Climate change and the potential distribution of <i>Xylella fastidiosa</i> in Europe
Bibliographic record
Abstract
Abstract The bacterium Xylella fastidiosa (Xf) is a plant endophyte native to the Americas that causes worldwide concern. Xf has been recently detected in several regions outside its natural range including Europe. In that context, accurate estimates of its response to climate change are required to design cost-efficient and environment-friendly control strategies. In the present study, we collected data documenting the native and invaded ranges of the three main subspecies of Xf: fastidiosa, pauca and multiplex , as well as two strains of Xf subsp. multiplex recently detected in southern France (ST6 and ST7). We fitted bioclimatic species distribution models (SDMs) to forecast their potential geographic range and impact in Europe under current and future climate conditions. According to model predictions, the geographical range of Xf as presently reported in Europe is small compared to the large extent of suitable areas. The European regions most threatened by Xf encompass the Mediterranean coastal areas of Spain, Greece, Italy and France, the Atlantic coastal areas of France, Portugal and Spain as well as the south-western regions of Spain and lowlands in southern Italy. Potential distribution of the different subspecies / strains are contrasted but all are predicted to increase by 2050, which could threaten several of the most economically important wine-, olive- and fruit-producing regions of Europe, warranting the design of control strategies. Bioclimatic models also predict that the subspecies multiplex might represent a threat to most of Europe under current and future climate conditions. These results may serve as a basis for future design of a spatially informed European-scale integrated management strategy, including early detection surveys in plants and insect vectors, quarantine measures as well as agricultural practices.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".