Who spawns where? Temperature, elevation, and discharge differentially affect the distribution of breeding by six Pacific salmonids within a large river basin
Bibliographic record
Abstract
Within the geographic range of salmonid fishes, many apparently suitable rivers and streams are used for reproduction by some species but not others. This is widely known but seldom addressed, as studies often examine factors determining the distribution of one or only a few species. We examined physical factors associated with the spawning distribution of six native Pacific salmonids (pink, chum, coho, and Chinook salmon, bull trout, and steelhead) in the Skagit River basin, Washington. Annual mean temperature and catchment elevation had the strongest association with spawning assemblage distribution, but stream length, annual discharge, seasonal hydrology, and land use were also influential. Some species (e.g., pink and Chinook salmon) were more closely associated with each other and with common variables than others, and bull trout were the most distinctive. For interpretation, we investigated the roles of adult body size, timing of spawning, and duration of juvenile rearing, but none of these factors explained groupings in the data. Interspecific differences in habitat association remained, suggesting fundamental constraints on species distributions with implications for conservation and restoration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".