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Record W2793176206 · doi:10.1111/evo.13458

Temperature-dependent oxygen limitation and the rise of Bergmann's rule in species with aquatic respiration

2018· article· en· W2793176206 on OpenAlexafffund
Njal Rollinson, Locke Rowe

Bibliographic record

VenueEvolution · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEctothermBiologyEcologyOxygenRespirationAquatic ecosystemInvertebrateBergmann's rulePhenotypic plasticityBotanyLatitude

Abstract

fetched live from OpenAlex

Bergmann's rule is the propensity for species-mean body size to decrease with increasing temperature. Temperature-dependent oxygen limitation has been hypothesized to help drive temperature-size relationships among ectotherms, including Bergmann's rule, where organisms reduce body size under warm oxygen-limited conditions, thereby maintaining aerobic scope. Temperature-dependent oxygen limitation should be most pronounced among aquatic ectotherms that cannot breathe aerially, as oxygen solubility in water decreases with increasing temperature. We use phylogenetically explicit analyses to show that species-mean adult size of aquatic salamanders with branchial or cutaneous oxygen uptake becomes small in warm environments and large in cool environments, whereas body size of aquatic species with lungs (i.e., that respire aerially), as well as size of semiaquatic and terrestrial species do not decrease with temperature. We argue that oxygen limitation drives the evolution of small size in warm aquatic environments for species with aquatic respiration. More broadly, the stronger decline in size with temperature observed in aquatic versus terrestrial salamander species mirrors the relatively strong plastic declines in size observed previously among aquatic versus terrestrial invertebrates, suggesting that temperature-dependent oxygen availability can help drive patterns of plasticity, micro- and macroevolution.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.155

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 head0.011
GPT teacher head0.201
Teacher spread0.190 · 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 designBench or experimental
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

Citations48
Published2018
Admission routes2
Has abstractyes

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