Forest carbon stock budget development following extreme drought- induced dieback of coniferous stands in Central Europe – a CBM-CFS3 model application
Bibliographic record
Abstract
Abstract Background The aim of this contribution is to analyze the forest carbon budget development following the recent historically unprecedented dieback of coniferous stands in the Czech Republic. The drought-induced bark-beetle infestation resulted in record-high sanitary logging, turning the Czech forestry from a long-term carbon sink offsetting about 6% of the country's greenhouse gas (GHG) emissions since 1990 to a significant source of CO2 emissions in recent years (2018–2021). In 2020, the forestry sector accounted for almost 10% of the country's overall GHG emissions. Using the nationally calibrated Carbon Budget Model of the Canadian Forest Sector at a regional spatial resolution, we analyzed the trend and scenarios of forest carbon budget development until 2070. Two critical points arise: the short-term prognosis for reducing current emissions from forestry and the implementation of adaptive forest management focused on tree species change and sustained carbon accumulation. Results This study used four different scenarios to assess the impact of adaptive forest management on the forest carbon budget and CO2 emissions, tree species composition, harvest possibilities, and forest structure in response to the recent calamitous dieback in the Czech Republic. The model analysis indicates that the Czech forestry may stabilize by 2025, or at the latest, by the end of this decade. Subsequently, it may become a sustained sink of about 3 Mt CO2 eq./year (excluding contribution of harvested wood products), while enhancing forest resilience by the gradual implementation of necessary adaptation measures that ensure the provision of all expected forest functions to society. The speed of adaptation is linked to harvest intensity and severity of the current calamity. Under the most severe black scenario, the proportion of unstable spruce stands declines from the current 43% to approximately 25% by 2070, in favor of more suited tree species such as fir and broadleaves. These species would also constitute about 50% of the harvest potential, while maintaining levels generated by the Czech forestry prior to the current calamity. Conclusion The results show progress of stabilizing CO2 emissions, implementing tree species change, and quantifying the expected harvest and mitigation potential in Czech forestry until 2070.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".