Sound level measurements from audio recordings provide objective distance estimates for distance sampling wildlife populations

Abstract

Distance sampling is widely used to estimate animal population densities by accounting for imperfect detection of individuals with increasing distance from an observer. Distance sampling assumes that distances are measured without error; however, it is often applied to human estimated distances, which are known to be inconsistent, inaccurate, and biased. We present an objective technique for estimating distance to vocalizing individuals that relies on the relative sound level (RSL) of the vocalization extracted from autonomous recording unit (ARU) recordings and show the error is less than human estimated error extracted from a literature case study. RSL predicted distances can be obtained by manual measurement in sound viewing software, or automatically with automated signal recognition software. We built calibration datasets of Ovenbirds (Seiurus aurocapilla) and Common Nighthawks (Chordeiles minor) recorded at known distances and used regression of RSL from those recordings to predict distance. There was no error bias of RSL predicted distances when compared to known distances for Common Nighthawk, minimal error bias for Ovenbird, and error from all RSL predicted distances was less than human estimated error extracted from the literature. We then simulated ARU point count surveys with a known density and estimated that density with distance sampling to test whether RSL distance prediction does not violate the assumption that distances are measured without error. There was no difference in density estimates from known distance and density estimates obtained from RSL predicted distance, while density estimates contaminated with human estimated error were significantly lower than density estimates from known distance. We found that a calibration dataset of approximately 300 vocalizations was suitable to minimize error for both species, and so conclude that RSL distance prediction is an accessible method of improving distance estimates relative to human estimation. We provide general recommendations on how to collect calibration recordings for the application of RSL distance prediction to other species and areas.

Autonomous sound recording outperforms human observation for sampling birds: a systematic map and user guide

“…, Yip et al. ). Taking all the evidence together, bird densities can be obtained both from human observer and sound recording surveys.…”

“…In that context, simultaneous point counts can be useful to gather reference material from aural bird detection at measured distances. Reference recordings of birds at known distances can also be used to fit models of how the vocalization loudness decreases with distance to infer detection distances (Sebastian-Gonzalez et al 2018, Yip et al 2019). Taking all the evidence together, bird densities can be obtained both from human observer and sound recording surveys.…”

A roadmap for survey designs in terrestrial acoustic monitoring

“…; Yip et al. ), and together with models of sound attenuation over heterogeneous environments (Royle ), they should support the standardizing of spatial sampling efforts in PAM.…”

“…; Yip et al. ). Use this information to determine the appropriate distance among sampling sites. Make a list of potential recording schedules based on behavioral and ecological aspects of focal taxa and research goal.…”

Ecoacoustics: acoustic sensing for biodiversity monitoring at scale

“…They demonstrate in a field study that it can produce comparable results as manual field transects, with less than a quarter of effective survey effort. Yip et al (2019) demonstrate that sound level measurements can improve population density estimates, serving as a proxy measure for the distance between sound event and autonomous recording unit.…”