Weak Coherence in Abundance Patterns Between Bacterial Classes and Their Constituent OTUs Along a Regulated River
Bibliographic record
Abstract
Deductions about the ecology of high taxonomic bacterial ranks (i.e., phylum, class, order) are often based on their abundance patterns, yet few studies have quantified how accurately variations in abundance of these bacterial groups represent the dynamics of the taxa within them. Using 454-pyrosequencing of the 16S rRNA gene, we investigated whether the changes in abundance of six dominant bacterial classes (Actinobacteria, Beta-/Alpha-/Gamma-proteobacteria, Flavobacteria, and Sphingobacteria) along a large dam-regulated river are reflected by those of their constituent Operational Taxonomic Units (OTUs; 97% similarity level). The environmental impact generated by the reservoirs promoted clear compositional shifts in all bacterial classes that resulted from changes in the abundance of individual OTUs rather than from the appearance of new taxa along the river. Abundance patterns at the class level represented the dynamics of only a small but variable proportion of their constituting OTUs, which were not necessarily the most abundant ones. Within most classes, we detected sub-groups of OTUs showing contrasting responses to reservoir-induced environmental changes. Overall, we show that the patterns observed at the class level fail to capture the dynamics of a significant fraction of their constituent members, calling for caution when the ecological attributes of high-ranks are to be interpreted.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".