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Behavioural responses of wild Pacific salmon and herring to boat noise

2021· article· en· W4200463936 on OpenAlexfundno aff
Inge van der Knaap, Erin Ashe, Dave Hannay, Asila Ghoul Bergman, Kimberly A. Nielsen, Catherine F. Lo, Rob Williams

Bibliographic record

VenueMarine Pollution Bulletin · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersFisheries and Oceans CanadaUniversiteit GentVictoria UniversityMarine Environmental Observation Prediction and Response NetworkPew Charitable Trusts
KeywordsOncorhynchusPacific herringHerringClupeaPredationForagingFisheryPopulationJuvenileForage fishEnvironmental scienceBiologyEcologyGeographyFish <Actinopterygii>Demography

Abstract

fetched live from OpenAlex

There is growing concern about impacts of ship and small boat noise on marine wildlife. Few studies have quantified impacts of anthropogenic noise on ecologically, economically, and culturally important fish. We conducted open net pen experiments to measure Pacific herring (Clupea pallasii) and juvenile salmon (pink, Oncorhynchus gorbuscha, and chum, Oncorhynchus keta) behavioural response to noise generated by three boats travelling at different speeds. Dose-response curves for herring and salmon estimated 50% probability of eliciting a response at broadband received levels of 123 and 140 dB (re 1 μPa), respectively. Composite responses (yes/no behaviour change) were evaluated. Both genera spent more time exhibiting behaviours consistent with anti-predator response during boat passings. Repeated elicitation of vigilance or anti-predatory responses could result in increased energy expenditure or decreased foraging. These experiments form an important step toward assessing population-level consequences of noise, and its ecological costs and benefits to predators and prey.

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
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 score0.159
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.018
GPT teacher head0.230
Teacher spread0.212 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations39
Published2021
Admission routes1
Has abstractyes

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