Abstract

While individual animal tracking data provides high resolution information about animal behavior, the most important management and ecological questions are at the population level. As individual tracking data are typically not representative of the entire population of interest, we propose methods of formally linking individual telemetry data with population level species distribution data, such as eBird relative abundance estimates. While traditional approaches for scaling from individual models to population dynamics rely on differential equation scaling, we propose a novel general framework based on probabilistic modeling of population variation, and show the major benefits of this approach. We consider two migratory bird species: golden eagles and mallard ducks, and show multiple approaches for modeling individual movement, including using stochastic differential equations (SDEs) and using nonparametric clustering approaches, and show computational approaches for fitting these movement models jointly to both individual and species distribution data. We show how these analyses can help guide management of migratory species through improved understanding of the risk that different hazards pose to different subpopulations of a migratory species.