Extreme weather events threaten biodiversity and functions of river ecosystems: evidence from a meta‐analysis
Bibliographic record
Abstract
Both gradual and extreme weather changes trigger complex ecological responses in river ecosystems. It is still unclear to what extent trend or event effects alter biodiversity and functioning in river ecosystems, adding considerable uncertainty to predictions of their future dynamics. Using a comprehensive database of 71 published studies, we show that event - but not trend - effects associated with extreme changes in water flow and temperature substantially reduce species richness. Furthermore, event effects - particularly those affecting hydrological dynamics - on biodiversity and primary productivity were twice as high as impacts due to gradual changes. The synthesis of the available evidence reveals that event effects induce regime shifts in river ecosystems, particularly affecting organisms such as invertebrates. Among extreme weather events, dryness associated with flow interruption caused the largest effects on biota and ecosystem functions in rivers. Effects on ecosystem functions (primary production, organic matter decomposition and respiration) were asymmetric, with only primary production exhibiting a negative response to extreme weather events. Our meta-analysis highlights the disproportionate impact of event effects on river biodiversity and ecosystem functions, with implications for the long-term conservation and management of river ecosystems. However, few studies were available from tropical areas, and our conclusions therefore remain largely limited to temperate river systems. Further efforts need to be directed to assemble evidence of extreme events on river biodiversity and functioning.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".