TY - JOUR

T1 - Acoustic indices perform better when applied at ecologically meaningful time and frequency scales

AU - Metcalf, O.C.

AU - Barlow, J.

AU - Devenish, C.

AU - Marsden, S.

AU - Berenguer, E.

AU - Lees, A.C.

N1 - This is the peer reviewed version of the following article: Metcalf, OC, Barlow, J, Devenish, C, Marsden, S, Berenguer, E, Lees, AC. Acoustic indices perform better when applied at ecologically meaningful time and frequency scales. Methods Ecol Evol. 2021; 12: 421– 431. https://doi.org/10.1111/2041-210X.13521 which has been published in final form at https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13521 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

PY - 2021/3/31

Y1 - 2021/3/31

N2 - 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. 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. 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. 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. © 2020 British Ecological Society

AB - 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. 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. 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. 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. © 2020 British Ecological Society

KW - acoustic indices

KW - Amazonia

KW - bioacoustics

KW - biodiversity

KW - ecoacoustics

KW - remote sensing

KW - soundscape

KW - tropical ecology

U2 - 10.1111/2041-210X.13521

DO - 10.1111/2041-210X.13521

M3 - Journal article

VL - 12

SP - 421

EP - 431

JO - Methods in Ecology and Evolution

JF - Methods in Ecology and Evolution

SN - 2041-210X

IS - 3

ER -