A spatially‐explicit assessment of the fish population response to flow management in a heterogeneous landscape
Bibliographic record
Abstract
Abstract Ecological processes are structured in space and there are important benefits in incorporating spatial information for the analysis of data sets obtained from field studies. Assessing the effect of different flow management practices on river ecosystems is an example where such an exercise is highly relevant. Human activities such as hydroelectric power production are known to modify the temporal variability in river flow. Flow management strategies may have a direct influence on fishes and may trigger complex cascades of interactions involving different features of the river ecosystem. In this study, we performed an assessment of the effect of different flow management practices on fish count density (no. fish/m2), biomass density (g/m2), and species richness. Data were collected in 941 sites located along 28 Canadian rivers. These rivers were either naturally flowing or had altered flows from one of three flow management strategies: run of the river dams, storage with gradual release, or storage with peak release. Each site (300 m2) was surveyed using paired snorkeling and electrofishing techniques; environmental variables (water depth and velocity, and substrate composition) were also measured. The study spanned a broad geographic range (3497 km, geodesic distance) and involved repeated local observations (16–50 sites/river), and was therefore inherently spatially organized. We used spatial modeling to obtain a baseline to estimate the effect of flow management strategies on fishes. Our results indicate that rivers downstream of flow peaking storage dams have, by far, the lowest fish densities (count and biomass) and species richness, whereas those downstream of gradual release storage dams had higher fish biomass density than the unregulated rivers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".