Passive acoustic monitoring reveals group ranging and territory use: A case study of wild chimpanzees (Pan troglodytes)

Abstract

Background: Assessing the range and territories of wild mammals traditionally requires years of data collection and often involves directly following individuals or using tracking devices. Indirect and non-invasive methods of monitoring wildlife have therefore emerged as attractive alternatives due to their ability to collect data at large spatiotemporal scales using standardized remote sensing technologies. Here, we investigate the use of two novel passive acoustic monitoring (PAM) systems used to capture long-distance sounds produced by the same species, wild chimpanzees (Pan troglodytes), living in two different habitats: forest (Taï, Côte d'Ivoire) and savanna-woodland (Issa valley, Tanzania). Results: Using data collected independently at two field sites, we show that detections of chimpanzee sounds on autonomous recording devices were predicted by direct and indirect indices of chimpanzee presence. At Taï, the number of chimpanzee buttress drums detected on recording devices was positively influenced by the number of hours chimpanzees were seen ranging within a 1 km radius of a device. We observed a similar but weaker relationship within a 500 m radius. At Issa, the number of indirect chimpanzee observations positively predicted detections of chimpanzee loud calls on a recording device within a 500 m but not a 1 km radius. Moreover, using just seven months of PAM data, we could locate two known chimpanzee communities in Taï and observed monthly spatial variation in the center of activity for each group. Conclusions: Our work shows PAM is a promising new tool for gathering information about the ranging behavior and habitat use of chimpanzees and can be easily adopted for other large territorial mammals, provided they produce long-distance acoustic signals that can be captured by autonomous recording devices (e.g., lions and wolves). With this study we hope to promote more interdisciplinary research in PAM to help overcome its challenges, particularly in data processing, to improve its wider application.