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Influence of population decline, fishing, and spawner variability on the recovery of marine fishes

2001· article· en· W2134788935 on OpenAlexaff
Jeffrey A. Hutchings

Bibliographic record

VenueJournal of Fish Biology · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsFishingBiologyPopulationFisheryPopulation dynamics of fisheriesExtinction (optical mineralogy)Mortality ratePopulation declineEcologyFish <Actinopterygii>Demography

Abstract

fetched live from OpenAlex

Based on an analysis of 90 marine fish populations, collapses (the greatest proportional reduction in spawner biomass over 15 years) are predicated typically by dramatic increases in fishing mortality and recoveries are more likely to occur when exploitation is reduced. However, among populations for which fishing mortality declined after collapse, recovery was independent of exploitation rate, even when fishing mortality (F) post‐collapse was expressed as a function of each population's maximum growth rate (r). After a period of 15 years, many populations that experienced 15 year declines &gt;60% exhibited little or no recovery, despite considerable reductions in fishing mortality. This suggests that factors other than fishing may be considerably more important to recovery, and fishing less important, than previously thought. Furthermore, among populations for which fishing mortality decreased post‐collapse, rate of population decline was a reliable predictor of recovery. With the possible exception of clupeids, variation in marine fish breeding population size was found to differ little from that of other vertebrates, and such variability appears to have no effect on rate of recovery. In addition to providing an empirical framework for the study of population collapse and recovery, the analyses presented here provide a means of assessing the precautionary nature of various population‐decline thresholds used to assign extinction risks to marine fish.

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)0.001
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 score0.023
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0020.000

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 head0.016
GPT teacher head0.259
Teacher spread0.243 · 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

Citations78
Published2001
Admission routes1
Has abstractyes

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