Integration of UAS-based spatial surveys and bio-logging tracking enhances precision in population size estimation

Accurately estimating wildlife population sizes, essential for ecological theory and conservation management, yet remains challenging. Although unmanned aerial systems (UASs) combined with machine learning, have revolutionized population estimation, they face limitations in addressing the hierarchical population processes from individual behavior to colony-and population-level dynamics. To overcome this limitation, we developed a data integration framework that jointly analyzes multiple datasets, representing different scales of the same underlying process, were jointly analyzed. Using a seabird colony as a model system, we integrated UAS-based count data in the colony with bio-logging-based tracking data to estimate population size by quantifying both the number of individuals present and the proportion absent from the surveyed area. These complementary datasets were linked using state-space models allowing accurate population estimates with explicit uncertainty quantification. Furthermore, we evaluated the robustness of the estimations with respect to sample size. Sub-sampling simulations revealed that estimation uncertainty was more sensitive to sample size in bio-logging-based tracking data than in UAS-based count data. This finding highlights the importance of understanding dataset-specific properties when designing effective investigations. Overall, our resource-efficient framework is broadly applicable across species and populations and demonstrates how integrating complementary observation methods can improve population estimates and inform conservation practice.