Experimental assessment of salinization effects on freshwater zooplankton communities and their trophic interactions under eutrophic conditions
Bibliographic record
Abstract
Freshwater ecosystems are becoming saltier due to human activities. The effects of increased salinity can lead to cascading trophic interactions, affecting ecosystem functioning and energy transfer, through changes in community and size structure. These effects can be modulated by other environmental factors, such as nutrients. For example, communities developed under eutrophic conditions could be less sensitive to salinization due to cross-tolerance mechanisms. In this study, we used a mesocosm approach to assess the effects of a salinization gradient on the zooplankton community composition and size structure under eutrophic conditions and the cascading effects on algal communities. Our results showed that zooplankton biomass, size diversity and mean body size decreased with increased chloride concentration induced by salt addition. This change in the zooplankton community did not have cascading effects on phytoplankton. The phytoplankton biomass decreased after the chloride concentration threshold of 500 mg L−1 was reached, most likely due to direct toxic effects on the osmotic regulation and nutrient uptake processes of certain algae rather than as a response to community turnover or top-down control. Our study can help to put in place mitigation strategies for salinization and eutrophication, which often co-occur in freshwater 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".