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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Coral loss alters guarding and farming behavior of a Caribbean damselfish

Coral loss is a major element of global ecological change in the oceans that may shape the behavior of keystone species such as habitat-attached animals. Farming damselfishes cultivate and aggressively protect algal garden within their territory, thereby shaping local fish assemblages and macro-algae diversity. Following the widespread loss of live branching coral throughout Florida and the Caribbean Sea, dusky damselfish (Stegastes adustus) have modified their territorial behavior. We compared antagonistic behavior, and size and algal species composition of territories of dusky damselfish on dead branching coral rubble and live boulder coral in the Dry Tortugas National Park, USA. Even though similar numbers of “intruders” entered each habitat type, dusky damselfish on rubble territories chased out significantly more fishes, had larger territories and more species of algae were present in their gardens. This study shows that dusky damselfish successfully occupy living and dead coral areas; however, there are potential trade-offs between defending a larger territory and cultivating fewer algal species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

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 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.

59 BASIC BIOLOGICAL SCIENCES↗