Integrated management of post-spread emerald ash borer populations in urban forests
Bibliographic record
Abstract
Emerald ash borer (Agrilus planipennis), known as EAB, invaded North America during the 1990s and devastated urban forests. Long-term integrated pest management (IPM) programs are essential during the post-spread phase—defined as the period after an invasive species has established itself across all suitable habitats in a landscape—to mitigate EAB damage and maintain forest canopies. However, these programs remain underdeveloped due to several factors. Among those explored in this thesis are limited understanding of EAB population dynamics in urban forests, potential incompatibility between introduced biological control agents and chemical control tactics, and insufficient knowledge of host-parasitoid interactions, particularly concerning host age. In Chapter 2, I assessed EAB population dynamics and its local habitat determinants using the long-term EAB monitoring data collected across the City of Toronto. Even 15+ years post-introduction, EAB populations remain in an outbreak population phase with local habitat features (i.e., host tree density and past EAB infestation) as significant drivers. In Chapter 3, I designed a novel laboratory bioassay to evaluate the non-target impacts of azadirachtin—a systemic, botanically-derived insecticide used in urban EAB management—on Tetrastichus planipennisi, an introduced EAB larval parasitoid. Exposure to azadirachtin at concentrations causing 30% and 50% mortality in EAB larvae significantly reduced the fitness of T. planipennisi. In Chapter 4, I determined the impact of EAB age (of both eggs and females) on the fitness of Oobius agrili, an introduced EAB egg parasitoid. Parasitizing older EAB eggs reduced O. agrili fitness, whereas maternal age influenced trade-offs among parasitism rates, immature survival, development time, and adult size. My work supports: 1) the need for continued targeted chemical treatments and EAB population monitoring in the City of Toronto to manage EAB populations and sustain its urban ash canopy, 2) strategic applications of insecticides to minimize conflicts with EAB biocontrol agents, and 3) the potential of O. agrili for integration into IPM programs aimed at managing EAB in urban forests. This research contributes to broader frameworks for understanding and managing invasive insect pests such as EAB that are in the post-spread invasion phase in urban forests to better support resilient urban forest ecosystems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".