The utility of different acoustic indicators to describe biological sounds of a coral reef soundscape
Bibliographic record
Abstract
Monitoring coral reefs is vital to the conservation of these at-risk ecosystems. While most current monitoring methods are costly and time-intensive, passive acoustic monitoring (PAM) could provide a cost-effective, large scale reef monitoring tool. However, for PAM to be reliable, the results must be field tested to ensure that the acoustic methods used accurately represent the certain ecological components of the reef being studied. For example, recent acoustic studies have attempted to describe the diversity of coral reef fish using the Acoustic Complexity Index (ACI) but despite inconsistent results on coral reefs, ACI is still being applied to these ecosystems. Here, we investigated the potential for ACI and sound pressure level (SPL – another common metric used), to accurately respond to biological sounds on coral reefs when calculated using three different frequency resolutions (31.2 Hz, 15.6 Hz, and 4 Hz). Acoustic recordings were made over two to three-week periods in 2017 and 2018 at sites around Kiritimati (Christmas Island), in the central equatorial Pacific. We hypothesized that SPL would be positively correlated with the number of nearby fish sounds in the low frequency band and with snapping shrimp snaps in the high frequency band, but that ACI would rely on its settings, specifically its frequency resolution, to describe sounds in both frequency bands. We found that nearby fish sounds were partially responsible for changes in low frequency SPL in the morning, during crepuscular chorusing activity, but not at other times of day. Snapping shrimp snaps, however, were responsible for large changes in high frequency SPL. ACI results were reliant on the frequency band chosen, with the 31.2 Hz frequency resolution models being chosen as the best models. In the low frequency band, the effect of fish knocks was positive and significant only in the 31.2 Hz and 15.6 Hz models while in the high frequency band snapping shrimp snaps were negatively associated with ACI in all frequency resolutions. These results contribute to a growing body of evidence against the continued use of ACI without standardization on highly energetic underwater ecosystems like coral reefs and highlight the importance of extensive field testing of new acoustic metrics prior to their adoption and proliferation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".