Trophic cascades in streams: effects of nutrient enrichment on autotrophic and consumer benthic communities under two different fish predation regimes
Bibliographic record
Abstract
We tested the hypothesis that differences in top-level predators could mediate the importance of top-down versus bottom-up forces in stream food chains using three streams dominated by Galaxias (fish native to New Zealand) and three dominated by brown trout (Salmo trutta) (an introduced species). These two fish species have quite different predation strategies and energetic requirements. Periphyton in the Galaxias streams formed relatively low biomass (indicative of high invertebrate grazing), whereas periphyton in two of the three trout streams formed relatively high biomass (indicative of lower grazing pressure). Periphyton biomass response to inorganic nutrient enrichment varied by fish type. Invertebrate densities on artificial substrates were higher in the Galaxias streams, with the exception of chironomids. Nutrient enrichment of periphyton led to increases in total invertebrate densities, but these increases were greater in the Galaxias streams where invertebrate predation was thought to be lower. Our results suggest that the specific feeding behavior traits of the consumers should be considered when investigating trophic cascades or predicting effects of bottom-up nutrient enrichment on autotrophy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".