Perspectives: Five organizing themes for invasive forest insect and disease management in Canada and the United States
Bibliographic record
Abstract
Forests provide crucial support for ecological communities and play a vital role in human well-being and livelihoods. Protecting forests from the impacts of invasive species is a challenge that spans epistemologies, governmental levels, and academic fields. Yet, sharing new information, existing practices, and challenges among relevant groups has often been limited in Canada and the United States. To address this challenge, we began with a review of all academic and Canadian and US grey literature to reveal major published themes in forest invader research and policy. We refined these through a survey and workshop with participants encompassing Indigenous knowledge holders, government scientists, non-government organization employees, and academic researchers based in Canada and the US. Our deliberations resulted in five organizing themes for research and practitioner action to address species invasions: 1) Overcoming barriers to knowledge sharing, for instance, through the employment of governmental liaisons, 2) Assessing risks and benefits of alternative forms of management, for instance through scenario models of spatial management decisions, 3) Making effective use of new technologies, such as advancements in genomics tools and sentinel plots, 4) Broadening the focus on invasion pathways, especially related to urban forests and the nursery trade, and 5) Considering equity and making space for differing epistemologies, for example through the improved engagement of Indigenous Peoples in forest invader management. We elicited semi-quantitative scores for the importance, uncertainty, feasibility, complexity, and time requirements of tactics aligned with these major themes. We also identified discrepancies in public attention and funding compared to forest experts’ priorities, including in the role of the nursery trade as a pathway of secondary invader spread. We illustrate how these themes can inform priorities for management in three important areas of North American biosecurity: solid wood packaging, and emerald ash borer ( Agrilus planipennis ) and Asian longhorned beetle ( Anoplophora glabripennis ) management. This work provides organization to the growing set of tools and outlines priority management tactics for invasive forest pests. • We conducted a survey and workshop with forest experts from Canada and the US. • We developed five themes for research and management action in forest invasions. • One-way communication and overly general strategies have led to past poor management. • Urban forests and tree nursery trade are considered key pathways for future risk. • Scenario models can provide a more explicit case for taking proactive action.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.068 | 0.034 |
| Scholarly communication | 0.027 | 0.006 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.007 | 0.013 |
| 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".