Advancing environmental DNA as a tool for fisheries management by predicting salmon abundance across a range of spawning habitats
Bibliographic record
Abstract
Advancing environmental DNA (eDNA) beyond conventional species detection applications to support wildlife and fisheries management has garnered great interest. The management of spawning Pacific salmon (Oncorhynchus spp.) could especially benefit from highly scalable eDNA tools for estimating local salmon abundance due to their extensive freshwater habitat distributions. Past studies have shown great promise for using salmonid eDNA to estimate abundance or biomass, though this predictive ability has rarely been evaluated beyond individual systems. We evaluated the potential for broadscale applicability of eDNA to estimate salmonid spawner abundance across a suite of disparate stream and river systems representing a range of different environmental conditions. We collected eDNA, spawner counts, and environmental variables at 39 salmon spawning grounds and/or migratory routes that encompassed a variety of freshwater habitats. We demonstrate that eDNA concentrations, when corrected for discharge, can be used to predict live salmon abundance across streams, whereas dead salmon were not well predicted. We also show that in the absence of equipment-intensive discharge measurements, point measurements of flow velocity and channel width may be used as a sufficient discharge proxy. Furthermore, discharge-corrected salmonid eDNA concentrations were not significantly influenced by water temperature or turbidity, but were positively related to bacterial eDNA concentrations. Collectively, these findings support the use of eDNA as a versatile tool for enumerating salmonids across systems and for integration of eDNA into salmonid monitoring programs. This work more broadly represents an important advancement of eDNA for applications beyond species detection and towards estimating abundance across lotic systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".