Inoculation with Ericoid Mycorrhizal Associations Alleviates Drought Stress in Lowland and Upland Velvetleaf Blueberry (Vaccinium myrtilloides) Seedlings
Bibliographic record
Abstract
Although velvetleaf blueberry (Vaccinium myrtilloides) is usually associated with sandy (upland) areas of the North American boreal forest, lowland populations can be also found in bogs, suggesting possible adaptations to different site conditions. In this study, we examined the role of ericoid mycorrhizal (ERM) fungi in conferring drought resistance to the upland and lowland velvetleaf blueberry seedlings. The seedlings were inoculated with four ERM fungi (Pezicula ericae, Pezoloma ericae, Meliniomyces variabilis, and Oidiodendron maius) isolated from the roots of ericaceous plants and grown under controlled environmental conditions in sterilized soil. The inoculated and non-inoculated (inoculation control) plants were subsequently subjected to three cycles of drought stress by withdrawing watering. Lowland plants appeared to benefit relatively more from mycorrhizal colonization, compared with the upland plants, in terms of plant growth and drought survival. After three weeks of treatments, the dry weights of non-inoculated well-watered upland plants were higher compared to the non-inoculated lowland plants. However, these differences were offset by the inoculation of plants with ERM fungi, some of which also significantly improved drought resistance characteristics of the upland and lowland plants. There were no major differences in the effects of different ERM fungal species on drought responses of upland and lowland plants. Of the examined ericoid mycorrhizal fungi, inoculation with Pezicula ericae was the most effective in conferring drought resistance characteristics to both upland and lowland seedlings and helped maintain higher shoot water potentials, net photosynthetic, and transpiration rates in plants subjected to drought stress.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".