Site-based climate-smart tree species selection for forestation under climate change
Bibliographic record
Abstract
Global climate change threatens ecosystem functions and resilience, prompting large-scale planting initiatives to mitigate its impacts. To ensure new plantations are adaptive to future climates, it is crucial to consider climate mismatches resulting from climate change when selecting tree species. However, current research is all species-based, which is not effective for species selection across species at specific plantation sites. Our research developed a novel site-based approach that can identify optimal tree species for specific planting sites under projected future climates. We evaluated the feasibility and effectiveness of this method across 10 representative sites in diverse climatic zones in China based on climate niche projections for 100 key tree species. Our findings demonstrated the necessity and effectiveness of this approach, which can select a suit of suitable tree species tailored for any potential planting site across China under different climate change scenarios. For instance, at Tibet Dongjiu Forest farm, Aibes densa and Quercus pannosa currently showed high suitability scores above 0.8 (on a scale of 0–1). However, by the 2080s, Aibes densa 's suitability was projected to drop to 0.25, while Quercus pannosa was expected to maintain its suitability. Conversely, Quercus aquifolioides currently had a low suitability of 0.08, but it was projected to increase to 0.74 by the 2080s. These findings demonstrate the importance of using this approach to avoid selecting the wrong species or overlooking potentially suitable species. In addition, our simulation analysis suggests that a dataset of 40–50 species is necessary to ensure that most planting sites can identify 2–3 suitable species. This advancement significantly enhances the precision and effectiveness of tree species selection strategies for local practitioners, offering vital insights for forestry, conservation, and ecological restoration projects. These results highlight the tremendous potential and practical applicability of our site-based approach in enhancing forestry adaptation and ecological functions in response to global climate change. • A site-based approach was developed to identify optimal species for future climates. • The method was evaluated at 10 sites with climate niche projections for 100 species. • The approach effectively identified species tailored for any planting site. • A dataset with 40-50 species is necessary to ensure effective species selection. • Our approach enhances the effectiveness of tree selection strategies for tree planting projects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".