Learning affects prey selection in larvae of a generalist coccinellid predator
Bibliographic record
Abstract
Abstract Under natural conditions, generalist predatory insects have to cope with a variety of potential prey species that are not all equally suitable. Under these circumstances, learning may be adaptive if it allows adjustment to variations in resource quality and availability. Under laboratory conditions, we examined the learning ability and memory in the prey selection process of larvae of the predatory coccinellid Coleomegilla maculata ssp. lengi Timberlake (Coleoptera: Coccinellidae). Using choice tests, we studied prey rejection behaviour of C. maculata fourth instars towards prey of different quality and we also tested the influence of hunger and prior experience with other food types on the prey rejection behaviour of coccinellid larvae. Coleomegilla maculata larvae gradually changed their behaviour and rejected low‐quality hosts more frequently, whereas high‐quality hosts were nearly always accepted. After 48 h, the learned behaviour appeared to be partially forgotten. Hunger and experience with other food types prior to the test had little effect on the gradual change of behaviour but the quality of the food ingested influenced the initial level of prey rejection. Our results demonstrate that (1) C. maculata larvae can adjust their prey selection behaviour with experience to reject progressively less suitable prey, and (2) previous experience with other prey types can influence their initial preference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".