Is root DNA a reliable proxy to assess arbuscular mycorrhizal community structure?
Bibliographic record
Abstract
Arbuscular mycorrhizal (AM) fungi are widespread plant symbionts that extensively colonize both soil and roots. Given their influence on ecosystem processes, such as plant growth, soil carbon storage, and nutrient cycling, there is great interest in understanding the drivers of their community structure. AM fungal communities are increasingly characterized by selectively amplifying their DNA from plant roots, thus assuming that AM fungal community structure within roots provides a reliable portrait of the total (i.e., soil + roots) community. Through numerical simulations, we test this assumption using published data. We show that community structure and diversity is well preserved when analyzing only a subset of the community biomass (i.e., roots or soil), provided that the community shows a typical skewed abundance distribution, with few very dominant species and a high prevalence of rare species. Given that this community structure has been shown to be common in natural AM fungal communities, the present work would suggest that characterizing AM fungal communities using only roots or soil can provide a reliable portrait of the overall community. However, we show through additional analyses that the proportion of sample biomass used for molecular methods must be over a minimal threshold to properly characterize the community. Using published molecular data sets, we validate those results, which suggest that typical molecular protocols using low amounts of biomass may strongly influence AM fungal community characterization. Finally, we also discuss other assumptions implied by the molecular analysis of AM fungal communities, and point out urgent knowledge gaps.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".