Using optimality models to improve the efficacy of parasitoids in biological control programmes
Bibliographic record
Abstract
Abstract Biological control of insect pests relies on the ability of natural enemies to limit pest populations. The behaviours expressed by natural enemies against their prey or hosts are modulated by a number of factors and a better understanding of these factors is key to obtaining more efficacious pest control. We propose here that optimality models based upon a behavioural ecology approach can provide a framework that should enable optimisation of biological control practices. We limit our discussion to parasitoid natural enemies and review the factors known to influence the behaviour of these insects. The most important areas that have been studied extensively in the behavioural ecology of insect parasitoids are addressed here: (1) residence time in a host patch, (2) clutch size, (3) sex ratio, (4) host and patch marking, and (5) diet choice. We discuss the implications of the incorporation of these optimality models into efficacious biological control practices and suggest areas where a better knowledge of the behavioural ecology of these insects could improve the efficacy of parasitoid‐based pest control.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".