DNA barcode data reveal biogeographic trends in Arctic non-biting midges
Bibliographic record
Abstract
Chironomid flies (non-biting midges) are among the most abundant and diverse animals in Arctic regions, but detailed analyses of species distributions and biogeographical patterns are hampered by challenging taxonomy and reliance on morphology for species-level identification. Here we take advantage of available DNA barcode data of Arctic Chironomidae in BOLD to analyse similarities in species distributions across a northern Nearctic - West Palearctic gradient. Using more than 260 000 barcodes representing 4666 BINs (Barcode Index Numbers) and 826 named species (some with interim names) from a combination of public and novel data, we show that the Greenland chironomid fauna shows affinities to both the Nearctic and the West Palearctic regions. While raw taxon counts indicate a strong Greenland - North American affinity, comparisons using Chao's dissimilarity metric support a slightly higher similarity between Greenland and West Palearctic chironomid communities. Results were relatively consistent across different definitions of species taxonomic units, including morphologically determined species, BINs, and superBINs based on a ∼4.5% threshold. While most taxa found in Greenland are shared with at least one other region, reflecting circum-Arctic dispersal, our results also reveal that Greenland harbours a small endemic biodiversity. Our exploratory study showcases how DNA barcoding efforts using standardized gene regions contribute to an understanding of broad-scale patterns in biogeography by enabling joint analysis of public DNA sequence data derived from diverse prior studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".