The direct and indirect effects of temperature on a predatorprey relationship
Bibliographic record
Abstract
Abiotic factors may directly influence community structure by influencing biotic interactions. In aquatic systems, where gape-limited predators are common, abiotic factors that influence organisms' growth rates potentially mediate predatorprey interactions indirectly through effects on prey size. We tested the hypothesis that temperature influences interactions between aquatic size-limited insect predators (Notonecta kirbyi) and their larval anuran prey (Hyla regilla) beyond its indirect effect on prey size. Notonecta kirbyi and H. regilla were raised and tested in predatorprey trials at one of three experimentally maintained temperatures, 9.9, 20.7, or 25.7°C. Temperature strongly influenced anuran growth and predator success; mean tadpole mass over time was positively related to temperature, while the number of prey caught was negatively related. At higher temperatures tadpoles attained greater mass more quickly, allowing them to avoid capture by notonectids. However, the probability of capture is a function of both mass and temperature; temperature was a significant explanatory variable in a logistic regression equation predicting prey capture. For a given prey mass, tadpoles raised in warmer water experienced a higher probability of capture by notonectids. Thus, rather than being static, prey size refugia are influenced directly by abiotic factors, in this case temperature. This suggests that temperature exerts differential effects on notonectid and larval anurans, leading to differences in the probability of prey capture for a given prey mass. Therefore, temperature can influence predatorprey interactions via indirect effects on prey size and direct effects on prey.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".