Evolution of mixed strategies of plant defense against herbivores
Bibliographic record
Abstract
Plants have evolved an impressive diversity of defenses to protect themselves from a correspondingly diverse assemblage of herbivores. These defenses include toxic chemicals, trichomes, tough leaves, resins, volatiles that recruit extra defenders such as parasitoids, and compensatory growth following damage, to name a few. Understanding the mechanisms that plants use to defend themselves, and the ecological drivers of plant defense evolution, have been major research problems for over a century While many studies still focus on pairwise plant-herbivore interactions, it is becoming increasingly clear that the ecological diversity of herbivore communities is an important factor shaping the evolution of plant defense strategies In this issue of New Phytologist, Carmona & Fornoni (pp. 574-583) report on a field experiment in which they test whether patterns of natural selection on the two principal plant defense strategiesresistance (i.e. traits that reduce damage) and tolerance (i.e. traits that reduce the fitness impacts of a given amount of damage)depend on the complexity of herbivore communities. They show that this ecological complexity selects for mixed resistance-tolerance strategies, contrary to a long-standing prediction that resistance and tolerance are mutually exclusive defenses (van der This result highlights the importance of understanding ecological context and complexity when studying evolution of traits mediating species interactions, and it provides unique insight into the evolutionary ecology of plant defenses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".