Predicting invertebrate herbivory from plant traits: evidence from 51 grassland species in experimental monocultures
Bibliographic record
Abstract
Invertebrate herbivores can impact plant performance and plant communities. Conversely, plants can affect the ability of herbivores to find, choose, and consume them through their functional traits. While single plant traits have been related to rates of herbivory, most often involving single herbivore-plant pairs, much less is known about which suite of plant traits is important for determining herbivory for a pool of plant species interacting with a natural herbivore community. In this study we measured aboveground herbivore damage on 51 herbaceous species growing in monocultures of a grassland biodiversity experiment and collected 42 different plant traits representing four trait groups: physiological, morphological, phenological, and herbivore related. Using the method of random forests and multiple regression, we identified seven traits that are important predictors of herbivore damage (leaf nitrogen and lignin concentration, number of coleopteran and hemipteran herbivores potentially feeding on the plants, leaf life span, stem growth form, and root architecture); leaf nitrogen and lignin concentration were the two most important predictors. The final model accounted for 63% of the variation in herbivore damage. Traits from all four trait groups were selected, showing that a variety of plant characteristics can be statistically important when assessing folivory, including root traits. Our results emphasize that it is necessary to use a multivariate approach for identifying traits affecting complex ecological processes such as herbivory.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.003 | 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 teacher head, 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".