The DOI matches its Crossref record; 103 of the 103 references it cites were found in a registry or an index.
Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring
Open accessAbstract
High‐throughput environmental sensing technologies are increasingly central to global monitoring of the ecological impacts of human activities. In particular, the recent boom in passive acoustic sensors has provided efficient, noninvasive, and taxonomically broad means to study wildlife populations and communities, and monitor their responses to environmental change. However, until recently, technological costs and constraints have largely confined research in passive acoustic monitoring (PAM) to a handful of taxonomic groups (e.g., bats, cetaceans, birds), often in relatively small‐scale, proof‐of‐concept studies. The arrival of low‐cost, open‐source sensors is now rapidly expanding access to PAM technologies, making it vital to evaluate where these tools can contribute to broader efforts in ecology and biodiversity research. Here, we synthesise and critically assess the current emerging opportunities and challenges for PAM for ecological assessment and monitoring of both species populations and communities. We show that terrestrial and marine PAM applications are advancing rapidly, facilitated by emerging sensor hardware, the application of machine learning innovations to automated wildlife call identification, and work towards developing acoustic biodiversity indicators. However, the broader scope of PAM research remains constrained by limited availability of reference sound libraries and open‐source audio processing tools, especially for the tropics, and lack of clarity around the accuracy, transferability and limitations of many analytical methods. In order to improve possibilities for PAM globally, we emphasise the need for collaborative work to develop standardised survey and analysis protocols, publicly archived sound libraries, multiyear audio datasets, and a more robust theoretical and analytical framework for monitoring vocalising animal communities.
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Biodiversity loss and its impact on humanity
Cardinale, B. J., Duffy, J. E., Gonzalez, A., Hooper, D. U., Perrings, C., Venail, P., Narwani, A., Mace, G. M., Tilman, D., Wardle, D. A., et al. (2012) · Nature · doi:10.1038/nature11148 · Cited by 7,457
A digital acoustic recording tag for measuring the response of wild marine mammals to sound
Johnson, M. P., & Tyack, P. L. (2003) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2002.808212 · Cited by 898
Muda, L., Begam, M. H., & Elamvazuthi, I. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1003.4083 · Cited by 803
Low-Shot Visual Recognition by Shrinking and Hallucinating Features
Hariharan, B., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.328 · Cited by 792
Estimating animal population density using passive acoustics
Marques, T. A., Thomas, L., Martin, S. W., Mellinger, D. K., Ward, J. A., Moretti, D. J., Harris, D., & Tyack, P. L. (2013) · Biological Reviews · doi:10.1111/brv.12001 · Cited by 746
What is soundscape ecology? An introduction and overview of an emerging new science
Pijanowski, B. C., Farina, A., Gage, S. H., Dumyahn, S. L., & Krause, B. L. (2011) · Landscape Ecology · doi:10.1007/s10980-011-9600-8 · Cited by 730
Principles of Animal Communication
Howard, R. (1999) · American Entomologist · doi:10.1093/ae/45.2.126 · Cited by 728
Rapid Acoustic Survey for Biodiversity Appraisal
Sueur, J., Pavoine, S., Hamerlynck, O., & Duvail, S. (2008) · PLoS ONE · doi:10.1371/journal.pone.0004065 · Cited by 684
Pieretti, N., Farina, A., & Morri, D. (2011) · Ecological Indicators · doi:10.1016/j.ecolind.2010.11.005 · Cited by 658
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Guidelines for the use of acoustic indices in environmental research
Bradfer‐Lawrence, T., Gardner, N., Bunnefeld, L., Bunnefeld, N., Willis, S. G., & Dent, D. H. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13254 · Cited by 319
AudioMoth: A low-cost acoustic device for monitoring biodiversity and the environment
Hill, A. P., Prince, P., Snaddon, J. L., Doncaster, C. P., & Rogers, A. (2019) · HardwareX · doi:10.1016/j.ohx.2019.e00073 · Cited by 282
Acoustic indices as proxies for biodiversity: a meta‐analysis
Alcocer, I., Lima, H., Sugai, L. S. M., & Llusia, D. (2022) · Biological Reviews · doi:10.1111/brv.12890 · Cited by 273
“Recent developments in acoustic sensors are enabling simultaneous and noninvasive monitoring at multiple sites during prolonged periods of time (Gibb et al., 2019).”
BackgroundCryptic species conservation: a review
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Metadata
- Authors
- Gibb, R., Browning, E., Glover‐Kapfer, P., & Jones, K. E.
- Venue
- Methods in Ecology and Evolution
- Publisher
- Wiley
- Published
- 2018-11-14
- Volume
- 10
- Issue
- 2
- Pages
- 169-185
- ISSN
- 2041-210X (ISSN-L)
- OpenAlex
- W2894847505
- Cited by
- 730
- References
- 103
- FWCI
- 30.07
- Type
- journal article
- Language
- en
- Open access
- hybrid
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