Context <p>Linking individual movements to population-level distributions remains a central challenge in ecology. Animal distributions arise from interdependent movement decisions across spatiotemporal scales. However, most studies focus on a single scale or treat them in isolation, constraining our ability to uncover the mechanisms shaping landscape-level patterns.</p> Objectives <p>We evaluated whether hierarchical foraging decisions operating across nested scales can be recovered from movement data alone. Specifically, we tested whether multi-state step-selection functions (HMM–SSFs) can identify scale-dependent behavioural organization arising from mechanistic decision rules.</p> Methods <p>We developed a model in which agents exploit resource sites grouped into camps within home ranges. Movement decisions were governed by energetic gains and the costs of travel, missed opportunities, and predation risk. We fitted HMM–SSFs to simulated movements to infer behavioural states and transitions.</p> Results <p>HMM–SSFs accurately identified latent behavioural states corresponding to within-site resource exploitation, within-camp movements, and between-camp relocations. Transitions among states responded to spatial variation in resources and risk. States emerged from model inference without being imposed a priori. The recovered behavioural hierarchy aligned with predictions from foraging theory. These results demonstrate that hierarchical decision-making leaves identifiable signatures in movement trajectories.</p> Conclusions <p>We demonstrate that multiscale processes can be inferred from movement data within a unified framework. Although results may be noisier for field observations than for simulations, complementary field methods can help mitigate this limitation by informing both the analysis and its interpretation. This approach therefore provides a valuable foundation for linking behaviour across scales in dynamic environments.</p>