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Approximate Bayesian computation reveals the factors that influence genetic diversity and population structure of foxsnakes

2011· article· en· W2146259681 on OpenAlexafffund
Jeffrey R. Row, Ronald J. Brooks, Carrie A MacKinnon, Ann Marie Lawson, Brian I. Crother, Mary E. White, Stephen C. Lougheed

Bibliographic record

VenueJournal of Evolutionary Biology · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsUniversity of GuelphQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaWorld Wildlife Fund
KeywordsBiologyGenetic diversityApproximate Bayesian computationGenetic structureEcologyPopulationPhylogeographyEvolutionary biologyRange (aeronautics)Demographic historyEffective population sizeHabitat fragmentationMicrosatelliteBiodiversityDemographyPhylogenetic treeGenetics

Abstract

fetched live from OpenAlex

Contemporary geographical range and patterns of genetic diversity within species reflect complex interactions between multiple factors acting across spatial and temporal scales, and it is notoriously difficult to disentangle causation. Here, we quantify patterns of genetic diversity and genetic population structure using mitochondrial DNA sequences (101 individuals, cytochrome b) and microsatellites (816 individuals, 12 loci) and use Approximate Bayesian computation methods to test competing models of the demographic history of eastern and western foxsnakes. Our analyses indicate that post-glacial colonization and past population declines, probably caused by the infilling of deciduous forest and cooler temperatures since the mid-Holocene, largely underpin large-scale genetic patterns for foxsnakes. At finer geographical scales, our results point to more recent anthropogenic habitat loss as having accentuated genetic population structure by causing further declines and fragmentation.

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 score0.253

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 head0.019
GPT teacher head0.231
Teacher spread0.212 · 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

Citations20
Published2011
Admission routes2
Has abstractyes

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