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Record W2612245049 · doi:10.1111/ecog.02869

Environmental marginality and geographic range limits: a case study with <i>Arabidopsis lyrata</i> ssp. <i>lyrata</i>

2017· article· en· W2612245049 on OpenAlexafffund
Julie A. Lee‐Yaw, Marco Fracassetti, Yvonne Willi

Bibliographic record

VenueEcography · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsNicheRange (aeronautics)EcologyEcological nicheEnvironmental niche modellingBiologyEnvironmental changeSpecies distributionGeographyClimate changeHabitat

Abstract

fetched live from OpenAlex

Understanding the factors that govern the distribution of species is a central goal of evolutionary ecology. It is commonly assumed that geographic range limits reflect ecological niche limits and that species experience increasingly marginal conditions towards the edge of their ranges. Using spatial data and ecological niche models we tested these hypotheses in Arabidopsis lyrata . Specifically, we asked whether range limits coincide with predicted niche limits in this system and whether the suitability of sites declines towards the edge of the species’ range in North America. We further explored patterns of environmental change towards the edge of the range and asked whether genome‐wide patterns of genetic diversity decline with increasing peripherality and environmental marginality. Our results suggest that latitudinal range limits coincide with niche limits. Populations experienced increasingly marginal environments towards these limits – though patterns of environmental change were more complex than most theoretical models for range limits assume. Genomic diversity declined towards the edge of the species’ range and with increasing distance from the estimated centre of the species’ niche in environmental space, but not with the suitability of sites based on niche model predictions. Thus while latitudinal range limits in this system are broadly associated with niche limits, the link between environmental conditions and genetic diversity (and thus the adaptive potential of populations) is less clear.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score1.000

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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.018
GPT teacher head0.234
Teacher spread0.216 · 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

Citations34
Published2017
Admission routes2
Has abstractyes

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