Workflow and training materials for validating species detections from passive acoustic monitoring: a case study from the northern Blue Mountains

Abstract

Passive acoustic monitoring (PAM) has revolutionized wildlife monitoring by lowering barriers associated with data collection. Data from PAM can support adaptive management programs aimed at meeting multiple objectives in the face of uncertainty. Pairing PAM with acoustic classifiers capable of identifying sounds produced by wildlife can generate thousands of species detections across managed landscapes. However, such species detections represent both true- and false-positive detections, and human review is required to generate accurate detection data. Although collecting audio recordings is highly cost-effective and scalable, making reliable inferences from audio recordings presents a new challenge for wildlife practitioners faced with processing many audio recordings and reviewing automated species detections. Here, we provide guidance for wildlife practitioners using PAM and acoustic classifiers to monitor wildlife populations. We offer (1) guidance on how to effectively implement sampling designs that support robust inferences and (2) open-source code and training materials to facilitate efficient review of thousands of automated species detections. More specifically, we highlight important considerations for selecting sites to deploy autonomous recording units, the times of day to record, and potential species detections for human review. We also introduce and share open-source code to implement the Simple Passive Acoustic Monitoring protocol, a highly efficient and scalable approach for reviewing species detections generated by PAM and acoustic classifiers. We use managed forests in the northern Blue Mountains of Oregon and Washington as a case study for the techniques described in this report. Overall, this report facilitates the use of adaptive management programs by providing wildlife practitioners guidance on how to integrate rapidly developing technologies and tools capable of effectively and efficiently monitoring wildlife populations.

Citation

Vernasco, Ben J.; Duarte, Adam; Weldy, Matthew J.; Finch, Savannah R.; Ratliff, Jamie. 2025. Workflow and training materials for validating species detections from passive acoustic monitoring: a case study from the northern Blue Mountains. Gen. Tech. Rep. PNW-GTR-1039. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 54 p. https://doi.org/10.2737/pnw-gtr-1039.