Which sequencing depth is sufficient to describe patterns in bacterial α‐ and β‐diversity?
Bibliographic record
Abstract
The vastness of microbial diversity implies that an almost infinite number of individuals needs to be identified to accurately describe such communities. Practical and economical constraints may therefore prevent appropriate study designs. However, for many questions in ecology it is not essential to know the actual diversity but rather the trends among samples thereof. It is, hence, important to know to what depth microbial communities need to be sampled to accurately measure trends in diversity. We used three data sets of freshwater and sediment bacteria, where diversity was explored using 454 pyrosequencing. Each data set contained 6-15 communities from which 15 000-20 000 16S rRNA gene sequences each were obtained. These data sets were subsampled repeatedly to 10 different depths down to 200 sequences per community. Diversity estimates varied with sequencing depth, yet, trends in diversity among samples were less sensitive. We found that 1000 denoised sequences per sample explained to 90% the trends in β-diversity (Bray-Curtis index) among samples observed for 15 000-20 000 sequences. Similarly, 5000 denoised sequences were sufficient to describe trends in α-diversity (Shannon index) with the same accuracy. Further, 5000 denoised sequences captured to more than 80% the trends in Chao1 richness and Pielou's evenness.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".