Common drivers shaping niche distribution and climate change responses of one hundred tree species
Bibliographic record
Abstract
Climate change is increasingly contributing to climatic mismatches, in which habitat suitability changes outpace the dispersal abilities of species. Climate niche models (CNM) have been widely used to assess such impacts on tree species. However, most studies have focused on either a single or a limited number of species, or have employed a fixed set of climate variables for multiple species. These limitations are largely due to the constraints of data availability, the complexity of the modeling algorithms, and integration approaches for the projections of diverse species. Therefore, whether specific climatic drivers determine the climatic niches of multiple tree species remains unclear. In this study, CNMs were developed for 100 economically and ecologically important tree species in China and were used to project their future distribution individually and collectively. Continentality was the predominant climate variable, affecting 71 species, followed by seasonal precipitation, which also significantly influenced over 50 species. Of the 100 tree species, the climate niche extent was projected to expand for 29 ("winners"), contract for 36 ("losers"), be stable for 27, and fluctuate for the remaining eight species. Principal component analysis showed that winners and losers were differentiated by geographic variables and the top five climatic variables, however, not by species type (deciduous vs. evergreen or conifer vs. broadleaf). The regions with the highest species richness were mainly distributed in the Hengduan Mountains, a global biodiversity hotspot, and were predicted to increase from 5.2% to 7.5% of the total area. Areas with low species richness were projected to increase from 33.0% to 42.4%. Significant shifts in species composition were anticipated in these biodiversity-rich areas, suggesting potential disruption owing to species reshuffling. This study highlights the urgent need for proactive forest management and conservation strategies to address the impacts of climate change on tree species and preserve ecological functions by mitigating climatic mismatches. In addition, this study establishes a framework to identify the common environmental drivers affecting niche distribution and evaluates the collective patterns of multiple tree species, thereby providing a scientific reference for enhanced forestry management and climate change mitigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".