Evaluating Fish Stranding Downstream of E.B. Campbell Hydroelectric Station on the Saskatchewan River, SK
Bibliographic record
Abstract
The number of hydroelectric stations is increasing globally to generate renewable energy resources. To provide energy when it is needed, stations can be operated under a hydropeaking regime, which results in a risk of fish stranding when water levels fall rapidly downstream. With the lack of regulation on hydropeaking and a limited understanding of fish stranding on a large riverscale, there is a need for methods to identify where and when fish stranding occurs due to hydropeaking in relation to ambient environmental conditions, as well as a way to quantify the number of fish impacted on a large riverscale that can be applied to other hydropeaking operations. Here, I studied fish stranding in three reaches downstream of E. B. Campbell Generating Station on the Saskatchewan River, Saskatchewan, Canada along a 16 km riverscape. Using an innovative remote photography approach with 45 trail cameras and traditional transect monitoring, conducting 323 transects, I observed fish stranding between June and October associated with hydropeaking. Juvenile sport and commercial fish species stranded in higher-than-expected proportions compared with small-bodied fish species. The remote photography approach provided more precisely identifies fish stranding timing and allowed me to associate the environmental and physical conditions with a given stranding event. The comparison of the two approaches resulted in similar stranded fish densities, but remote photography allowed for continuous observations whereas transect monitoring was limited by observer availability in the field. Remote photography allowed for additional information on the scavenging of stranded fish, with scavenging occurring on average within 240 min of stranding. The probability of fish stranding increased significantly with increasing water temperature and decreasing substrate particle size. \nBy applying a combination of physical habitat assessments, hydrodynamic modeling, and observations on fish stranding, I estimated the number of fish stranding over five-month sampling periods for three consecutive years. I modelled how many fish stranded during 2019, 2020, and 2021 along the 16 km study reach, to predict the number of stranded fish during an average, low and high flow year, respectively. I determined a change in wetted area of 90.05 km2, 53.02 km2, and 80.74 km2 for 2019, 2020, and 2021, respectively during June through October each year. The fish stranding density obtained from the field observations was applied to the change in wetted area determined from a hydrodynamic model. The highest number of stranded fish was estimated for 2021, the low flow year, where estimates ranged from 170,700 to 1,638,000 stranded fish, based on remote photography and transect monitoring densities, respectively. \nThese findings have important implications for hydroelectric flow management by introducing an innovative, standardized methodology to study the effects of hydropeaking events on fish stranding that can be applied to determine impacts on fish communities. Coupling in-field observations with hydrodynamic modeling to estimate fish stranding on a large scale allows us to gain a greater understanding of the timing and magnitude of effects of hydropeaking on downstream ecosystems, and can be transferred to other riverscapes threatened by hydropeaking.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.168 | 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".