Declines in prey production during the collapse of a tailwater Rainbow Trout population are associated with changing reservoir conditions
Bibliographic record
Abstract
Abstract Objective Understanding how energy moves through food webs and limits productivity at various trophic levels is a central question in aquatic ecology and can provide insight into drivers of fish population dynamics since many fish populations are food limited. In this study, we seek to better understand what factors drove a decline of >85% in the number of Rainbow TroutOncorhynchus mykiss found in the tailwater portion of the Colorado River below Glen Canyon Dam during 2012–2016. Methods We estimate the production of dominant prey using data from previously published studies of Rainbow Trout abundance and growth alongside drift and diet samples. We test how prey production correlates to both proximate (e.g., nutrients) and distal (e.g., limnological conditions in the upriver reservoir) drivers. Result Results suggest that gross consumption of invertebrate prey by the Rainbow Trout population declined from an annual mean of 423 to 69 kg/d. Daily production rates of dominant prey in aggregate declined from a high of 0.173 to 0.018 g·m−2·d−1. Chironomids accounted for 70% of the decline in prey production. Foraging efficiency by Rainbow Trout (range, 0.99–0.67) was high across the range of prey production rates. After the Rainbow Trout population had declined by ~90%, prey consumption saturated at higher rates of prey production and the gross quantity of daily drift exported from the reach increased from 8.9 to 12.7 kg/d. Conclusion Rainbow Trout population dynamics are largely influenced by changes in prey production, which is itself driven by soluble reactive phosphorus (SRP) concentrations in the reservoir. The SRP model predicted that prey production would increase by 32 kg/d (SE, 9) for each 1 μg/L increase in SRP. These concentrations were indirectly influenced by reservoir hydrology and biogeochemistry, linkages that may extend far beyond the confines of this tailwater fishery and into the downstream reaches of the Grand Canyon's Colorado River ecosystem.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".