The Footprint of Wildfires on Mediterranean Forest Ecosystem Services in Vesuvius National Park
Bibliographic record
Abstract
Wildfires are one of the most important natural disturbances in vegetation biomes. In recent decades, both the number and severity of fires have significantly increased in Mediterranean forests, frequently resulting in catastrophic events. In this scenario, we aimed to explore the flow of ecosystem services and their related economic value that was disrupted by human-induced megafires in the Mediterranean forest of Vesuvius National Park in the summer of 2017. We adopted an innovative approach by merging two methodologies: an ecological approach to evaluate the status of the forest ecosystem after the wildfires and an economics methodology to estimate the monetary value of the interruption to ecosystem services. Losses related to the following six services were estimated: woody biomass, soil erosion control, habitat maintenance, pollination, carbon stock, and ecotourism. In 2017, 3350 ha of forest (88% of the total forested area of Vesuvius National Park) burnt over a period of 49 days. The total estimated monetary loss amounted to €14.363 M, 56.9% of which comprised of provisioning ecosystem services, while 34.7% encompassed maintenance and regulation services, and 8.5% were so-called cultural services. Suppression costs accounted for 16% of the total estimated economic loss of ecosystem services. Our results provide useful insights for decision-makers when allocating financial resources, suggesting that they should invest in fire prevention rather than fire suppression and post-fire restoration. This explicit valuation of the footprint of the wildfires, although not exhaustive, can also lead to greater awareness among the public regarding the benefits conferred by Mediterranean forest ecosystems. This is the first study to economically evaluate the interruption of ecosystem services after megafires in the Mediterranean basin.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".