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Record W4225927969 · doi:10.3354/meps14060

Failure of bivalve foundation species recruitment related to trophic changes during an extreme heatwave event

2022· article· en· W4225927969 on OpenAlexafffund
Alana Correia-Martins, Réjean Tremblay, Béatrice Bec, Cécile Roques, Ariane Atteia, Angélique Gobet, Marion Richard, Masami Hamaguchi, Toshihiro Miyajima, Michio Hori, Gilles Miron, Stéphane Pouvreau, Franck Lagarde

Bibliographic record

VenueMarine Ecology Progress Series · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Bivalve and Aquaculture Studies
Canadian institutionsUniversité de MonctonUniversité du Québec à Rimouski
FundersNatural Sciences and Engineering Research Council of CanadaMitacsInstitut Français de Recherche pour l'Exploitation de la Mer
KeywordsTrophic levelOysterBiologyEcologyContext (archaeology)Foundation speciesEcosystemCrassostreaPlanktonFishery

Abstract

fetched live from OpenAlex

Bivalves are regulators of coastal lagoons and provide a wide range of ecosystem services. However, coastal lagoons are sensitive to climate change. Our objective was to describe the drivers of the cascade of ecological events that occurred during a summer heatwave and which resulted in recruitment failure of the Pacific oyster Crassostrea gigas . Results show that elevated temperatures and salinity caused a shift in planktonic food availability toward smaller taxa. These trophic changes did not affect food accumulation by oyster larvae or their fatty acid composition but did affect post-metamorphosis success, with up to 24% fewer young metamorphosed postlarvae at some sites and no development of juveniles at all sites. This resulted in the failure of oyster recruitment and in the development of tubeworms, a trophic and spatial competitor that can better ingest small particles. This knowledge suggests that, in the context of marine heatwaves, the ecological limits of oyster larvae are narrower than their physiological limits.

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 score0.055
Threshold uncertainty score0.991

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.267
Teacher spread0.237 · 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

Citations20
Published2022
Admission routes2
Has abstractyes

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