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ORIGINAL ARTICLE: Life‐history traits and energetic status in relation to vulnerability to angling in an experimentally selected teleost fish

2009· article· en· W2113074556 on OpenAlexafffund
Tara Redpath, Steven J. Cooke, Robert Arlinghaus, David H. Wahl, David P. Philipp

Bibliographic record

VenueEvolutionary Applications · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Illinois at Urbana-ChampaignLeibniz-GemeinschaftLeibniz-Institut für Gewässerökologie und Binnenfischerei
KeywordsBiologyFishingFish <Actinopterygii>Vulnerability (computing)FisheryRelation (database)Life historyEcology

Abstract

fetched live from OpenAlex

In recreational fisheries, a correlation has been established between fishing-induced selection pressures and the metabolic traits of individual fish. This study used a population of largemouth bass (Micropterus salmoides) with lines of low vulnerability fish (LVF) and high vulnerability fish (HVF) that were previously established through artificial truncation selection experiments. The main objective was to evaluate if differential vulnerability to angling was correlated with growth, energetics and nutritional condition during the sub-adult stage. Absolute growth rate was found to be between 9% and 17% higher for LVF compared with HVF over a 6-month period in three experimental ponds. The gonadosomatic index in females was lower for LVF compared with HVF in one experimental pond. No significant differences in energy stores (measured using body constituent analysis) were observed between LVF and HVF. In addition, both groups were consuming the same prey items as evidenced by stomach content analysis. The inherent reasons behind differential vulnerability to angling are complex, and selection for these opposing phenotypes appears to select for differing growth rates, although the driving factors remain unclear. These traits are important from a life-history perspective, and alterations to their frequency as a result of fishing-induced selection could alter fish population structure. These findings further emphasize the need to incorporate evolutionary principles into fisheries management activities.

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 score0.029
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.241
Teacher spread0.231 · 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

Citations39
Published2009
Admission routes2
Has abstractyes

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