Development and application of an eDNA method to detect and quantify a pathogenic parasite in aquatic ecosystems
Bibliographic record
Abstract
Approaches based on organismal DNA found in the environment (eDNA) have become increasingly utilized for ecological studies and biodiversity inventories as an alternative to traditional field survey methods. Such DNA-based techniques have largely been used to establish the presence of free-living organisms, but have much potential for detecting and quantifying infectious agents in the environment, which is necessary to evaluate disease risk. We developed an eDNA method to examine the distribution and abundance of the trematode Ribeiroia ondatrae, a pathogenic parasite known to cause malformations in North American amphibians. In addition to comparing this eDNA approach to classical host necropsy, we examined the detectability of R. ondatrae in water samples subject to different degradation conditions (time and temperature). Our test exhibited high specificity and sensitivity to R. ondatrae, capable of detecting as little as 14 fg (femtograms) of this parasite's DNA (1/2500th of a single infectious stage) from field water samples. Compared to our results from amphibian host necropsy, quantitative PCR was -90% concordant with respect to R. ondatrae detection from 15 field sites and was also a significant predictor of host infection abundance. DNA was still detectable in lab samples after 21 days at 25°C, indicating that our method is robust to field conditions. By comparing the advantages and disadvantages of eDNA vs. traditional survey methods for determining pathogen presence and abundance in the field, we found that the lower cost and effort associated with eDNA approaches provide many advantages. The development of alternative tools is critical for disease ecology, as wildlife management and conservation efforts require reliable establishment and monitoring of pathogens.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".