Assessing the impact of climate change and a water management programme on white sturgeon physiology in the Nechako River, British Columbia
Bibliographic record
Abstract
Abstract Climate change is impacting river ecosystems, underlining the need for water management strategies to protect native species within these ecosystems. Here, we evaluate the impact of climate change and water management on the physiology of white sturgeon (Acipenser transmontanus) in the Nechako River, British Columbia (Canada). Using the CEQUEAU hydrological–thermal model, we simulated daily water temperatures from 1980 to 2099 under two climate scenarios (SSP2-4.5 and SSP5-8.5). We assessed thermal exposure risk (Te) for different developmental stages of white sturgeon, focusing on the warmest 6-month period. Our findings show that embryos and yolk-sac larvae exhibit resilience, with Te values consistently <1 under both scenarios, signifying low thermal stress. In contrast, feeding larvae and juveniles experience elevated Te values, indicating significant future thermal stress. For feeding larvae, Te values exceeded 1 under both scenarios, reaching up to 1.5 by the mid-century (2050s) and up to 1.8 by the end of the century (2090s) under SSP5-8.5. Juvenile white sturgeon also faced increased thermal risks, with Te values rising >1 during July and August, reaching 1.4 and 1.8 by the 2050s and 1.8 and 2.0 by the 2090s under SSP5-8.5, compared to the 1980s. These results underscore the need to evaluate the existing water management programme to better accommodate the projected changes in thermal conditions associated with climate change. Additionally, regulated river discharge, which can both increase and decrease downstream temperatures, offers a strategic opportunity to mitigate some climate impacts through strategic dam discharge management.
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 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) | 0.000 | 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".