Environmental <scp>DNA</scp> survey of the Winter Salmonosphere in the Gulf of Alaska
Bibliographic record
Abstract
Abstract Pacific salmon ( Oncorhynchus spp.) are a foundation species around the north Pacific, yet many wild populations have experienced steep declines over the last decades associated with decreased marine survival. Due to limited sampling, our understanding of the dynamic North Pacific Ocean ecosystem remains poor and factors impacting marine survival of salmon remain largely unknown, specifically in the winter when salmon face limited resources. Here, we present a late winter ecosystem‐wide environmental DNA (eDNA) survey performed in 2019 and 2020 in the Gulf of Alaska. Our eDNA data refines the ocean distribution and relative abundance of Pacific salmon, as well as their prey such as copepods. Vertical diurnal migrators like myctophids and squid that are prey and/or competitors of salmon and represent the bulk of the open ocean biomass in the Gulf of Alaska. We detected these species irrespective of time of day in the eDNA data, resolving the nighttime bias associated with trawl catches. Furthermore, eDNA uncovered cryptic predators of salmon such as salmon sharks ( Lamna ditropis ) and beaked whales ( Ziphiidae ) not evident from catch or observer data. Open‐ocean communities of the central Gulf of Alaska were characterized by low species richness in contrast to hotspots of biological activity associated with oceanographic features such as eddies and the continental shelf. Network analysis revealed the ecological interactions that salmon navigate in the open‐ocean ecosystem by identifying key prey species, competitors, and predators and revealed the negative impact of the northern sea nettle ( Chrysaora melanaster ) on salmon that was associated with a marine heatwave in 2019. Finally, we highlight the utility of eDNA for open ocean ecosystem research and provide recommendations for further surveys. This dataset, complementing traditional survey methods, illustrates the winter salmonosphere in the North Pacific Ocean at improved resolution and provides a baseline for future research.
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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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".