Targeted Next Generation Sequencing improves detection and quantification of rare species from eDNA
Bibliographic record
Abstract
Targeted species detection from eDNA is central to identifying and quantifying rare (i.e. invasive or endangered) species to inform conservation and resource management. Here we introduce a new targeted Next Generation Sequencing (tNGS) assay that shows improved detection relative to quantitative (q)PCR at low eDNA concentrations and increased precision to detect spatial variation in eDNA concentration related to species abundance. We compare the tNGS and qPCR methods using invasive European green crab ( Carcinus maenas ) in the northeast Pacific Ocean as a test case, and find that crab abundance measured by traditional trapping is significantly correlated with eDNA concentration across multiple sites for both methods. However, the tNGS assay outperformed qPCR in all tests: (1) increased precision of eDNA concentration estimation; (2) a 7-10% increase in detection probability at low abundance sites; and (3) greater power to detect spatial variation in eDNA concentration. The accuracy of predicting green crab abundance from eDNA concentration increased with the number of field replicates sampled and did not change appreciably over a tidal cycle. Green crab eDNA concentration behaving similarly to abundance measured from trapping demonstrates great promise for this tool to be implemented for early detection and routine monitoring surveys. The tNGS assay is easily accessible for surveying other species with existing qPCR assays and can thus be potentially important for detection and quantification of any species of high interest to management.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".