A Transferable Individual‐Based Framework for Quantifying Demographic Structure and Spatial Organization in Colonial Species Using UAV Imagery

ABSTRACT

Remote sensing of colonial wildlife has largely focused on estimating population size, providing limited insight into internal demographic structure and spatial organization. We present a transferable, individual‐based framework that integrates morphology‐informed classification and spatial metrics to extract demographic composition and social spacing from high‐resolution unmanned aerial vehicle (UAV) imagery. The workflow combines gray‐value discrimination, geometry‐based posture identification, aggregation area estimation, and centroid‐based distance analysis within a unified object‐level pipeline. Applied to a large Antarctic seabird colony, the framework quantified age structure, behavioral states, aggregation patterns, and inter‐individual spacing, with overall extraction errors below 15%. As a case demonstration, this approach offers a replicable workflow that extends wildlife remote sensing beyond abundance estimation toward structural and spatial metrics, enabling investigation of population organization and ecological processes in dense animal aggregations.