The relative importance of ecological drivers of arbuscular mycorrhizal fungal distribution varies with taxon phylogenetic resolution
Bibliographic record
Abstract
The phylogenetic depth at which arbuscular mycorrhizal (AM) fungi harbor a coherent ecological niche is unknown, which has consequences for operational taxonomic unit (OTU) delineation from sequence data and the study of their biogeography. We tested how changes in AM fungi community composition across habitats (beta diversity) vary with OTU phylogenetic resolution. We inferred exact sequence variants (ESVs) to resolve phylotypes at resolutions finer than provided by traditional sequence clustering and analyzed beta diversity profiles up to order-level sequence clusters. At the ESV level, we detected the environmental predictors revealed with traditional OTUs or at higher genetic distances. However, the correlation between environmental predictors and community turnover steeply increased at a genetic distance of c. 0.03 substitutions per site. Furthermore, we observed a turnover of either closely or distantly related taxa (respectively at or above 0.03 substitutions per site) along different environmental gradients. This study suggests that different axes of AM fungal ecological niche are conserved at different phylogenetic depths. Delineating AM fungal phylotypes using DNA sequences should screen different phylogenetic resolutions to better elucidate the factors that shape communities and predict the fate of AM symbioses in a changing environment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".