Predicting growth and habitat responses of Ginkgo biloba L. to climate change
Bibliographic record
Abstract
Abstract Key message We developed a climatic response function using 20-year tree height observed from 45Ginkgo bilobaplantations in China and used it to predict the growth and habitat responses to anticipated climate change. We projected northward and upward shifts in the species habitat and productive areas, but a dramatic contraction of the species distribution is unlikely to occur at least during the present century. Context Ginkgo biloba is the only living species in the division Ginkgophyta. The species exists in small natural populations in southeastern China but is cultivated across China and the world. The species’ future under climate change is of concern. Aims This study was initiated to model the species’ growth response to climate change and to predict its range of suitable habitat under future climates. Methods Using height data from 45 20 years old plantations growing under a wide range of climatic conditions across China, we developed univariate and bivariate climatic response functions to identify the climate requirements of the species. Results According to the amount of variance explained (> 70%) and the high level of agreement (> 99%) with independent species occurrence coordinates, the developed climate response function was highly accurate and credible. Projections for future periods (2011–2040, 2041–2070, and 2071–2100) under the Representative Concentration Pathway 4.5 (RCP4.5) scenario indicated that the areas of potential suitable habitat would increase (25–67 million hectares). It would also be associated with northward (0.21–0.62° in latitude) and elevational (24–75 m) shifts. Conclusion Global climate change is projected to increase the area of potential suitable habitats for Ginkgo and shift its spatial distributions northward and upward.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".