Egg cannibalism by <i>Coleomegilla maculata lengi</i> neonates: preference even in the presence of essential prey
Bibliographic record
Abstract
Abstract 1. Egg cannibalism among coccinellids has been reported widely, however reasons why this type of behaviour is observed so frequently have been neglected. This experiment was undertaken to clarify whether cannibalistic behaviour is advantageous to Coleomegilla maculata lengi Thimberlake neonates and to understand the reasons for high levels of egg cannibalism. 2. Benefits gained by neonate cannibals were ascertained by comparing survival, developmental time, and second‐instar weight of C. maculata larvae that were allowed to cannibalise conspecific eggs or not. Preference and behaviour tests were also conducted to assess the reasons for high levels of egg cannibalism. 3. Cannibal neonates grew faster and were heavier than non‐cannibals. The developmental time of neonates was influenced more by prey vulnerability than by prey quality. 4. In choice tests, where three different proportions of conspecific eggs and aphids were offered (33–67, 50–50, and 67–33%), C. maculata neonates always consumed significantly more eggs. Manly's preference indexes indicated that neonates showed a consistent preference for conspecific eggs. 5. Seventy‐five per cent of neonates observed went directly towards eggs and 90% of the first prey consumed by neonates were an egg. When aphids were painted with extract of crushed eggs and eggs with crushed aphids to determine whether neonates found eggs by chemical cues, neonates preferred aphids painted with egg extract to eggs painted with aphid extract. 6. It was concluded that C. maculata neonates benefited from cannibalistic behaviour. Moreover, egg cannibalism is not related only to frequency of encounter; chemical cues are also involved in egg searching.
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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".