An experimental test of the effects of temperature and resource quality on carrying capacity
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".