Environmental DNA detection and abundance estimates comparable to conventional methods for three freshwater larval species at a power plant discharge
Bibliographic record
Abstract
Abstract Early life stage (ELS) fishes provide a valuable metric for species, population, and ecosystem monitoring. Industrial, manufacturing, and power generating facilities in the United States can be required to monitor ELS fishes to assess impacts of facility intake waters. Traditional methods for collecting, identifying, and enumerating ELS fishes include ichthyoplankton netting and pumping from intake and discharge waters. However, sampling at these sites can prove challenging from logistical and safety standpoints, with added challenges of identifying and post‐processing of ELS fishes to quantify facility impacts on waterbodies for regulatory purposes. Methods utilizing environmental DNA (eDNA) may offer improvements in these areas. This study assessed the utility of novel, species‐specific qPCR assays to detect eDNA from three differentially abundant fish species (alewife ( Alosa pseudoharengus ; Wilson , 1811 ), gizzard shad ( Dorosoma cepedianum ; Lesueur , 1818 ), and yellow perch ( Perca flavescens ; Mitchill , 1814 )) at the outflow of an industrial site. eDNA concentrations were compared with abundance estimates derived from two conventional collection methods to explore the potential utility of eDNA‐based methods in future monitoring. The likelihood of detecting gizzard shad, yellow perch, and alewife in the discharge waters of the power generation station was not significantly different among eDNA, plankton netting, or pump sampling. Results suggest successful detections differ by species and time of year for each method. Gizzard shad eDNA relative abundance correlated strongly with total larval abundances captured by the pumping but not the netting methods, whereas yellow perch eDNA abundance was found to correlate with both conventional methods. Alewife was not detected by any method, consistent with documentation of the decline in this species within the lake. Overall, our study found a positive relationship between eDNA abundance and larval fish abundance in both daily and seasonal sampling, suggesting that fluctuations in eDNA concentration may be linked to larval abundance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".