Various methods for estimating animal density from visual data, including distance sampling (DS) and spatially explicit capture-recapture (SECR), have recently been adapted for estimating call density using passive acoustic monitoring (PAM) data, e.g., recordings of animal calls. Here we summarize three methods available for passive acoustic density estimation: plot sampling, DS, and SECR. The first two require distances from the sensors to calling animals (which are obtained by triangulating calls matched among sensors), but SECR only requires matching (not localizing) calls among sensors. We compare via simulation what biases can arise when assumptions underlying these methods are violated. We use insights gleaned from the simulation to compare the performance of the methods when applied to a case study: bowhead whale call data collected from arrays of directional acoustic sensors at five sites in the Beaufort Sea during the fall migration 2007–2014. Call detections were manually extracted from the recordings by human observers simultaneously scanning spectrograms of recordings from a given site. The large discrepancies between estimates derived using SECR and the other two methods were likely caused primarily by the manual detection procedure leading to non-independent detections among sensors, while errors in estimated distances between detected calls and sensors also contributed to the observed patterns. Our study is among the first to provide a direct comparison of the three methods applied to PAM data and highlights the importance that all assumptions of an analysis method need to be met for correct inference.

用于从视觉数据估计动物密度的各种方法，包括距离采样 (DS) 和空间显式捕获-重新捕获 (SECR)，最近已适用于使用被动声学监测 (PAM) 数据（例如动物叫声记录）估计叫声密度。在这里，我们总结了三种可用于被动声密度估计的方法：绘图采样、DS 和 SECR。前两个需要从传感器到呼叫动物的距离（通过对传感器之间匹配的呼叫进行三角测量获得），但 SECR 只需要传感器之间匹配（而不是定位）呼叫。我们通过模拟比较当违反这些方法的假设时会出现哪些偏差。我们使用从模拟中收集到的见解来比较这些方法在应用于案例研究时的性能：2007-2014 年秋季迁徙期间从波弗特海五个地点的定向声学传感器阵列收集的弓头鲸叫声数据。呼叫检测是由人类观察者从录音中手动提取的，同时扫描来自给定站点的录音频谱图。使用 SECR 和其他两种方法得出的估计值之间的巨大差异可能主要是由手动检测程序导致的，导致传感器之间的非独立检测，而检测到的呼叫和传感器之间的估计距离误差也导致了观察到的模式。我们的研究是最早对应用于 PAM 数据的三种方法进行直接比较的研究之一，并强调了需要满足分析方法的所有假设才能正确推断的重要性。