Whitebark pine in the United States projected to experience an 80% reduction in climatically suitable area by the mid-21st century
Bibliographic record
Abstract
Abstract Whitebark pine (WBP; Pinus albicaulis) is a five-needle conifer tree species native to the western United States (US) and southwestern Canada. In the US, this ecologically important species is listed as ‘threatened’ under the Endangered Species Act because of population declines from compound stressors, including climate change, introduced white pine blister rust (WPBR), widespread outbreaks of mountain pine beetle, and altered fire regimes. Species recovery depends primarily on planting WBP seedlings that are resistant to WPBR, but suitable planting locations are likely to shift under climate warming. We modeled and mapped WBP climatic suitability in the US using forest inventory data and fine-scale (∼220 m) climate datasets under reference period (1961–1990) and mid-21st century climatic conditions. We projected an 80% reduction in the area climatically suitable for WBP by the mid-21st century. Moreover, 75% of the climatically suitable area for WBP under mid-21st century climate is located in designated wilderness areas and national parks. This could challenge WBP recovery efforts, as these protected areas strive to reduce human manipulation of ecological systems. WBP climate suitability maps resulting from our models can be used to identify priority or ‘target areas’ where planting will have the highest likelihood of success and to identify areas within the WPB’s current range that are most vulnerable to rapid change should high-severity wildfires or mountain pine beetle outbreaks occur. Mapping WBP reference period and mid-21st century climate suitability is a fundamental step to efficiently prioritize restoration areas and develop a successful recovery program.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".