State‐space models and inference approaches for aquatic animal tracking with passive acoustic telemetry and biologging sensors

Abstract Passive acoustic telemetry systems are widely deployed to track animals in aquatic environments. However, investments in integrative methods of data analysis have remained comparatively limited, with current workflows typically considering individual movements separately from space use, home ranges and residency. This review presents a unifying perspective that bridges this divide. We argue that the core of animal‐tracking analyses lies in the estimation of individual locations based on probabilistic principles. We formalise a generic state‐space model for individual movements and a set of targets for statistical inference, unifying existing literature in a common framework. We critically assess inference algorithms and connect model‐based inference to downstream ecological analyses of individual centres of activity, occurrence, residency, home ranges, habitat selection and behaviour. We provide guidance to practitioners on model formulation, algorithm choice and software suitability in different contexts and identify key avenues for future research. This review provides a roadmap for integrative data analysis in passive acoustic telemetry systems that should support research into the ecology and conservation of many aquatic species.