Incorporating diapause to predict the interannual dynamics of an important agricultural pest
Bibliographic record
Abstract
Abstract We develop a new population‐scale model incorporating diapause induction and termination that allows multi‐year predictions of pest dynamics. In addition to predicting phenology and voltinism, the model also allows us to study the degree of overlapping among the life‐stages across time; a quantity not generally predicted by previous models yet a key determinant of how frequently management must be done to maintain control. The model is a physiological, stage‐structured population model that includes temperature‐dependent vital rates, diapause processes, and plasticity in development. The model is statistically fitted with a 33‐year long weekly term time series of Cydia pomonella adults captured in pheromone‐baited traps from a research orchard in southern Pennsylvania. The multiannual model allows investigation of both within season control strategies, as well as the likely consequences of climate change for this important agricultural pest. The model predicts that warming temperatures will cause earlier spring emergence, additional generations, and increased overall abundance. Most importantly, by calculating the circular variance, we find that warmer temperatures are associated with an increase in overlap among life‐stages especially at the beginning of the growing season. Our findings highlight the importance of modeling diapause to fully understand C. pomonella lifecycle and to better inform management for effectively controlling this pest in a warmer future.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".