Climate-induced trends in predator–prey synchrony differ across life-history stages of an anadromous salmonid
Bibliographic record
Abstract
Differential climate-induced shifts in phenology can create mismatches between predators and prey, but few studies have examined predator–prey mismatch across multiple life-history stages. We used long-term data from a warming stream with shifting salmonid migration timings to quantify intra-annual migration synchrony between predatory Dolly Varden (Salvelinus malma) and Pacific salmon prey and examined how predator–prey synchrony has been influenced by climate change. We demonstrate that Dolly Varden have become increasingly mismatched with spring downstream migrations of abundant pink salmon (Oncorhynchus gorbuscha) juveniles. However, Dolly Varden have remained matched with fall upstream migrations of spawning Pacific salmon, including coho (Oncorhynchus kisutch), sockeye (Oncorhynchus nerka), and pink salmon. Downstream predator–prey migration synchrony decreased over time and with higher temperatures, particularly with pink salmon. In contrast, upstream migration synchrony was temporally stable and increased with rising temperatures. Differing trends in Dolly Varden predator–prey synchrony may be explained by the direct use of salmon to cue upstream migration, but not downstream migration. Overall, we show that climate change can have differing impacts on predator–prey synchrony across life-history stages.
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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) | 0.001 | 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".