Analytical approaches for evaluating passive acoustic monitoring data: A case study of avian vocalizations
1. The interface between field biology and technology is energizing the collection of vast quantities of environmental data. Passive acoustic monitoring, the use of unattended recording devices to capture environmental sound, is an example where technological advances have facilitated an influx of d...
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ftpubmed:oai:pubmedcentral.nih.gov:9022445 2023-05-15T15:34:42+02:00 Analytical approaches for evaluating passive acoustic monitoring data: A case study of avian vocalizations Symes, Laurel B. Kittelberger, Kyle D. Stone, Sophia M. Holmes, Richard T. Jones, Jessica S. Castaneda Ruvalcaba, Itzel P. Webster, Michael S. Ayres, Matthew P. 2022-04-21 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022445/ http://www.ncbi.nlm.nih.gov/pubmed/35475182 https://doi.org/10.1002/ece3.8797 en eng John Wiley and Sons Inc. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022445/ http://www.ncbi.nlm.nih.gov/pubmed/35475182 http://dx.doi.org/10.1002/ece3.8797 © 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. CC-BY Ecol Evol Research Articles Text 2022 ftpubmed https://doi.org/10.1002/ece3.8797 2022-05-01T00:34:09Z 1. The interface between field biology and technology is energizing the collection of vast quantities of environmental data. Passive acoustic monitoring, the use of unattended recording devices to capture environmental sound, is an example where technological advances have facilitated an influx of data that routinely exceeds the capacity for analysis. Computational advances, particularly the integration of machine learning approaches, will support data extraction efforts. However, the analysis and interpretation of these data will require parallel growth in conceptual and technical approaches for data analysis. Here, we use a large hand‐annotated dataset to showcase analysis approaches that will become increasingly useful as datasets grow and data extraction can be partially automated. 2. We propose and demonstrate seven technical approaches for analyzing bioacoustic data. These include the following: (1) generating species lists and descriptions of vocal variation, (2) assessing how abiotic factors (e.g., rain and wind) impact vocalization rates, (3) testing for differences in community vocalization activity across sites and habitat types, (4) quantifying the phenology of vocal activity, (5) testing for spatiotemporal correlations in vocalizations within species, (6) among species, and (7) using rarefaction analysis to quantify diversity and optimize bioacoustic sampling. 3. To demonstrate these approaches, we sampled in 2016 and 2018 and used hand annotations of 129,866 bird vocalizations from two forests in New Hampshire, USA, including sites in the Hubbard Brook Experiment Forest where bioacoustic data could be integrated with more than 50 years of observer‐based avian studies. Acoustic monitoring revealed differences in community patterns in vocalization activity between forests of different ages, as well as between nearby similar watersheds. Of numerous environmental variables that were evaluated, background noise was most clearly related to vocalization rates. The songbird community included one cluster ... Text Avian Studies PubMed Central (PMC) Ecology and Evolution 12 4 |
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Research Articles Symes, Laurel B. Kittelberger, Kyle D. Stone, Sophia M. Holmes, Richard T. Jones, Jessica S. Castaneda Ruvalcaba, Itzel P. Webster, Michael S. Ayres, Matthew P. Analytical approaches for evaluating passive acoustic monitoring data: A case study of avian vocalizations |
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Research Articles |
description |
1. The interface between field biology and technology is energizing the collection of vast quantities of environmental data. Passive acoustic monitoring, the use of unattended recording devices to capture environmental sound, is an example where technological advances have facilitated an influx of data that routinely exceeds the capacity for analysis. Computational advances, particularly the integration of machine learning approaches, will support data extraction efforts. However, the analysis and interpretation of these data will require parallel growth in conceptual and technical approaches for data analysis. Here, we use a large hand‐annotated dataset to showcase analysis approaches that will become increasingly useful as datasets grow and data extraction can be partially automated. 2. We propose and demonstrate seven technical approaches for analyzing bioacoustic data. These include the following: (1) generating species lists and descriptions of vocal variation, (2) assessing how abiotic factors (e.g., rain and wind) impact vocalization rates, (3) testing for differences in community vocalization activity across sites and habitat types, (4) quantifying the phenology of vocal activity, (5) testing for spatiotemporal correlations in vocalizations within species, (6) among species, and (7) using rarefaction analysis to quantify diversity and optimize bioacoustic sampling. 3. To demonstrate these approaches, we sampled in 2016 and 2018 and used hand annotations of 129,866 bird vocalizations from two forests in New Hampshire, USA, including sites in the Hubbard Brook Experiment Forest where bioacoustic data could be integrated with more than 50 years of observer‐based avian studies. Acoustic monitoring revealed differences in community patterns in vocalization activity between forests of different ages, as well as between nearby similar watersheds. Of numerous environmental variables that were evaluated, background noise was most clearly related to vocalization rates. The songbird community included one cluster ... |
format |
Text |
author |
Symes, Laurel B. Kittelberger, Kyle D. Stone, Sophia M. Holmes, Richard T. Jones, Jessica S. Castaneda Ruvalcaba, Itzel P. Webster, Michael S. Ayres, Matthew P. |
author_facet |
Symes, Laurel B. Kittelberger, Kyle D. Stone, Sophia M. Holmes, Richard T. Jones, Jessica S. Castaneda Ruvalcaba, Itzel P. Webster, Michael S. Ayres, Matthew P. |
author_sort |
Symes, Laurel B. |
title |
Analytical approaches for evaluating passive acoustic monitoring data: A case study of avian vocalizations |
title_short |
Analytical approaches for evaluating passive acoustic monitoring data: A case study of avian vocalizations |
title_full |
Analytical approaches for evaluating passive acoustic monitoring data: A case study of avian vocalizations |
title_fullStr |
Analytical approaches for evaluating passive acoustic monitoring data: A case study of avian vocalizations |
title_full_unstemmed |
Analytical approaches for evaluating passive acoustic monitoring data: A case study of avian vocalizations |
title_sort |
analytical approaches for evaluating passive acoustic monitoring data: a case study of avian vocalizations |
publisher |
John Wiley and Sons Inc. |
publishDate |
2022 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022445/ http://www.ncbi.nlm.nih.gov/pubmed/35475182 https://doi.org/10.1002/ece3.8797 |
genre |
Avian Studies |
genre_facet |
Avian Studies |
op_source |
Ecol Evol |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022445/ http://www.ncbi.nlm.nih.gov/pubmed/35475182 http://dx.doi.org/10.1002/ece3.8797 |
op_rights |
© 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1002/ece3.8797 |
container_title |
Ecology and Evolution |
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12 |
container_issue |
4 |
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1766365004496371712 |