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Record W2013047421 · doi:10.1086/671058

Phenotypic Selection Favors Missing Trait Combinations in Coexisting Annual Plants

2013· article· en· W2013047421 on OpenAlexaff
Sarah Kimball, Jennifer R. Gremer, Travis E. Huxman, D. Lawrence Venable, Amy L. Angert

Bibliographic record

VenueThe American Naturalist · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of British Columbia
FundersNational Science Foundation
KeywordsSelection (genetic algorithm)TraitTrade-offBiologyEcologyBiodiversityNatural selectionGrowth ratePhenotypic traitEcological selectionPhenotypeGeneMathematics

Abstract

fetched live from OpenAlex

Trade-offs among traits are important for maintaining biodiversity, but the role of natural selection in their construction is not often known. It is possible that trade-offs reflect fundamental constraints, negative correlational selection, or directional selection operating on costly, redundant traits. In a Sonoran Desert community of winter annual plants, we have identified a trade-off between relative growth rate and water-use efficiency among species, such that species with high relative growth rate have low water-use efficiency and vice versa. We measured selection on water-use efficiency, relative growth rate, and underlying traits within populations of four species at two study sites with different average climates. Phenotypic trait correlations within species did not match the among-species trade-off. In fact, for two species with high water-use efficiency, individuals with high relative growth rate also had high water-use efficiency. All populations experienced positive directional selection for water-use efficiency and relative growth rate. Selection tended to be stronger on water-use efficiency at the warmer and drier site, and selection on relative growth rate tended to be stronger at the cooler and wetter site. Our results indicate that directional natural selection favors a phenotype not observed among species in the community, suggesting that the among-species trade-off could be due to pervasive genetic constraints, perhaps acting in concert with processes of community assembly.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score0.995

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 head0.028
GPT teacher head0.234
Teacher spread0.205 · 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
Published2013
Admission routes1
Has abstractyes

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