Impact of seasonal variations, nutrients, pollutants and dissolved oxygen on the microbial composition and activity of river biofilms
Bibliographic record
Abstract
Biofilm communities were cultivated in rotating annular bioreactors using water from the South Saskatchewan River. The impacts of seasonal variations, nutrients, pollutants and dissolved oxygen on the activity and composition of the biofilms were assessed by using a combination of microcosm assays and molecular biology techniques. The seasonal pattern in nitrification, denitrification and hexadecane mineralization, and in the occurrence of nirK in the South Saskatchewan River biofilms was: fall greater than winter, which was equivalent to spring. Hexadecane mineralization was higher in fall 1999 than in fall 2001, denitrification was similar in these two years, and no seasonal pattern of nitrification was observed. The addition of combined nutrients (C, N, and P) resulted in significant increases in the measured bacterial activities and in the predominance of alkB, nirS and nirK in all seasons and years. The addition of individual nutrients did not stimulate hexadecane mineralization, denitrification, and the PCR amplification of nirS and nirK. In fall 1999, CNP and, to a lesser extent P, stimulated nitrification, whereas in fall 2001, no pattern was observed. The results showed that nutrients, especially P, were limiting for bacterial activities, and that the biofilm activities and composition varied with nutrient availability and time of year. At the concentration assessed (1 ppb), hexadecane partially inhibited denitrification to similar extents in both years, had a negative impact on nitrification and hexadecane mineralization in fall 1999, and a positive impact on these two latter activities in fall 2001. Nickel (0.5 mg liter-1 ) negatively affected denitrification but had no effect on hexadecane mineralization. The alkB and nirS genes were less predominant and absent, respectively, in biofilms grown in the presence of nickel. DGGE analyses indicated that nickel reduced the biofilm bacterial diversity. The results presented herein provide much needed information on the microbial ecology of river biofilms, and on the impact and interactive effects of pollutant and nutrient inputs on these biofilms. These results and the techniques used in this project can be applied to monitor environmental effects of anthropogenic activities on aquatic biofilms, and can contribute to establish or revise environmental regulations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".