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Record W2340545879 · doi:10.1111/nph.13971

The scale of local adaptation in <i>Mimulus guttatus</i>: comparing life history races, ecotypes, and populations

2016· article· en· W2340545879 on OpenAlexaff
Megan L. DeMarche, Kathleen M. Kay, Amy L. Angert

Bibliographic record

VenueNew Phytologist · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsUniversity of British Columbia
FundersCalifornia Native Plant SocietyNational Science Foundation
KeywordsEcotypeLocal adaptationAdaptation (eye)BiologyEcologyScale (ratio)Life historyLife history theoryGeographyPopulationDemography

Abstract

fetched live from OpenAlex

Fitness trade-offs between environments are central to the evolution of biodiversity. Although transplant studies often document fitness trade-offs consistent with local adaptation (LA), many have also found an advantage of foreign genotypes (foreign advantage (FA)). Understanding the mechanisms driving the magnitude and distribution of fitness variation requires comparative approaches that test the ecological scales at which these different patterns emerge. We used a common garden transplant experiment to compare the relative fitnesses of native vs foreign genotypes at three nested ecological scales within Mimulus guttatus: annual vs perennial life history races, perennial ecotypes across an elevational range, and populations within perennial elevational ecotypes. We integrated fitness across the life-cycle and decomposed LA vs FA into contributions from different fitness components. We found LA, measured as home-site advantage, between annual and perennial races and a trend towards LA among populations within montane habitats. Conversely, we found strong FA of low-elevation perennials in a montane environment. LA between life history races reflects the fitness advantages of adult survival and vegetative growth in a mesic environment. Within the perennial race, recent climate conditions or nonselective processes, such as dispersal limitation or mutational load, could explain FA of low-elevation perennials in a montane environment.

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 score0.217
Threshold uncertainty score0.257

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 head0.036
GPT teacher head0.244
Teacher spread0.208 · 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

Citations55
Published2016
Admission routes1
Has abstractyes

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