From rings to regions: modeling and mapping climate-driven timber production zones for Cyclocarya paliurus by integrating niche models and growth traits
Bibliographic record
Abstract
Introduction: , a native hardwood species with multi-functional value, has been prioritized in China's National Reserve Forest Program. However, uncertainties related to its habitat stability and timber productivity under climate change pose challenges to effective conservation and afforestation planning aligned with national carbon neutrality goals. Methods: In this study, we constructed species distribution models using Random Forest (RF) and Maximum Entropy (MaxEnt), based on verified field occurrence records and climatic data. Habitat suitability was projected under current and future climate scenarios (SSP2-4.5 and SSP5-8.5). We further analyzed the relationships between climate suitability and growth traits across 27 natural populations. Results: Both models demonstrated high predictive performance (RF AUC = 0.970, MaxEnt AUC = 0.942), identifying temperature variability and water availability as key limiting factors. Climate suitability was significantly correlated with 20-year diameter growth (R² = 0.625) and wood basic density (R² = 0.463). A stronger correlation was observed between annual growth and climate suitability of the preceding year (R² = 0.695), suggesting a lag effect. Discussion: By integrating trait-climate relationships, we projected spatial shifts in fast-growing, high-quality timber zones. Future projections suggest a 49.2-60.0% decline in highly suitable habitats and timber forests by the 2050s, with marginal zones shifting northward and toward higher latitudes. This trait-integrated modeling framework offers a scientific basis for climate-resilient conservation and afforestation planning.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".