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Record W2788811834 · doi:10.1098/rspb.2017.1796

Clownfishes evolution below and above the species level

2018· article· en· W2788811834 on OpenAlexaff
Jonathan Rolland, Daniele Silvestro, Glenn Litsios, Laurélène Faye, Nicolas Salamin

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
FundersVetenskapsrådetKnut och Alice Wallenbergs StiftelseSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsInterspecific competitionIntraspecific competitionBiologyMicroevolutionMacroevolutionEvolutionary biologyEcologyMolecular evolutionPopulationSelection (genetic algorithm)Parallel evolutionNatural selectionAdaptation (eye)ZoologyPhylogeneticsGeneGeneticsDemography

Abstract

fetched live from OpenAlex

The difference between rapid morphological evolutionary changes observed in populations and the long periods of stasis detected in the fossil record has raised a decade-long debate about the exact role played by intraspecific mechanisms at the interspecific level. Although they represent different scales of the same evolutionary process, micro- and macroevolution are rarely studied together and few empirical studies have compared the rates of evolution and the selective pressures between both scales. Here, we analyse morphological, genetic and ecological traits in clownfishes at different evolutionary scales and demonstrate that the tempo of molecular and morphological evolution at the species level can be, to some extent, predicted from parameters estimated below the species level, such as the effective population size or the rate of evolution within populations. We also show that similar codons in the gene of the rhodopsin RH1 , a light-sensitive receptor protein, are under positive selection at the intra and interspecific scales, suggesting that similar selective pressures are acting at both levels.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.010
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.029
GPT teacher head0.227
Teacher spread0.198 · 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.

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

Citations43
Published2018
Admission routes1
Has abstractyes

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