The effect of nutrient transport downstream on food web stability in an experimental freshwater meta-ecosystem
Bibliographic record
Abstract
Recent spatial nutrient transport theory suggests that accumulation of nutrients downstream in riverine systems can amplify the magnitude of phytoplankton and zooplankton blooms and/or lead to competitive replacement of phytoplankton by less edible species, such as cyanobacteria. We used an experimentally controlled three-node network of freshwater mesocosms with green algae, cyanobacteria, and Daphnia magna to test these hypotheses. Nutrients and detritus accumulated significantly downstream, reaching maximum values in the terminal nodes, resulting in small increases in abundance for green algae and D. magna populations. Stability analysis from the Lotka–Volterra competition model for green algae and cyanobacteria did not provide evidence of complete competitive exclusion, but cyanobacteria projected to equilibrate at densities 50% higher than those of green algae in all nodes. Our results support the theoretical prediction that unidirectional flow in riverine systems contributes to accumulation of nutrients downstream and increased heterotrophic bacterial activity, but these changes were of insufficient magnitude to produce variance- or mean-driven destabilization of food web relationships downstream in our experimental system.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".