Geology Reports⌕ Search

USGS · 70276526

Passive acoustic monitoring is a cost-effective approach to conduct inland surveys for marbled murrelets

Abstract

The marbled murrelet ( Brachyramphus marmoratus ) is a seabird of conservation concern that nests in late-successional, old-growth forests in Oregon, Washington, and California, USA. Traditional audio-visual surveys have been the standard method for detecting murrelets in forests. Given that murrelets can be extremely cryptic in forest environments, audio-visual surveys are difficult, time consuming, and expensive to conduct. Passive acoustic monitoring (PAM) may be a useful and cost-effective alternative. Using a paired murrelet survey design, we compared detection probabilities across 88 survey points in forests of Oregon and Washington. We conducted 2–4 early morning audio-visual surveys and 13–53-day PAM surveys from 14 June through 5 August 2022 and 2023, when murrelets typically make the most frequent inland flights during the nesting season. We found the average detection probability for murrelets on any given day was higher for an audio-visual survey visit (0.40, SD = 0.17) than for a PAM survey day (0.19, SD = 0.10). However, the cumulative detection probability for PAM surveys was 0.98 (95% CI = 0.96–1.0) after 19 survey days, whereas the same cumulative detection probability would require 9 audio-visual survey visits. Using simulations to compare the cost-effectiveness of both methods, we found audio-visual surveys cost more when compared with PAM surveys in 61–91% of scenarios because they require specialized personnel training and additional pre-dawn visits. Further, PAM per survey costs decreased as the number of survey days per point increased and over time after initial costs were incurred, particularly when autonomous recording units were reused for multiple years. While audio-visual surveys are the standard method for murrelet surveys in forest habitats, PAM represents a more cost-effective approach to determine the presence of murrelets.

Explore related subjects

90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 41.72311163735378° to 49.44066172891584° latitude; -126.468941266289° to -116.90084276070849° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Alaina D. Thomas, Adam Duarte, Matt J. Gostin, James T. Peterson, Damon B. Lesmeister. 2025-11-25. Passive acoustic monitoring is a cost-effective approach to conduct inland surveys for marbled murrelets. https://doi.org/10.1002/wsb.1640

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Considerations for using autonomous recording units to monitor northern bobwhite occupancy at large spatial scales

Northern bobwhites ( Colinus virginianus ) have experienced widespread declines across their range, increasing the need for population monitoring across broad spatial and temporal scales. Yet large-scale monitoring of post-breeding bobwhites remains limited by the time-intensive nature of fall (i.e., autumn) covey counts. Passive acoustic monitoring with autonomous recording units (ARUs) and automatic detection software may help overcome these constraints but requires further study. From 2017 to 2021, we paired in-person observer point counts with ARU surveys during the fall in southern Iowa, USA to 1) evaluate how ARU detections compare to observer detections, 2) assess the accuracy of automatic classification software for covey calls, 3) gauge the influence of weather and ordinal date on detection, and 4) offer best practices for processing ARU acoustic data. We found high correspondence (89.2%) in detection and non-detection outcomes between paired ARU and observer surveys. Across manually processed recordings, 95.7% of covey call detections occurred within a 35-min window (50–15 min before sunrise). Increasing wind speed and cloud cover were associated with reduced detection probabilities. Wind speeds ≥5 m/s resulted in detection probabilities <0.5. The cumulative probability of detecting bobwhites at a site exceeded 0.995 (95% Credible Interval [CrI] = 0.987, 0.999) after 4 survey days when technicians processed the files. Manual processing had the highest mean detection probability (0.75, 95% CrI = 0.66, 0.82) compared with automatic detection software with 2 different confidence thresholds (0.46 [95% CrI = 0.39, 0.53] and 0.45 [95% CrI = 0.36, 0.55]). Automatic detection software allowed for substantially faster processing with relatively few false positives (false positive rate of 0.014 to 0.038). Autonomous recording units coupled with automatic detection software provided a promising alternative to in-person observer point counts of bobwhite covey calls for determining site occupancy and have the potential to greatly increase capacity for landscape-level bobwhite monitoring.

Iowa↗

Developing a low-cost drone-based method for deploying and retrieving autonomous recording units in inaccessible areas

Autonomous Recording Units (ARUs) are increasingly used in research to support passive acoustic monitoring, but deployment in remote or inaccessible locations remains logistically challenging. Traditional manual placement is labor-intensive, time-consuming, potentially hazardous, and often limited to habitat edges, creating sampling biases and restricting spatial coverage. To address placement limitations in wetland systems, we developed and field-tested a low-cost, lightweight floating platform paired with a drone-based deployment and retrieval system. The drone-deployable ARU platform, constructed from inexpensive, off-the-shelf materials (~US $21 per unit), weighed ~560 g and was mounted with an ARU (AudioMoth) at ~1.2 m above water. The platform was designed for stability, portability, and compliance with Federal Aviation Administration Part 107 regulations. Field trials were conducted across 50 operations in various types of historical rice-field impoundments along the South Carolina coast, which provide critical habitat for secretive marsh birds such as rails (e.g ., Rallus, Laterallus , and Porzana ) and bitterns (e.g ., Botaurus and Ixobrychus ). All 50 deployments and retrievals were successful, demonstrating the robustness of the method under diverse hydrologic and vegetative conditions. Deployment times were significantly shorter than retrieval times (median 6 vs. 9 min), with retrieval requiring greater precision for hook engagement. Operation times scaled predictably with distance: deployment increased by 0.93 min/100 m (R 2 = 0.94) and retrieval by 1.17 min/100 m (R 2 = 0.95). No platform damage or displacement was observed, as emergent vegetation was likely providing natural anchoring. Our approach offers an affordable and effective solution for expanding ARU coverage in large, inaccessible wetlands, reducing sampling bias and enhancing biodiversity monitoring. With continued advances in drone payload capacity, battery endurance, and beyond-visual-line-of-sight flight regulations, our workflow could be adapted for deploying multi-sensor platforms (e.g., ARUs, eDNA samplers, water and air quality sensors, plant sample collectors, insect traps, and trail cameras) to support integrated biodiversity monitoring in challenging landscapes worldwide.

South Carollina↗