Net rate of energy intake predicts reach-level steelhead (<i>Oncorhynchus mykiss</i>) densities in diverse basins from a large monitoring program
Bibliographic record
Abstract
Substantial research effort has been devoted to understanding stream-dwelling salmonids’ use of summer rearing and growth habitat, with a subset of studies focusing on foraging position selection and the energetic trade-offs of differential habitat use. To date, however, cost–benefit analyses for most foraging model studies have focused on small sampling areas such as individual habitat units. To address this knowledge gap, we applied a mechanistic foraging model to 22 stream reaches (100–400 m) from two watersheds within the Columbia River Basin. We found a strong, positive correlation (R2 = 0.61, p < 0.001) between predicted carrying capacities and observed fish densities. Predicted proportion of suitable habitat was weakly correlated with observed fish density (R2 = 0.18, p = 0.051), but the mean net rate of energy intake prediction in sampling reaches was not a significant predictor of observed fish biomass. Our results suggest spatial configuration of habitat, in addition to quantity and quality, is an important determinant of habitat use. Further, carrying capacity predicted by the model shows promise as a habitat metric. We also evaluated the feasibility of applying this data-intensive modeling approach in a large-scale monitoring program to examine habitat quality and quantity. Though the approach can be computationally expensive, we feel the model’s ability to integrate physical habitat metrics (e.g., depth, velocity) with important biological considerations like food availability and temperature is a benefit that far outweighs associated costs. We feel this modeling approach has great potential as a tool to help understand habitat use in drift-feeding fishes.
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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 | 0.000 | 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) | 0.001 | 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".