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Coral reefs comprise some of the most biodiverse habitats on the planet. These ecosystems face a range of stressors, making quantifying community assemblages and potential changes vital to effective management. To understand short- and long-term changes in biodiversity and detect early warning signals of decline, new methods for quantifying biodiversity at scale are necessary. Acoustic monitoring techniques have proven useful in observing species activities and biodiversity on coral reefs through aggregate approaches (i.e. energy as a proxy). However, few studies have ground-truthed these acoustic analyses with human-based observations. In this study, we sought to expand these passive acoustic methods by investigating biological sounds and fish call rates on a healthy reef, providing a unique set of human-confirmed, labeled acoustic observations. We analyzed acoustic data from Tektite Reef, St. John, US Virgin Islands, over a 2 mo period. A subset of acoustic files was manually inspected to identify recurring biotic sounds and quantify reef activity throughout the day. We found a high variety of acoustic signals in this soundscape. General patterns of call rates across time conformed to expectations, with dusk and dawn showing important and significantly elevated peaks in soniferous fish activity. The data reflected high variability in call rates across days and lunar phases. Call rates did not correspond to sound pressure levels, suggesting that certain call types may drive crepuscular trends in sound levels while lower-level critical calls, likely key for estimating biodiversity and behavior, may be missed by gross sound level analyses.
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Unsupervised clustering reveals acoustic diversity and niche differentiation in pulsed calls from a coral reef ecosystem
Coral reefs are biodiverse marine ecosystems that are undergoing rapid changes, making monitoring vital as we seek to manage and mitigate stressors. Healthy reef soundscapes are rich with sounds, enabling passive acoustic recording and soundscape analyses to emerge as cost-effective, long-term methods for monitoring reef communities. Yet most biological reef sounds have not been identified or described, limiting the effectiveness of acoustic monitoring for diversity assessments. Machine learning offers a solution to scale such analyses but has yet to be successfully applied to characterize the diversity of reef fish sounds. Here we sought to characterize and categorize coral reef fish sounds using unsupervised machine learning methods. Pulsed fish and invertebrate sounds from 480 min of data sampled across 10 days over a 2-month period on a US Virgin Islands reef were manually identified and extracted, then grouped into acoustically similar clusters using unsupervised clustering based on acoustic features. The defining characteristics of these clusters were described and compared to determine the extent of acoustic diversity detected on these reefs. Approximately 55 distinct calls were identified, ranging in centroid frequency from 50 Hz to 1,300 Hz. Within this range, two main sub-bands containing multiple signal types were identified from 100 Hz to 400 Hz and 300 Hz–700 Hz, with a variety of signals outside these two main bands. These methods may be used to seek out acoustic diversity across additional marine habitats. The signals described here, though taken from a limited dataset, speak to the diversity of sounds produced on coral reefs and suggest that there might be more acoustic niche differentiation within soniferous fish communities than has been previously recognized.
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- PAR ID:
- 10652415
- Publisher / Repository:
- Frontiers
- Date Published:
- Journal Name:
- Frontiers in Remote Sensing
- Volume:
- 5
- ISSN:
- 2673-6187
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/frsen.2024.1429227
