Multifunctionality in European mountain forests — an optimization under changing climatic conditions
Bibliographic record
Abstract
Forests provide countless ecological, societal, and climatological benefits. With changing climate, maintaining certain services may lead to a decrease in the quantity or quality of other services available from that source. Accordingly, our research objective is to analyze the effects of the provision of a certain ecosystem service on the economically optimized harvest schedules and how harvest schedules will be influenced by climate change. Based on financial portfolio theory, we determined, for two case study regions in Austria and Slovakia, treatment schedules based on nonlinear programming, which integrates climate-sensitive biophysical risks and risk-averting behavior of the management. In both cases, results recommend reducing the overaged stocking volume within several decades to establish new ingrowth, leading to an overall reduction of age and related risk, as well as an increase in growth. Under climate change conditions, the admixing of hardwoods towards spruce–fir–beech (Austria) or spruce–pine–beech (Slovakia) stands should be emphasized to account for the changing risk and growth conditions. Moreover, climate change scenarios either increased (Austria) or decreased (Slovakia) the economic return slightly. In both cases, the costs for providing the ecosystem service “rock fall protection” increases under climate change. Although in the Austrian case there is no clear tendency between the management options, in the Slovakian case, a close-to-nature management option is preferred under climate change conditions. Increasing tree species richness, increasing structural diversity, replacing high-risk stands, and reducing average growing stocks are important preconditions for a successful sustainable management of European mountain forests in the long term.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".