Machine learning analysis reveals relationship between pomacentrid calls and environmental cues
Sound production rates of fishes can be used as an indicator for coral reef health, providing an opportunity to utilize long-term acoustic recordings to assess environmental change. As acoustic datasets become more common, computational techniques need to be developed to facilitate analysis. Convolutional neural networks demonstrate an advantage in the identification of fish sounds over manual sampling approaches. Here we evaluate the ability of convolutional neural networks to identify and monitor call patterns for pomacentrids (damselfishes) in a tropical reef region of the western Pacific. A stationary hydrophone was deployed for 39 months in the National Park of American Samoa to record the local marine acoustic environment. A neural network was trained—achieving 94% identification accuracy of pomacentrids—to demonstrate the applicability of machine learning in fish acoustics and ecology. The distribution of sound production was found to vary on diel and interannual timescales. Additionally, the distribution of sound production was found to be correlated with wind speed, water temperature, tidal amplitude, and sound pressure level. This research has broad implications for state-of-the-art acoustic analysis and promises to be an efficient, scalable asset for ecological research and in environmental and conservation management plans.
Published: January 13, 2023
Munger J.E., D.P. Herrerra, S.M. Haver, L. Waterhouse, M.F. McKenna, R.P. Dziak, and J. Gedamke, et al. 2022.Machine learning analysis reveals relationship between pomacentrid calls and environmental cues.Marine Ecology Progress Series 681.PNNL-SA-159507.doi:10.3354/meps13912