Endosymbionts in cranberry: Diversity, effect on plant growth, and pathogen biocontrol
Bibliographic record
Abstract
Societal impact statement This investigation addresses the diversity of microbial endosymbionts in cranberry, which are among the least understood and ill‐defined ericoid symbionts. There is excellent potential for Ericaceous plants, such as cranberry and blueberry, to be farmed more sustainably once the properties and functioning of their associated microbiomes are known in more detail. Here, we demonstrate that some bacterial and fungal endosymbionts of cranberry stimulate plant growth and suppress fungal pathogens. They have the potential for field applications as a first step toward sustainable cranberry farming. Summary Virtually, all vascular plants harbor bacterial and fungal endosymbionts, which colonize predominantly but not exclusively, roots. Most common among fungal partners are arbuscular mycorrhizal fungi (AMF; Glomeromycotina) that live within plant roots and provide soil nutrients to the plant while receiving from the plant organic carbon sources. Ericaceae are an exception as they do not host AMF but rather the taxonomically ill‐defined “ericoid mycorrhizal fungi,” including select ascomycete and basidiomycete species. Because the diversity of endosymbionts in Ericaceae is poorly investigated, we set out to explore the microbiome of Vaccinium macrocarpon Aiton (cranberry). Here, we report the isolation and ribotyping of ~180 distinct bacterial and fungal endophytes collected from roots, stems, and leaves of cranberry plants cultivated in Quebec, Canada. Plant growth promotion was assessed after inoculating plant cuttings with these microbes, whereas pathogen suppression was tested on agar plates by growth confrontation. We show that the cranberry microbiome varies substantially between tissues, cultivars, and across fields of the same farm. Among the isolates, 16 bacterial and 8 fungal strains exhibit biofertilization or biocontrol properties with potential application in sustainable cranberry farming. We propose to move towards a more rigorous, molecular‐based definition of ericoid mycorrhiza, accounting for their broad evolutionary and morphological diversity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".