Early detection strategies for invading tree pests: Targeted surveillance and stakeholder perspectives
Bibliographic record
Abstract
Abstract Trees are at an increasing risk from pests and diseases as global trade of trees and their products increases. One of the most destructive pests found outside its native range is the emerald ash borer (Agrilus planipennis Fairmaire), responsible for the death of millions of ash trees in the United States, Canada, Russia and Eastern Europe. Its early detection in countries where it is not yet present is essential for effective control. One of the most likely introduction pathways for emerald ash borer into Great Britain (GB) is through firewood imports from Eastern Europe, with potential spread from ports, firewood depots and households using wood‐burning fires. We developed a novel modelling framework accounting for the likely invasion pathways of emerald ash borer, its population dynamics, spread and detection sensitivities to determine sampling locations that maximise the probability of detection within 2, 4 and 8 years. To provide a sociological perspective, we interviewed firewood stakeholders to understand biosecurity implications of importing and moving firewood and used scenario workshops to explore landowners' willingness to adopt early detection methods for the emerald ash borer. Optimised sampling strategies significantly improve detection compared with ranked entry points (REPS) if detection resources are plentiful and optimisation targets detection within 8 years of emerald ash borer arrival. For detection within less than 4–6 years or fewer than 70 detection devices REPS are almost as effective as optimised strategies. The methods' detection sensitivity and knowledge of likely entry pathways influence the optimal spatial sampling design. Firewood imports are actively inspected, and samples taken to ensure biosecurity measures are followed, but compliance at source remains uncertain. Landowners with many ash trees were more open to tree girdling, which may lead to increased detection. Synthesis and applications: We provide the first surveillance map for emerald ash borer incursions in GB with potential for deployment by government agencies and stakeholders concerned with biosecurity. Our framework establishes optimal surveillance locations depending on factors, including detection within different timeframes, knowledge certainty of entry pathways and sensitivity of detection methods. This methodological framework is applicable to other invasive threats.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".