Strength in numbers? Effects of multiple natural enemy species on plant performance
Bibliographic record
Abstract
While plants are invariably attacked by numerous insects and pathogens, the consequences of multiple enemies for plant performance are poorly understood. In particular, a predictive framework is lacking for when to expect enemies to have independent versus non-independent effects on their host plant. This is problematic for weed biological control programmes where multiple enemies are frequently released with the possibility of antagonistic interactions that may reduce control. Here, we conduct an analysis of 74 unique plant-enemy-enemy combinations from 51 studies to determine the frequency of non-independent effects of natural enemies on host plant performance, and test a number of a priori predictions for determinants of independent and antagonistic effects of multiple enemies. For three-quarters of plant response measurements, enemies had independent effects on plant performance. In most of the remainder, multiple enemies led to less reduction in performance than that predicted from each enemy alone. Antagonistic effects occurred when enemies attacked the same plant part concurrently or attacked plant reproductive structures. These two predictors explained why antagonistic effects were particularly prevalent for weeds, plants in the family Asteraceae and enemies in the order Diptera. Our results suggest that a few simple rules about avoiding particular combinations of multiple enemies could improve biological control success.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".