Short-term effect of thinning on inter- and intra-annual radial increment in Mediterranean Scots pine-oak mixed forests
Bibliographic record
Abstract
Thinning treatments along with the establishment of mixed forest stands have been put forward as possible adaptation strategies to cope with climate change, although the effectiveness of combining these two measures has scarcely been studied and may vary depending on stand conditions and the thinning regime employed. The aim of this study was to better understand the effect of commercial thinning and of the different growth behavior of two coexisting species on their inter- and intra-annual cumulative radial increment patterns. For this purpose, we studied radial increment in a Scots pine-Pyrenean oak (Pinus sylvestris L.-Quercus pyrenaica Willd.) Mediterranean mixed forest in north-west Spain over two climatically contrasting years (2016–2017). The data came from a thinning trial consisting of a randomized latin square design with a control and two commercial thinning treatments from below; one moderate and the other heavy, removing 25% and 50 % of initial basal area, respectively, of both species. The radial increment was analyzed based on bi-weekly readings from band dendrometers installed on 90 oak and pine trees. A non-linear mixed model based on double-Richards curve was fitted to explore the differences between thinning treatments and species response in the intra-annual cumulative radial increment patterns. Inter-annual basal area increments for each species at stand level were quantified by aggregating the tree estimates obtained from the model fitted in the first step. Tree and stand level growth were greater in Scots pine, which also showed a greater growth response to early spring droughts than the Pyrenean oak. Heavy thinning increased radial increment in trees of both species at the expense of decreased total stand basal area. At species level, basal area growth in Scots pine decreased through thinning, whereas for Pyrenean oak, the heavy thinning intensity resulted in the same basal area growth as the control. Thus, heavy thinning induced a trade-off between total stand growth and tree-level response to climatic conditions for Scots pine but with no loss in productivity in the case of the Pyrenean oak. Hence, heavy thinning may be an appropriate measure to attain productive stability of the oak coppice in the studied mixed forest as well as to adapt tree growth to future droughts associated with climate change.
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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 | 0.001 |
| 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) | 0.000 | 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 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".