Mosquito Identification From Bulk Samples Using DNA Metabarcoding: a Protocol to Support Mosquito-Borne Disease Surveillance in Canada
Bibliographic record
Abstract
Approximately 80 species of mosquitoes (Diptera: Culicidae) have been documented in Canada. Exotic species such as Aedes albopictus (Skuse) (Diptera: Culicidae) are becoming established. Recently occurring endemic mosquito-borne diseases (MBD) in Canada including West-Nile virus (WNV) and Eastern Equine Encephalitis (EEE) are having significant public health impacts. Here we explore the use of DNA metabarcoding to identify mosquitoes from CDC light-trap collections from two locations in eastern Canada. Two primer pairs (BF2-BR2 and F230) were used to amplify regions of the cytochrome c oxidase subunit I (CO1) gene. High throughput sequencing was conducted using an Illumina MiSeq platform and GenBank-based species identification was applied using a QIIME 1.9 bioinformatics pipeline. From a site in southeastern Ontario, Canada, 26 CDC light trap collections of 72 to >300 individual mosquitoes were used to explore the capacity of DNA metabarcoding to identify and quantify captured mosquitoes. The DNA metabarcoding method identified 33 species overall while 24 species were identified by key. Using replicates from each trap, the dried biomass needed to identify the majority of species was determined to be 76 mg (equivalent to approximately 72 mosquitoes), and at least two replicates from the dried biomass would be needed to reliably detect the majority of species in collections of 144-215 mosquitoes and three replicates would be advised for collections with >215 mosquitoes. This study supports the use of DNA metabarcoding as a mosquito surveillance tool in Canada which can help identify the emergence of new mosquito-borne disease potential threats.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".