Adaptive Management of Flows in a Regulated River: Flow-ecology Relationships Revealed by a 26-year, Five-treatment Flow Experiment
Bibliographic record
Abstract
Adaptive management (AM) is often proposed as a means to resolve uncertainty in the management of socio-ecological systems but successful implementation of AM is rare. We report results from a 26 year, five-treatment, AM experiment designed to inform decision makers about the response of juvenile salmonids (Oncorhynchus spp.) to flow releases from a dam on the regulated Bridge River, British Columbia, Canada. Treatments consisted of a baseline (no dam release) and four different dam release regimes that followed a semi-natural hydrograph but varied in the magnitude of spring-summer freshet flows. We found total salmonid biomass was highest at the lowest flow release, and decreased with increasing flow, consistent with a priori predictions made by an expert solicitation process. Species-specific responses were observed that in some cases could be attributed to interactions between the flow regime and life history. The relationship between juvenile biomass and flow resulting from the experiment can inform decisions on water management for this river. The documentation of successful AM experiments is sorely needed to allow for reflection on the circumstances when AM is likely to deliver desirable outcomes, and to improve other decision processes that require fewer resources and less time to implement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".