Effects of the Filter-Feeding Benthic Bivalve Corbicula fluminea on Plankton Community and Water Quality in Aquatic Ecosystems: A Mesocosm Study
Bibliographic record
Abstract
The influence of filter-feeding bivalves on plankton communities, nutrients, and water quality in a given aquatic ecosystem is so profound that they can be considered ecosystem engineers. In a 70-day mesocosm experiment, we tested the hypothesis that Corbicula fluminea would change plankton community structure by reducing small zooplankton and large phytoplankton and improve water quality by reducing nutrients. We monitored levels of nitrogen and phosphorus, organic suspended solids (OSS), and light at the sediment surface. Within the plankton, phytoplankton biomass (as Chl a, >0.45 μm), the biomass of microphytoplankton (>20 μm), nanophytoplankton (2–20 μm), picophytoplankton (0.2–2 μm), and zooplankton were determined. Compared with the controls, C. fluminea reduced the abundance of rotifers and the biomass of phytoplankton, and picophytoplankton, thereby modifying the plankton community structure. We did not observe reductions in TN and TP concentration, but OSS concentrations were reduced, and light intensity at the sediment surface was increased as a result of the improved water transparency. Our research shows that colonization by C. fluminea may modify plankton community structure and improve water quality of eutrophic shallow lakes, shedding further light on the ecological roles of filter-feeding bivalves in aquatic ecosystems.
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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) | 0.001 | 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 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".