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Record W2115448168 · doi:10.1093/icesjms/fst179

Renaissance of a caveat: Allee effects in marine fish

2013· article· en· W2115448168 on OpenAlexafffund
Jeffrey A. Hutchings

Bibliographic record

VenueICES Journal of Marine Science · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAllee effectPopulationAbundance (ecology)OverfishingEcologyBiologyPopulation dynamics of fisheriesPopulation sizeDefaunationFisheryFish <Actinopterygii>DemographyFishing

Abstract

fetched live from OpenAlex

Abstract The population dynamics of marine fish at low abundance has long been of interest. One century ago, Johan Hjort drew attention to the importance of understanding “the laws which govern the renewal of the animal population”. Integral to the current work on the recovery of collapsed fish stocks is the association between abundance and per capita population growth rate (r), a negative correlation being representative of compensation and a positive correlation indicative of an Allee effect, also termed depensation. Allee effects are predicted to slow the rate, and increase the uncertainty, of recovery. Based on studies having sufficient data at low abundance, the magnitude of depletion experienced by some fish populations appears to have been sufficient to have generated either an Allee effect or a transition from strong to weak (or absent) compensatory dynamics. To a first approximation, empirically based Allee-effect reference points are consistent with suggested thresholds for overfishing and stock collapse. When evaluating Allee effects in marine fish, it is important not to conflate causal mechanism(s) with the pattern between r and abundance; the latter is of greater practical import. An additional caveat is that the longer a population remains at low abundance, the more likely it is that the environment around it will change in ways that are unfavourable to recovery. It might be this “temporal tyranny” of small population size that is most likely to produce an emergent Allee effect and depensatory dynamics in some collapsed marine fish populations.

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 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 score0.199
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.009
GPT teacher head0.247
Teacher spread0.237 · 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

Citations63
Published2013
Admission routes2
Has abstractyes

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