Patterns of microbial abundance and heterotrophic activity along nitrogen and salinity gradients in coastal wetlands
Bibliographic record
Abstract
Abstract Coastal wetlands are valuable aquatic ecosystems with high biological productivity, which provide services such as a reduction in nitrogen loading into coastal waters and storage of organic carbon acting as carbon dioxide sinks. The predicted rise of sea level or freshwater extractions, particularly in the arid Mediterranean biome, will salinize many coastal wetlands. However, there is considerable uncertainty about how salinization will affect microbial communities and biogeochemical processes. We determined the abundance of total prokaryotes, cyanobacteria, and viruses and quantified the heterotrophic production of prokaryotes sensitive- (predominantly Bacteria) and resistant- (predominantly Archaea) to erythromycin in 112 ponds from nine coastal wetlands. We explored the main drivers of prokaryotic abundance and heterotrophic production using generalized linear models (GLMs). The best GLM, including all the wetlands, indicated that the concentration of total dissolved nitrogen (TDN) positively affected the total abundance of prokaryotes and the heterotrophic erythromycin-resistant (ery-R) production. In contrast, heterotrophic erythromycin-sensitive (ery-S) production was negatively related to TDN. This negative relationship appeared to be mediated by salinity and virus abundance. Heterotrophic ery-S production declined as salinity and virus abundance increased. Consequently, we observed a switch from heterotrophic ery-S production towards ery-R production as salinity and virus abundance increased. Our results imply that microbial activity will change from heterotrophic bacterial-dominated processes to archaeal-dominated processes with anthropogenic nitrogen and salinization increases. However, more studies are required to link the mineralization rates of dissolved nitrogen and organic carbon with specific archaeal taxa to enable more accurate predictions on future scenarios in coastal wetlands.
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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 | 0.000 | 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 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".