<i>Corylus avellana</i> disease management: using metagenomics to illuminate the rhizosphere microbiome of hazelnut
Bibliographic record
Abstract
ABSTRACT The European hazelnut, Corylus avellana , is one of the most economically important tree nut crops globally. The biotrophic ascomycete pathogen Anisogramma anomala , found naturally associated with wild C. americana , continues to pose a significant threat to European hazelnut production across North America. Here, metagenomics was used to examine the taxonomic and functional features of the rhizosphere microbial communities of hazelnut trees differing in their levels of resistance to A. anomala : highly tolerant Corylus americana , and resistant and susceptible Corylus avellana . No statistically significant differences in microbial alpha diversity or beta diversity were noted between the three rhizosphere groups. Compared to bulk soil, all three rhizosphere groups were enriched for the fungal phylum Basidiomycota and bacterial phylum “ Candidatus Rokubacteriota”. At the genus level, the bacterial genera Actinospica , Occallatibacter , and “ Candidatus Sulfotelmatobacter” were under-represented, while the genus Rhizobacter was over-represented, in the resistant and susceptible C. avellana rhizosphere samples compared to the bulk soil. A total of 45 dereplicated, high-quality metagenome-assembled genomes (MAGs) were generated, corresponding to 41 bacteria and 4 archaea. Many of the MAGs carried multiple biosynthetic gene clusters, including MAGs corresponding to the genera Lysobacter and Actinospica . Overall, the low differentiation of the rhizosphere microbiomes suggest that differences in A. anomala disease expression are likely not associated with differences in the rhizosphere microbiome. Nevertheless, the results shed new light on the rhizosphere communities of two species of hazelnut, and woody perennials more broadly, and identify potential avenues for future research into the development of microbial inoculants for Corylus spp..
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".