Monitoring feral pigs ( Sus scrofa ): Complementarity between autonomous sensing methods increases detection probability

Abstract Invasive alien species are a major threat to biodiversity, with significant impacts on threatened species and priority sites. Monitoring is essential to inform appropriate management strategies, and autonomous sensors are increasingly used to address data collection at large spatio‐temporal scales. Feral pigs ( Sus scrofa ) are a major threat to native fauna in Australia. Here, the utility of passive acoustic monitoring for detecting feral pigs and its complementarity to camera trap detection was tested. A custom‐built deep‐learning BirdNET recogniser was used to automatically scan sound for pig presence; image data was manually scanned. Detection probabilities and effects of covariates were compared for detections of each method, separately and combined, using multi‐season occupancy models. There was little spatio‐temporal overlap between image and sound detections. Modelled detection probability was the highest when sound and image detections were combined, followed by sound and, lastly, images. Seasonality affected detectability: camera traps were most successful in the Late Wet, when sound detection was poor. Sound detection was more successful in all other seasons, with the highest detection probability in the Late Dry. The intrinsic variation across survey methods along with the effects of environmental factors in species behaviour can be accounted for by combining methods, improving overall detections and providing complementary information on the same species. Autonomous sensors can provide comprehensive data to inform land management decisions, including population control and impact mitigation of invasive species. However, the utility of different sensors is context‐dependent. Combining multiple technologies can harness the strengths of each and mitigate against weaknesses. Increasing technology accessibility and decreasing costs is key to facilitate uptake by land managers.