Prey size and availability limits maximum size of rainbow trout in a large tailwater: insights from a drift-foraging bioenergetics model
Bibliographic record
Abstract
The cold and clear water conditions present below many large dams create ideal conditions for the development of economically important salmonid fisheries. Many of these tailwater fisheries have experienced declines in the abundance and condition of large trout species, yet the causes of these declines remain uncertain. Here, we develop, assess, and apply a drift-foraging bioenergetics model to identify the factors limiting rainbow trout (Oncorhynchus mykiss) growth in a large tailwater. We explored the relative importance of temperature, prey quantity, and prey size by constructing scenarios where these variables, both singly and in combination, were altered. Predicted growth matched empirical mass-at-age estimates, particularly for younger ages, demonstrating that the model accurately describes how current temperature and prey conditions interact to determine rainbow trout growth. Modeling scenarios that artificially inflated prey size and abundance demonstrate that rainbow trout growth is limited by the scarcity of large prey items and overall prey availability. For example, shifting 10% of the prey biomass to the 13 mm (large) length class, without increasing overall prey biomass, increased lifetime maximum mass of rainbow trout by 88%. Additionally, warmer temperatures resulted in lower predicted growth at current and lower levels of prey availability; however, growth was similar across all temperatures at higher levels of prey availability. Climate change will likely alter flow and temperature regimes in large rivers with corresponding changes to invertebrate prey resources used by fish. Broader application of drift-foraging bioenergetics models to build a mechanistic understanding of how changes to habitat conditions and prey resources affect growth of salmonids will benefit management of tailwater fisheries.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".