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Record W1555337881 · doi:10.1111/bij.12268

Ecological preference between generalist and specialist rodents: spatial and environmental correlates of phenotypic variation

2014· article· en· W1555337881 on OpenAlexaff
Juan José Martínez, Virginie Millien, Ivana Simone, José Priotto

Bibliographic record

VenueBiological Journal of the Linnean Society · 2014
Typearticle
Languageen
FieldMathematics
TopicMorphological variations and asymmetry
Canadian institutionsMcGill University
Fundersnot available
KeywordsGeneralist and specialist speciesBiologyEcologySexual dimorphismSigmodontinaeEcomorphologySpatial variabilityZoologyHabitatRodent

Abstract

fetched live from OpenAlex

Different ecological preferences among species may result in differences in response to similar environmental variation. To test this hypothesis, we assessed the patterns of skull and mandible size and shape variation in three Sigmodontinae mice from agroecosystems of central Argentina with increasing degree of specialization: Calomys musculinus, Akodon azarae and Oxymycterus rufus. Spatial patterns in size and shape were analysed after controlling for allometry and sexual dimorphism using a total of 697 specimens. We then evaluated the covariation between shape, climatic and environmental variables and assessed the contribution of distinct climatic and environmental variables to phenotypic variability. Oxymycterus rufus displayed a marked spatial structure, and there was a high correlation between shape, climatic and environmental variables in this species. Climatic and environmental variables had a moderate effect on the phenotype of A. azarae, and were not correlated with morphological variation in C. musculinus. Our study highlights the difference in phenotypic responses to spatial and environmental gradients across coexisting species, specialist species displaying a more marked spatial structure in morphology than generalist 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)0.001
metaresearch head score (Gemma)0.001
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 score0.013
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.054
GPT teacher head0.255
Teacher spread0.201 · 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

Citations30
Published2014
Admission routes1
Has abstractyes

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