Biology subjects

Gasc, A.

Publications and source records attributed to Gasc, A..

3 recordsLinked to original sources

bioRxiv2026

Overcoming software bottlenecks for scalable passive acoustic monitoring: insights from a global expert assessment

1.Passive acoustic monitoring (PAM) enables non-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 create fragmented and complex analytical workflows. To identify the key barriers and opportunities shaping the implementation of PAM surveys, we conducted a structured expert solicitation involving 30 international practitioners working across terrestrial and aquatic ecosystems. Experts identified and ranked their most critical pain points 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. Participants also proposed 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.

ecology↗

bioRxiv2025

Quantifying the effect of forest edge on tropical fauna using explainable ecoacoustics metrics

Tropical forests are biodiversity hotspots but increasingly fragmented by human activities, multiplying forest edges that alter microclimates and species dynamics. While edge effect on plants is well documented, its impact on fauna remains less ex-plored. Ecoacoustics provides a non-invasive and passive approach to monitor faunal communities, yet the complexity of soundscapes and the opacity of machine learning tools often limit ecological interpretability. Here, we show that acoustic diversity in New Caledonian ultramafic forests exhibits a detectable edge effect, with sound-scapes converging towards homogeneity starting 100 m from the forest edge. Using passive acoustic monitoring and 59 ecoacoustic indices, we combined machine learn-ing, Representational Similarity Analysis, and explainable statistical methods from psychophysics. Results reveal a significant correlation between distance to edge and acoustic diversity, particularly pronounced during day-time. Indices such as NDSI, Hf, and SKEWf contributed most to detecting the effect, whereas some other in-dices added noise rather than informative signal. These findings match the botanical threshold described by Blanchard et al. (2023) and highlight both the promise and limitations of indice-based ecoacoustics. By integrating explicability methods, we offer a transparent framework to disentangle complex ecological signals, advancing ecoacoustics as a pertinent tool for studying forest fragmentation and guiding biodi-versity conservation.

ecology↗

bioRxiv2024

Worldwide soundscape ecology patterns across realms

Abstract and keywordsO_ST_ABSAimC_ST_ABSThe urgency for remote, reliable, and scalable biodiversity monitoring amidst mounting human pressures on ecosystems has sparked worldwide interest in Passive Acoustic Monitoring (PAM), which can track life underwater and on land. However, we lack a unified methodology to report this sampling effort and a comprehensive overview of PAM coverage to gauge its potential as a global research and monitoring tool. To remediate this, we created the Worldwide Soundscapes project, a collaborative network and growing database comprising metadata from 409 datasets across all realms (terrestrial, marine, freshwater, and subterranean). LocationWorldwide, 12 200 sites, all ecosystems. Time period1991 to present. Major taxa studiedAll soniferous taxa. MethodsWe synthesise sampling coverage across spatial, temporal, and ecological scales using metadata describing sampling locations, deployment schedules, focal taxa, and recording parameters. We explore global trends in biological, anthropogenic, and geophysical sounds based on 168 recordings from twelve ecosystems across all realms. ResultsTerrestrial sampling is spatially denser (45 sites/Mkm2) than aquatic sampling (0.3 and 1.8 sites/Mkm2 in oceans and fresh water) with only two subterranean datasets. Although diel and lunar cycles are well-covered in all realms, only marine datasets (56%) comprehensively sample all seasons. Across twelve ecosystems, biological sounds show contrasting diel patterns, decline with distance from the equator and negatively correlate with anthropogenic sounds. Main conclusionsPAM can inform macroecology studies as well as global conservation and phenology syntheses, but representation can be improved by expanding terrestrial taxonomic scope, sampling coverage in the high seas and subterranean ecosystems, and spatio-temporal replication in freshwater habitats. Overall, this global PAM network holds promise to support global biodiversity research and monitoring efforts.

ecology↗