How does the cladoceran Daphnia pulex affect the fate of Escherichia coli in water?
Bibliographic record
Abstract
The faecal indicator Escherichia coli plays a central role in water quality assessment and monitoring. It is therefore essential to understand its fate under various environmental constraints such as predation by bacterivorous zooplankton. Whereas most studies have examined how protozooplankton communities (heterotrophic nanoflagellates and ciliates) affect the fate of E. coli in water, the capacity of metazooplankton to control the faecal indicator remains poorly understood. In this study, we investigated how the common filter-feeding cladoceran, Daphnia pulex, affects the fate of E. coli under different experimental conditions. Daphnia ingested E. coli and increased its loss rates in water, but the latter rates decreased from 1.65 d-1 to 0.62 d-1 after a 1,000-fold reduction in E. coli initial concentrations, due to lower probability of encounter between Daphnia and E. coli. The combined use of culture and PMA qPCR (viability-qPCR) demonstrated that exposure to Daphnia did not result into the formation of viable but non-culturable E. coli cells. In lake water, a significant part of E. coli population loss was associated with matrix-related factors, most likely due to predation by other bacterivorous biota and/or bacterial competition. However, when exposing E. coli to a D. pulex gradient (from 0 to 65 ind.L-1), we observed an increasing impact of Daphnia on E. coli loss rates, which reached 0.47 d-1 in presence of 65 ind.L-1. Our results suggest that the filter-feeder can exert a non-negligible predation pressure on E. coli, especially during seasonal Daphnia population peaks. Similar trials using other Daphnia species as well as stressed E. coli cells will increase our knowledge on the capacity of this widespread zooplankter to control E. coli in freshwater resources. Based on our results, we strongly advocate the use of natural matrices to study these biotic interactions in order to avoid overestimation of Daphnia impact.
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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 | 0.001 |
| 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".