Predicting invertebrate herbivory from plant traits: Polycultures show strong nonadditive effects
Bibliographic record
Abstract
Plant functional traits affect the capacity of herbivores to find, choose, and consume plants. However, in a community composed of different plant species, it is unclear what proportion of herbivory on a focal plant is explained by its own traits and which is explained by the characteristics of the surrounding vegetation (i.e., nonadditive effects). Moreover, nonadditive effects could be positive or negative, and it is not known if they are related to community properties such as diversity. To quantify nonadditive effects, we developed four different additive models based on monoculture herbivory rates or plant traits and combined them with measurements of standing invertebrate herbivore damage along an experimental plant diversity gradient ranging from monocultures to 60-species mixtures. In all four models, positive nonadditive effects were detected, i.e., herbivory levels were higher in polycultures than what was expected from monoculture data, and these effects contributed up to 25% of the observed variance in herbivory. Importantly, the nonadditive effects, which were defined as the deviance of the models' predictions from the observed herbivory, were positively correlated with the communities' plant species richness. Consequently, interspecific interactions appear to have an important impact on the levels of herbivory of a community. Identifying those community properties that capture the effects of these interactions is a next important challenge for our understanding of how the environment interacts with plant traits to drive levels of herbivory.
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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) | 0.001 | 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 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".