Plant and insect functional traits influence herbivore performance under climate change
Bibliographic record
Abstract
Climate change is expected to disrupt many trophic interactions, including those between insect herbivores and their host plants, which could have detrimental effects at the ecosystem level. However, the response of insect herbivory to climate change can vary widely across species, and an understanding of the mechanisms underlying this variation is lacking. Here we examine whether functional traits of insect herbivores and their host plants influence how climate change affects herbivore performance. Information on sixteen functional traits across 86 insect and 93 plant species were collated from values in literature and combined with a dataset of 102 climate manipulation studies measuring herbivore performance. We identified clusters within both plant and insect functional trait values which aligned closely with the fast-slow continuum of life history strategies (woody, perennial plants and chewing insects were larger and had greater longevity/slow development, while non-woody, annual plants and sucking insects were smaller and had shorter longevity/fast development). We found that herbivores performed better on drought-stressed woody plants but not on non-woody plants. Sucking insects performed worse on plants exposed to elevated CO2, while chewing insect performance improved but only on annuals when both plants and insects were exposed to elevated CO2. When insects were exposed to elevated temperatures, we found that both chewing and sucking insects performed better, but when both plants and insects were exposed to elevated temperatures, sucking insects performed worse and chewing insects performed better. Generally, we found the slower life history strategies appeared to be less vulnerable to climate change, except in the case of woody vs. non-woody plants exposed to drought or elevated temperature. This research identifies key functional trait relationships that could enhance our ability to predict the vulnerability of plant-insect interactions to projected climate change and guide conservation efforts.
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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 | 0.001 |
| 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) | 0.001 | 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".