Rising water temperature in rivers: Ecological impacts and future resilience
Bibliographic record
Abstract
Abstract Rising water temperatures in rivers due to climate change are already having observable impacts on river ecosystems. Warming water has both direct and indirect impacts on aquatic life, and further aggravates pervasive issues such as eutrophication, pollution, and the spread of disease. Animals can survive higher temperatures through physiological and/or genetic acclimation, behavioral and phenological change, and range shifts to more suitable locations. As such, those animals that are adapted to cool‐water regions typically found in high altitudes and latitudes where there are fewer dispersal opportunities are most at risk of future extinction. However, sub‐lethal impacts on animal physiology and phenology, body‐size, and trophic interactions could have significant population‐level effects elsewhere. Rivers are vulnerable to warming because historic management has typically left them exposed to solar radiation through the removal of riparian shade, and hydrologically disconnected longitudinally, laterally, and vertically. The resilience of riverine ecosystems is also limited by anthropogenic simplification of habitats, with implications for the dispersal and resource use of resident organisms. Due to the complex indirect impacts of warming on ecosystems, and the species‐specific physiological and behavioral response of organisms to warming, predicting how river ecosystems will change in the future is challenging. Restoring rivers to provide connectivity and heterogeneity of conditions would provide resilience to a range of expected co‐occurring pressures, including warming, and should be considered a priority as part of global strategies for climate adaptation and mitigation. This article is categorized under: Science of Water > Water and Environmental Change Water and Life > Nature of Freshwater Ecosystems Water and Life > Stresses and Pressures on Ecosystems
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".