Elevated Genetic Diversity in the Emerging Blueberry Pathogen Exobasidium maculosum
Bibliographic record
Abstract
Emerging diseases caused by fungi are increasing at an alarming rate. Exobasidium leaf and fruit spot of blueberry, caused by the fungus Exobasidium maculosum, is an emerging disease that has rapidly increased in prevalence throughout the southeastern USA, severely reducing fruit quality in some plantings. The objectives of this study were to determine the genetic diversity of E. maculosum in the southeastern USA to elucidate the basis of disease emergence and to investigate if populations of E. maculosum are structured by geography, host species, or tissue type. We sequenced three conserved loci from 82 isolates collected from leaves and fruit of rabbiteye blueberry (Vaccinium virgatum), highbush blueberry (V. corymbosum), and southern highbush blueberry (V. corymbosum hybrids) from commercial fields in Georgia and North Carolina, USA, and 6 isolates from lowbush blueberry (V. angustifolium) from Maine, USA, and Nova Scotia, Canada. Populations of E. maculosum from the southeastern USA and from lowbush blueberry in Maine and Nova Scotia are distinct, but do not represent unique species. No difference in genetic structure was detected between different host tissues or among different host species within the southeastern USA; however, differentiation was detected between populations in Georgia and North Carolina. Overall, E. maculosum showed extreme genetic diversity within the conserved loci with 286 segregating sites among the 1,775 sequenced nucleotides and each isolate representing a unique multilocus haplotype. However, 94% of the nucleotide substitutions were silent, so despite the high number of mutations, selective constraints have limited changes to the amino acid sequences of the housekeeping genes. Overall, these results suggest that the emergence of Exobasidium leaf and fruit spot is not due to a recent introduction or host shift, or the recent evolution of aggressive genotypes of E. maculosum, but more likely as a result of an increasing host population or an environmental change.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".