1. Spatial capture–recapture (SCR) models are commonly used to estimate animal population density from detections and subsequent redetections of individuals across space. In particular, acoustic SCR models deal with detections of animal vocalisations across an array of acoustic detectors. Previously published acoustic SCR methods either estimate call density (calls per unit space per unit time) rather than animal density itself, require an independently estimated call rate to estimate animal density, or discard data from all but one detected call from each individual.
2. In this manuscript, we develop a new spatial capture–recapture model that estimates both call rate and animal density from the acoustic survey alone, without requiring an independently estimated call rate. Our approach therefore alleviates the need for the additional fieldwork of physically locating and monitoring individual animals. We illustrate our method and compare it to an existing approach using a simulation study and an application to data collected on an acoustic survey of the visually cryptic Cape peninsula moss frog Arthroleptella lightfooti.
3. In the context of our acoustic survey, our calling animal density estimator has low bias, good precision and confidence intervals with appropriate coverage, yielding results that are consistent with previous studies of the same species.
4. Our method can obtain accurate and precise estimates of animal density while eliminating the fieldwork burden associated with separately estimating call rate. We discuss how the development of our model's likelihood reveals a clear path to further extensions, which may incorporate features such as animal movement processes and uncertain individual identification.

中文翻译：

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一种空间捕获-捕获模型，可从被动声学调查中估算通话率和人口密度

1. 空间捕获-捕获（SCR）模型通常用于根据跨空间个体的检测和随后的重新检测来估计动物种群密度。尤其是，声学SCR模型可处理整个声学检测器阵列中动物发声的检测。先前发布的声学SCR方法要么估计呼叫密度（单位时间每单位空间的呼叫），而不是动物密度本身，要么需要独立估计的呼叫速率来估计动物密度，要么丢弃每个个体中除了一个检测到的呼叫以外的所有数据。
2. 在本手稿中，我们开发了一个新的空间捕获-捕获模型，该模型可以仅通过声学调查来估计出声率和动物密度，而无需独立估计出声率。因此，我们的方法减轻了对物理定位和监视单个动物的其他野外工作的需求。我们说明了我们的方法，并将其与现有方法进行了模拟研究，并将其与视觉隐秘的海角半岛苔藓蛙Arthroleptella lightfooti的声学调查收集的数据进行了比较。
3. 在我们的声学调查中，我们的动物密度估算器具有较低的偏差，良好的精确度和置信区间，并具有适当的覆盖范围，其结果与以前对相同物种的研究相一致。
4. 我们的方法可以获得准确而精确的动物密度估算值，同时消除了与单独估算呼叫率相关的野外工作负担。我们讨论了模型可能性的发展如何揭示进一步扩展的清晰路径，其中可能包含诸如动物运动过程和不确定的个体识别之类的特征。