When mating disruption does not disrupt mating: fitness consequences of delayed mating in moths
Bibliographic record
Abstract
Abstract The goals of pheromone‐mediated mating disruption are to interfere with mate finding, suppress insect population growth, and prevent crop damage. In addition to prevention of mating, pheromone treatment can also result in a delay of mating so that the fitness and subsequent population dynamics of the target insect pest are impacted. Females have a limited time to mate, mature eggs, and find suitable oviposition hosts, and a delay in mating can have large consequences on female fitness. As a result, delayed mating could be considered an indirect method by which mating disruption works to control pest populations. We perform a meta‐analysis on 24 experimental studies to assess the consequences of delayed mating on female moth fecundity, egg fertility, adult longevity, and pre‐oviposition period. Our goal is to determine whether the effect of delayed mating on female moth fitness is influenced by the following explanatory variables: moth family, voltinism, larval diet breadth, adult dispersal capacity, female mating strategy, and egg development strategy. Across species, the effect of delayed mating on female moths significantly decreases fecundity, fertility, and pre‐oviposition period and increases female longevity. The effect of delayed mating on female fitness is only marginally affected by the explanatory variables tested. We discuss the observed patterns and argue that delayed mating can be an important method by which population regulation is achieved through pheromone‐mediated mating disruption. Finally, we highlight areas where future research could add to the growing body of knowledge on mating disruption‐imposed delayed mating of female moths.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".