Accommodating false positives within acoustic spatial capture-recapture, with variable source levels, noisy bearings and an inhomogeneous spatial density

TL;DR

This paper introduces a robust acoustic spatial capture-recapture method that accounts for false positives, variable source levels, noisy bearings, and inhomogeneous spatial density, improving wildlife population estimates from passive acoustic data.

Contribution

It presents a novel extension of ASCR that effectively handles false positives and individual detection heterogeneity, validated through simulation and applied to bowhead whale data.

Findings

The method yields near-unbiased population estimates when correctly specified.

Ignoring source level variation causes negative bias in estimates.

Ignoring inhomogeneous density causes severe positive bias.

Abstract

Passive acoustic monitoring is a promising method for surveying wildlife populations that are easier to detect acoustically than visually. When animal vocalisations can be uniquely identified on an array of sensors, the potential exists to estimate population density through acoustic spatial capture-recapture (ASCR). However, sound classification is imperfect, and in some situations a high proportion of sounds detected on just a single sensor ('singletons') are not from the target species. We present a case study of bowhead whale calls (Baleana mysticetus) collected in the Beaufort Sea in 2010 containing such false positives. We propose a novel extension of ASCR that is robust to false positives by truncating singletons and conditioning on calls being detected by at least two sensors. We allow for individual-level detection heterogeneity through modelling a variable sound source level, model inhomogeneous call spatial density, and include bearings with varying measurement error. We show via simulation that the method produces near-unbiased estimates when correctly specified. Ignoring source level variation resulted in a strong negative bias, while ignoring inhomogeneous density resulted in severe positive bias. The case study analysis indicated a band of higher call density approximately 30km from shore; 59.8% of singletons were estimated to have been false positives.