Predicting the Bioclimatic Habitat Suitability of Ginkgo biloba L. in China with Field-Test Validations
Bibliographic record
Abstract
Ginkgo (Ginkgo biloba L.) is not only considered a ‘living fossil’, but also has important ecological, economic, and medicinal values. However, the impact of climate change on the performance and distribution of this plant is an increasing concern. In this study, we developed a bioclimatic model based on data about the occurrence of ginkgo from 277 locations, and validated model predictions using a wide-ranging field test (12 test sites, located at the areas from 22.49° N to 39.32° N, and 81.11° E to 123.53° E). We found that the degree-days below zero were the most important climate variable determining ginkgo distribution. Based on the model predictions, we classified the habitat suitability for ginkgo into four categories (high, medium, low, and unsuitable), accounting for 9.29%, 6.09%, 8.46%, and 76.16% of China’s land area, respectively. The ANOVA results of the validation test showed significant differences in observed leaf-traits among the four habitat types (p < 0.05), and importantly the rankings of the leaf traits were consistent with our classification of the habitat suitability, suggesting the effectiveness of our classification in terms of biological and economic significance. In addition, we projected that suitable (high and medium) habitats for ginkgo would shrink and shift northward under both the RCP4.5 and RCP8.5 climate change scenarios for three future periods (the 2020s, 2050s, and 2080s). However, the area of low-suitable habitat would increase, resulting in a slight decrease in unsuitable habitats. Our findings contribute to a better understanding of climate change impact on this plant and provide a scientific basis for developing adaptive strategies for future climate.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".