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Functional ecology of ecotypic differentiation in the Californian serpentine sunflower (<i>Helianthus exilis</i>)

2007· article· en· W1992686453 on OpenAlexaff
Julianno B. M. Sambatti, Kevin J. Rice

Bibliographic record

VenueNew Phytologist · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRiparian zoneBiologyEcotypeEdaphicHelianthusLocal adaptationSunflowerCompetition (biology)Phenotypic plasticityAbiotic componentPopulationBotanyEcologyAgronomySoil waterHabitat

Abstract

fetched live from OpenAlex

* Here, we examined phenotypic differences between locally adapted serpentine and riparian populations of the serpentine sunflower Helianthus exilis from northern California, USA. * Within a common environment, plants from serpentine and riparian sites were grown in regular potting soil or serpentine soil. Physiology, morphology, phenology and fitness-related traits were measured. * Overall, riparian plants grew more rapidly, attained a larger final size, produced larger leaves, and smaller flowering heads. Riparian plants also invested less in root biomass and were more water-use-efficient than the serpentine plants. Serpentine and riparian plants also differed in leaf concentrations of boron, magnesium, sodium and molybdenum. * These ecotypic differences suggest contrasting adaptive strategies to cope with either edaphic stress in serpentine sites or intense above-ground competition at riparian sites. There was a significant population origin x soil type crossing interaction in one fitness trait (average dry weight) that mirrored local adaptation previously documented for these riparian and serpentine ecotypes. However, because all other fitness traits did not exhibit this crossing interaction in our common garden study, it is possible that phenotypic differences underlying local adaptation may be amplified in the field as a result of biotic and abiotic interactions.

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 score0.063
Threshold uncertainty score0.954

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 head0.038
GPT teacher head0.226
Teacher spread0.188 · 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

Citations60
Published2007
Admission routes1
Has abstractyes

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