The role of the geophysical template and environmental regimes in controlling stream-living trout populations
Bibliographic record
Abstract
The importance of multiple processes and instream factors to aquatic biota has been explored extensively, but questions remain about how local spatiotemporal variability of aquatic biota is tied to environmental regimes and the geophysical template of streams. We used an individual-based trout model to explore the relative role of the geophysical template versus environmental regimes on biomass of trout (Oncorhynchus clarkii clarkii). We parameterized the model with observed data from each of the four headwater streams (their local geophysical template and environmental regime) and then ran 12 simulations where we replaced environmental regimes (stream temperature, flow, turbidity) of a given stream with values from each neighboring stream while keeping the geophysical template fixed. We also performed single-parameter sensitivity analyses on the model results from each of the four streams. Although our modeled findings show that trout biomass is most responsive to changes in the geophysical template of streams, they also reveal that biomass is restricted by available habitat during seasonal low flow, which is a product of both the stream’s geophysical template and flow regime. Our modeled results suggest that differences in the geophysical template among streams render trout more or less sensitive to environmental change, emphasizing the importance of local fish–habitat relationships in streams.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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".