Phenological Diversity of Salmon Smolt Migration Timing within a Large Watershed
Bibliographic record
Abstract
Abstract Although there is growing concern that climate change might drive phenological mismatches between predators and prey, it is possible that within‐ and among‐species phenological variation provides resilience against such a mismatch. One key life history event that may be vulnerable to climate‐induced mismatch is the seaward migration of juvenile salmon relative to the spring bloom of their marine zooplankton prey. Here, we quantified phenological diversity of out‐migration timing among salmon populations within a large watershed and its implications for climate mismatches with marine zooplankton. Specifically, we sampled juvenile Sockeye Salmon Oncorhynchus nerka throughout the spring and early summer in the estuary of the Skeena River, a vast watershed with numerous locally adapted salmon populations that support commercial, recreational, and First Nations fisheries, and we used genetic stock identification to link the fish to their population of origin. We found that Sockeye Salmon were migrating through the estuary for more than 50 d, with peak emigration for different populations varying by over 5 weeks. The out‐migration timing of specific populations was related to geographic factors, including elevation of the rearing lake and the river distance between individual rearing lakes and the estuary, with different populations arriving 1.5 d later for every 100 m of elevation or 3 d later for every 100 km of river distance. Concurrent with sampling, we quantified the estuarine prey of juvenile Sockeye Salmon; zooplankton species composition and abundance varied throughout the smolt migration period, and the different salmon populations encountered different prey abundances upon ocean entry. Together, these results indicate underappreciated phenological diversity in this harvested metapopulation, which may contribute to response diversity and metapopulation‐level resilience to climate change.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".