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.

翻译：被动声学监测是一种有望用于调查野生动物种群的方法，尤其适用于那些通过听觉比视觉更容易检测的物种。当动物发声能在传感器阵列中被唯一识别时，存在通过声学空间捕获-再捕获（ASCR）估算种群密度的潜力。然而，声音分类并非完美，在某些情况下，仅在单个传感器上检测到的声音（"单次事件"）中有很大比例并非来自目标物种。我们以2010年在波弗特海采集的弓头鲸（Baleana mysticetus）叫声为案例，其中包含此类假阳性。我们提出了一种对假阳性具有鲁棒性的ASCR新扩展方法，通过截断单次事件并将条件限制为至少在两个传感器上检测到的叫声。我们通过建模可变声源级来考虑个体水平检测异质性，模拟非均匀叫声空间密度，并纳入具有可变测量误差的方位角。通过模拟，我们证明该方法在正确指定时能产生近乎无偏的估计。忽略声源级变化会导致强烈负偏差，而忽略非均匀密度则会导致严重正偏差。案例研究分析表明，距海岸约30公里处存在一条较高叫声密度带；据估计，59.8%的单次事件为假阳性。