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Record W7099151677

Top-down vs. bottom-up drivers of recruitment in a key marine invertebrate: investigating early life stages of snow crab

2014· article· en· W7099151677 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytoplasmas and Hemiptera pathogens
Canadian institutionsnot available
Fundersnot available
KeywordsGroundfishJuvenileAbundance (ecology)CannibalismBenthic zoneSnow
DOInot available

Abstract

fetched live from OpenAlex

Many snow crab fisheries have fluctuatedwidely over time in a quasi-cyclic way due to highly variable recruitment. The causes of this variability are still debated. Bottom-up processes related to climate variabilitymay strongly affect growth and survival during early life, whereas top-down preda-tor effects may be amajor source of juvenile mortality. Moreover, intrinsic density-dependent processes, which have receivedmuch less attention, are hypothetically responsible for the cycles in recruitment. This study explored how climate, larval production, intercohort cannibalism and groundfish predation may have affected recruitment of early juvenile snow crab in the northwest Gulf of St Lawrence (eastern Canada) over a period of 23 years. Abundance of early juvenile snow crabs (2.5–22.9 mm in carapace width), representing the first 3 years of benthic life, came from an annual trawl survey and was used to determine cohort strength. Analyses revealed a cyclic pattern in abundance of 0+ crabs that may arise fromcohort resonant effects. This pattern consistedof three recruitmentpulses butwas reduced to twopulses byage2+, while the interannual variability of cohort strengthwas dampened. This reconfiguration of the earliest recruitment patternwas dictated primarily by bottomwater tem-perature and cannibalism, which progressively overruled the pre-settlement factors of larval production and surface water temperature that best explained abundance of 0+ crabs. The results strongly suggest that bottom-up and density-dependent processes prevail over top-down control in

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.664

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.235
Teacher spread0.197 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2014
Admission routes1
Has abstractyes

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