Using acoustic metrics to characterize underwater acoustic biodiversity in the Southern Ocean.
Acoustic metrics (AM) assist our interpretation of acoustic environments by aggregating a complex signal into a unique number. Numerous AM have been developed for terrestrial ecosystems, with applications ranging from rapid biodiversity assessments to characterizing habitat quality. However, there h...
| Published in: | Remote Sensing in Ecology and Conservation |
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| Main Authors: | , |
| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
Wiley & Sons Ltd
2019
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| Subjects: | |
| Online Access: | https://epic.awi.de/id/eprint/50382/ https://epic.awi.de/id/eprint/50382/1/Roca_VanOpzeeland2019_UsingAcousticMetrics.pdf https://zslpublications.onlinelibrary.wiley.com/doi/pdf/10.1002/rse2.129 https://hdl.handle.net/10013/epic.183b976c-5b9a-4b74-b567-ebcd11be3cf7 |
| Summary: | Acoustic metrics (AM) assist our interpretation of acoustic environments by aggregating a complex signal into a unique number. Numerous AM have been developed for terrestrial ecosystems, with applications ranging from rapid biodiversity assessments to characterizing habitat quality. However, there has been comparatively little research aimed at understanding how these metrics perform to characterize the acoustic features of marine habitats and their relation with ecosystem biodiversity. Our objectives were to 1) assess whether AM are able to capture the spectral and temporal differences between two distinct Antarctic marine acoustic environment types (i.e., pelagic vs. on-shelf), 2) evaluate the performance of a combination of AM compared to the signal full frequency spectrum to characterize marine mammals acoustic assemblages (i.e., species richness–SR–and species identity) and 3) estimate the contribution of SR to the local marine acoustic heterogeneity measured by single AM. We used 23 different AM to develop a supervised machine learning approach to discriminate between acoustic environments. AM performance was similar to the full spectrum, achieving correct classifications for SR levels of 58% and 92% for pelagic and on-shelf sites respectively and > 88% for species identities. Our analyses show that a combination of AM is a promising approach to characterize marine acoustic communities. It allows an intuitive ecological interpretation of passive acoustic data, which in the light of ongoing environmental changes, supports the holistic approach needed to detect and understand trends in species diversity, acoustic communities and underwater habitat quality |
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