High tree diversity exposed to unprecedented macroclimatic conditions even under minimal anthropogenic climate change
Bibliographic record
Abstract
Tree species worldwide face increasing exposure to unprecedented macroclimatic conditions due to anthropogenic climate change, which may trigger biome shifts and ecosystem disruptions. We quantified climate change exposure–shifts to species’ currently unoccupied climate zones–for 32,089 tree species globally by 2100, assessing both species-level and local tree diversity risks. On average, 69% of species are predicted to experience macroclimatic shifts in at least 10% of their range, while 14% face exposure in over 50% of their range under a high-emission (4 °C warming) future scenario. This suggests that most species retain substantial climate refugia within their current range. However, local tree diversity exposure is predicted to be severe in vast regions, including Eurasia, the northwestern United States and Canada, northern Chile, and the Amazon Delta. Under a moderate (2 °C warming) scenario, high tree diversity exposure is mostly restricted to taiga regions in the Northern Hemisphere. These findings provide conservative estimates of climate-driven biodiversity risk, as our approach focuses solely on macroclimate and does not account for additional stressors such as land-use change or species interactions. Identifying tree species and areas of high macroclimatic shift exposure allows for targeted conservation strategies, including species stability monitoring, assisted migration, and the protection of climate refugia. Our results offer a foundation for prioritizing conservation actions in a rapidly changing climate, ensuring long-term ecosystem resilience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".