Modelling <i>Pinus pinea</i> forest management to attain natural regeneration under present and future climatic scenarios
Bibliographic record
Abstract
Natural regeneration-based silviculture has been increasingly regarded as a reliable option in sustainable forest management. However, successful natural regeneration is not always easy to achieve. Recently, new concerns have arisen because of changing future climate. To date, regeneration models have proved helpful in decision-making concerning natural regeneration. The implementation of such models into optimization routines is a promising approach in providing forest managers with accurate tools for forest planning. In the present study, we present a stochastic multistage regeneration model for Pinus pinea L. managed woodlands in Central Spain, where regeneration has been historically unsuccessful. The model is able to quantify recruitment under different silviculture alternatives and varying climatic scenarios, with further application to optimize management scheduling. The regeneration process in the species showed high between-year variation, with all subprocesses (seed production, dispersal, germination, predation, and seedling survival) having the potential to become bottlenecks. However, model simulations demonstrate that current intensive management is responsible for regeneration failure in the long term. Specifically, stand densities at rotation age are too low to guarantee adequate dispersal, the optimal density of seed-producing trees being around 150 stems·ha −1 . In addition, rotation length needs to be extended up to 120 years to benefit from the higher seed production of older trees. Stochastic optimization confirms these results. Regeneration does not appear to worsen under climate change conditions; the species exhibiting resilience worthy of broader consideration in Mediterranean silviculture.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".