1. With one million animal species at risk of extinction, there is an urgent need to regularly monitor threatened species. However, in practice this is challenging, especially with wide-ranging, elusive and cryptic species or those that occur at low density.
2. Here we compare two non-invasive methods, passive acoustic monitoring (n=12) and camera trapping (n=53), to detect chimpanzees (Pan troglodytes) in a savanna-woodland mosaic habitat at the Issa Valley, Tanzania. With occupancy modelling we evaluate the efficacy of each method, using the estimated number of sampling days needed to establish chimpanzee absence with 95% probability, as our measure of efficacy.
3. Passive acoustic monitoring was more efficient than camera trapping in detecting wild chimpanzees. Detectability varied over seasons, likely due to social and ecological factors that influence party size and vocalization rate. The acoustic method can infer chimpanzee absence with less than ten days of recordings in the field during the late dry season, the period of highest detectability, which was five times faster than the visual method.
4. Synthesis and applications: Despite some technical limitations, we demonstrate that passive acoustic monitoring is a powerful tool for species monitoring. Its applicability in evaluating presence/absence, especially but not exclusively for loud call species, such as cetaceans, elephants, gibbons or chimpanzees provides a more efficient way of monitoring populations and inform conservation plans to mediate species-loss.

This dataset was collected with a passive acoustic monitoring and camera trap systems. We manually processed audio files and videos to confirm chimpanzee detections and create matrices of presence/absence for occupancy modelling. These matrices presented in the archive are the raw matrices: for the passive acoustic monitoring, the values correspond to detection/non detection per 30min files and for the camera traps, the values corresponds to detection/non detection per day of deployment. Further steps, as detailed in the manuscript (i.e. divide the sampling period into sampling occasions of 8 days) have been conducted to create the occupancy model.

Detection histories were compiled into matrices containing two different values: (0) non detection and (1) detection. When no survey was conducted during a sampling occasion (e.g. due to camera or audio recorder malfunctioning or not deployed), a value of NA was assigned. The value (1) corresponds to at least one detection during a sampling occasion.