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Record W2412134223 · doi:10.1111/jeb.12914

The context dependence of assortative mating: a demonstration with conspecific salmonid populations

2016· article· en· W2412134223 on OpenAlexaff
Zoé Gauthey, Andrew P. Hendry, Arturo Elosegi, Cédric Tentelier, Jacques Labonne

Bibliographic record

VenueJournal of Evolutionary Biology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMcGill University
FundersInterregEuropean Union Agency for Network and Information SecurityEuropean Commission
KeywordsAssortative matingBiologyReproductive isolationContext (archaeology)Brown troutMatingSalmoGene flowPopulationEcologyEvolutionary biologyZoologyGenetic variationFisheryGeneticsDemographyGene

Abstract

fetched live from OpenAlex

Assortative mating is thought to play a key role in reproductive isolation. However, most experimental studies of assortative mating do not take place in multiple natural environments, and hence, they ignore its potential context dependence. We implemented an experiment in which two populations of brown trout (Salmo trutta) with different natural flow regimes were placed into semi-natural stream channels under two different artificial flow regimes. Natural reproduction was allowed, and reproductive isolation was measured by means of parentage assignment to compare within-population vs. between-population male-female mating and relative offspring production. For both metrics, reproductive isolation was highly context dependent: no isolation was evident under one flow regime, but strong isolation was evident under the other flow regime. These patterns were fully driven by variance in the mating success of males from one of the two populations. Our results highlight how reproductive isolation through assortative mating can be strongly context dependent, which could have dramatic consequences for patterns of gene flow and speciation under environmental change.

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 score0.020
Threshold uncertainty score0.263

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 studies0.0000.001
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 head0.019
GPT teacher head0.250
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

Citations13
Published2016
Admission routes1
Has abstractyes

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