Abstract Climate and land‐use change are dramatically altering the frequency, intensity, and extent of ecological disturbances, which threaten the persistence of at‐risk species. To curb the pace and scale of disturbances, balance management and conservation priorities, and alleviate associated population declines, managers require high‐quality information on species' responses to disturbance and their population trends across broad spatial scales that challenge the capacity of traditional, local‐scale monitoring programs. Passive acoustic monitoring is a scalable approach to obtain occurrence data, but the extent to which it can be used to model occupancy dynamics and their environmental drivers remains uncertain. Here, we demonstrate how passive acoustic surveys can be analyzed within a Bayesian dynamic occupancy modeling framework to robustly estimate occupancy dynamics and responses to disturbance in the California spotted owl ( Strix occidentalis occidentalis ), which is threatened by increasingly large, severe “megafires.” From 2021 to 2024, we collected ~2 million hours of audio from autonomous recording units deployed across seven national forests in the Sierra Nevada, California, USA. Spotted owls were less likely to initially occupy and colonize sites that were severely burned, and more likely to go locally extinct following high‐severity fire. Further, we observed declining postfire occupancy trajectories, particularly when sites burned ≥50% high severity. Occupancy trends varied by national forest, but declined by 2% across the entire region. Our findings—which closely align with those from intensive, traditional demographic studies—demonstrate that large‐scale passive acoustic monitoring paired with dynamic occupancy models can effectively detect species' responses to disturbance and estimate population trends, offering valuable insights for management across multiple spatial scales. Finally, we provide specific recommendations to help other passive acoustic monitoring programs successfully detect ecological responses to disturbance and track population changes.
Winiarski et al. (Thu,) studied this question.