Temperature responses of a plant‐insect system using a food‐web performance approach
Bibliographic record
Abstract
Abstract Evaluation of the performance of a plant‐herbivore system as a whole is difficult due to the lack of fitness parameters that can be applied to both components. The individual use of traditional measures of performance (e.g., rm, biomass) can provide useful, but incomplete information on the performance of insect herbivores and seldom incorporates plant performance. We propose the use of the net generational productivity (NGP) to evaluate the fitness of the herbivore, which can then be compared directly with the performance of the plant in biomass units, to obtain the food‐web performance ratio (φH/P). We compared three biotypes of the potato aphid,Macrosiphum euphorbiaeThomas (Hemiptera:Aphididae), when raised on three different host plants: potato (Solanum tuberosumL. cv.Norland) and two bell peppers (Capsicum annuumL. cv.Fascinato and cv.Crosby) (allSolanaceae) at temperatures ranging from 8 to 36 °C. The temperature profiles of the potato aphid biotypes suggest that this aphid is better suited to temperate climates, and its performance generally depends on the particular host‐plant/biotype association. Plant growth performance showed that potato has a lower thermal tolerance, but has a faster growth rate than bell peppers, especially in the range of 16–24 °C. Temperature variation in the φH/Pratio shows that aphids have a greater performance than plants, especially at lower temperatures, at which they can accumulate biomass up to 148 times faster. Because of the aphid's biological inability to withstand long exposures to temperatures above 28 °C, plants have a slight advantage over aphids. Nonetheless, as the performance of plants is extremely reduced at high temperatures, this advantage cannot withstand long‐term exposures to extreme temperatures. This is the first attempt to obtain a parameter capable of determining the climatic profile and performance of a food web in an inclusive yet simple manner.
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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 | 0.000 |
| 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".