Keywords :
Artificial intelligence; Bioacoustics; Data management; Passive acoustic monitoring; Scalable ecological monitoring; Software bottlenecks; Workflow optimisation; Ecology, Evolution, Behavior and Systematics; Modeling and Simulation; Ecology; Ecological Modeling; Computer Science Applications; Computational Theory and Mathematics; Applied Mathematics
Abstract :
[en] Passive acoustic monitoring (PAM) enables minimally invasive sampling of wildlife across broad spatial, temporal and taxonomic scales. Its ongoing and widespread use has generated unprecedented volumes of acoustic data, shifting the primary bottleneck from data collection to the storage, processing, integration, and interpretation of PAM outputs. Although many software tools exist to address these challenges, differences in their design, scope, and usability often result in fragmented, complex analytical workflows. To identify the key barriers and opportunities shaping the implementation of PAM surveys, we conducted a structured, community-informed consultation developed through iterative engagement, comprising international practitioners working across terrestrial and aquatic ecosystems. Thirty experts identified and ranked their most critical bottlenecks in current PAM workflows, spanning data storage, processing, and interpretation. The top challenge identified related to accurate species identification using deep learning and artificial intelligence (AI) models, especially in noisy soundscapes or for underrepresented taxa. Eight additional priority challenges included workflow fragmentation, limited availability of user-friendly analytical and visualisation tools, uneven access to software, manual validation bottlenecks, computational constraints, and difficulties in data handling, standardisation, and sharing. Using a follow-up consultation with 24 of the original 30 experts, we propose practical mitigation strategies for these priority challenges, supported by step-by-step guidance to help overcome key barriers. Together, these insights provide a roadmap toward more scalable, open-access, and collaborative software systems, which are increasingly essential to realise the full potential of PAM in global biodiversity monitoring.
Funders :
European Union. Marie Skłodowska-Curie Actions
CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
FAPESP - Fundação de Amparo à Pesquisa do Estado de São Paulo
Government of Western Australia
WBI - Wallonie-Bruxelles International
ARC - Australian Research Council
MICINN - Ministerio de Ciencia, Innovación y Universidades
BAEF - Belgian American Educational Foundation
Funding text :
MEM thanks the support of the Australian Government through an Australian Research Council grant (project ID DE220100752 ). CPG was funded by the Ram\u00F3n y Cajal 2024 Programme ( RYC2024-048830-I ) of the Spanish Ministry of Science, Innovation and Universities, funded by MICIU/AEI/10.13039/501100011033 and FSE+ . We acknowledge the use of generative artificial intelligence tools to correct grammatical errors and improve the clarity of the text of an earlier version of this article. XR thanks the Belgian American Educational Foundation (BAEF) and Wallonia-Brussels International (WBI) for their support. Grants and fellowships were provided to LFT by the S\u00E3o Paulo Research Foundation (FAPESP #2022/11096-8 ) and the National Council for Scientific and Technological Development (CNPq #302834/2020-6 ). IM thanks the support of the Spanish National Parks Autonomous Agency (OAPN) for funding through the RarAvis project (Reference; 3377/2025). CD was supported by the European Union's Horizon 2020 research and innovation programme under the Marie Sk\u0142odowska-Curie grant agreement No AMD-101106862-1 . KMB was supported in part by the National Science Foundation under award OAC-2513947 .
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