Development of continuous cover forests under different levels of global warming – a methodological case study in Southern Germany
Bibliographic record
Abstract
Informing forest decision makers about the impacts of climate change on forests is challenging because the representative concentration pathway scenarios (RCPs) impose deep uncertainty and complexity that is difficult to integrate in management planning. A user-oriented translation of the RCPs would facilitate the integration of climate change impacts in forest decisions and improve the understanding of how different climate policy actions would affect forests.We applied a translation of the RCPs by analyzing how three global warming scenarios related to climate policy actions – the Paris targets (1.5°C and 2°C warming) and a higher warming level without climate policy (3°C) – would impact forest dynamics. We developed indices of forest processes (e.g., species succession, biomass, harvest) that capture changes induced by the global warming scenarios relative to a reference period (1981 – 2010). The methodology was adapted from the JRC PESETA IV project, where climate indices had been developed and impacts on forest vulnerability was explored.We applied this method with a large-scale forest model (LandClim) on a complex and highly diverse 5000 ha forest landscape over an elevation gradient from lowland deciduous to high montane conifer forests in the area of Freiburg, Southern Germany. Simulations started from the state of the forest in the year 2010, and both no-management and a business-as-usual management (BAU) was simulated. For the initial state of the forest, we applied a state-of-the-art initialization procedure that makes use of a detailed inventory network (over 2000 inventory points in the study area) to depict the current forest conditions (e.g., species distribution, stem numbers, tree ages, stem diameters at breast height) at high resolution. BAU was applied in the form of close-to-nature management based on the guidelines by the State Forestry Department. It includes >10 forest types with both younger and older stands.Simulation results indicate reductions of biomass and species richness at lower elevations, including both lowland and submontane zones, connected to an upslope shift of species. As these changes intensify with increasing global warming, the largest impacts are observed under the 3°C warming scenario, leading to the loss of biodiversity associated with dominant species capitalizing on the changing ecological conditions.In summary, by applying this method for a diversity in continuous cover forests over a large elevation gradient, our study outlines important forest dynamics representative for temperate forests in Central Europe under three global warming scenarios. Moreover, the evaluation of close-to-nature management can give important insights for forest decision making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".