Passive acoustic monitoring can be an effective way of monitoring wildlife
populations that are acoustically active but difficult to survey visually.
Digital recorders allow surveyors to gather large volumes of data at low cost,
but identifying target species vocalisations in these data is non-trivial.
Machine learning (ML) methods are often used to do the identification. They can
process large volumes of data quickly, but they do not detect all vocalisations
and they do generate some false positives (vocalisations that are not from the
target species). Existing wildlife abundance survey methods have been designed
specifically to deal with the first of these mistakes, but current methods of
dealing with false positives are not well-developed. They do not take account
of features of individual vocalisations, some of which are more likely to be
false positives than others. We propose three methods for acoustic spatial
capture-recapture inference that integrate individual-level measures of
confidence from ML vocalisation identification into the likelihood and hence
integrate ML uncertainty into inference. The methods include a mixture model in
which species identity is a latent variable. We test the methods by simulation
and find that in a scenario based on acoustic data from Hainan gibbons, in
which ignoring false positives results in 17% positive bias, our methods give
negligible bias and coverage probabilities that are close to the nominal 95%
level.

非主动声学监测是一种监测难以通过视觉调查的有声野生动物种群的有效方法。机器学习方法常用于识别目标物种的鸣叫，但很难检测到所有鸣叫并且常常产生误报。我们提出了三种声学空间捕获 - 释放推断的方法，将机器学习的不确定性融入推断过程中，以解决误报问题。通过模拟测试，我们发现在基于海南长臂猿的声学数据场景中，采用我们的方法可以减少 17% 的正向偏差，并且覆盖概率接近标准的 95% 水平。