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Record W3128529515 · doi:10.1016/j.ecolind.2021.107435

The utility of different acoustic indicators to describe biological sounds of a coral reef soundscape

2021· article· en· W3128529515 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEcological Indicators · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsWildlife Conservation Society CanadaUniversity of Victoria
FundersCentre for Asia-Pacific InitiativesBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaLiber Ero FoundationMitacsW. Garfield Weston FoundationUniversity of Victoria
KeywordsReefCoral reefSoundscapeEnvironmental scienceCoralShrimpSound (geography)OceanographyFisheryGeologyBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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.

Opus teacher head0.030
GPT teacher head0.258
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it