Temperature and Flow Effects on Migration Timing of Chinook Salmon Smolts
Bibliographic record
Abstract
Abstract Physiological and behavioral changes occur in the spring when juvenile Pacific salmon Oncorhynchus spp. undergo smolting. Survival is maximized if the timing of these changes coincides with migration from fresh to marine environments. Therefore, understanding how environmental conditions influence the onset, duration, and termination of smolting can have substantial management implications, particularly for flow‐controlled rivers. We used an information‐theoretic model comparison analysis to investigate the roles of daily mean temperature, temperature experience (accumulated thermal units [ATU]), photoperiod, and flow on the timing of the downstream migration of Chinook salmon O. tshawytscha smolts from the Nechako River in central British Columbia. Both binary (migration or not) and count (the total number of migrants) models were developed that predicted the downstream migration of Chinook salmon based on data collected from fish captured at rotary‐screw traps from 1992 to 2004. The analyses identified a combination of temperature experience, flow, and the number of spawners as best able to describe the observed migration patterns. In addition, increasing ATU had a positive influence on migration, while increasing flow had a negative influence. Temperature experience was found to have more influence on migration than daily mean temperature. The predictive ability of each model was tested with 2 years of independent data. The count model accurately predicted the general trends in migration and, in particular, the termination of migration, but not the daily fluctuations in movement. By contrast, the binary model predicted whether fish would migrate on a given day with accuracies of 93% and 99%, respectively, for the 2 years tested. Temperature experience was more strongly linked to migration than the daily or threshold temperature; warmer temperatures resulted in earlier migration. Our data suggest that flow plays an important role once migration is under way and may even serve as a termination cue. Furthermore, the number of migrants and the probability of migration was positively related to the number of spawners. Based on the results of this study, flow manipulations that change the timing, duration, or magnitude of temperature and flow in the spring could affect the migration of Chinook salmon. Both temperature and river flow should be considered when one is managing flow‐controlled watersheds for salmon productivity.
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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 | 0.000 |
| 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 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".