Applying intrinsic potential models to evaluate salmon (Oncorhynchus spp.) introduction into main-stem and tributary habitats upstream from the Skagit River Hydroelectric Project, northern Washington
Bibliographic record
Abstract
First posted December 18, 2023 For additional information, contact: Director, Western Fisheries Research CenterU.S. Geological Survey6505 NE 65th StreetSeattle, Washington 98115-5016 We assessed habitat suitability for salmonids across selected tributaries upstream from three hydroelectric dams on the upper Skagit River in Whatcom County, northern Washington. We used NetMap, a commercial toolset within the ArcMap geographic information system (GIS), to analyze stream attributes based upon a synthetic stream channel network derived from digital elevation models. The GIS-derived stream attributes—including gradient, bankfull width, valley width index, elevation, and stream flow—allowed us to examine the spatial distribution and relative quality of spawning and rearing habitat for salmonids based on existing intrinsic potential (IP) models. As a first step, we created maps of potential anadromous fish distribution by identifying potential migration barriers within the synthetic stream network. Next, we applied a suite of existing IP models for steelhead, coho, and Chinook salmon (Oncorhynchus mykiss, O. kisutch, and O. tshawytscha, respectively) to estimate low, medium, and high IP habitat for each species. Three different IP models were used for each species, based on species preference curves from populations from coastal Oregon, northern California, Alaska, and western Washington. We found that at least 25 tributaries that were greater than third order and contained habitat with the potential for anadromous fish, totaling about 470 river kilometers in 4,453 synthetic stream reaches averaging about 100 meters (m) in length. The IP of each of these reaches was calculated and placed into low, medium, and high IP categories. For Chinook salmon, the only stream with significantly (in other words, greater than 1 kilometer [km]) high IP reaches was the upper Skagit River upstream from Ross Lake reservoir in Canada, upstream from the third dam in the hydroelectric system. There were differences among the three models evaluated, with the model derived for the lower Skagit River showing more high and medium IP habitat than the other two models that were designed for the Columbia River Basin. For coho salmon, all three models showed similar results favoring medium IP over low and high IP habitat. Of the 3 species examined with existing IP models, steelhead had the most habitat rated as high IP with 19 targeted tributaries showing greater than 1 km of high intrinsic potential habitat.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".