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.

中文翻译：

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使用视频的人工智能分类检查配置行为的环境，进化和生理限制

人工智能（AI）技术的使用可以彻底改变我们在行为生态学中进行数据收集和分析的方式。这样的例子之一就是供应行为。选择了其他种类的父母为其后代提供父母照料（例如食物供应），但是这种照料水平存在很大差异。配给率可以通过环境，父母的生理能力和雏鸟的需求或进化动机来确定。我们量化了20个紫色马丁Progne subis中的配置率在实验性减少巢外寄生虫的背景下筑巢。10巢进行了寄生虫减少处理，而10巢为对照。通过使用AI对嵌套摄像机视频进行自动分析，我们能够在嵌套期间的前半部分以高时间分辨率获得近乎连续的预配率信息。我们使用随机森林模型来评估决定调配率的因素，并找到环境，进化和生理限制以及调动激励的证据。在凉爽，潮湿的条件下，尤其是在繁殖季节后期，家禽的饲养似乎受到了环境的限制；但要根据雏鸟不断变化的生理需求调整其配置。我们发现了对寄生虫负荷增加做出补偿性反应的证据，