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Ecological disturbances are becoming more extensive and intensive globally, a trend exemplified by 'megafires' and industrial deforestation, which cause widespread losses of forest cover. Yet the hypothesis that contemporary environmental disturbances are affecting biodiversity has been difficult to test directly.The novel combination of landscape‐scale passive acoustic monitoring, a new machine learning algorithm, BirdNET and improved Bayesian model‐fitting engines enables cohesive, community‐level before‐after, control‐impact studies of disturbances. We conducted such a study of a 2020 megafire in the Sierra Nevada, USA. We used a bespoke dynamic multi‐species occupancy modelling approach, which enabled us to account for imperfect detection, misclassifications, and to share information among species.There was no community‐level difference in colonization between burned and unburned forest. In contrast, the probability of site extinction in burned forest, 0.36, was significantly higher than in unburned forest, 0.12. Of the 67 species in our study, 6 (9%) displayed a positive colonization response to the fire, while 28 (41%) displayed a significant extinction response.We observed a 12% decrease in avian biodiversity 1 year post‐fire, and a substantial shift in community composition. However, in this ecosystem, many species display time‐dependent responses to the fire that are unobservable after just 1 year.Synthesis and applications. We have shown that three emerging conservation technologies, passive acoustic monitoring, machine learning animal sound identification algorithms, and advances in Bayesian statistical tools, can provide previously unattainable information about biodiversity responses to ecological change. Critically, our approach is transferrable and scalable, as the workflow is agnostic to species or ecosystem and each component is either freely available (all relevant software) or relatively inexpensive (recording hardware). Environmental change is unfolding rapidly, but new analytical techniques may help our understanding and—thus interventions—keep pace. [ABSTRACT FROM AUTHOR]

The California spotted owl (Strix occidentalis occidentalis) is an older‐forest associated species that resides at the center of forest management planning in the Sierra Nevada and Southern California, USA, which are experiencing increasingly large and severe wildfires and drought‐related tree mortality. We leveraged advances in passive acoustic survey technologies to develop an acoustically assisted survey design that could increase the efficiency and effectiveness of project‐level surveys for spotted owls, allowing surveys to be completed in a single year instead of in multiple years. We deployed an array of autonomous recording units (ARUs) across a landscape and identified spotted owl vocalizations in the resulting audio using BirdNET. We then evaluated spatio‐temporal patterns in spotted owl vocalizations near occupied territories and the ability of a crew naïve to the location of occupied territories to locate spotted owls based on patterns of acoustic detections. After only 3 weeks of acoustic surveys, ≥1 ARU within 750 m of all 17 occupied territories obtained spotted owl detections across ≥2 nights. When active surveys using broadcast calling were conducted near ARUs with spotted owl detections by surveyors naïve to territory occupancy status and locations, surveyors located owls in 93% to 100% of occupied territories with ≤3 surveys. To further improve the efficiency of spotted owl surveys, we developed a statistical model to identify and prioritize areas across the Sierra Nevada for different survey methods (active only, acoustically assisted, no surveys) based on the expected probability of occupancy predicted from remotely sensed measurements of tree height and historical occupancy. Depending on managers' tolerance for false negatives, this model could help identify large areas that might not benefit from surveys based on low expected occupancy probabilities and areas where acoustically assisted surveys might enhance survey effectiveness and efficiency. Collectively, these findings can help managers streamline the survey process and thus increase the pace of forest restoration while minimizing potential near‐term adverse effects on California spotted owls. [ABSTRACT FROM AUTHOR]

Passive acoustic monitoring has emerged as a scalable, noninvasive tool for monitoring many acoustically active animals. Bioacoustics has long been employed to study wolves and coyotes, but the process of extracting relevant signals (e.g., territorial vocalizations) from large audio datasets remains a substantial limitation. The BirdNET algorithm is a machine learning tool originally designed to identify birds by sound, but it was recently expanded to include gray wolves (Canis lupus) and coyotes (C. latrans). We used BirdNET to analyze 10,500 h of passively recorded audio from the northern Sierra Nevada, USA, in which both species are known to occur. For wolves, real-world precision was low, but recall was high; careful post-processing of results may be necessary for an efficient workflow. For coyotes, recall and precision were high. BirdNET enabled us to identify wolves, coyotes, and apparent intra- and interspecific acoustic interactions. Because BirdNET is freely available and requires no computer science expertise to use, it may facilitate the application of passive acoustic surveys to the research and management of wolves and coyotes, two species with continental distributions that are frequently involved in high-profile and sometimes contention management decisions. [ABSTRACT FROM AUTHOR]