Climate change and human footprint increase insect defoliation across central boreal forests of Canada
Bibliographic record
Abstract
Anthropogenic climate change is contributing to increased insect infestation globally, leading to pest population growth, expansion of niche and geographic ranges as well as increased outbreak frequencies, resulting in economic losses and reduction in food security. In recent years, spongy moth (Lymantria dispar dispar), jack pine budworm (Choristoneura pinus pinus), large aspen tortrix (Choristoneura conflictana) and spruce budworm (Choristoneura fumiferana) caused widespread defoliation across one of Canada’s most forested provinces, Ontario. Observations of such outbreaks have been limited to field sightings around Ontario, with few studies focused on mapping of outbreak occurrence across the province or exploring potential anthropogenic and climatic drivers of infestation. Using random forest probability estimates and satellite data resampled to 1 km spatial resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS), we reveal greater expansion of insect defoliation across Ontario between 2018 and 2020 than previously recorded. Much of the geographic expansion of outbreaks was driven by increasing temperature, and the proximity of roads. With ongoing global warming and growing economic development, infestations will not only continue to increase across Ontario but also expand northward due to their responses to accelerated warming at higher latitudes. This expansion presents an important and alarming new challenge for forest conservation and management in Ontario, in particular, and Canada in general.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".