Landscape epidemiology of ash dieback
Bibliographic record
Abstract
Abstract Ash dieback is induced by Hymenoscyphus fraxineus , an invasive pathogenic fungus. It is causing severe damage to European ash populations. However, the local environment, such as climate or site conditions, is known to affect ash dieback. We studied the landscape epidemiology of the disease on a 22 km 2 area in north‐eastern France at two stages of the invasion process using Bayesian spatio‐temporal models fitted with integrated nested Laplace approximation (INLA). Several features characterizing disease severity, crown dieback, frequency of collar canker and density of infected leaf debris in the litter were determined on a regular grid over a 3.5 × 6.5 km area. We first analysed the effect of landscape features on the disease establishment stage in 2012, 2 years after the first report of the disease in the area, and then on further disease development, in 2016–2018. Landscape features had little impact on the disease at the establishment stage, but strongly determined its further development. Local fragmentation of tree cover was the most important factor, with trees that are isolated or in hedges far less affected than trees in a forest environment. We showed that they were subjected to different microclimates, with higher crown temperatures unfavourable to pathogen development. Low host density strongly reduced disease development. The presence of large ash populations in the vicinity affected local disease severity up to several hundred metres. Synthesis . We showed that the landscape characteristics strongly affect the development and spread of ash dieback. The disease is far less severe in forest conditions when ash density is low or in open canopies such as hedges and isolated trees. Ash trees are often in these types of landscapes, which should strongly limit the overall impact of ash dieback.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.007 | 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 teacher head, 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".