Abstract
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 of the massive data files produced by long-term monitoring. Machine learning techniques demonstrate an advantage in the identification of fish sounds over manual sampling approaches. Here we evaluated 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 mo (2014-2018) in the National Park of American Samoa to continuously 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 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, environmental monitoring, and conservation planning.
Highlights
Marine environments are notoriously difficult to study over extended time frames, but long-term monitoring is an effective means to evaluate changes in ecosystem status
The accuracy of the trained convolutional neural network (CNN) model based on the validation dataset was 0.9421, suggesting that 94% of all 2 s segments were correctly classified as containing either a pomacentrid sound or noise
This research provides an efficient methodology for characterizing the temporal distribution of pomacentrid sound production over a 4 yr period through the utilization of CNN techniques
Summary
Marine environments are notoriously difficult to study over extended time frames, but long-term monitoring is an effective means to evaluate changes in ecosystem status. Passive acoustic monitoring (PAM) allows for cost-effective, protracted environmental sampling that can be used to monitor ecosystem health and species diversity. PAM complements other techniques by sampling marine environments at night, in low-visibility conditions, and over longer temporal scales. The teleosts (bony fishes) are excellent candidates for using PAM to understand behavior and evaluate the effects of environmental change. An important step in PAM analysis for teleost sound production is the development of automated analytical techniques capable of efficiently processing the vast quantity of acoustic data produced during long-term PAM deployments.
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