Regeneration patterns of three Mediterranean pines and forest changes after a large wildfire in northeastern Spain
Bibliographic record
Abstract
Fire has favored pines throughout their natural range in environments subject to continuous disturbances, such as the Mediterranean Basin. However, recovery of pine species after large fires is not always successful. In this study, we analyze the post-fire regeneration pattern of Pinus halepensis, P. nigra and P. sylvestris three years after fire, in an area affected by a large wildfire in 1994. Moreover, we develop a model of succession to predict medium-term changes in forest composition 30 years after fire from the regeneration monitored during the first years after fire. The results show that, although the three pine species regenerate quite well in the absence of fire, their post-fire regeneration is very different: P. halepensis shows high seedling density after fire, but P. nigra and P. sylvestris almost disappear from burned plots. The model simulations of the future forest composition 30 years after fire indicate that 77-93% of plots dominated by these two pines change after fire to communities dominated by oaks (Quercus ilex, Q. cerrioides). There is also a considerable number (7-16%) of these burned pine plots that change to shrublands. Thus, these observational and modelling results suggest that large fire events, which have increased considerably in the Mediterranean region in the last decades, may decrease the overall distribution of these pine species, especially that of P.nigra and P. sylvestris.
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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 | 0.000 | 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) | 0.001 | 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".