What we can find in what’s left behind: DNA metabarcoding of amphibiotic insect exuviae
Bibliographic record
Abstract
Abstract The phenomenon of insect emergence represents a transfer of mass and energy from aquatic to terrestrial systems and is a critical part of ecosystem connectivity and functioning. Traditional methods of studying insect emergence rely on the capture of insects as they emerge and on morphological identification with taxonomic keys. This can be both time consuming and impact study populations, obstacles that can be removed with DNA obtained from biological remnants. The present proof-of-concept study investigated the potential of using exuviae collected from the water surface as a DNA source. Emergence trap samples and insect exuviae were collected from a pond and a small creek. Sample types were generally not statistically distinguishable, but the exuviae samples identified more orders containing amphibiotic insects and a higher level of diversity within these orders than the trap samples did. This higher level of diversity seen in exuviae samples may be due to limitations of emergence traps, including that they alter environmental variables in their collection area. We demonstrated that identification of emerging aquatic insects through metabarcoding of exuviae is a useful method for the study of insect emergence and could be used for biodiversity assessments and studies on emergence times and to better understand ecosystem connectivity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".