Response of pine natural regeneration to small‐scale spatial variation in a managed Mediterranean mountain forest
Bibliographic record
Abstract
Abstract Questions:What influence do management practices and previous tree and shrub stand structure have on the occurrence and development of natural regeneration ofPinus sylvestrisin Mediterranean mountain forests? How are the fine‐scale and environmental patterns of resources affected and what impact does this have on the distribution of the regeneration? Location:APinus sylvestrisMediterranean mountain forest in central Spain. Methods:Upperstory trees and regeneration (seedlings and saplings) were mapped in four 0.5‐ha plots located in two types of stand with different management intensities (even‐aged and uneven‐aged stands). Environmental variables were recorded at the nodes of a grid within the plots. The relationships between the upperstory and regeneration were evaluated by bivariate point pattern analysis; redundancy analysis ordination and variation partitioning were performed to characterize regeneration niches and the importance of the spatial component. Results:Seedlings and saplings presented a clumped structure under both types of management and their distribution was found to be related to the spatial distribution of favourable microsites. Regeneration was positively related to conditions of partial cover with high soil water content during the summer. More than half of the explained variance was spatially structured in both types of stand. This percentage was particularly high in the even‐aged stands where the pattern of regeneration was highly influenced by the gaps created by harvesting. Conclusions:The spatial distribution of the tree and shrub upperstory strongly influences regeneration patterns ofP. sylvestris. Current management practices, promoting small gaps, partial canopy cover and moderate shade in even‐aged stands, or favouring tree and shrub cover in the case of uneven‐aged stands, appears to provide suitable conditions for the natural regeneration ofP. sylvestrisin a Mediterranean climate.
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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 | 0.001 |
| 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".