Ectomycorrhizal fungal communities of black spruce differ between wetland and upland forests
Bibliographic record
Abstract
Ectomycorrhizal (ECM) communities of black spruce (Picea mariana (Mill.) BSP) seedlings were characterized from three habitats spanning a moisture gradient in central British Columbia: black spruce dominated wetlands, black spruce tamarack wetlands, and black spruce lodgepole pine uplands. Morphological and molecular (PCR-RFLP) analyses indicated a diverse community of root-associated ECM fungi consisting of 33 morphotypes and 65 genotypes. ECM abundance varied significantly between habitats for six morphotypes. Although many occurred in all three habitats, some occurred in only one or two, and some genotypes had distributions that suggested habitat specificity across the moisture gradient. Intraspecific variation (defined as genotype variation within morphotypes) ranged from one to seven genotypes, depending on ECM morphotype. Both morphological and molecular analyses showed that ECM diversity was greater in upland than in wetland habitats and greater in black spruce tamarack wetlands than in black spruce dominated wetlands (α ≤ 0.05). Morphological assessment captured contributions to diversity by both abundant and less abundant ECM morphotypes, whereas molecular analysis revealed patterns of genetic variation and habitat distribution at a finer resolution. The study presents the first comprehensive information on black spruce ECM and suggests that ECM community composition and richness varies across the moisture gradient in response to soil heterogeneity and alternate hosts (tamarack and lodgepole pine).
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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 | 0.001 | 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".