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 severely impacted by signal masking. Signal masking can occur when acoustic responses sensitive to the effect being monitored 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 including 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 highly 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.
Detailes of data collection are provided in the paper. Species richness and other diversity metrics are calculated in the vegan package in R.