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Record W2601164573 · doi:10.1139/cjfas-2016-0269

When does hypoxia affect management performance of a fishery? A management strategy evaluation of Dungeness crab (<i>Metacarcinus magister</i>) fisheries in Hood Canal, Washington, USA

2017· article· en· W2601164573 on OpenAlexvenueno aff
Halley E. Froehlich, Timothy E. Essington, P. Sean McDonald

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicCrustacean biology and ecology
Canadian institutionsnot available
Fundersnot available
KeywordsFisheryHypoxia (environmental)FishingFisheries managementManagement strategyEcosystemPopulationHabitatBiologyEcologyBusiness

Abstract

fetched live from OpenAlex

Management strategies for fisheries typically do not account for environmental stressors, such as hypoxia (dissolved oxygen &lt; 2 mg·L −1 ). Hypoxia can lead to shoaling of organisms into normoxic habitats, enhancing catchability, which could reduce the performance of fishery management strategies. Here, we conducted a management strategy evaluation of Dungeness crab (Metacarcinus magister) fisheries in Hood Canal, Washington, a seasonally hypoxic fjord in Puget Sound. Specifically, we asked whether the current management strategy was robust to hypoxia-induced catchability changes under alternative scenarios of illegal take, incidental capture mortality, and reproductive limitation. We find that the management strategy performed well to changes in catchability when illegal and incidental fishing mortality was low and fishing did not lead to reproductive limitation. However, the performance eroded markedly (reduced long-term catch and (or) population and higher catch variation) under the alternative scenarios. These findings underscore the benefit of applying an ecosystem approach to fisheries management because it identifies potential risks to management strategies in systems subject to environmental change.

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)0.002
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 score0.309
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.033
GPT teacher head0.248
Teacher spread0.215 · 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

Citations18
Published2017
Admission routes1
Has abstractyes

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