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Record W2128917332 · doi:10.3354/meps10897

The role of harp seals, fisheries and food availability in driving the dynamics of northern cod

2014· article· en· W2128917332 on OpenAlexafffundabout
Alejandro D. Buren, Mariano Koen‐Alonso, GB Stenson

Bibliographic record

VenueMarine Ecology Progress Series · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsFisheries and Oceans CanadaMemorial University of Newfoundland
FundersMemorial University of NewfoundlandEli Lilly and Company
KeywordsCapelinGadusFisheryAtlantic codPredationFishingTrophic cascadeStock (firearms)Fisheries managementStock assessmentApex predatorFish stockGeographyFood webEcologyBiologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

The Atlantic cod Gadus morhua population off Newfoundland collapsed in the early 1990s due to over-exploitation, and despite marked reduction in fishing effort the stock remains depressed. Harp seal Pagophilus groenlandicus predation has been repeatedly proposed as an explanation for this lack of recovery, but other hypotheses include reduced prey availability and/or food quality (i.e. lack of capelin Mallotus villosus), as well as fisheries catches and environmental effects. Using a bioenergetics-allometric model, we gauged the relative contributions of these drivers on the lack of recovery and dynamics of the northern cod stock. Biomass dynamics were best explained by a combination of fisheries removals and capelin availability, whereas seal consumption was found not to be an important driver of the northern cod stock. Prey availability was linked to reduced somatic condition during the 1990s and 2000s. We discuss evidence that suggests that cod may be experiencing depensatory dynamics, but not related to a 'predator pit effect'. Our study suggests that trophic control is bottom-up, and that a depressed capelin stock could be a serious impediment for cod rebuilding.

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)0.001
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 score0.388
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.191
Teacher spread0.186 · 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

Citations49
Published2014
Admission routes3
Has abstractyes

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