Simulating the effects of a climate-change scenario on the geographical range and activity of forest-pathogenic fungi
Bibliographic record
Abstract
The aim of the present study was to explore possible effects of climate change on the geographic range or local impact of several forest-pathogenic fungi. To this aim, (i) the parameters of species' responses to climatic variables were determined, in two types of models (specific statistical models and the generic model CLIMEX); (ii) these models were used to make simulations under a future climatic scenario based on a general circulation model of climate, which was regionalized over France. A range of pathogens commonly reported in Europe were studied: Melampsora larici-populina, Melampsora allii-populina, and Melampsora medusae, causal agents of poplar rust; Mycosphaerella pini, an agent of red-band disease of pines; Melampsora pinitorqua, an agent of pine-twisting rust; Cryphonectria parasitica, an agent of chestnut blight; Phytophthora cinnamomi, causal agent of ink disease on European chestnut (Castanea sativa) and oaks; and Sphaeropsis sapinea and Biscogniauxia mediterranea, which are opportunistic pathogens (cortical endophytes) on pines and oaks, respectively. The predicted warming would be favourable to most of the studied species, especially those for which winter survival is a limiting factor linked to low temperatures (P. cinnamomi and Melampsora medusae). For species such as Mycosphaerella pini, the favourable effect of warming would be counterbalanced by the negative effect of a decrease in summer rainfall, leading to a stable or decreased impact of these pathogens by the end of the century. Conversely, B. mediterranea and S. sapinea, which are favoured by water stress, should have an increased impact. Interest and limitations of the different models were discussed. Some implications of the projected changes in "forest phytosanitary landscape" were presented in terms of research and management issues.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".