MULTIPLE PLANT EXPLOITERS ON A SHARED HOST: TESTING FOR NONADDITIVE EFFECTS ON PLANT PERFORMANCE
Bibliographic record
Abstract
The combined impact of multiple plant parasites on plant performance can either be additive (the total damage equals the sum of the individual effects) or nonadditive (synergistic or antagonistic damage). Two statistical models are available for testing the independent (=additive) effects of two factors. Here we suggest that the natural history of the plant-parasite system should motivate the choice of a statistical model to test for additivity. Using in-field, manipulative experiments, we examined the interactions between the herbivorous mite Calacarus flagelliseta Fletchmann, De Moraes and Barbosa (Acari: Eriophyidae), the fungal pathogen Oidium caricae F. Noack (a powdery mildew), and their host plant Carica papaya L. in Hawaii. First, we found that herbivorous mites had a moderate negative effect on powdery mildew: when mites were absent, powdery mildew colonies were larger and more numerous. Second, we showed that each plant parasite, when evaluated alone, significantly reduced several measures of plant performance. Third, we found that the combined impact of mites and mildew on plant performance is mostly additive and, for a few variables, less than additive. Finally, we explored compensatory responses and found no evidence for nonlinearities in the relationship between plant performance and cumulative parasite impact. Plants are almost universally subject to attack by multiple herbivores and pathogens; thus a deeper understanding of how multiple plant parasites shape each other's population dynamics and plant performance is essential to understanding plant-parasite interactions.
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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 | 0.034 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".