Invasion of emerald ash borer Agrilus planipennis Fairmaire (Coleoptera: Buprestidae): the tactic of actions
Bibliographic record
Abstract
This review summarizes the behaviour of emerald ash borer (EAB) and aims as contributing to the challenges of detecting and managing this pest, along with an array of EAB impacts recorded in USA, Canada, and Russia, including variation in host preference and prediction of invasion spread. It provides an overview of tactics available to manage EAB infestations. EAB was first recorded in Ukraine on 20-22 June 2019. Ash trees in Starokozhiv Forest and field shelterbelts in its vicinity (Markivka district, Luhansk region of Ukraine) were examined and officially reported by National Plant Protection Organization of Ukraine in autumn 2019. EAB is included in the list of 20 priority quarantine pests of the EU (EU 2019) including Ukraine. Detection of EAB in Ukraine requires detailed research and growing awareness of its biology, ecology, interaction with hosts, spread prediction especially because the species is distinguished by its wide plasticity and ability to adapt to different ecological conditions within its native range. The pest is quickly spreading to the south and will undoubtedly occur soon in other European countries. The analyses of the forecasting model of EAB spread have shown no significant geographic barriers which could slow the spread of the pest to the neighbouring countries. Consequently, the probability of EAB detection in western Europe can be up to 15–40% in a couple of years. No case of serious A. planipennis damage to Fraxinus excelsior in European forests including Ukraine has been detected yet. Therefore, it is still unknown whether A. planipennis will become a devastating forest pest in Europe or just a pest of urban plantings. The biology and ecology of A. planipennis in Russia are similar to those in North America and China, with only two exceptions: parasitism is low, and host trees are highly susceptible. More research is needed to understand the behaviour of this pest in Ukraine, encouraging the scientists from universities, government agencies, and companies to continue to discover EAB-related issues in order to advance EAB management and ash conservation. Our understanding on how EAB can be managed/controlled continues in different countries and methods with available monitoring techniques (e.g. visual detection, an array of woodpeckers, use of pheromone traps, girdled ash trees, branch sampling techniques, insecticides, and native larval parasitoids or introduced parasitoids) were discussed in this review paper. It aims at developing adaptive forest management options/strategies and controlling the movement/spread of EAB for all interested stakeholders in Ukraine.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".