Choices to Landscapes: Mechanisms of Animal Movement Scale to Landscape Patterns of Space Use

Understanding animal space use is central to ecology and conservation, and movement-based habitat selection models provide powerful tools for identifying preferred environments. However, in heterogeneous landscapes, movement constraints limit habitat accessibility, decoupling realised space use from underlying preferences and complicating efforts to scale individual-level movement processes to landscape distributions. We present a steady-state distribution (SSD) framework that integrates locally estimated habitat selection and movement into sparse Markovian transition matrices to predict emergent space use. Across simulations and empirical case studies, SSD consistently outperformed occurrence-based models in predicting space use from individual- to population-level scales. Moreover, SSD proved to be more efficient-and sometimes even more accurate-than more assumptive agent-based model simulations (ABMs). Our approach bridges a key gap between mechanistic movement models and spatial prediction, providing a scalable and computationally efficient approach for translating individual movement behaviour into landscape-scale distribution patterns relevant for ecological inference and conservation planning.