Automated acoustic detection of Geoffroy's spider monkey highlights tipping points of human disturbance

Abstract

As more land is altered by human activity and more species become at risk of extinction, it is essential that we understand the requirements for conserving threatened species across human-modified landscapes. Owing to their rarity and often sparse distributions, threatened species can be difficult to study and efficient methods to sample them across wide temporal and spatial scales have been lacking. Passive acoustic monitoring (PAM) is increasingly recognized as an efficient method for collecting data on vocal species; however, the development of automated species detectors required to analyse large amounts of acoustic data is not keeping pace. Here, we collected 35 805 h of acoustic data across 341 sites in a region over 1000 km² to show that PAM, together with a newly developed automated detector, is able to successfully detect the endangered Geoffroy's spider monkey (Ateles geoffroyi), allowing us to show that Geoffroy's spider monkey was absent below a threshold of 80% forest cover and within 1 km of primary paved roads and occurred equally in old growth and secondary forests. We discuss how this methodology circumvents many of the existing issues in traditional sampling methods and can be highly successful in the study of vocally rare or threatened species. Our results provide tools and knowledge for setting targets and developing conservation strategies for the protection of Geoffroy's spider monkey.

Keywords: automated species detector; bioacoustics; passive acoustic monitoring; primate; threatened species; tipping points.

Figure 1.

Land use map of the Osa Peninsula. Map showing the nine land use categories in the region, created at a scale of 5 × 5 m using Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) [53]. White circles represent the sample sites where each audio recorder was placed.

Figure 2.

Land use model results. Probability of species occurrence in each land use type on the y-axis (X), error bars represent 95% confidence intervals and percentage of occupied sites in each land use type on the z-axis. Pairwise differences between land use are indicated with letters A and B, where different letters represent a significant difference between land use. R², X² and p-values are annotated on each plot.

Figure 3.

Continuous variables model results. Probability of occurrence on the y-axis and observed presence/absence on the z-axis (l) in relation to (a) forest cover (%) measured within 200 m. (b) Density of primary roads measured within a 1 km radius of the site, where what appears to be a single point denoting presence relates to multiple points. (c) Cover of buildings measured within a 1 km radius of the site. (d) Density of secondary roads measured within a 200 m radius of the site. Shaded areas represents 95% confidence intervals. R², X² and p-values are annotated on each plot.