Winners and losers under past and future climate change
Bibliographic record
Abstract
ABSTRACT Understanding the historical and physiological context of species’ vulnerabilities to climate change is a crucial step in predicting “winners” and “losers” under climate change. However, few studies have compared the magnitude and mechanisms of extant species’ responses to climate change in both the past and the future. By combining temporally contrasting range and niche projections, we show that range shifts in the next 50 years will need to be more extreme than in the past 6000 years to track climate niches in a large plant radiation. A new subset of physiological niche traits, particularly temperature and radiation tolerance, will be strong filters of range occupancy under anthropogenic compared with Holocene climate change. In the absence of migration, temperature niche shifts tracking the magnitude of climate change will also be required for many species to maintain their present ranges. Where range shifts occur, our results suggest that communities will be restructured differently in different habitats, with widespread range contraction in the mountains and potential latitudinal range expansion in the lowlands. Our study adds to a growing body of evidence that despite the threats posed by climate change to many species, not all species will experience unmitigated loss, and that it may be possible to predict which species are most at risk based on physiological and geographical traits.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".