Screening marker sensitivity: Optimizing eDNA‐based rare species detection
Bibliographic record
Abstract
Abstract Aim Environmental DNA (eDNA)‐based techniques are useful tools in disciplines such as conservation biogeography at local to global scales since they provide promising methods to locate organisms at low abundance. Here, we raise a largely overlooked issue that the marker (primer pairs and/or probes) sensitivity of eDNA‐based detection should be optimized and reported to improve detection performance and result interpretation. Location Global. Methods We analysed 250 articles published between 2008 and 2019 that sought to detect animals from environmental water samples using species‐specific markers to identify effort required. Results Most (66.0%) studies used newly designed markers, and real‐time quantitative PCR dominated the studies (72.4% of articles). The use of quantitative PCR increased significantly over time (p = .016), while conventional PCR decreased significantly (p = .005). In 82.4% of studies using newly designed markers, researchers did not screen their chosen markers for sensitivity, and 46.7% of these studies did not report the limit of detection (LoD). Limited knowledge of sensitivity screening and LoD was also found among aquatic species on the list of the world's worst alien invasive species, and many studies used published markers without such knowledge, potentially propagating errors. Main conclusions The rapidly growing use of eDNA‐based detection of low‐abundance species requires well‐designed protocols to improve sensitivity. Knowledge of the limits of eDNA technology is imperative, particularly when applied to conservation biogeography studies for detecting non‐indigenous or endangered species. Our results highlight the currently inadequate sensitivity screening of genetic markers used in most studies, contrasting the transition to highly sensitive PCR methods. Along with ongoing calls for standardization in the eDNA methods, we add that newly designed markers be screened to determine and optimize sensitivity before use to reduce the uncertainty of detection and benefit future applications within or beyond areas of their development.
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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 | 0.049 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| 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".