Multiple stressors in river networks: local and downstream effects on freshwater macroinvertebrates
Bibliographic record
Abstract
Abstract River networks are complex ecosystems characterized by a continuous exchange of material and energy through longitudinal gradients. These ecosystems are threatened by various human-induced stressors, which frequently co-occur and may interact in complex ways, potentially triggering cascading effects in the river network. Aiming at assessing single and combined effects of flow intermittency and light pollution on macroinvertebrate communities, we performed a multiple stressors experiment in 18 flow-through mesocosms. Each mesocosm was designed to mimic a simplified river network, with two upstream tributaries merging downstream, to assess both local and cascading effects. The experiment was performed in Summer 2021 for seven weeks (26 days of colonization, 23 days of treatment), applying the stressors either separately or combined in the upstream sections, in a randomized block design. Flow intermittency was simulated as the ponded phase of the drying process, whereas light pollution was applied with LED strips (set at 10 lux) that automatically turned on at sunset and off at sunrise. Drifting macroinvertebrates were sampled weekly during the treatment phase, and benthic macroinvertebrates at the end of the treatment phase. Both stressors individually applied had negative effects on the benthos, whereas drift decreased with flow intermittency and increased with light pollution. When combined upstream, stressors showed dominant effects of flow intermittency on the benthos and interactive effects on the drift. The effects of the single stressors and their interactions propagated along the river network, with stronger downstream effects when stressors co-occurred upstream. These findings showed that the spatial distribution of multiple stressors along the river network can affect their resultant downstream effects, highlighting the importance of framing multiple stressors research in a spatial context. Considering the pressing needs of the growing human population, our results represent a step forward in anticipating cumulative stressors effects, informing efficient conservation strategies for protecting freshwater 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 | 0.000 |
| 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".