A Bayesian State‐Space Approach for Invasive Species Management: The Case of Spotted Wing Drosophila
Bibliographic record
Abstract
Spotted wing drosophila (SWD) is an invasive pest with devastating effects on soft‐skinned fruit crops. Due to its high economic impacts, current SWD management strategies usually focus on preventive calendar‐based insecticide sprays. The industry is calling for adoption of monitoring‐based integrated pest management (IPM) strategies to reduce unnecessary insecticide applications. However, because traps are costly and do not provide perfect observations of the population size, most growers do not monitor. We develop a Bayesian state‐space bioeconomic framework to inform the optimal SWD management strategy when observational uncertainty exists. We use Bayesian inference via the Markov Chain Monte Carlo method to generate the posterior distribution of population model parameters, which we then use to simulate the economic performance of alternative SWD management strategies. We find that one of the monitoring‐based IPM strategies has a slightly lower total cost than the calendar‐based spray strategy. We also find evidence of misalignments between public and private incentives in the adoption of IPM strategies. Profit‐maximizing growers who ignore the negative externalities of insecticide spray have little incentive to adopt the IPM strategies. However, for environmentally conscious growers who take into account the external costs of insecticide sprays, IPM strategies are superior to calendar‐based spray strategy. Our results indicate that IPM strategies become more appealing to both types of growers as the trapping efficiency improves. Extension efforts, support for research to improve trapping efficiency, and monetary incentives can be used to encourage grower adoption of monitoring‐based IPM strategies to control SWD infestation.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".