intelligence (AI) technologies can revolutionize how we approach data collection and analysis in behavioral ecology. One such example is in provisioning behavior. Parents of altricial species are selected to provide parental care (such as food provisioning) for their offspring, but there is substantial variation in the level of this care. Provisioning rate may be determined environmentally, by the physiological ability of parents and needs of nestlings, or by evolutionary incentives.  We quantified provisioning rate in 20 purple martin (Progne subis) nests in the context of an experimental reduction of nest ectoparasites. 10 nests had a parasite reduction treatment, and 10 nests were controls. By using AI to automate the analysis of nest camera videos we were able to obtain nearly-continuous provisioning rate information at a high temporal resolution for the first half of the nestling period. We used random forest modeling to assess the factors determining provisioning rate and found evidence for environmental, evolutionary and physiological constraints and incentives on provisioning. Birds appeared to be environmentally limited in their provisioning in cool, wet conditions, especially later in the breeding season; but adjusted their provisioning according to the changing physiological needs of nestlings. We found evidence for a compensatory response to increased parasite load, in which parents increased provisioning to more heavily parasitized nests.