Temperature triggers a non‐linear response in resource–consumer interaction strength
Bibliographic record
Abstract
Abstract Although temperature is recognized as a major determinant of many ecological processes, it is still not clear whether temperature increase caused by climate change will strengthen or weaken species interactions. One hypothesis is that interactions will respond non‐monotonically to temperature because thermal performance curves, which determine the strength of these interactions, are also non‐monotonic. To evaluate this hypothesis, we developed a temperature‐dependent consumer–resource model and tested predictions from this model in large freshwater mesocosms populated with green algae (Chlorella vulgaris) and herbivorous zooplankton (Daphnia magna). We found both in the model simulations and empirical investigations that the suppressive effect of the consumer depended non‐monotonically on the temperature. As predicted by the model, Daphnia suppressed the algal maximum per capita growth rate at the temperature that maximized algal growth rate but had little effect on resource growth at either lower or higher temperatures. This finding could help explain why effects of temperature variation on species interaction are variable in the literature and suggests that predicting the effects of temperature on the strength of food web interactions requires knowledge of the thermal performance curves for multiple traits, for multiple species and over a range of temperatures.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".