The Influence of Habitat Features on the Biomass and Distribution of Three Species of Southern Ontario Stream Salmonines
Bibliographic record
Abstract
We developed models to predict habitat use and productive capacity of brook trout Salvelinus fontinalis, brown trout Salmo trutta, and rainbow trout Oncorhynchus mykiss in southern Ontario streams using readily measured habitat variables. We collected habitat and fish biomass data from 118 streams distributed throughout southern Ontario. Our habitat variables included those for morphology and substrate, water quality, instream physical habitat types, and bank vegetation. We used trout biomass, estimated from a single-pass electrofishing technique, as our indicator of site productive capacity. A discriminant function model showed modest separation among sites with low, moderate, high, and very high total trout biomass, based on differences in water temperature, percent pools, substrate, and cover. The discriminant function correctly classified sites in 80 of 118 cases. A regression tree model indicated that water temperature was by far the most important habitat variable at distinguishing sites with differing total trout biomass. A second, species-level discriminant analysis showed better separation among sites (65 of 82 cases correctly classified) where trout biomass was dominated by rainbow, brook, or brown trout; this was based on differences in water temperatures, percent pools, substrate size, average competitor biomass, and cover. A classification tree model yielded similar results. Our results are consistent with an earlier modeling effort to predict trout biomass from habitat in southern Ontario streams. Our findings add to those by (1) increasing the geographic breadth of the data used to fit the models, (2) providing a model for which all the requisite data for a site can be collected in a single day, and (3) showing that species-level models are better at linking habitat to fish biomass than are total trout biomass models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".