Choices on sampling, sequencing, and analyzing DNA influence the estimation of community composition of plant fungal symbionts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it