Sediment addition reduces the importance of predation on ecosystem functions in experimental stream channels
Bibliographic record
Abstract
Sedimentation is a pervasive cause of biological impairment in streams, and predation exerts strong control over lower trophic levels. However, studies combining these two factors are lacking. In a factorial experiment in flow-through channels, addition of sand (<0.5 mm) and predatory stoneflies (Perlidae) caused independent effects on benthic invertebrates, algal biomass, and leaf breakdown. Sand reduced invertebrate density by 55% and also reduced leaf breakdown and algal biomass. Predators reduced invertebrate densities by 40%, with the strongest impact on algal-feeding invertebrates. Predators also decreased densities of leaf-shredding invertebrates and reduced leaf breakdown, thereby inducing a trophic cascade via detritus-based food web. The two treatments exhibited an antagonistic interaction whereby sand obscured any effect of predators on algae, indicating that an abiotic stress may modify a trophic cascade. By contrast, we found no support for synergistic interactions between sand and predation. The strong effects of sedimentation on key ecosystem processes illustrate that stream management needs to exploit riparian and in-stream measures to reduce sediment inputs to headwater streams.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".