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Record W4417410674 · doi:10.1098/rsbl.2025.0380

An experimental test of the effects of temperature and resource quality on carrying capacity

2025· article· en· W4417410674 on OpenAlexafffund
Matthew A. Barbour, Tess Nahanni Grainger

Bibliographic record

VenueBiology Letters · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversité de SherbrookeUniversity of Guelph
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsCarrying capacityResource (disambiguation)PopulationOrganismMetabolic rateClimate change

Abstract

fetched live from OpenAlex

The metabolic theory of ecology links the effects of temperature on metabolic rates at the cellular scale to larger-scale ecological processes, providing a framework for predicting how individuals, populations and communities will respond to climate change. Metabolic theory predicts that carrying capacity will have a unimodal (hump-shaped) response to temperature, and that this relationship could be altered by resource availability. However, few studies have empirically tested these predictions, despite the fundamental role that carrying capacity plays in governing population and community dynamics. To test the effect of temperature on carrying capacity and to determine whether this is mediated by resource quality, we conducted a fully crossed experiment in which we grew populations of the model organism Tribolium castaneum for 22 weeks at all 12 treatment combinations of four temperatures (27.5, 30, 32.5, 35°C) and three resource qualities (flour mixtures). Our results support the prediction that carrying capacity has a unimodal relationship with temperature, however resource quality did not alter this relationship. These findings support emerging theory describing temperature's effect on carrying capacity, and contribute to our understanding of how population dynamics will shift 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 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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score0.189

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 studies0.0000.001
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 head0.251
Teacher spread0.240 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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