Effects of an invasive consumer on zooplankton communities are unaltered by nutrient inputs
Bibliographic record
Abstract
Summary Interactions between multiple anthropogenic stressors can have unexpected synergistic or antagonistic effects, making it difficult to predict their combined effects using single‐stressor studies. The interaction between invasive consumers and nutrient enrichment is particularly important as both stressors frequently co‐occur, and their respective bottom‐up and top‐down effects have the potential to interact across multiple trophic levels. We conducted a mesocosm experiment that crossed an increasing nutrient addition gradient against an increasing zebra mussel invasion gradient. Native zooplankton communities were added to the mesocosms, and after 3 months, we examined how the single‐stressor effects on available resources and the zooplankton community were altered by their multiple‐stressor interaction. Added nutrients had no effect on small phytoplankton, but increased the abundance and dominance of copepods and reduced the density of large phytoplankton, probably due to increased top‐down predation pressure. Zebra mussels reduced large phytoplankton concentration by about 80%, rotifer abundance by about 75%, and shifted communities towards dominance by cladocerans and adult/juvenile copepods. When combined, the top‐down control exerted by the mussels interacted antagonistically to prevent any bottom‐up influence of nutrient enrichment on the zooplankton community. These results provide insight into the potential outcomes of nutrient and invasive consumer stressor interactions, and illustrate the need for researchers to consider environmental change in a multiple‐stressor context.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".