Piscine predation on juvenile salmon in sub‐arctic Alaskan rivers: Associations with season, habitat, predator size and streamflow
Bibliographic record
Abstract
Abstract Predation on anadromous salmon can have important consequences for both predators and prey. Salmon provide large seasonal pulses of energy and nutrients via carcasses, eggs and juveniles to many freshwater consumers, and conversely, predation can represent a significant source of mortality for juvenile salmon. Recent declines of Chinook salmon ( Oncorhynchus tshawytscha ) populations in Alaska have raised concern that predation might inhibit their recovery. Here, we quantify patterns of predation by freshwater fishes on juvenile salmon across seasons, habitats, predator sizes and streamflow levels in the Arctic‐Yukon‐Kuskokwim region of Alaska. We analysed piscivore stomach contents and identified prey using DNA sequence “barcoding.” In coastal rivers, juvenile pink ( O . gorbuscha ) and chum ( O . keta ) salmon contributed heavily to Arctic grayling ( Thymallus arcticus ) and Dolly Varden char ( Salvelinus malma ) diets, coho salmon ( O . kisutch ) prey were rare, and Chinook salmon were not detected. In interior rivers, Arctic grayling, burbot ( Lota lota ) and northern pike ( Esox lucius ) consumed small numbers of Chinook salmon. Predation on Chinook salmon was documented disproportionately in sloughs during a summer of exceptionally high streamflow. Dietary and distributional patterns suggested northern pike and burbot may exclude salmon from sloughs in low‐gradient river reaches that would otherwise provide suitable rearing habitat. The data also provided tentative support for the hypothesis that high streamflow induces juvenile Chinook salmon to move from mainstem habitats into sloughs, where they face an increased risk of mortality. Incorporating predation risk into climate adaptation, fisheries management and habitat restoration decisions may help to facilitate Chinook salmon recovery.
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 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) | 0.001 | 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 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".