Monitoring marine pollution effects through targeted environmental DNA (eDNA) testing in the Pacific northwest
Bibliographic record
Abstract
Globally, coastal waters experience degradation from pollution associated with multiple discharges, including industrial and agricultural runoff, and municipal wastewater. Certain benthic infaunal taxa are tolerant of high nutrient input and anoxic conditions, while others are sensitive to these conditions. Using these indicator taxa as proxies for assessing organic enrichment is well established to characterize subsequent pollution impacts. Conventional assessment of macroinfauna involves the detailed analysis of each individual specimen within a sample by taxonomic experts, a resource intensive process. As an alternative, we developed sensitive quantitative polymerase chain reaction (qPCR) assays to detect these indicator taxa in a scalable and reliable way. Using whole genome shotgun sequencing, we generated full mitogenome sequences of selected indicator macroinfaunal polychaetes routinely used for monitoring programs in Pacific Northwest marine environments. These sequences were used to design five new, rigorously validated environmental DNA (eDNA) assays capable of detecting low levels of DNA that can be isolated from environmental samples. For nine sites at a wastewater treatment plant outfall in Vancouver, British Columbia, we tested three eDNA sample collection types: active filtration, a passive dip filter from water containing collected macroinfauna, and active filtration from water collected near the sea floor. Generalized linear models indicated that eDNA signal strength correlated with organism count particularly with passive dip sample collection type. eDNA occupancy modelling techniques estimated detection probabilities corresponding with organism count. The present study emphasizes the value of integrating eDNA into marine outfall monitoring efforts to enhance the assessment of environmental effects. • We developed reliable qPCR eDNA assays for selected pollution indicator species. • eDNA assays targeting pollution indicators offer valuable pollution monitoring data. • We found significant correlation between eDNA signal strength and organism count. • Passive eDNA samplers are simple to integrate into conventional monitoring. • eDNA occupancy modelling provides detection probabilities useful for decision-makers.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".