Effects of Fluctuating Flows and a Controlled Flood on Incubation Success and Early Survival Rates and Growth of Age‐0 Rainbow Trout in a Large Regulated River
Bibliographic record
Abstract
Abstract Hourly fluctuations in flow from Glen Canyon Dam were increased in an attempt to limit the population of nonnative rainbow troutOncorhynchus mykissin the Colorado River, Arizona, due to concerns about negative effects of nonnative trout on endangered native fishes. Controlled floods have also been conducted to enhance native fish habitat. We estimated that rainbow trout incubation mortality rates resulting from greater fluctuations in flow were 23–49% (2003 and 2004) compared with 5–11% under normal flow fluctuations (2006–2010). Effects of this mortality were apparent in redd excavations but were not seen in hatch date distributions or in the abundance of the age‐0 population. Multiple lines of evidence indicated that a controlled flood in March 2008, which was intended to enhance native fish habitat, resulted in a large increase in early survival rates of age‐0 rainbow trout. Age‐0 abundance in July 2008 was over fourfold higher than expected given the number of viable eggs that produced these fish. A hatch date analysis indicated that early survival rates were much higher for cohorts that hatched about 1 month after the controlled flood (∼April 15) relative to those that hatched before this date. The cohorts that were fertilized after the flood were not exposed to high flows and emerged into better‐quality habitat with elevated food availability. Interannual differences in age‐0 rainbow trout growth based on otolith microstructure supported this hypothesis. It is likely that strong compensation in survival rates shortly after emergence mitigated the impact of incubation losses caused by increases in flow fluctuations. Control of nonnative fish populations will be most effective when additional mortality is applied to older life stages after the majority of density‐dependent mortality has occurred. Our study highlights the need to rigorously assess instream flow decisions through the evaluation of population‐level responses.
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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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".