Animal tracking with particle algorithms informs protected area design.

Animal movements affect their exposure to threats and the efficacy of conservation measures, such as marine protected areas (MPAs). However, many species' movements are difficult to reconstruct from available datasets, hampering conservation efforts. This is especially the case for aquatic species that rarely surface, for which data are often limited to observations from acoustic telemetry (detections) and ancillary sensors. Here, we pioneer the use of state-of-the-art particle algorithms to model movements, integrate datasets, and assess MPA design, leveraging a case study of a Critically Endangered elasmobranch. Our algorithms led to 5-fold improvements in space-use maps and 30-fold improvements in residency estimates compared to prevailing methods. By integrating tracking datasets, we were uniquely able to examine movements beyond acoustic receivers, MPA-scale residency, and specific habitats beyond protected areas that warrant protection. This work reveals a modeling framework that enhances the conservation value of acoustic telemetry, supporting analyses of MPA efficacy worldwide.