Heterogeneity of plant phenotypes caused by herbivore‐specific induced responses influences the spatial distribution of herbivores
Bibliographic record
Abstract
Abstract 1. Herbivory can induce resistance in a plant and the induced phenotype may be disfavoured by subsequent herbivores. Yet, as the distance between plants in a population increases, limited mobility may make a herbivore more likely to feed and oviposit on host plants in its immediate surroundings. 2. The present study tested whether a herbivore’s preference and distribution across plants with different induced phenotypes was influenced by the spatial distribution of plants. A fragmented population of Solanum dulcamara plants was created. This consisted of discrete, spatially separated patches with different histories of damage, either herbivory from adult flea beetles ( Psylliodes affinis ), tortoise beetles ( Plagiometriona clavata ), or mechanical damage. Each patch was separated by 7 m and consisted of 12 plants that were spaced 30 cm apart. Then a fixed number of adult tortoise beetles were introduced to each patch, and movement and oviposition within and between spatially separate homogeneous patches (receiving one type of damage) were compared with movement and oviposition within heterogeneous patches (containing all three types of damage) over the growing season. 3. Flea beetle and tortoise beetle herbivory consistently induced different phytochemical responses in S. dulcamara (polyphenol oxidase and peroxidase), and adult tortoise beetles avoided oviposition on the flea beetle induced plants within heterogeneous patches. However, between homogeneous patches, plant phenotype did not influence oviposition. Colonisation by naturally occurring flea beetle adults followed a similar pattern. 4. These results suggest that the heterogeneity of plant phenotypes can influence herbivore choice and distribution at small but not large spatial scales.
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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".