Competitive avoidance not edaphic specialization drives vertical niche partitioning among sister species of ectomycorrhizal fungi
Bibliographic record
Abstract
Soil depth partitioning is thought to promote the diversity of ectomycorrhizal (EM) fungal communities, but little is known about whether it is controlled by abiotic or biotic factors. In three bioassay experiments, we tested the role of vertical soil heterogeneity in determining the distributions and competitive outcomes of the EM sister species Rhizopogon vinicolor and Rhizopogon vesiculosus. We planted Pseudotsuga menziesii seedlings into soils that were either a homogenized mix of upper and lower depths or vertically stratified combinations mimicking natural field conditions. We found that both species colonized the upper or lower soil depths in the absence of competition, suggesting that their distributions were not limited by abiotic edaphic factors. In competition within homogeneous soils, R. vesiculosus completely excluded colonization by R. vinicolor, but R. vinicolor was able to persist when soils were stratified. The amount of colonization by R. vinicolor in the stratified soils was also significantly correlated with the number of multilocus genotypes present. Taken together, our findings suggest that the differential vertical distributions of R. vinicolor and R. vesiculosus in natural settings are probably attributable to competition rather than edaphic specialization, but that soil heterogeneity may play a key role in promoting EM fungal 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 | 0.001 |
| 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".