What we can find in what’s left behind: DNA metabarcoding of semi-aquatic insect exuviae
Bibliographic record
Abstract
Abstract Semi-aquatic insects contribute to critical ecological functioning of freshwater habitats in both the aquatic and terrestrial phase of their life. Traditional methods of studying their emergence rely on the capture of insects as they emerge, and morphological identification with taxonomic keys. This is not only time consuming but can have large impacts on the study population, obstacles that can be removed by the use of DNA. This study investigated the potential of using exuviae collected from the water surface as DNA source. Both emergence trap samples and insect exuviae were collected from a constructed wetland and a small creek in southern Ontario. Metabarcoding provided a total of 40 samples with 254 distinct taxa from emergence traps (26 samples), and 135 from exuviae (14 samples). There were many similarities between both two sample types, especially for the rich chironomid diversity. Nonetheless, the exuviae samples were able to identify more orders containing semi-aquatic insects. Furthermore, they showed a higher level of diversity within these orders. This higher level of diversity seen in exuviae samples may be due to limitations of emergence traps, such as they only account for a small defined surface area. In contrast, exuviae are representing a much larger area and are free floating, thus collection of emerging taxa is not limited to the emerging site. We were able to show that identification of emerging aquatic insects through metabarcoding of exuviae is a useful method for the study of insect emergence.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".