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Record W2123266081 · doi:10.3732/ajb.92.6.972

Evolutionary and ecological correlates of early seedling morphology in East African trees and shrubs

2005· article· en· W2123266081 on OpenAlexaff
Amy E. Zanne, Colin A. Chapman, Kaoru Kitajima

Bibliographic record

VenueAmerican Journal of Botany · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsMcGill University
FundersNational Science Foundation
KeywordsBiologySeed dispersalHabitatEcologyTraitBiological dispersalPhylogenetic comparative methodsSeedlingPhylogenetic treeBotany

Abstract

fetched live from OpenAlex

Seed size and cotyledon morphology are two key juvenile traits that have evolved in response to changes in plant species life-history strategies and habitat associations. Correlations of these traits with each other and with other juvenile traits were examined for 70 species of trees and shrubs in Kibale National Park, Uganda. Although species with photosynthetic cotyledons were more abundant than in other tropical floras, both univariate and multivariate analyses supported trait associations expected from the literature. Trait values varied continuously across species, yet mean trait values differed significantly among habitat association types. Species with large seeds, large seedlings, thick storage cotyledons, slow germination, large-stature adults, and dispersal by large animals were common in forest and gap habitats. An opposite suite of traits was common in open habitats (grassland and edge). Analyses incorporating phylogeny (independent contrasts and omnibus tests) confirmed that these suites of traits showed correlated evolution. Cotyledon functional morphology yielded a strong phylogenetic signal, while seed mass was labile. Nevertheless, contingent change tests found that evolutionary change from photosynthetic to reserve cotyledons was more likely when disperser and perhaps seed size of ancestral species were already large, suggesting a strong interdependency among these traits.

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 score0.005
Threshold uncertainty score0.373

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 studies0.0000.001
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 head0.006
GPT teacher head0.213
Teacher spread0.207 · 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

Citations62
Published2005
Admission routes1
Has abstractyes

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