Effects of Plant Gross Morphology on Predator Searching Behaviour
Bibliographic record
Abstract
Plant morphology influences insect predators' abilities to capture prey and control pest populations. Several mechanisms for this effect of plants on predator foraging have been proposed. In particular, it is often claimed that increased complexity of plant structures may increase search time and reduce foraging success. Using time-lapse photography we recorded search paths, and compared the total path lengths, percentages of plants searched, and path tortuosity of adult multicolored Asian lady beetles (Harmonia axyridis Pallas) and green lacewing larvae (Chrysoperla carnea Stephens) foraging for pea aphids (Acyrthosiphon pisum Harris) on pea near-isolines (Pisum sativum L.) that differed in shape. We found that H. axyridis searched leafy morphologies less thoroughly than those with more branches, while C. carnea larvae search paths did not differ on any of the pea morphologies. In addition, the ability of H. axyridis to attach to plants and maneuver was increased on morphologies with many branches and edges, while C. carnea was able to attach to all morphologies. Both species, however, had significantly reduced predation success on inverted leaf surfaces. We conclude that undersides of leaves, far from the leaf margin, may serve as partial prey refugia. In addition, we find increased plant branching or an increase in other morphological features which provide predator attachment points may promote foraging 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 | 0.000 | 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 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".