Drought-induced TREE MOrtalities and REwilding in Apulia (TREEMORE)
Bibliographic record
Abstract
In the last 30 years the Mediterranean region has increasingly been subjected to prolonged droughts, a phenomenon expected to worsen due to the rising levels of anthropogenic emissions. Although the scientific community has reached an emerging consensus regarding the physical processes driving these extreme events - such as the increased frequency and duration of atmospheric blocking and the expansion of subtropical zones - the broader impacts of water shortages on vegetation and feedback mechanisms within the climate-environment system remain poorly understood. Current evidence suggests that drought may lead to widespread tree mortality, heightened wildfire risks, and a gradual transformation from Mediterranean ecosystems to vegetation types typically associated with semi-arid environments. Apulia region, in Southern Italy has been selected as the study region, as it offers a unique case study to assess the consequences of extensive olive trees die-off after the spread of the pathogen/bacteria Xylella fastidiosa. We will investigate the effect of die-off and of different potential replanting strategies on the regional atmosphere. The study involves three different vegetation scenarios with a total of 12 new high-resolution sensitivity experiments under low and high-emission conditions (RCP2.6 or SSP1-2.6 and RCP8.5 or SSP5 8.5). One scenario will act as a reference with the current vegetation state. A deforestation scenario, accounting for 100% desertification, will represent the worst-case scenario. A regreening scenario will represent the afforestation/rewilding with native Mediterranean vegetation over the whole region. For this work, we will employ the regional version of the Global Environmental Multiscale Model (GEM) over the Euro-Cordex domain and the high-resolution Regional Climate Model (RegCM5, Giorgi et al., 2023) in convection-permitting setup, configured for the Southern Adriatic region over the domain 39.5°N - 42°N, 14.5°E - 18.5°E. The simulations will facilitate an in-depth analysis of the climatic effects of altered vegetation cover, focusing on key variables such as mean and extreme temperatures and precipitation, moisture distribution, and convection. We aim at identifying climate resilient planting strategies (e.g. restoring the historical land use, olive groves, or the native mediterranean vegetation) in Apulia, as a potentially practical approach to counteract or alleviate the effects of future compound extreme events, including severe droughts and heatwaves.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".