The use of eDNA and DNA based methods to assess and monitor alien and doorknocker species
Bibliographic record
Abstract
All organisms leave traces of their DNA in the environment they live in. This environmental DNA (eDNA) can be used to detect and monitor single species as well as communities, and potentially be utilized in early warning systems to detect alien species. In this report, we give an overview of eDNA-based methods used to detect and monitor alien species, and give examples of speciesspecific assays designed to identify species that are alien to Norwegian nature or have the potential to establish viable populations in Norway (doorknockers). We emphasize the need for several stages of testing before species-specific assays can be operational, and discuss the importance of including models to assess detection probabilities. Going through standards for sampling and analyses, we suggest a number of minimum requirements for eDNA sampling, laboratory practice, bioinformatics and the use of reference libraries, as well as for reporting results from eDNA studies. The origin and fate of eDNA in different environments can influence its usefulness in detecting and monitoring alien species. We outline factors for terrestrial, freshwater and marine ecosystems and provide examples from six case studies where eDNA has been used to detect and/or monitor alien invasive species: invertebrates and vascular plants in soil of imported ornamental plants, freshwater crayfish and crayfish plague, Gyrodactylus salaris, pink salmon, Canadian pondweed, and American lobster. We provide a decision diagram for detection and monitoring of invasive species starting with early considerations for implementation of a monitoring program, and ending with management decision points depending on detection outcomes at different stages. Finally, we provide some key recommendations for the use of eDNA in assessments of alien and doorknocker species.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".