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The genetic signature of rapid range expansion by flying squirrels in response to contemporary climate warming

2010· article· en· W2110516108 on OpenAlexafffundabout
Colin J. Garroway, Jeff Bowman, Gillian L. Holloway, Jay R. Malcolm, Paul J. Wilson

Bibliographic record

VenueGlobal Change Biology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of TorontoMinistry of Natural Resources and ForestryTrent University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRange (aeronautics)EcologyPopulationBiologyGenetic diversityDemography

Abstract

fetched live from OpenAlex

Climate is an important factor limiting species distributions. Historic climate-change related range movements have modified the genetic diversity of species by the merging and splitting of gene pools and by the effects associated with recurrent founder events. These effects are often inferred, either from retrospective analyses of current genetic patterns or from simulations. Rarely has it been possible for the population genetic effects of range expansion to be examined with contemporaneous demographic data. We characterized the genetic signature of rapid range expansion by southern flying squirrels (Glaucomys volans) and compared these results to a stationary population of the closely related northern flying squirrel (Glaucomys sabrinus) in Ontario, Canada. Samples were taken during an approximately 200 km range expansion by G. volans (1994–2003) and genotyped at 6 (G. sabrinus) and 8 (G. volans) microsatellite loci. For G. volans, but not G. sabrinus, we found evidence of a latitudinal gradient in allele frequencies and a decrease in allelic richness along the axis of expansion. We found no evidence of isolation-by-distance in either species or of genetic bottlenecks in the area of G. volans expansion. These results suggest that serial founder events can cause an immediate reduction in genetic diversity following rapid range expansion with high levels of gene flow giving rise to heterogeneity within what would classically be termed panmixia. Given the pace of anthropogenic climate change, and the increasing incidence of range movements in response, this may be an important, immediate consequence of climate change.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.284
Teacher spread0.251 · 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

Citations72
Published2010
Admission routes3
Has abstractyes

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