Predicting the Potential Geographical Distribution of Rhodiola L. in China under Climate Change Scenarios
Bibliographic record
Abstract
Rhodiola L. has high nutritional and medicinal value. Little is known about the properties of its habitat distribution and the important eco-environmental factors shaping its suitability. Rhodiola coccinea (Royle) Boriss., Rhodiola gelida Schrenk, Rhodiola kirilowii (Regel) Maxim., and Rhodiola quadrifida (Pall.) Fisch. et Mey., which are National Grade II Protected Plants, were selected for this research. Based on high-resolution environmental data for the past, current, and future climate scenarios, we modeled the suitable habitat for four species by MaxEnt, evaluated the importance of environmental factors in shaping their distribution, and identified distribution shifts under climate change scenarios. The results indicate that the growth distribution of R. coccinea, R. kirilowii, and R. quadrifida is most affected by bio10 (mean temperature of warmest quarter), bio3 (isothermality), and bio12 (annual precipitation), whereas that of R. gelida is most affected by bio8 (mean temperature of wettest quarter), bio13 (precipitation of wettest month), and bio16 (precipitation of wettest quarter). Under the current climate scenario, R. coccinea and R. quadrifida are primarily distributed in Tibet, eastern Qinghai, Sichuan, northern Yunnan, and southern Gansu in China, and according to the 2070 climate scenario, the suitable habitats for both species are expected to expand. On the other hand, the suitable habitats for R. gelida and R. kirilowii, which are primarily concentrated in southwestern Xinjiang, Tibet, eastern Qinghai, Sichuan, northern Yunnan, and southern Gansu in China, are projected to decrease under the 2070 climate scenario. Given these results, the four species included in our study urgently need to be subjected to targeted observation management to ensure the renewal of Rhodiola communities. In particular, R. gelida and R. kirilowii should be given more attention. This study provides a useful reference with valuable insights for developing effective management and conservation strategies for these four nationally protected plant species.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 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".