Timing of spawning and predicted fry emergence by naturalized Chinook salmon (Oncorhynchus tshawytscha) in a Lake Huron tributary
Bibliographic record
Abstract
We evaluated how well a naturalized Chinook salmon population matches their seasonal timing of spawning to the period of thermal suitability (i.e., phenological match) in the Sydenham River (Ontario), because this could be a factor in their persistence. In 2010 and 2011, the mean spawning date (= date of nest settlement) in the Sydenham River was in early October, about 7–12 days earlier than that of the source population at around the time of the transplant (Green River/Soos Creek, Washington, USA). Twenty-eight percent (2010) and 72% (2011) of females settled on nests when temperatures were above those considered suitable for spawning (> 12.8 °C). From nest settlement to death, average water temperature was 9.5 °C in 2010 ( n = 28) and 14.4 °C in 2011 ( n = 22). Median egg retention rate showed a significant seasonal decline in the warmer year (2011), but overall rates (2010: 0.078, n = 20; 2011: 0.264, n = 13) were not significantly higher than rates for native populations of Oncorhynchus spp. (0.10). By applying Belehrádek's model of temperature-dependent development to the Sydenham River population, we predicted fry to emerge from early April to early June at water temperatures of 7.3–17.3 °C, depending on the year and time of spawning. All but the latest emerging fry should experience thermally suitable conditions for growth (< 15.6 °C). We suggest that thermally appropriate timing of juvenile growth may compensate for weak phenological match during the spawning season and may have contributed to the naturalization of Chinook salmon in the Sydenham River.
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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 | 0.000 | 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".