Experimental evidence on the impact of climate-induced hydrological and thermal variations on glacier-fed stream biofilms
Bibliographic record
Abstract
Climate change is predicted to alter the hydrological and thermal regimes of high-mountain streams, particularly glacier-fed streams. However, relatively little is known about how these environmental changes impact the microbial communities in glacier-fed streams. Here, we operated streamside flume mesocosms in the Swiss Alps, where benthic biofilms were grown under treatments simulating climate change. Treatments comprised four flow (natural, intermittent, stochastic, and constant) and two temperature (ambient streamwater and warming of +2°C) regimes. We monitored microbial biomass, diversity, community composition, and metabolic diversity in biofilms over 3 months. We found that community composition was largely influenced by successional dynamics independent of the treatments. While stochastic and constant flow regimes did not significantly affect community composition, droughts altered their composition in the intermittent regime, favouring drought-adapted bacteria and decreasing algal biomass. Concomitantly, warming decreased algal biomass and the abundance of some typical glacier-fed stream bacteria and eukaryotes, and stimulated heterotrophic metabolism overall. Our study provides experimental evidence towards potential and hitherto poorly considered impacts of climate change on benthic biofilms in glacier-fed streams.
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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 | 0.000 |
| 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) | 0.001 | 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".