Bacterial communities in peat swamps reflect changes associated with catchment urbanisation
Bibliographic record
Abstract
Abstract Like many peat wetlands around the world, Temperate Highland Peat Swamps on Sandstone (THPSS), located in the Sydney Basin, Australia, have been impacted by urban development. In this paper, we used Illumina 16S rRNA DNA amplicon sequencing to characterise and compare the bacterial communities of surface (top 0–2 cm) and deep (50 cm) sediments in peat swamps that occur in both urbanised and non-urbanised catchments. Proteobacteria (32.2% of reads), Acidobacteria (23.6%) and Chloroflexi (10.7%) were the most common phyla of the dataset. There were significant differences in the bacterial community structure between catchment types and depths apparent at the phyla level. Proteobacteria, Bacteroidetes, Actinobacteria and Verrucomicrobia made up a greater proportion of the reads in the surface sediments than the deeper sediments, while Chloroflexi and Nitrospirae were relatively more common in the deeper than the surface sediment. By catchment type, Acidobacteria were more common in swamps occurring in non-urbanised catchments, while Nitrospirae, Bacteroidetes and Actinobacteria were more common in those in urbanised catchments. Microbial community structure was significantly correlated with sediment pH, as was the relative abundance of several phyla, including Acidobacteria (negative correlation) and Bacteroidetes (positive correlation).As an indicator of trophic shift from oligotrophic to copiotrophic conditions associated with urbanised catchment, we found significant differences ratios of β-Proteobacteria to Acidobacteria and Bacteriodetes to Acidobacteria between the catchment types. Based on SIMPER results we suggest the relative abundance of Nitrosomonadaceae family as a potential indicator of urban degradation. As the first study to analyse the bacterial community structure of THPSS using sequencing of 16S rDNA, we reveal the utility of such analyses and show that urbanisation in the Blue Mountains is impacting the microbial ecology of these important peatland ecosystems.
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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 | 0.000 |
| 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".