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Record W3015864766 · doi:10.1111/eva.12970

Fisheries‐induced evolution of alternative male life history tactics in Coho salmon

2020· article· en· W3015864766 on OpenAlexafffund
Kyle A. Young, Victoria A. Cluney, Laura K. Weir

Bibliographic record

VenueEvolutionary Applications · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsSaint Mary's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyOncorhynchusLife historyFisheryPhenotypic plasticitySelection (genetic algorithm)Life history theoryFish <Actinopterygii>EcologyZoology

Abstract

fetched live from OpenAlex

Abstract Fisheries‐induced evolution (FIE) can result when harvest imposes artificial selection on variation in heritable phenotypic traits. While there is evidence for FIE, it remains difficult to disentangle the contributions of within‐generation demographic adjustment, phenotypic plasticity, and genetic adaption to observed changes in life history traits. We present evidence for FIE using dozens of Coho salmon ( Oncorhynchus kisutch ) populations in which males adopt one of two age‐invariant, heritable life history tactics: most mature as large three‐year‐old “hooknose” and typically fight for spawning opportunities, while some mature as small two‐year‐old “jacks” and fertilize eggs through sneaking. The closure of a fishery targeting three‐year‐old fish provided an experimental test of the prediction that fishery‐imposed selection against hooknose males drives an evolutionary increase in the proportion of males adopting the jack tactic. The data support the prediction: 43 of 46 populations had higher jack proportions during than after the fishery. The data further suggest that changes in jack proportion were not solely the result of demographic adjustments to harvest. We suggest that systems where fisheries differentially exploit phenotypically discrete, age‐invariant life histories provide excellent opportunities for detecting FIE.

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 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 score0.438
Threshold uncertainty score0.999

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)0.0020.000

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 head0.023
GPT teacher head0.215
Teacher spread0.193 · 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

Citations7
Published2020
Admission routes2
Has abstractyes

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