Precision as a metric for acoustic survey design using occupancy or spatial capture-recapture

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.

Automated summary

Passive acoustic surveys are a convenient and cost-effective way to monitor animal populations, with methods undergoing rapid development. The effectiveness of such surveys in two common frameworks of population inference, occupancy modelling and spatially explicit capture-recapture, is explored in the article. Precision is considered as a possible metric of survey performance, with the study finding that it does not necessarily lead to optimal designs in occupancy modelling, but can be optimized in SCR density estimates with fewer experiment-specific parameters. Tradeoffs between missed calls and data throughput are accurately captured with the proposed metric, highlighting the flexibility of the SCR framework in evaluating different survey designs.