Temperature-dependent herbivore nutritional traits affect population dynamics and persistence
Bibliographic record
Abstract
Abstract The nutritional traits of herbivores affect demographic rates and regulate nutrient and energy fluxes among trophic levels. Herbivore nutritional requirements, and the nutrient contents of herbivore biomass, depend on temperature – at temperature extremes, more nutritious food is required to maximize growth rate and herbivore biomass contains fewer nutrients. Yet, the consequences of these thermal responses for the population dynamics of herbivore-autotroph systems have not been explored. Here, we develop and analyze a stoichiometrically-explicit, temperature-dependent model of herbivore-autotroph systems to answer the question: How does the thermal response of herbivore nutritional traits affect population responses to temperature and nutrient (phosphorus) supply? We find that temperature-dependent herbivore nutritional traits restrict the range of temperatures at which herbivore populations persist, reduce the stability of population dynamics at high phosphorus supplies, and limit the herbivore’s capacity to control autotroph population density. These results reflect temperature-dependent changes in the herbivore’s sensitivity to nutrient-poor autotroph biomass and ability to retain nutrients in biomass (and thereby dilute autotroph nutrient contents). The thermal response of herbivore nutritional traits may therefore be an important factor influencing population and community responses to warming and nutrient enrichment.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".