Multiple stressors in river networks: local and downstream effects on freshwater macroinvertebrates
Bibliographic record
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 throughout 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, allowing us to assess both local and cascading effects. The experiment was performed in summer 2021 over seven weeks, applying the stressors either separately or co‐occurring in the upstream sections, following 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 to 10 lux. Drifting macroinvertebrates were sampled weekly during the treatment phase, and benthic macroinvertebrates were sampled at the end of the treatment phase. Both stressors, when applied individually, reduced benthos richness and abundance, whereas drift decreased with flow intermittency and increased with light pollution. When co‐occurring upstream, stressors showed the dominant effects of flow intermittency on the benthos and interactive effects on the drift. The effects of the single stressors and their interactions cascaded along the river network, with stronger downstream effects when stressors co‐occurred upstream. These findings show 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 the effects of cumulative stressors and in informing efficient conservation strategies for protecting freshwater ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".