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Record W3115931500 · doi:10.1111/icad.12469

High genetic drift in endangered northern peripheral populations of the Behr's hairstreak butterfly ( <scp> <i>Satyrium behrii</i> </scp> )

2020· article· en· W3115931500 on OpenAlexafffundabout
Nusha Keyghobadi, Lindsay A. Crawford, Sylvie Desjardins

Bibliographic record

VenueInsect Conservation and Diversity · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaEnvironment and Climate Change CanadaWestern University
FundersNatural Sciences and Engineering Research Council of CanadaMitacsCanada Research ChairsWorld Wildlife Fund
KeywordsGenetic diversityButterflyThreatened speciesBiologyEndangered speciesEcologyGenetic driftPopulationRange (aeronautics)Genetic variationEffective population sizeEvolutionary biologyDemographyGeneticsHabitat

Abstract

fetched live from OpenAlex

Abstract The persistence and adaptation of leading‐edge peripheral populations may be critical for allowing species to shift their range limits under climate change. However, peripheral populations are potentially vulnerable to genetic, demographic, and environmental stochasticity. Here, we characterise genetic variation across space and among years in northern peripheral populations of the Behr's hairstreak butterfly in British Columbia, Canada. This butterfly is dependent on antelope‐brush ecosystems that are threatened in this part of the world and is federally listed as Endangered in Canada. Using a large panel of amplified fragment length polymorphism (AFLP) genetic markers, we found low diversity in these populations. We also detected a high degree of year‐to‐year variation in allele frequencies, resulting in low effective population size estimates. Our findings suggest that Canadian populations of the Behr's hairstreak experience high genetic drift and may be vulnerable to genetic stochasticity. Unstable demography, low effective population size, and low genetic diversity in these populations could impede their adaptation to rapidly changing environmental conditions and contribute to a contraction of the species' range under 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.441
Threshold uncertainty score0.877

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.203
Teacher spread0.162 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2020
Admission routes3
Has abstractyes

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