Enhancing automated analysis of marine soundscapes using ecoacoustic indices and machine learning

Abstract

Historically, ecological monitoring of marine habitats has primarily relied on labour-intensive, non-automated survey methods. The field of passive acoustic monitoring (PAM) has demonstrated the potential of this practice to automate surveying in marine habitats. This has primarily been through the use of ‘ecoacoustic indices’ to quantify attributes from natural soundscapes. However, investigations using individual indices have had mixed success. Using PAM recordings collected at one of the world’s largest coral reef restoration programmes, we instead apply a machine-learning approach across a suite of ecoacoustic indices to improve predictive power of ecosystem health. Healthy and degraded reef sites were identified through live coral cover surveys, with 90–95% and 0–20% cover respectively. A library of one-minute recordings were extracted from each. Twelve ecoacoustic indices were calculated for each recording, in up to three different frequency bandwidths (low: 0.05–0.8 kHz, medium: 2–7 kHz and broad: 0.05–20 kHz). Twelve of these 33 index-frequency combinations differed significantly between healthy and degraded habitats. However, the best performing single index could only correctly classify 47% of recordings, requiring extensive sampling from each site to be useful. We therefore trained a regularised discriminant analysis machine-learning algorithm to discriminate between healthy and degraded sites using an optimised combination of ecoacoustic indices. This multi-index approach discriminated between these two habitat classes with improved accuracy compared to any single index in isolation. The pooled classification rate of 1000 cross-validated iterations of the model had a 91.7% 0.8, mean SE) success rate at correctly classifying individual recordings. The model was subsequently used to classify recordings from two actively restored sites, established >24 months prior to recordings, with coral cover values of 79.1% (±3.9) and 66.5% (±3.8). Of these recordings, 37/38 and 33/39 received a classification as healthy respectively. The model was also used to classify recordings from a newly restored site established <12 months prior with a coral cover of 25.6% (±2.6), from which 27/33 recordings were classified as degraded. This investigation highlights the value of combining PAM recordings with machine-learning analysis for ecological monitoring and demonstrates the potential of PAM to monitor reef recovery over time, reducing the reliance on labour-intensive in-water surveys by experts. As access to PAM recorders continues to rapidly advance, effective automated analysis will be needed to keep pace with these expanding acoustic datasets.