Ecoacoustic indices as proxies for biodiversity on temperate reefs

Abstract

Summary Diversity measurement techniques can present logistical and financial obstacles to conservation efforts. Ecoacoustics has recently emerged as a promising solution to these issues, providing a mechanism for measuring diversity using acoustic indices, which have proven to be beneficial in terrestrial habitats. This study investigates the application of acoustic measures as a tool for quick and effective marine diversity monitoring via direct, in situ comparison of ecoacoustics indices with species assemblage diversity measures from temperate rocky reefs. Acoustic recordings and visual surveys of reef fish abundance were collected at nine sites in north‐eastern New Zealand. Three ecoacoustic indices originally developed for terrestrial use were then compared to three species assemblage diversity measures and compared using Pearson correlations. Additionally, four criteria for successful ecoacoustic indices were developed and tested as a means of standardizing future evaluation and use of acoustic indices: (i) positive correlations between species diversity and ecoacoustic indices in relevant frequency ranges, (ii) robustness to changes in spectral resolution, (iii) robustness to the presence of natural noise interference (i.e. wind) and (iv) robustness to the presence of anthropogenic noise. Acoustic Complexity Index (ACI) was significantly correlated with Pielou's Evenness (J′) and Shannon's index (H′). Neither Acoustic Richness (AR) nor ACI was impacted by changes in spectral resolution, but values of the Acoustic Entropy Index (H) increased significantly between fast Fourier transformation (FFT) sizes 512 and 1024. H was consistently positively correlated with both H′ and estimated number of species (S) above a spectral resolution of c. 140·6 Hz (FFT size 1024). Wind did not affect any of the acoustic indices. As anthropogenic noise was included in these investigations, both ACI and H were considered robust to its presence. While AR failed to meet all four criteria for a successful ecoacoustic indices, both ACI and H appeared to be appropriate for use on temperate reefs. In a time of accelerated global diversity loss, these two ecoacoustic indices show strong potential for use as efficient, non‐invasive marine diversity measures.

Improving biodiversity assessment via unsupervised separation of biological sounds from long-duration recordings

“…Several studies have also successfully tested the correlations between ecoacoustics indices and local biodiversity in tropical forests, temperate forests, and coastal reef environments 7, 10, 14 . However, not all indices perform equally regarding the correlations of biodiversity changes.…”

Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring

“…Similarly, there is growing interest in marine soundscape analysis, for instance, in studies of reef phenology (McWilliam, McCauley, Erbe, & Parsons, ), the use of acoustic cues by fish (Simpson et al., ), mapping biotic sound across oceanic habitats (Nedelec et al., ), and development of biodiversity indicators (Sueur et al., ). However, these efforts are complicated by the acoustic connectedness of underwater habitats, with long‐range sounds and anthropogenic noise potentially swamping local variations in biotic sound (Harris et al., ; McWilliam & Hawkins, ).…”

Tadarida: A Toolbox for Animal Detection on Acoustic Recordings

“…For example, the prolific development of acoustic indices in ecoacoustics can benefit from Tadarida-D by the way it detects and extract numerous features on every animal vocalization in a soundscape, thus allowing measures of alpha and beta diversity [5,20].…”

“…Reduced cost of acoustic recorders and increase in storage capacity has resulted in an exponential development of Passive Acoustic Monitoring (PAM) of a very wide range of animals in a few years [1,2,3], opening up a new field of research [4] and responding to an urgent need for biodiversity monitoring [5]. However, data analysis of this emerging big data is now impeded by limited software development and availability [6,7].…”