Multiple-Stressor Interactions in Tributaries Alter Downstream Ecosystems in Stream Mesocosm Networks
Bibliographic record
Abstract
We studied how multiple-stresssors in tributaries affect function, diversity, and physical habitat of recipient downstream ecosystems. Using a mesocosm model of a stream network, we manipulated sediment and nutrients individually and in combination in tributaries of second-order channels, to test the effect of complex stressor interactions within tributaries on recipient channels. Sedimentation in second-order channels increased with the level of disturbance of the tributaries. Moreover, Ephemeroptera, Plecoptera, and Trichoptera (EPT) density and EPT richness were higher in second-order channels fed by tributaries where the stressors were applied separately, compared to those fed by tributaries where the stressors were applied simultaneously. Our observations suggest this result was due to the combination of the two stressors within the same tributary reducing EPT drift from the tributaries further than the addition of the stressors in separate tributaries. These results support the hypothesis that cumulative upstream disturbance can influence downstream recipient ecosystems in stream networks. However, contrary to our expectations, most observed effects were due to impacts on dispersal patterns of EPT taxa, rather than downstream accumulation of disturbances throughout the network. Our results underscore the importance of metacommunity frameworks to understand how tributary disturbance may influence population dynamics in downstream ecosystems.
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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 | 0.001 | 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".