Microbial community and functional shifts across agricultural and urban landscapes within a Lake Erie watershed
Bibliographic record
Abstract
The role of sediment microbial communities in regulating the loss and retention of nutrients in aquatic ecosystems has been increasingly recognised. However, in the Great Lakes, where nutrient mitigation focuses on harmful algal blooms, there are limited studies examining the fundamental role of water/sediment microbes in nutrient biogeochemical cycling. Little is understood in this regard considering the increase in anthropogenic pressure on in-stream biological processes impacting nutrient flux to lakes. In this study, metagenomic and metatranscriptomic approaches were used to investigate the microbial community and gene regulation. The study focused on nitrogen (N) metabolism in a nutrient-polluted watershed of Lake Erie in southwestern Ontario, Canada. Nutrients and microbial analyses of water and sediments were collected in 2020 and 2021 from Sturgeon Creek headwaters to the nearshore of Lake Erie. Results showed no significant shifts in community structure with nutrient concentrations or land use. Metabolically, active genes involved in denitrification (consisting of 32–53% of N metabolic transcripts) showed the highest expression within agricultural and wetland dominant locations. Based on active gene expression patterns, the urbanised location coinciding with peak nitrate (NO 3 − ) concentrations showed the greatest potential for nitrous oxide (N 2 O) emission and nitrogen loss along this transect. In contrast to denitrification, direct nitrification (5–21% of N metabolic transcripts) increased two-fold approaching downstream and nearshore lake locations. Across this river-lake corridor, expression of key functional genes associated with N transformation showed strong correlation with the change in concentrations of aqueous NO 3 − and nitrite (NO 2 − ) and the ratio of NO 2 − /NO 3 − . Our findings demonstrated a clear link between sediment microbial metabolism and overlying water chemistry in this lotic system. We suggest that future studies assessing nutrient mitigation consider sediment biogeochemical processes and N-metabolising bacteria, and their fundamental role and cooperative relationship with nutrient and hydrological dynamics of overlying waters. • Positive correlations are found between microbial nitrogen (N) metabolism and N concentration along a nutrient impacted river-lake corridor based on land use. • Microbial gene expression provides specificity not achieved with 16S rRNA analyses. • Direct nitrification potential increases 2-fold approaching the river mouth and nearshore of the lake. • Microbial functional response suggests nitrous oxide correlates with increased nitrate concentration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".