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

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 several sensors, the potential exists to estimate population density through acoustic spatial capture-recapture (ASCR). Detections need to be correctly identified and associated across sensors so that capture histories can be built. 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, i.e., are false positives. We present a case study of bowhead whale calls (Baleana mysticetus) collected in the Beaufort Sea in 2010. We propose a novel extension of ASCR that is robust to these false positives by conditioning on calls being detected by at least two sensors. We allow for individual-level detection heterogeneity through modelling a variable sound source level, we model an inhomogeneous call spatial density, and we include bearings with varying measurement error. We show via simulation based on the case study 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; 60.7% of singletons were estimated to have been false positives.

The importance of independence in unmarked spatial capture-recapture analysis

“…When some detections cannot be assigned to individuals, they may be discarded (Tourani et al, 2020), incorporated along with detections of marked individuals (Sollmann et al 2013), or modeled as having lost their identity with some probability (Jiménez et al, 2020). Occasional misidentifications may also be accounted for (McClintock et al, 2014;Morrison et al, 2011;Petersma et al, 2023;Rakhimberdiev et al, 2022;Yoshizaki et al, 2009). However, if all individuals are entirely unmarked or have few identifying marks such that their full identities are unknown, then spatial count (SC) and spatial partial identity models (SPIM) are two SCR-based modelling alternatives that can be used for density estimation.…”