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Record W2767033347 · doi:10.1002/ece3.3495

Connectivity, persistence, and loss of high abundance areas of a recovering marine fish population in the Northwest Atlantic Ocean

2017· article· en· W2767033347 on OpenAlexafffundabout
Stephanie A. Boudreau, Nancy L. Shackell, Stuart Carson, Cornelia E. den Heyer

Bibliographic record

VenueEcology and Evolution · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsDalhousie UniversityBedford Institute of OceanographyFisheries and Oceans Canada
FundersGovernment of Canada
KeywordsHalibutHippoglossus hippoglossusGroundfishOverfishingGadusFisheryGeographyFisheries managementEcologyAbundance (ecology)FishingSeascapePopulationElectrofishingBiologyHabitat

Abstract

fetched live from OpenAlex

) has been increasing since the mid-2000s, concomitant with increasing interest from the fishing industry. Currently, our knowledge about halibut ecology is limited, and the lack of recovery in other collapsed groundfish populations has highlighted the danger of overfishing local concentrations. Here, we apply a Bayesian hierarchical spatiotemporal approach to model the spatial structure of juvenile Atlantic halibut over 36 years and three fisheries management regimes using three model parameters to characterize the resulting spatiotemporal abundance structure: persistence (similarity of spatial structure over time), connectivity (coherence of temporal pattern over space), and spatial variance (variation across the seascape). Two areas of high juvenile abundance persisted through three decades whereas two in the northeast are now diminished, despite the increased abundance and landings throughout the management units. The persistent areas overlap with full and seasonal area closures, which may act as refuges from fishing. Connectivity was estimated to be 250 km, an order of magnitude less than the distance assumed by the definition of the Canadian management units (~2,000 km). The underlying question of whether there are distinct populations within the southern stock unit cannot be answered with this model, but the smaller ~250 km scale of coherent temporal patterns suggests more complex population structure than previously thought, which should be taken into consideration by fishery management.

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 score0.070
Threshold uncertainty score0.982

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)0.0000.000

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 head0.012
GPT teacher head0.223
Teacher spread0.211 · 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

Citations24
Published2017
Admission routes3
Has abstractyes

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