Mapping the climate niches of forest insects and diseases in Canada under current and future climate
Bibliographic record
Abstract
Insects and diseases are important disturbance agents in Canadian forests and there is concern that their impacts will intensify under climate change. Here we report on an effort to model and map the climate niches of more than 4000 forest insect and fungus species in Canada - including high-profile pest species that are already, or may soon become, established in the country. This work employs occurrence data from historical, national-scale forest insect and disease surveys, several research collections, and the Global Biodiversity Information Facility (GBIF). We further employ national forest inventory products (gridded maps) to summarize forest host volumes at risk of infestation by selected insect and disease species. Maps of current and projected climate suitability have been made publicly available via a web application ( https://cfs.cloud.nrcan.gc.ca/bmfid/ ), which allows the products to be explored and downloaded. We demonstrate use of the products through examples, including brown spruce longhorn beetle (Tetropium fuscum), southern pine beetle (Dendroctonus frontalis), oak wilt (Bretziella fagacearum), and map overlays that show hotspots for bark beetles under current and projected climate. We hope this tool will help pest managers to better understand how these species may respond to projected climate change over the course of the current century.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".