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Record W2078885542 · doi:10.1111/ele.12346

Latitudinal gradients in climatic‐niche evolution accelerate trait evolution at high latitudes

2014· article· en· W2078885542 on OpenAlexafffund
Adam Matthew Lawson, Jason T. Weir

Bibliographic record

VenueEcology Letters · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsNicheLatitudeEcologyBiologyEcological nicheTraitTropicsEcological speciationReproductive isolationParallel evolutionEcological selectionEvolutionary biologySelection (genetic algorithm)GeographyPopulationGenetic variationPhylogenetic treeHabitat

Abstract

fetched live from OpenAlex

Despite the importance of divergent selection to the speed of evolution, it remains poorly understood if divergent selection is more prevalent in the tropics (where species richness is highest), or at high latitudes (where paleoclimate change has been most intense). We tested whether the rate of climatic-niche evolution - one proxy for divergent selection - varies with latitude for 111 pairs of bird species. Using Brownian motion and Ornsetin-Ulhenbeck models, we show that evolutionary rates along two important axes of the climatic-niche - temperature and seasonality - have been faster at higher latitudes. We then tested whether divergence of the climatic-niche was associated with evolution in traits important in ecological differentiation (body mass) and reproductive isolation (song), and found that climatic divergence is associated with faster rates in both measures. These results highlight the importance of climate-mediated divergent selection pressures in driving evolutionary divergence and reproductive isolation at high latitudes.

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 score0.111
Threshold uncertainty score0.980

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 head0.195
Teacher spread0.169 · 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

Citations100
Published2014
Admission routes2
Has abstractyes

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