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Record W4388456066 · doi:10.1038/s41467-023-42786-2

Candidate genes under selection in song sparrows co-vary with climate and body mass in support of Bergmann’s Rule

2023· article· en· W4388456066 on OpenAlexafffund
Katherine Carbeck, Peter Arcese, Irby J. Lovette, Christin L. Pruett, Kevin Winker, Jennifer Walsh

Bibliographic record

VenueNature Communications · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaCornell Lab of OrnithologyCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsSelection (genetic algorithm)Bergmann's ruleGeneBiologyClimate changeEvolutionary biologyEcologyGeneticsGeographyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Ecogeographic rules denote spatial patterns in phenotype and environment that may reflect local adaptation as well as a species' capacity to adapt to change. To identify genes underlying Bergmann's Rule, which posits that spatial correlations of body mass and temperature reflect natural selection and local adaptation in endotherms, we compare 79 genomes from nine song sparrow (Melospiza melodia) subspecies that vary ~300% in body mass (17 - 50 g). Comparing large- and smaller-bodied subspecies revealed 9 candidate genes in three genomic regions associated with body mass. Further comparisons to the five smallest subspecies endemic to California revealed eight SNPs within four of the candidate genes (GARNL3, RALGPS1, ANGPTL2, and COL15A1) associated with body mass and varying as predicted by Bergmann's Rule. Our results support the hypothesis that co-variation in environment, body mass and genotype reflect the influence of natural selection on local adaptation and a capacity for contemporary evolution in this diverse species.

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 score0.361
Threshold uncertainty score0.371

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 head0.020
GPT teacher head0.328
Teacher spread0.308 · 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

Citations13
Published2023
Admission routes2
Has abstractyes

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