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Loss of genetic integrity correlates with stocking intensity in brook charr (Salvelinus fontinalis)

2010· article· en· W2131685791 on OpenAlexaffabout
Amandine D. Marie, Louis Bernatchez, Dany Garant

Bibliographic record

VenueMolecular Ecology · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsUniversité LavalUniversité de Sherbrooke
Fundersnot available
KeywordsSalvelinusFontinalisBiologyStockingEcologyEvolutionary biologyZoologyTroutFish <Actinopterygii>Fishery

Abstract

fetched live from OpenAlex

Supportive breeding and stocking performed with non-native or domesticated fish to support sport fishery industry is a common practice throughout the world. Such practices are likely to modify the genetic integrity of natural populations depending on the extent of genetic differences between domesticated and wild fish and on the intensity of stocking. The purpose of this study is to assess the effects of variable stocking intensities on patterns of genetic diversity and population differentiation among nearly 2000 brook charr (Salvelinus fontinalis) from 24 lakes located in two wildlife reserves in Québec, Canada. Our results indicated that the level of genetic diversity was increased in more intensively stocked lakes, mainly due to the introduction of new alleles of domestic origin. As a consequence, the population genetic structure was strongly homogenized by intense stocking. Heavily stocked lakes presented higher admixture levels and lower levels of among lakes genetic differentiation than moderately and un-stocked lakes. Moreover, the number of stocking events explained the observed pattern of population genetic structure as much as hydrographical connections among lakes in each reserve. We discuss the implications for the conservation of exploited fish populations and the management of stocking practices.

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 score0.371
Threshold uncertainty score0.586

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)0.0000.000

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 head0.008
GPT teacher head0.208
Teacher spread0.200 · 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

Citations119
Published2010
Admission routes2
Has abstractyes

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