Estimation of salmonid habitat growth potential through measurements of invertebrate food abundance and temperature
Bibliographic record
Abstract
Criteria used to characterize lotic salmonid habitat suitability are often based on correlations between physical habitat characteristics and salmonid abundance. Focusing on physical habitat features ignores other habitat components, such as an adequate food supply, that limit the amount of energy available for growth and survival. We tested the degree that food availability and temperature influence lotic salmonid consumption and growth rates and outline an approach for assessing habitat quality based on measurements of these features. We collected benthic and drifting invertebrate abundances, stream temperatures, and juvenile steelhead – rainbow trout (Onchorhynchus mykiss gairdneri) summer growth rates among nine stream segments in central Oregon. Stream temperatures and growth rates were used in bioenergetics model simulations to estimate O. mykiss consumption rates. The variation in O. mykiss consumption rates was explained by measurements of total drift biomass along a type II predator response curve (R 2 = 0.71). This simplified foraging relationship between food abundance and consumption is then used to estimate the consumption component of the bioenergetics model to allow estimation of salmonid growth potential. Validation of the growth potential model produced reasonably accurate estimates of fish growth rates at reaches within the study area and precise but biased estimates in novel systems. While additional reach-level habitat information may be required to make the model more generalizable, the assessment of invertebrate food availability offers a simple yet powerful approach for describing the growth potential of stream habitat.
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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".