Projections of suitable habitat for rare species under global warming scenarios
Bibliographic record
Abstract
PREMISE OF THE STUDY: Modeling the contemporary and future climate niche for rare plants is a major hurdle in conservation, yet such projections are necessary to prevent extinctions that may result from climate change. • METHODS: We used recently developed spline climatic models and modified Random Forests statistical procedures to predict suitable habitats of three rare, endangered spruces of Mexico and a spruce of the southwestern USA. We used three general circulation models and two sets of carbon emission scenarios (optimistic and pessimistic) for future climates. • KEY RESULTS: Our procedures predicted present occurrence perfectly. For the decades 2030, 2060, and 2090, the ranges of all taxa progressively decreased, to the point of transient disappearance for one species in the decade 2060 but reappearance in 2090. Contrary to intuition, habitat did not develop to the north for any of the Mexican taxa; rather, climate niches for two taxa re-materialized several hundred kilometers southward in the Trans-Mexican Volcanic Belt. The climate niche for a third Mexican taxon shrank drastically, and its two mitotypes responded differently, one of the first demonstrations of the importance of intraspecific genetic variation in climate niches. The climate niche of the U.S. species shrank northward and upward in elevation. • CONCLUSION: The results are important for conservation of these species and are of general significance for conservation by assisted colonization. We conclude that our procedures for producing models and projecting the climate niches of Mexican spruces provide a way for handling other rare plants, which constitute the great bulk of the world's endangered and most vulnerable flora.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".