Nitrification in the water column of Lake Erie: Seasonal patterns, community dynamics, and competition with cyanobacterial harmful algal blooms
Bibliographic record
Abstract
Abstract This study reports directly measured nitrification rates in the water column of western Lake Erie, which is affected by annual cyanobacterial harmful algal blooms, and across all three Lake Erie basins. Over three field seasons, 15NH4+ stable isotope tracers were employed to quantify nitrification rates, and relative abundances of ammonia-oxidizing bacteria and ammonia-oxidizing archaea were determined via qPCR. Nitrification rates ranged from undetectable to 1,270 nmol L-1 d-1 and were generally greatest in the western basin near the Maumee River mouth (a major nutrient source). Nitrification rates were highest in early summer, and often lowest during peak cyanobacterial harmful algal blooms months (August and September), before increasing again in October. In the western basin, nitrification was negatively correlated with cyanobacterial biomass. There were no consistent differences in nitrification rates between the three Lake Erie basins. Over the three years in western Lake Erie, ammonia-oxidizing bacteria and ammonia-oxidizing archaea were often present in high and similar abundances, but overall, ammonia-oxidizing bacteria exceeded ammonia-oxidizing archaea, particularly in 2017. No relationships were observed between nitrification rates and ammonia-oxidizing bacteria and ammonia-oxidizing archaea abundances. Thus, despite abundant ammonia-oxidizer DNA, lower nitrification rates during cyanobacterial harmful algal blooms suggest that nitrifiers were poor competitors for regenerated and available NH4+ during these blooms, as also observed in similar systems. Low nitrification rates during cyanobacterial harmful algal blooms could limit system nitrogen removal via denitrification, a natural pathway for its removal and a valuable ecosystem service. Lower denitrification rates allow more bioavailable nitrogen to remain in the system and support biomass and microcystin production; therefore, these results help explain how non-nitrogen-fixing cyanobacterial harmful algal blooms persist, despite low bioavailable nitrogen concentrations during these blooms, and support management efforts to reduce external nitrogen loading to eutrophic systems.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".