Variable performance of DNA barcoding and morphological characteristics for the identification of Arctic black-legged Aedes (Diptera: Culicidae), with a focus on the Punctor subgroup
Bibliographic record
Abstract
Abstract Arctic ecosystems face increasing risks from vector-borne diseases due to climate-driven shifts in disease patterns and vector distribution. However, species identification challenges impact vector-borne disease surveillance, necessitates accurate identification. Aedes species are predominant among Arctic mosquitoes and pose health risks, with some species potentially carrying Jamestown Canyon and Snowshoe hare viruses. However, identifying Aedes species is challenging, especially under Arctic conditions and with complex adult traits. This study assessed the suitability of DNA barcoding (COI and ITS2 regions) and morphological characteristics for the identification of Arctic black-legged Aedes. It also aimed to evaluate the reliability of publicly available sequences. Our analysis focused on Aedes impiger, Aedes nigripes, and two species from the Punctor subgroup – Aedes hexodontus and Aedes punctor. In our study, the COI barcoding region distinguished Ae. impiger and Ae. nigripes but not within the species of the Punctor subgroup. In addition, the ITS2 barcoding region did not differentiate the species. When we evaluated GenBank and BOLD sequences, we found issues of under-representation and misidentifications, particularly within the Punctor subgroup. Based on these results, we recommend addressing identification difficulties, particularly within the Punctor subgroup, and advocate for more comprehensive morphological and molecular identification strategies. Integrating morphology and DNA barcoding holds promise for robust disease surveillance in Arctic regions, yet challenges persist, especially in complex species groups like the Punctor subgroup. Tackling these issues is pivotal to ensuring accurate vector status determination and reliable disease risk assessments in a rapidly changing Arctic ecosystem.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".