Scholarly record
ACOUSTIC INDICATORS FOR SDG 15: MONITORING LIFE ON LAND THROUGH BIOLOGICAL ACTIVITY IN TEMPERATE BROADLEAF FORESTS
Abstract
Sustainable Development Goal 15 ( Life on Land ) emphasizes the conservation and restoration of terrestrial ecosystems, yet monitoring biodiversity across large landscapes remains a challenge, particularly for species not yet under direct threat but that serve as indicators of ecosystem health. Terrestrial wildlife monitoring has traditionally relied on direct visual observations, such as point counts or mark-recapture methods; however, these approaches can be challenging to apply to highly vagile, cryptic, rare, or nocturnal taxa. Technological advancements, such as radio telemetry, camera trapping, and passive acoustic monitoring, now enable researchers to study elusive populations. Yet, they come with their own suite of challenges, including vast data sets and a lack of robust, standardized approaches for their interpretation. This study presents a remote acoustic monitoring framework designed for the temperate broadleaf biome, integrating autonomous recording units and AI-assisted classification tools to quantify and interpret patterns of biological activity. Using the B-simple index, a biologic activity metric derived from all detectable acoustic events filtered through verified biotic signatures, we demonstrate an approach that captures the collective pulse of ecosystem vitality. Unlike single-species monitoring, this composite acoustic metric provides an early indicator of biodiversity change and ecosystem resilience with potential applications across temperate broadleaf biomes worldwide. Results highlight the importance of developing conservation metrics tailored to specific biomes, enabling generalization across diverse regions while maintaining sensitivity to local ecological processes. By focusing on proactive monitoring of common species and their soundscapes, this work contributes to building community awareness, adaptive management capacity, and long-term sustainability aligned with the targets of SDG 15.
Publication Impact Profile
Publication details
References14
Davison, C. W., Rahbek, C., & Morueta-Holme, N. (2021). Land-use change and biodiversity: Challenges for assembling evidence on the greatest threat to nature. Global Change Biology, 27(21), 5414-5429. DOI: 10.1111/gcb.15846
Sparrow, B. D., Edwards, W., Munroe, S. E., Wardle, G. M., Guerin, G. R., Bastin, J. F.,... & Lowe, A. J. (2020). Effective ecosystem monitoring requires a multi-scaled approach. Biological reviews, 95(6), 1706-1719. DOI: 10.1111/brv.12636
Breed, D., Meyer, L. C. R., Steyl, J. C. A., Goddard, A., Burroughs, R., & Kohn, T. A. (2019). Conserving wildlife in a changing world: Understanding capture myopathy a malignant outcome of stress during capture and translocation. Conservation Physiology, 7(1), coz027. DOI: 10.1093/conphys/coz027
Sugai, L. S. M., Silva, T. S. F., Ribeiro Jr, J. W., & Llusia, D. (2019). Terrestrial passive acoustic monitoring: review and perspectives. BioScience, 69(1), 15 25. DOI: 10.1093/biosci/biy147
Sunde, P., Jessen, L. It counts who counts: an experimental evaluation of the importance of observer effects on spotlight count estimates. Eur J Wildl Res 59, 645 653 (2013). DOI: 10.1007/s10344-013-0717-8
Winiarska, D., Szymanski, P., & Osiejuk, T. S. (2025). Methods of acoustic data processing affect species detectability in passive acoustic monitoring of multi-species playback. Ibis, 167(3), 789-802. DOI: 10.1111/ibi.13405
Bradfer-Lawrence, T., Gardner, N., Bunnefeld, L., Bunnefeld, N., & Dent, D. H. (2019). Guidelines for the use of acoustic indices in environmental research. Methods in Ecology and Evolution, 10(10), 1796 1807. DOI: 10.1111/2041-210x.13254
Kahl, S., Wood, C. M., Eibl, M., & Klinck, H. (2021). BirdNET: A deep learning solution for avian diversity monitoring. Ecological Informatics, 61, 101236. DOI: 10.1016/j.ecoinf.2021.101236
Stowell, D., Wood, M. D., Pamula, H., Stylianou, Y., & Glotin, H. (2019). Automatic acoustic detection of birds through deep learning: the first Bird Audio Detection challenge. Methods in Ecology and Evolution, 10(3), 368 380. DOI: 10.1111/2041-210x.13103
Meyer, C., Kreft, H., Guralnick, R. et al. Global priorities for an effective information basis of biodiversity distributions. Nat Commun 6, 8221 (2015). DOI: 10.1038/ncomms9221
Darras, K. F., Rountree, R. A., Van Wilgenburg, S. L., Cord, A. F., Pitz, F., Chen, Y.,... & Mammides, C. (2025). Worldwide Soundscapes: a synthesis of passive acoustic monitoring across realms. Global Ecology and Biogeography, 34(5), e70021. DOI: 10.1111/geb.70021
Farina, A., & Gage, S. H. (2017). Ecoacoustics: The Ecological Role of Sounds. Wiley-Blackwell. DOI: 10.1002/9781119230724
Emmert-Streib, F., & Dehmer, M. (2019). High-dimensional LASSO-based computational regression models: regularization, shrinkage, and selection. Machine Learning and Knowledge Extraction, 1(1), 359-383. DOI: 10.3390/make1010021
Metcalf, O. C., Baccaro, F., Barlow, J., Berenguer, E., Bradfer-Lawrence, T., Rossi, L. C.,... & Lees, A. C. (2024). Listening to tropical forest soils. Ecological Indicators, 158, 111566. DOI: 10.1016/j.ecolind.2024.111566
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.