Using acoustic indices in ecology: Guidance on study design, analyses and interpretation

Abstract

The rise of passive acoustic monitoring and the rapid growth in large audio datasets is driving the development of analysis methods that allow ecological inferences to be drawn from acoustic data. Acoustic indices are currently one of the most widely applied tools in ecoacoustics. These numerical summaries of the sound energy contained in digital audio recordings are relatively straightforward and fast to calculate but can be challenging to interpret. Misapplication and misinterpretation have produced conflicting results and led some to question their value. To encourage better use of acoustic indices, we provide nine points of guidance to support good study design, analysis and interpretation. We offer practical recommendations for the use of acoustic indices in the study of both whole soundscapes and individual taxa and species, and point to emerging trends in ecoacoustic analysis. In particular, we highlight the critical importance of understanding the links between soundscape patterns and acoustic indices. Acoustic indices can offer insights into the state of organisms, populations, and ecosystems, complementing other ecological research techniques. Judicious selection, appropriate application and thorough interpretation of existing indices is vital to bolster robust developments in ecoacoustics for biodiversity monitoring, conservation and future research.

Time and habitat structure shape insect acoustic activity in the Amazon

“…two months), within the same period (dry season), and with several replicates per habitat, we believe that our results are robust. Finally, we are aware that acoustic indices need to be used with caution [24], but our results suggest that acoustic indices can greatly advance insect automated monitoring after carefully selecting the optimal frequency bands for certain insect groups and with a validation of low interferences in the selected frequencies [25].…”

“…It takes the amplitude measurement of signals in each bin above a selected threshold (−50 dBFS) and then applies the Gini index to these bins to measure if they are equally occupied. It returns a value close to one when sound intensity is restricted to few frequencies and a value close to zero when soundscapes are saturated with sounds [24,33]. Therefore, recordings with lower values of AEI can be interpreted as recordings with higher sound activity, while recordings with higher AEI values can be interpreted as recordings with limited sound activity (electronic supplementary material, figures S1-S8).…”

“…However, two recent meta-analysis revealed only moderate positive relationships between acoustic indices and diversity metrics, highlighting their limitations [22,23]. These findings do not undermine their usage but further encourage the development of alternative applications and more tests [22,24].…”

Assessing the potential of BirdNET to infer European bird communities from large-scale ecoacoustic data

“…Using only the Za-Pygar dataset, we further analyzed how BirdNET performance might be influenced by factors related to the acoustic dataset analyzed (total duration recorded, habitat recorded, passive recorder used, preponderance of biophony over anthropophony and geophony (Bradfer-Lawrence et al, 2023; see Appendix S6), and number of species vocalizing at the same time) as well as the number of recordings available online for each species as a proxy for the size of the training dataset used by BirdNET.…”

“…Additionally, the analysis of soundscape variation across time and space allows to study potential correlations between the presence of anthropogenic sounds and the acoustic activity of sound-producing animals (Blumstein et al, 2011). This means that PAM could serve as a reliable way to obtain complementary information to cover the gaps left by traditional monitoring methods (Bradfer-Lawrence et al, 2023). Indeed, recent studies suggest that the combined use of PAM and AAM may provide a more accurate picture of the presence and abundance of target species than the use of only one of the two methods (Bobay et al, 2018; Shaw et al, 2021).…”