Robotics‐assisted acoustic surveys could deliver reliable, landscape‐level biodiversity insights
Author(s)
Type
Journal Article
Abstract
Terrestrial remote sensing approaches, such as acoustic monitoring, deliver finely resolved and reliable biodiversity data. However, the scalability of surveys is often limited by the effort, time and cost needed to deploy, maintain and retrieve sensors. Autonomous unmanned aerial vehicles (UAVs, or drones) are emerging as a promising tool for fully autonomous data collection, but there is considerable scope for their further use in ecology. In this study, we explored whether a novel approach to UAV-based acoustic monitoring could detect biodiversity patterns across a varied tropical landscape in Costa Rica. We simulated surveys of UAVs employing intermittent locomotion-based sampling strategies on an existing dataset of 26,411 h of audio recorded from 341 static sites, with automated detections of 19 bird species (n = 1819) and spider monkey (n = 2977) vocalizations. We varied the number of UAVs deployed in a single survey (sampling intensity) and whether the UAVs move between sites randomly, in a pre-determined route to minimize travel time, or by adaptively responding to real-time detections (sampling strategy), and measured the impact on downstream ecological analyses. We found that avian species detections and spider monkey occupancy were not impacted by sampling strategy, but that sampling intensity had a strong influence on downstream metrics. Whilst our simulated UAV surveys were effective in capturing broad biodiversity trends, such as spider monkey occupancy and avian habitat associations, they were less suited for exhaustive species inventories, with rare species often missed at low sampling intensities. As autonomous UAV systems and acoustic AI analyses become more reliable and accessible, our study shows that combining these technologies could deliver valuable biodiversity data at scale.
Date Issued
2026-04-01
Date Acceptance
2025-09-23
Citation
Remote Sensing in Ecology and Conservation, 2026, 12 (2), pp.260-274
ISSN
2056-3485
Publisher
Wiley
Start Page
260
End Page
274
Journal / Book Title
Remote Sensing in Ecology and Conservation
Volume
12
Issue
2
Copyright Statement
© 2025 The Author(s). Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
10.1002/rse2.70037
Subjects
Automation
machine learning
passive acoustic monitoring (PAM)
species monitoring
unmanned aerial vehicles (UAVs)
Publication Status
Published
Date Publish Online
2025-10-19