Acoustic indices perform better when applied at ecologically meaningful time and frequency scales
Type
Journal
Authors
Category
Article
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Publication Year
2020
Publisher
Methods In Ecology and Evolution, United Kingdom
URL
[ private ]
Volume
12 (3)
Pages
421-431
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Abstract
1) Acoustic indices are increasingly employed in the analysis of soundscapes to ascertain biodiversity value. However, conflicting results and lack of consensus on best practices for their usage has hindered their application in conservation and land-use management contexts. Here we propose that the sensitivity of acoustic indices to ecological change and fidelity of acoustic indices to ecological communities are negatively impacted by signal masking. Signal masking can occur when acoustic responses of taxa sensitive to the effect of interest are masked by less-sensitive acoustic groups, or target taxa sonification is masked by non-target noise. We argue that by calculating acoustic indices at ecologically appropriate time and frequency bins, masking effects can be reduced and the efficacy of indices increased.
2) We test this on a large acoustic dataset collected in Eastern Amazonia spanning a disturbance gradient of undisturbed, logged, burned, logged-and-burned and secondary forests. We calculated values for two acoustic indices: the Acoustic Complexity Index and the Bioacoustic Index, across the entire frequency spectrum (0–22.1 kHz), and four narrower subsets of the frequency spectrum; at dawn, day, dusk and night.
3) We show that signal masking has a large impact on the sensitivity of acoustic indices to forest disturbance classes. Calculating acoustic indices at a range of narrower time–frequency bins substantially increases the classification accuracy of forest classes by random forest models. Furthermore, signal masking led to misleading correlations, including spurious inverse correlations, between biodiversity indicator metrics and acoustic index values compared to correlations derived from manual sampling of the audio data.
4) Consequently, we recommend that acoustic indices are calculated either at a range of time and frequency bins, or at a single narrow bin, predetermined by a priori ecological understanding of the soundscape.
2) We test this on a large acoustic dataset collected in Eastern Amazonia spanning a disturbance gradient of undisturbed, logged, burned, logged-and-burned and secondary forests. We calculated values for two acoustic indices: the Acoustic Complexity Index and the Bioacoustic Index, across the entire frequency spectrum (0–22.1 kHz), and four narrower subsets of the frequency spectrum; at dawn, day, dusk and night.
3) We show that signal masking has a large impact on the sensitivity of acoustic indices to forest disturbance classes. Calculating acoustic indices at a range of narrower time–frequency bins substantially increases the classification accuracy of forest classes by random forest models. Furthermore, signal masking led to misleading correlations, including spurious inverse correlations, between biodiversity indicator metrics and acoustic index values compared to correlations derived from manual sampling of the audio data.
4) Consequently, we recommend that acoustic indices are calculated either at a range of time and frequency bins, or at a single narrow bin, predetermined by a priori ecological understanding of the soundscape.
Description
https://doi.org/10.1111/2041-210X.13521
Number of Copies
1
