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Record W2538846932 · doi:10.1111/ele.12687

Body size phenology in a regional bee fauna: a temporal extension of Bergmann's rule

2016· article· en· W2538846932 on OpenAlexaff
Sergio Osorio‐Canadas, Xavier Arnán, Anselm Rodrigo, Anna Torné‐Noguera, Roberto Molowny, Jordi Bosch

Bibliographic record

VenueEcology Letters · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Toronto
FundersAgència de Gestió d'Ajuts Universitaris i de RecercaMinisterio de Ciencia e InnovaciónConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsBergmann's rulePhenologyEcologyFaunaBiologyGeography

Abstract

fetched live from OpenAlex

Bergmann's rule originally described a positive relationship between body size and latitude in warm-blooded animals. Larger animals, with a smaller surface/volume ratio, are better enabled to conserve heat in cooler climates (thermoregulatory hypothesis). Studies on endothermic vertebrates have provided support for Bergmann's rule, whereas studies on ectotherms have yielded conflicting results. If the thermoregulatory hypothesis is correct, negative relationships between body size and temperature should occur in temporal in addition to geographical gradients. To explore this possibility, we analysed seasonal activity patterns in a bee fauna comprising 245 species. In agreement with our hypothesis of a different relationship for large (endothermic) and small (ectothermic) species, we found that species larger than 27.81 mg (dry weight) followed Bergmann's rule, whereas species below this threshold did not. Our results represent a temporal extension of Bergmann's rule and indicate that body size and thermal physiology play an important role in structuring community phenology.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.279

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 head0.024
GPT teacher head0.202
Teacher spread0.177 · 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

Citations53
Published2016
Admission routes1
Has abstractyes

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