  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.
