Forest Management under Climate Change: A Decision Analysis of Thinning Interventions for Water Services and Biomass in a Norway Spruce Stand in South Germany
Bibliographic record
Abstract
Climate change is producing threats to forests’ capacity of regulating water regimes. Therefore, thinning strategies can be applied to mitigate climate change impacts more efficiently by providing more spaces for trees to utilize resources e.g., water and nutrients. This study examined the effects of different thinning intensities and intervals on water characteristics and biomass growth of a 75-year-old Norway spruce (Picea abies) stand in the Black Forest, Germany. Here we used a water and management sensitive update of the process-based forest growth model 3PG, 3PG-Hydro. We applied light (10%), moderate (30%), and heavy thinning (50% intensity) in the interval of 10, 25, and 50 years of the management period. We simulated growth with climate change scenario RCP 8.5 data from 1995 to 2065. We analyzed the effects of the different thinning regimens on biomass, evapotranspiration as well as water yield. Thinning intensity and interval as well as their interaction have significant influence on production of stand biomass and water yield for all thinning regimes applied (p < 0.05). However, there is no significant difference (p > 0.05) in accumulated biomass (thinned biomass added to the stand biomass) between the applied thinning regimes. Light thinning in a long interval (50 years) produced highest stand biomass among the applied thinning regimes. Furthermore, the prediction showed that accumulated water yield increased with increasing thinning intensity. Our study concludes that repeated moderate thinning at intermediate intervals results in a high water yield without losing biomass production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".