A new approach to molecular biosurveillance of invasive species using DNA metabarcoding
Bibliographic record
Abstract
Non-indigenous species (NIS) reach every corner of the world, at times wreaking havoc on ecosystems and costing the global economy billions of dollars. A rapid and accurate biosurveillance tool tailored to a particular biogeographic region is needed to detect NIS when they are first introduced into an area as traditional detection methods are expensive and require specialized expertise. Metabarcoding of environmental and community DNA meets those biosurveillance requirements; a novel tool tailored to the Northwest Pacific Ocean is presented here using an approach that could revolutionize early detection of NIS. Eight newly designed genetic markers for multiple gene regions were implemented to meet the stringent taxonomic requirements for the detection of NIS across four major marine phyla. The tool was considered highly successful because it identified 12 known NIS in the study area and a further seven species representing potential new records. Overall community composition detected here was statistically different between substrate types; zooplankton sampling accounted for significantly higher species richness than filtered sea water in most cases, but this was dominated by mollusk and arthropod species. Both substrate types sampled were required to identify the wide taxonomic breadth of known NIS in the study area. Intensive sampling is known to be paramount for the detection of rare species, including new incursions of NIS, thus it is recommended to include diverse DNA sampling protocols based on species' life-history characteristics for broad detection capacity. Application of a metabarcoding-based molecular biosurveillance tool optimized for biogeographic regions enables rapid and accurate early detection across a wide taxonomic range to allow quick implementation of eradication or control efforts and potentially mitigate some of the devastating effects of NIS worldwide.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 teacher head, 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".