Using ecoacoustics metrices to track grassland bird richness across landscape gradients
Ecoacoustics is an emerging field that allows inferences about biodiversity trends and ecosystem health. Several acoustic indices have been developed for fast, automated assessments of ecosystem condition and are often used to assess changes in species richness/diversity across space or time. Howeve...
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2021
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oai:doaj.org-article:e05164bd6bfb4f40883588c103b7708f2021-12-01T04:29:33ZUsing ecoacoustics metrices to track grassland bird richness across landscape gradients1470-160X10.1016/j.ecolind.2020.106928https://doaj.org/article/e05164bd6bfb4f40883588c103b7708f2021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X20308670https://doaj.org/toc/1470-160XEcoacoustics is an emerging field that allows inferences about biodiversity trends and ecosystem health. Several acoustic indices have been developed for fast, automated assessments of ecosystem condition and are often used to assess changes in species richness/diversity across space or time. However, studies have reported inconclusive relationships between acoustic indices and species richness/diversity and the conclusions regarding “best” performing index differ between ecosystems. Here we assess the use of acoustic indices, using birds as a proxy for ecosystem health in a Northern Great Plains grassland system. We recorded soundscapes during bird morning chorus at 47 sites for 12–14 consecutive days and used these data to assess the accuracy of four acoustic indices (i.e. Bioacoustic Index; Acoustic Evenness Index; Acoustic Diversity Index; and Acoustic Complexity Index). We compared indices to bird richness derived from a trained ornithologist listening to sound recordings. The Bioacoustic Index (BI) and Acoustic Complexity Index (ACI) had the absolute highest correlation with bird richness in Northern Great Plains systems. Where Bioacoustic Index had a positive correlation and Acoustic Complexity Index had a negative correlation with grassland birds. The latter had the opposite direction from the ‘theoretical’ prediction. We further explored the relationship between indices and landscape gradients with known influences on bird diversity to further validate each index. We also discuss implications of acoustic indices as a tool for monitoring grassland birds. We found BI model outcomes align with known relationships between grassland birds and spatial covariates, whereas ACI did not concur these trends. We discuss these outcomes in relation to habitat-specific bird biophonic characteristics and provide possible explanations for differences between studies.Hila ShamonZoe ParaskevopoulouJustin KitzesEmily CardJessica L. DeichmannAndy J. BoyceWilliam J. McSheaElsevierarticleAcoustic indicesGrassland birdsEcoacousticsSound characteristicsSoundscapesEcologyQH540-549.5ENEcological Indicators, Vol 120, Iss , Pp 106928- (2021) |
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Acoustic indices Grassland birds Ecoacoustics Sound characteristics Soundscapes Ecology QH540-549.5 |
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Acoustic indices Grassland birds Ecoacoustics Sound characteristics Soundscapes Ecology QH540-549.5 Hila Shamon Zoe Paraskevopoulou Justin Kitzes Emily Card Jessica L. Deichmann Andy J. Boyce William J. McShea Using ecoacoustics metrices to track grassland bird richness across landscape gradients |
description |
Ecoacoustics is an emerging field that allows inferences about biodiversity trends and ecosystem health. Several acoustic indices have been developed for fast, automated assessments of ecosystem condition and are often used to assess changes in species richness/diversity across space or time. However, studies have reported inconclusive relationships between acoustic indices and species richness/diversity and the conclusions regarding “best” performing index differ between ecosystems. Here we assess the use of acoustic indices, using birds as a proxy for ecosystem health in a Northern Great Plains grassland system. We recorded soundscapes during bird morning chorus at 47 sites for 12–14 consecutive days and used these data to assess the accuracy of four acoustic indices (i.e. Bioacoustic Index; Acoustic Evenness Index; Acoustic Diversity Index; and Acoustic Complexity Index). We compared indices to bird richness derived from a trained ornithologist listening to sound recordings. The Bioacoustic Index (BI) and Acoustic Complexity Index (ACI) had the absolute highest correlation with bird richness in Northern Great Plains systems. Where Bioacoustic Index had a positive correlation and Acoustic Complexity Index had a negative correlation with grassland birds. The latter had the opposite direction from the ‘theoretical’ prediction. We further explored the relationship between indices and landscape gradients with known influences on bird diversity to further validate each index. We also discuss implications of acoustic indices as a tool for monitoring grassland birds. We found BI model outcomes align with known relationships between grassland birds and spatial covariates, whereas ACI did not concur these trends. We discuss these outcomes in relation to habitat-specific bird biophonic characteristics and provide possible explanations for differences between studies. |
format |
article |
author |
Hila Shamon Zoe Paraskevopoulou Justin Kitzes Emily Card Jessica L. Deichmann Andy J. Boyce William J. McShea |
author_facet |
Hila Shamon Zoe Paraskevopoulou Justin Kitzes Emily Card Jessica L. Deichmann Andy J. Boyce William J. McShea |
author_sort |
Hila Shamon |
title |
Using ecoacoustics metrices to track grassland bird richness across landscape gradients |
title_short |
Using ecoacoustics metrices to track grassland bird richness across landscape gradients |
title_full |
Using ecoacoustics metrices to track grassland bird richness across landscape gradients |
title_fullStr |
Using ecoacoustics metrices to track grassland bird richness across landscape gradients |
title_full_unstemmed |
Using ecoacoustics metrices to track grassland bird richness across landscape gradients |
title_sort |
using ecoacoustics metrices to track grassland bird richness across landscape gradients |
publisher |
Elsevier |
publishDate |
2021 |
url |
https://doaj.org/article/e05164bd6bfb4f40883588c103b7708f |
work_keys_str_mv |
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