Journal Article

Machine learning analysis reveals relationship between pomacentrid calls and environmental cues

Herrera, Daniel P., Haver, Samara M., Dziak, Robert P., Haxel, Joseph H., Waterhouse, Lynn, Heppell, Scott A., McKenna, Megan F., Munger, Jill E., Gedamke, Jason

2022

Overview

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.

• Publisher: Inter-Research Science Center,Inter-Research Science Publisher
• Subject: Acoustics / American Samoa / Analysis / Artificial neural networks / BASIC BIOLOGICAL SCIENCES / Biological noise / Computer applications / convolutional neural network / Coral reefs / Damselfishes / Distribution / Ecological monitoring / Ecological research / Environmental assessment / Environmental changes / Environmental monitoring / Fish / fish acoustics / Hydrophones / Identification / Learning algorithms / Machine learning / Marine invertebrates / National parks / Neural networks / passive acoustic monitoring / Sound / Sound pressure / Sound production / Tidal amplitude / Tropical climate / Water temperature / Wind speed