Pea aphid dropping behavior diminishes foraging efficiency of a predatory ladybeetle
Bibliographic record
Abstract
Abstract Antipredator defensive behaviors are a well‐studied and often crucial part of prey life histories, but little has been done to quantify how such behaviors affect natural enemies, their foraging, and their effectiveness as biological control agents. We explored how the generalist predatory coccinellid Harmonia axyridis Pallas (Coleoptera: Coccinellidae) affects the dropping behavior of the pea aphid, Acyrthosiphon pisum (Harris) (Homoptera: Aphididae), and in turn, how that defensive behavior affects the foraging efficiency of the predator. Experimental arenas that allowed or prevented pea aphid dropping were compared to determine how dropping influences the foraging of multiple life stages of H. axyridis: second instars, fourth instars, and adults. Dropping reduced predation on aphids by all ladybeetle life stages. Despite older predators inducing more dropping, aphid dropping reduced predation by approximately 40% across all ladybeetle life stages. Aphid dropping and predator consumption of aphids were both correlated with how much the predator moved, which also increased with predator life stage. We suggest that the high rates of dropping induced by H. axyridis and the subsequent decrease in H. axyridis foraging efficiency may partially explain why H. axyridis is less effective at controlling pea aphids than it is at controlling other aphid species that do not drop.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".