Patterns in whitebark pine regeneration and their relationships to biophysical site characteristics in southwest Montana, central Idaho, and Oregon, USA
Bibliographic record
Abstract
Declines of whitebark pine ( Pinus albicaulis Engelm.) have occurred across much of the species’ range over the last 40 years due to mountain pine beetle outbreaks and white pine blister rust infection. Management efforts to stem these declines are increasing, yet the long-term success of whitebark pine depends on the species itself adapting to the modern environment. Natural regeneration will be a critical part of this process. We examined patterns in natural whitebark pine regeneration as related to the biophysical environment in sixty 0.1 ha plots in Montana, Idaho, and Oregon. Whitebark pine regeneration was present in 97% of our plots and varied widely in density from 0 to 17 000 seedlings/ha and 0 to 2680 saplings/ha. Using nonparametric correlation analysis and ordination techniques, we found whitebark pine regeneration abundance was unrelated to stand age but significantly related to several biophysical site characteristics, including positive relationships with elevation and canopy tree mortality caused by mountain pine beetle and negative relationships with moisture availability, temperature, and subalpine fir importance. Our findings indicate that whitebark pine is regenerating in many areas and that the widespread mortality from recent mountain pine beetle outbreaks may provide suitable settings for whitebark pine regeneration given sufficient seed sources.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".