Poor regeneration of pine after mountain pine beetle attack in colder boreal regions of Canada
Bibliographic record
Abstract
The recent expansion of mountain pine beetle (MPB, Dendroctonus ponderosae) from its native range in western North America into northern boreal pine forests in Alberta, Canada has resulted in conditions for tree regeneration that are dramatically different from those after wildfire, the predominant natural disturbance in these forests. We assessed natural regeneration post-MPB for northern boreal lodgepole pine sites in Alberta, Canada via intensive surveys of small plots at 33 severely attacked pine stands and using data from 205 permanent sample plots representing various site types and levels of MPB mortality. We used model selection to identify factors explaining regeneration. Overall, pine regeneration was very poor 6–9 years post-MPB; only 42% of the 33 intensively surveyed plots and only 9% of the 205 permanent plots had pine seedlings. This poor regeneration is attributed to high levels of cone serotiny in these populations, unsuitable regeneration microsites due to undisturbed litter or feathermoss layers, and competition from the residual canopy and understory vegetation. Other species (aspen, birch, poplar, and black and white spruce) were found on most sites, either as post-attack regeneration or regeneration established in advance. Without intervention, many of these stands will likely transition away from pine, to broadleaf and other conifer species.
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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 | 0.000 |
| 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".