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Climate change alters reproductive isolation and potential gene flow in an annual plant

2009· article· en· W2080220387 on OpenAlexaff
Steven J. Franks, Arthur E. Weis

Bibliographic record

VenueEvolutionary Applications · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Toronto
FundersUniversity of California, Irvine
KeywordsBiologyAssortative matingGene flowReproductive isolationPhenologyEcologyPopulationMatingBrassica rapaClimate changeAgronomyBrassicaGeneGenetic variationGenetics

Abstract

fetched live from OpenAlex

Climate change will likely cause evolution due not only to selection but also to changes in reproductive isolation within and among populations. We examined the effects of a natural drought on the timing of flowering in two populations of Brassica rapa and the consequences for predicted reproductive isolation and potential gene flow. Seeds were collected before and after a 5-year drought in southern California from two populations varying in soil moisture. Lines derived from these seeds were raised in the greenhouse under wet and drought conditions. We found that the natural drought caused changes in reproductive timing and that the changes were greater for plants from the wet than from the dry site. This differential shift caused the populations to become more phenological similar, which should lead to less reproductive isolation and increased gene flow. We estimated a high level of assortative mating by flowering time, which potentially contributed to the rapid evolution of phenological traits following the drought. Estimates of assortative mating were higher for the wet site population, and assortative mating was reduced following the drought. This study shows that climate change can potentially alter gene flow and reproductive isolation within and among populations, strongly influencing evolution.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.030
GPT teacher head0.223
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 source (direct Gemma or distilled Codex), 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

Citations61
Published2009
Admission routes1
Has abstractyes

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