Integrating individual tracking data and spatial surveys to improve estimation of animal spatial distribution

Tracking data and spatial surveys (e.g., counts) contribute to understanding animal distribution despite highlighting complementary aspects of habitat selection, from detailed insights on few individuals to raw inferences for the population, respectively. Here, we showcased how to combine individual tracking and count data to estimate habitat selection at the population level. We developed an integrated model that provides a joint estimation of habitat selection for tracking data fitted with a resource selection function (RSF) and count data fitted with a Poisson generalized linear model (GLM), both respecting the statistical conditions for converging with an inhomogeneous Poisson point process. We tested our integrated habitat selection model using simulated movement data and a real case study of GPS-tracked Sandwich terns (Thalasseus sandvicensis) in the French Mediterranean Sea. Simulations showed that the integrated model correctly estimated habitat selection coefficients and benefited from both data sources with better accuracy and precision than RSF and Poisson GLM alone, especially when data are limited. Overall, our study formalized an easy-to-use approach for the integration of tracking and count data to estimate habitat selection, contributing to a promising research avenue, since individual tracking and spatial survey monitoring are abundant in many ecological contexts.