Choices on sampling, sequencing, and analyzing DNA influence the estimation of community composition of plant fungal symbionts
Bibliographic record
Abstract
Abstract Plant root symbionts, namely mycorrhizal fungi, can be characterized using a variety of methods, but most of these rely on DNA. While Sanger sequencing still fulfills particular research objectives, next‐generation sequencing currently dominates the field, thus understanding how the two methods differ is important for identifying both opportunities and limitations to characterizing fungal communities. In addition to testing sequencing methods, we also examined how roots and soils may yield different fungal communities and how disturbance may affect those differences. We sequenced DNA from ectomycorrhizal fungi colonizing roots of Pinus banksiana and found that operational taxonomic unit richness was higher, and compositional variance lower, for Illumina MiSeq–sequenced communities compared to Sanger‐sequenced communities. We also found that fungal communities associated with roots were distinct in composition compared to those associated with soils and, moreover, that soil‐associated fungi were more clustered in composition than those of roots. Finally, we found community dissimilarity between roots and soils was insensitive to disturbance; however, rarefying read counts had a sizeable influence on trends in fungal richness. Although interest in mycorrhizal communities is typically focused on the abiotic and biotic filters sorting fungal species, our study shows that the choice of methods to sample, sequence, and analyze DNA can also influence the estimation of community composition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".