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:被动声学监测是调查野生生物群落的一个很有希望的方法,在声学上比视觉上更容易探测到。当动物声学可以在一系列传感器上得到独特识别时,就存在通过声音空间捕捉(ASCR)来估计人口密度的潜力。然而,声学分类不完善,在某些情况下,仅在单一传感器(“星盘”)上检测到的声音比例很高,并不是来自目标物种。我们介绍了2010年在博福特海收集的弓头鲸呼唤(Baleana mysticetus)的案例研究,其中含有此类虚假的阳性。我们建议了动物声学的新型扩展,通过对至少两个传感器探测到的电话进行调试和调节,对假阳性反应具有强力。我们允许通过模拟一个可变声源级别(“singletons”),模型不来自目标物种。我们通过模拟来显示2010年在博福特海采集的弓头鲸呼唤(Baleana mystiteetusetus)包含此类假阳性估计。我们建议了源水平变化的结果是强烈的负偏差,同时忽略了30个恒度密度密度,同时忽略了直系直系直度分析结果。</s>