Passive Acoustic Monitoring within the Northwest Forest Plan Area: 2025 Annual Report

Abstract

The Northwest Forest Plan (NWFP) Passive Acoustic Monitoring (PAM) program is a large-scale interagency biodiversity monitoring framework designed to assess the status and trends of northern spotted owls (Strix occidentalis caurina), barred owls (Strix varia), marbled murrelets (Brachyramphus marmoratus), and broader forest biodiversity across federally administered lands in the Pacific Northwest. In 2025, we deployed autonomous recording units at 2,095 sampling stations within 532 5-km2 hexagons randomly selected across approximately 24 million acres of federal forest lands in Washington, Oregon, and California. These deployments generated 1.37 million hours of acoustic recordings that we processed using the convolutional neural network PNW-Cnet v5 for automated species identification and subsequent human validation of focal species detections. Northern spotted owls were detected in 28% of sampled hexagons range-wide, including 16% in Washington, 25% in Oregon, and 60% in California. Occupancy patterns remained consistent compared to previous years, with higher occupancy concentrated in southern portions of the geographic range and continued low occupancy across much of the Washington Cascades and Oregon Coast Range. Barred owls were detected in 86% of sampled hexagons and remained broadly distributed throughout most of the NWFP area. Marbled murrelets were detected in 50% of reviewed hexagons within NWFP marbled murrelet management zones, with highest occupancy occurring in coastal forests of Oregon and Washington. The 2025 field season occurred under substantial operational constraints that reduced sampling effort by approximately half relative to 2023 and 2024 because of staffing limitations affecting participating federal agencies. Despite these reductions, the NWFP-PAM framework continued to provide broad-scale, spatially representative ecological information across the NWFP area. Results highlight the growing importance of passive acoustic monitoring and machine learning approaches for long-term biodiversity monitoring under changing environmental and operational conditions.