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

Abstract

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.

Simultaneous passive acoustic monitoring uncovers evidence of potentially overlooked temporal variation in an Amazonian bird community

“…Many Amazonian bird species vocalize only in brief temporal windows, especially those surrounding dawn and dusk (Scott et al . 1986, Parker 1991, Metcalf et al . 2020, pers.…”

“…Additionally, the dawn soundscape in Amazonia features far less anthropogenic activity and insect noise than other periods, reducing interference with avian species detection (Hart et al . 2015, Metcalf et al . 2020).…”

“…Species richness and Shannon diversity were included as standard measures of ecological diversity (Okansen et al . 2019, Metcalf et al . 2020).…”

“…The dawn-hour was selected because it contains the highest level of vocal activity and diversity among neotropical birds (Berg et al 2006), including many species that only sing in narrow time windows around first light (Parker 1991, Blake 1992. Additionally, the dawn soundscape in Amazonia features far less anthropogenic activity and insect noise than other periods, reducing interference with avian species detection (Hart et al 2015, Metcalf et al 2020.…”

“…For each site, day, and individual recording, four metrics were calculated: species richness, Shannon diversity, vocal prevalence, and Bray-Curtis dissimilarity. Species richness and Shannon diversity were included as standard measures of ecological diversity (Okansen et al 2019, Metcalf et al 2020. Vocal prevalence (VP), the number of 15-second blocks in a recording containing at least one foreground vocalization from a species or taxa, was used to quantify vocal activity levels or soundscape abundance.…”