Effects of multiple stressors on river biofilms depend on the time scale
Bibliographic record
Abstract
Abstract Global change exposes ecosystems to a myriad of stressors differing in their spatial (i.e. surface of stressed area) and temporal (i.e. exposure time) components. Among freshwater ecosystems, rivers and streams are subject to physical, chemical and biological stressors, which interact with each other and might produce diverging effects depending on exposure time. We conducted a manipulative experiment using 24 artificial streams to examine the individual and combined effects of warming (1.6 °C increase in water temperature), hydrological stress (simulated low-flow situation) and chemical stress caused by pesticide exposure (15.1–156.7 ng L −1 ) on river biofilms. We examined whether co-occurring stressors could lead to non-additive effects, and if these differed at two different exposure times. Specifically, structural and functional biofilm responses were assessed after 48 hours (short-term effects) and after 30 days (long-term effects) of exposure. Hydrological stress caused strong negative impacts on river biofilms, whereas effects of warming and pesticide exposure were less intense, although increasing on the long term. Most stressor combinations (71%) resulted in non-significant interactions, suggesting overall additive effects, but some non-additive interactions also occurred. Among non-additive interactions, 59% were classified as antagonisms after short-term exposure to the different stressor combinations, rising to 86% at long term. Our results indicate that a 30-day exposure period to multiple stressors increases the frequency of antagonistic interactions compared to a 48-hour exposure to the same conditions. Overall, the impacts of multiple-stressor occurrences appear to be hardly predictable from individual effects, highlighting the need to consider temporal components such as duration when predicting the effects of multiple stressors.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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; both teacher heads agree on what is shown here.
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".