Disentangling key drivers for counteracting growth loss in stone pine under climate change: results based on multisite provenance tests
Bibliographic record
Abstract
Abstract Increasing forest productivity through afforestation under climate change is challenging, as maladaptation to current conditions may reduce trees’ growth capacity. Hence, understanding adaptation mechanisms can boost new tree plantations success, particularly in drought-prone environments such as the Mediterranean Basin. Here, we provide an example on how tree height measured in common gardens can be used to generate growth models and management scenarios for stone pine, an emblematic conifer widely used for restoration, landscaping, and nut production in Mediterranean countries. We used a multisite international network with nine common gardens testing 56 range-wide provenances and fitted Linear Mixed-Effects models of tree height at age 10, accounting for mortality, the climate of both the provenance and the common garden, tree density and microenvironment. Aridity at the common garden and tree density were the main drivers of tree height. The best-fitted model was used to project management scenarios under the current climate and for the period 2041–2070 for the Shared Socioeconomic Pathway 3-7.0. Taller trees resulted when using provenances from colder origins growing in high densities across the aridity range covered by the common gardens. Therefore, establishing new plantations with high densities during the juvenile stage might, albeit reducing nut production, enhances protective reforestation and carbon assimilation, provided that site-specific constraints, such as fire risk, are not limiting. Our results highlight the value of networks of common gardens to support reforestation programs and identify populations with high growth potential for protective and restorative afforestation under adverse climatic conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".