Microbial nitrification in urban streams: from single cell activity to ecosystem
Bibliographic record
Abstract
The main goal of this PhD thesis was the study of the ammonium oxidation process in high nutrient loaded urban streams. We aimed to unveil regulating factors and driving mechanisms from the organisms to the ecosystem scales using a combined biogeochemistry-microbial ecology approach. \n\n\t\t\t\t Ammonia oxidization is the first and rate-limiting step of nitrification. Nitrification is the key process linking nitrogen (N) inputs (fixation, mineralization) and losses (denitrification, anamox) in the aquatic ecosystem. Ammonia oxidizing archaea (AOA) and bacteria (AOB) drive this process through the enzyme ammonia monooxygenase. Although sharing a common function, AOB and AOA are phylogenetically distinct, suggesting different evolution and phenotypic characteristics. \n\n\t\t\t\t AOA and AOB were detected in the stream biofilms. The abundance, community composition and distribution of these microbial components were driven by environmental physical and chemical conditions, mainly ammonia (NH4) concentrations and sun irradiance.\n\t\t\t\t Ammonia oxidizing activity in biofilms under low NH4 availability was low and only 2 % of the inorganic NH4 was nitrified. Under these conditions AOA dominated ammonia oxidizing community and were key players of the observed ammonia oxidation (Nitrososophaera cluster). Conversely, under high NH4 load in the stream up to 100 % of the inorganic NH4 was oxidized to nitrate (NO3). Such high ammonia oxidizing activity was mostly driven by AOB (Nitrosospira and N. oligotropha clusters). Under these conditions AOB outnumbered AOA by orders of magnitude. AOA in contrast were poorly active under high NH4 concentrations and a consistent community composition shift was observed between high and low NH4 conditions. \n\n\t\t\t\t In laboratory cultures the growth of AOA and AOB was immediately inhibited by light. In particular, at lower light intensities, archaeal growth was much more photosensitive than bacterial growth and unlike AOB, AOA showed no evidence of recovery during dark phases. These findings provide evidence for niche differentiation in aquatic environments and suggested light as a main driving factor for the distribution and activity of ammonia oxidizers in the aquatic environment. Accordingly, in early stage biofilms developing on streams cobbles the percentage of ammonia oxidizers was higher in darkness (i.e., sediment facing side or dark-side biofilms) than in biofilms grown on the upper, light exposed side of the cobbles (light-side biofilm). However, this spatial segregation was missed in mature biofilms suggesting that the complex microbial structure present in light-side biofilms may protect both AOA and AOB against photoinhibition. This finding was further confirmed by a significant relationship found between light-side biofilm biomass and the abundance of ammonia oxidizers in situ. In contrast, for dark-side biofilms the relationship was missed. Therefore, irradiance was not an inhibitory factor for AOA and AOB in mature light-side biofilms probably due to an “umbrella effect”. The umbrella effect and the fact that AOA and AOB were highly abundant in the sediment (light avoiding strategy) are probably the reasons why nitrification at the ecosystem scale was independent from both light intensity and dial light cycling. Altogether these results highly contributed to improve the current knowledge on nitrification in urban streams and provided insights on niche differentiation between AOA and AOB.
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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 | 0.000 | 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) | 0.001 | 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".