Connectivity and nutrient enrichment affect the productivity and stability of aquatic meta-ecosystems
Bibliographic record
Abstract
g. agriculture, sewage), empirical studies on the combined impacts of these effects are rare. To better understand the interactive role of connectivity and nutrient enrichment in shaping meta-ecosystem stability, we set up a mesocosm experiment mimicking a minimal meta-ecosystem composed of two ponds (upstream and downstream). The upstream pond received varying water volumes from a mesotrophic lake (ranging from 0% to 40% mesocosm volume per week) and five levels of nutrient enrichment (phosphorus and nitrogen). The experiment featured a fractional factorial design, with 13 unique treatment combinations monitored over 14.5 weeks. We found that connectivity increased phytoplankton biomass in highly nutrient-enriched meta-ecosystems, that connectivity and nutrient enrichment independently promoted synchrony and spatial homogeneity of phytoplankton biomass within meta-ecosystems and that our treatments did not influence temporal stability beyond the initial nutrient-induced biomass increase. Furthermore, while intermediate levels of connectivity stimulated zooplankton biomass and diversity, the increase was counteracted with nutrient enrichment. We conclude that increased ecosystem connectivity is likely to exacerbate the negative effects of nutrient enrichment on freshwater meta-ecosystems across watersheds while homogenizing temporal population dynamics.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".