Decision analysis of Integrated Pest Management: A case study on invasive sea lamprey in the Great Lakes Basin
Bibliographic record
Abstract
Integrated Pest Management (IPM) provides a powerful framework for addressing threats to human well-being caused by nuisance species including invasives. We examined the hypothesis that adaptive management could erode barriers to IPM implementation by developing a decision-analytic adaptive management framework for invasive sea lamprey (Petromyzon marinus) IPM in the Laurentian Great Lakes of North America. The framework addressed objectives associated with coordinating multiple sea lamprey control actions at the regional scale and objectives associated with internal validity of control actions. We reduced the scope of possible management actions by orders of magnitude to the set of 6432 alternatives expected to be both socially acceptable and technically feasible. Using utility theory, we identified the management actions that optimized expected utility for all possible objective weighting schemes that considered tradeoffs between maximizing learning about control tactic efficacy and minimizing cost to the IPM program. Sensitivity analyses revealed that assumptions about the social acceptability of deploying electric weirs to control invasive sea lamprey influenced selection of the optimal control action, suggesting that resolving this source of uncertainty through iterative application of the framework may lead to improved sea lamprey control outcomes. Overall, we found that adaptive management enabled learning processes useful for overcoming barriers to IPM of invasive sea lamprey. It formalized learning about sea lamprey control tactic efficacy as an objective of the IPM institution, questioned previously held assumptions about what constitutes a viable control strategy, and enabled a management experiment with temporal and spatial replication.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".