Passive acoustic surveys provide a convenient and cost-effective way to monitor animal populations, and methods for conducting and analysing such surveys are undergoing rapid development. However, no standard metric exists to evaluate the proposed changes. Furthermore, the metrics that are commonly used are specific to a single stage of the survey workflow, and may not reflect the overall effects of a design choice. Here, we attempt to define the effectiveness of acoustic surveys conducted in two common frameworks of population inference—occupancy modelling and spatially explicit capture-recapture (SCR). Specifically, we investigate precision as a possible metric of survey performance, but we observe that it does not lead to generally optimal designs in occupancy modelling. In contrast, the precision of the SCR density estimate can be optimised with fewer experiment-specific parameters. We illustrate these issues using simulations. We further demonstrate how SCR precision can be used to evaluate design choices on a field survey of little spotted kiwi (Apteryx owenii). We compare call recognition by software and human experts. The resulting tradeoff between missed calls and faster data throughput was accurately captured with the proposed metric, while common metrics failed to identify optimal improvements and could be inflated by deleting data. Due to the flexibility of SCR framework, the approach presented here can be applied to a wide range of different survey designs. As the precision is directly related to the power of subsequent inference, this metric evaluates design choices at the application level and captures tradeoffs that are missed by stage-specific metrics, enabling reliable comparison of survey methods.

被动声学调查提供了一种方便且具有成本效益的方式来监测动物种群，并且进行和分析此类调查的方法正在快速发展。但是，不存在评估提议更改的标准指标。此外，常用的指标特定于调查工作流程的单个阶段，可能无法反映设计选择的整体效果。在这里，我们试图定义在人口推理的两个常见框架中进行的声学调查的有效性 - 占用建模和空间显式捕获 - 重新捕获（SCR）。具体而言，我们将精度作为调查性能的一个可能指标进行研究，但我们观察到它不会导致占用建模中的一般最佳设计。相比之下，可以使用较少的实验特定参数来优化 SCR 密度估计的精度。我们使用模拟来说明这些问题。我们进一步展示了如何使用 SCR 精度来评估对小斑点猕猴桃的实地调查的设计选择（Apteryx owenii )。我们比较了软件和人类专家的呼叫识别。所提出的指标准确地捕获了未接来电和更快数据吞吐量之间的权衡，而通用指标未能确定最佳改进，并且可能会因删除数据而膨胀。由于 SCR 框架的灵活性，这里介绍的方法可以应用于各种不同的调查设计。由于精度与后续推理的能力直接相关，因此该指标会评估应用程序级别的设计选择，并捕获特定阶段指标所遗漏的权衡，从而能够对调查方法进行可靠的比较。