Restoring Whitebark Pine Ecosystems of the West in the Face of Climate Change
Bibliographic record
Abstract
The combined effects of mountain pine beetle (Dendroctonus ponderosae) outbreaks, fire exclusion policies, and the exotic disease white pine blister rust (caused by the pathogen Cronartium ribicola) has caused a severe decline in high elevation whitebark pine (Pinus albicaulis) forests across western North America. Predicted changes in climate may exacerbate this decline by (1) accelerating succession to more shade tolerant conifers, (2) creating environments unsuitable for whitebark pine, (3) increasing the frequency and severity of mountain pine beetle outbreaks and wildland fire events, and (4) facilitating spread of blister rust. Since more than 90 percent of whitebark pine forests occur on public lands in the U.S. and Canada, a trans-boundary, a range-wide whitebark pine restoration strategy was developed for public lands to coordinate and inform restoration efforts across federal and provincial land management agencies. In this presentation, we will discuss the fire ecology of this valuable ecosystem to provide a context for restoration. Then, the range-wide strategy will be presented and the full suite of restoration activities will be explored. Last, we will present guidelines for restoring whitebark pine under future climates using the rangewide restoration strategy structure. The information on adjusting whitebark pine restoration effects for climate change impacts come from two sources: we conducted a comprehensive review of the literature to assess climate change impacts on whitebark pine ecology and management and then we used the spatially explicit, ecological process model FireBGCv2 to simulate various climate change, management, and fire exclusion scenarios The paper is written as a general guide to be used with the rangewide strategy for planning, designing, implementing, and evaluating fine-scale restoration activities for whitebark pine by public land management agencies by addressing climate change impacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".