<scp>eDNA</scp> Sampling Systems for Salmon Ecosystem Monitoring
Bibliographic record
Abstract
ABSTRACT Environmental DNA (eDNA) is transforming the way aquatic ecosystems are monitored and managed by scientists, resource managers, ENGOs, First Nations communities, and citizen scientists alike. However, available sampling systems currently don't allow for combined high filtration volumes, rapid sample collection, and preservation in the field, thus far hindering broad scale eDNA studies in the ocean specifically for small and medium scale organizations. To overcome these challenges, several modular water sampling systems that utilize hollow‐membrane (HM) filtration cartridges were developed by RKS laboratories and tested by the Fisheries and Oceans, Canada, Molecular Genetics Laboratory. Compared to Sterivex filters, an industry standard for eDNA filtration, the HM filtration cartridges allowed for a six‐fold increase in filtration volume and threefold increase in filtration speed. The field sampling systems, which combine pumps, a programmable controller, an air pump, an ozone generator, and up to eight filters at once, enabled efficient direct eDNA filtration from diverse aquatic environments, from creeks to the open ocean. To evaluate ease of deployment, we present the results of a 3 day workshop where technical staff of an Indigenous resource management organization, without any prior knowledge in eDNA sampling, were trained and performed independent eDNA sample collection. The samples were analyzed by metabarcoding and qPCR to reveal the distributions of salmon and other species co‐occurring in salmon ecosystems, from large ephemeral predators, to the planktonic prey of salmon, even including their pathogens. In this example study, we further observed a substantial shift in community composition in the vicinity of aquaculture facilities where marine species associated with aquaculture feed were detected in freshwater at high relative abundance. This study demonstrates how these sampling systems provide an efficient entry point for small and medium scale organizations to utilize eDNA to fulfill their research and monitoring objectives.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".