Seasonal effects of a hydropeaking dam on a downstream benthic macroinvertebrate community
Bibliographic record
Abstract
Abstract As more hydroelectric dams regulate rivers to meet growing energy demands, there is ongoing concern about downstream effects, including impacts on downstream benthic macroinvertebrate (BMI) communities. Hydropeaking is a common hydroelectric practice where short‐term variation in power production leads to large and often rapid fluctuations in discharge and water level. There are key knowledge gaps on the ecosystem impacts of hydropeaking in large rivers, the seasonality of these impacts, and whether dams can be managed to lessen impacts. We assessed how patterns of hydropeaking affect abundance, taxonomic richness, and relative tolerance of BMIs in the Saskatchewan River (Saskatchewan, Canada). Reaches immediately (<2 km) downstream of the dam generally had high densities of BMIs and comparable taxonomic diversity relative to upstream locations but were characterized by lower ratios of sensitive (e.g., Ephemeroptera, Plecoptera, and Trichoptera) to tolerant (e.g., Chironomidae) taxa. The magnitude of effect varied with seasonal changes in discharge. Understanding the effects of river regulation on BMI biodiversity and river health has implications for mitigating the impacts of hydropeaking dams on downstream ecosystems. Although we demonstrated that a hydropeaking dam may contribute to a significantly different downstream BMI assemblage, we emphasize that seasonality is a key consideration. The greatest differences between upstream and downstream locations occurred in spring, suggesting standard methods of late summer and fall sampling may underestimate ecosystem‐scale impacts.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".