Impacts of waterpower management on select fish in the Ottawa River, Canada, with an emphasis on lake sturgeon
Bibliographic record
Abstract
The impacts of waterpower management on select fish in the Ottawa River are examined in this study and are presented in four manuscripts: (1) a systematic review of published literature on water power management; (2) examining potential causes of variation in lake sturgeon abundance among river reaches; (3) community structure and abundance among water management regimes; and (4) evaluating the predictive power of a lake sturgeon habitat suitability model at multiple scales. (1) Three meta-analyses were completed: (i) macroinvertebrate abundance is lower in zones or areas that have been dewatered due to water fluctuations or low flows (overall effect size -1.64 (-2.51/-0.77; 95% CI)); (ii) a hypolimnetic draw reduces the abundance of aquatic communities (overall effect size -0.84 (-1.38/-0.33; 95% CI)) and macroinvertebrates (overall effect size -0.73 (-1.24/-0.22; 95% CI)) downstream of a dam; (iii) altered flows has a negative effect on the abundance fluvial specialists (-0.42 (-0.81/-0.02; 95% CI) but does not affect habitat generalists (overall effect size -0.14 (-0.61/0.32; 95% CI)). (2) Three main stressors were examined to explain variation in lake sturgeon abundance among river reaches: contaminants, commercial harvest and water power management. Results were consistent with water power management impeding lake sturgeon recovery: abundance was significantly greater in unimpounded river reaches; growth rates were significantly greater in impounded reaches suggesting food was not limiting; and size/age structure in managed reaches was skewed to larger, older fish suggesting recruitment was impaired. (3) Variation in community structure and abundance was assessed for 11 fish species among three water management regimes: winter reservoirs, run-of-the-river and unimpounded. Littoral zone benthivores were significantly lower in abundance in winter reservoirs whereas species that are planktivorous for portions of their life were significantly greater. Lake sturgeon was the only fast water spawning species affected in run-of-the-river reaches. (4) The predictive power of a lake sturgeon habitat suitability model was assessed by paired net sets in good (habitat suitability index (HSI)>0.6) and poor (HSI<0.3) habitat. Lake sturgeon catch-per-unit-effort (CUE) was significantly greater for adults and juveniles in good habitat however, predictive power of the model was low (r2 < 0.18). Standard index netting lake sturgeon CUE corresponded with overall foraging component which suggests the model is a good predictor of sturgeon abundance at multiple scales.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".