Climate change likely to reshape vegetation in North America's largest protected areas
Bibliographic record
Abstract
Abstract Climate change poses a serious threat to biodiversity and unprecedented challenges to the preservation and protection of natural landscapes. We evaluated how climate change might affect vegetation in 22 of the largest and most iconic protected area (PA) complexes across North America. We use a climate analog model to estimate how dominant vegetation types might shift under mid‐ (2041–2070) and late‐century (2071–2100) climate according to the RCP 8.5 scenario. Maps depicting vegetation for each PA and time period are provided. Our analysis suggests that half (11 of 22) of the PAs may have substantially different vegetation by late‐21st century compared with reference period conditions. The overall trend is toward vegetation associated with warmer or drier climates (or both), with near complete losses of alpine communities at the highest elevations and high latitudes. At low elevation and latitudes, vegetation communities associated with novel climate conditions may assemble in PAs. These potential shifts, contractions, and expansions in vegetation portray the possible trends across landscapes that are of great concern for conservation, as such changes imply cascading ecological responses for associated flora and fauna. Overall, our findings highlight the challenges managers may face to maintain and preserve biodiversity in key PAs across North America.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".