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Record W2901014653 · doi:10.1002/ece3.4676

Maladaptive secondary sexual characteristics reduce the reproductive success of hybrids between native and non‐native salmonids

2018· article· en· W2901014653 on OpenAlexafffund
Sho Fukui, Shannan L. May‐McNally, Eric B. Taylor, Itsuro Koizumi

Bibliographic record

VenueEcology and Evolution · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of British ColumbiaRoyal British Columbia Museum
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIntrogressionBiologyReproductive successHybridOutbreeding depressionZoologySexual selectionSalvelinusReproductive isolationEcologyTroutPopulationFish <Actinopterygii>DemographyFisheryBotanyGenetics

Abstract

fetched live from OpenAlex

Abstract Human‐mediated hybridization between introduced and native species is one of the most serious threats to native taxa. Although field studies have attempted to quantify the relative fitness or reproductive success of parental species and their hybrids , only a few studies have unraveled the factors determining the fitness of hybrids. Here, we hypothesized that maladaptive secondary sexual characteristics may reduce fitness of hybrids between two fish species. To test this, we evaluated the reproductive success of introduced brook trout (BT: Salvelinus fontinalis ), native white‐spotted charr (WSC: S. leucomaenis ) and their hybrids in a natural stream in Hokkaido, Japan, where the two parental species show remarkably different male secondary sexual characteristics, such as elongated jaws and deeper bodies. We predicted that introgression from WSC is maladaptive for BT males because the BT male has more prominent secondary sexual characteristics. Our results suggest that both sexual selection and outbreeding depression in males and females significantly influence an individual's reproductive success. Our results also suggest that asymmetric introgression may increase the risks to persistence in the recipient species.

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 score0.008
Threshold uncertainty score0.918

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 studies0.0010.002
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 head0.233
Teacher spread0.223 · 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

Citations23
Published2018
Admission routes2
Has abstractyes

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