Diversity pattern of Symplocos tree species in China under climate change scenarios: Toward conservation planning
Bibliographic record
Abstract
Symplocos, a genus rich in endemic and keystone species in China's subtropical forests, plays a vital role in maintaining biodiversity. Climate change impacts on Symplocos trees affect ecosystem composition, dynamics, and functions. This study delves into the potential ramifications of climate change on the distribution and richness of Symplocos species across China under different climatic conditions. A comprehensive analysis was conducted using occurrence records of 29 species of Symplocos , focusing on habitat suitability modeling and spatial analysis, to demonstrate the possible effects of climate change on the distribution and diversity patterns of Symplocos species in China. Environmental data, including bioclimatic variables, were collected, and the future climate scenarios were projected using the combined average of the two global climate models. Four modeling algorithms were applied in an ensemble species distribution models to assess the distribution of Symplocos species and decipher the pattern of species richness and endemism. The results indicate high accuracy and performance of the models with a mean AUC of 0.94 and TSS of 0.77, particularly the random forest. The analysis identified key bioclimatic variables influencing Symplocos species distribution, with precipitation patterns (precipitation of driest month and precipitation of warmest quarter) and temperature variability (temperature mean diurnal range) playing significant roles. Predictions suggest a reduction in Symplocos species richness over the next six decades, particularly in southern and southwestern China, with potential gains in other regions. Overall, this study highlights the vulnerability of Symplocos species to climate change and underscores the importance of proactive conservation efforts and forest plantations to mitigate future loss in Symplocos species diversity .
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".