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THE QUANTITATIVE GENETICS OF INCIPIENT SPECIATION: HERITABILITY AND GENETIC CORRELATIONS OF SKELETAL TRAITS IN POPULATIONS OF DIVERGING FAVIA FRAGUM ECOMORPHS

2011· article· en· W2126451325 on OpenAlexaff
David B. Carlon, Ann F. Budd, Catherine Lippé, Rose L. Andrew

Bibliographic record

VenueEvolution · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiologyHeritabilityGenetic algorithmEvolutionary biologyQuantitative geneticsMorphology (biology)ZoologyGenetic variationGenetics

Abstract

fetched live from OpenAlex

Recent speciation events provide potential opportunities to understand the microevolution of reproductive isolation. We used a marker-based approach and a common garden to estimate the additive genetic variation in skeletal traits in a system of two ecomorphs within the coral species Favia fragum: a Tall ecomorph that is a seagrass specialist, and a Short ecomorph that is most abundant on coral reefs. Considering both ecomorphs, we found significant narrow-sense heritability (h(2) ) in a suite of measurements that define corallite architecture, and could partition additive and nonadditive variation for some traits. We found positive genetic correlations for homologous height and length measurements among different types of vertical plates (costosepta) within corallites, but negative correlations between height and length within, as well as between costosepta. Within ecomorphs, h(2) estimates were generally lower, compared to the combined ecomorph analysis. Marker-based estimates of h(2) were comparable to broad-sense heritability (H) obtained from parent-offspring regressions in a common garden for most traits, and similar genetic co-variance matrices for common garden and wild populations may indicate relatively small G × E interactions. The patterns of additive genetic variation in this system invite hypotheses of divergent selection or genetic drift as potential evolutionary drivers of reproductive isolation.

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 score0.019
Threshold uncertainty score0.317

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 head0.026
GPT teacher head0.245
Teacher spread0.220 · 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

Citations23
Published2011
Admission routes1
Has abstractyes

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