Species delimitation under allopatry: genomic insights within and across continents in Lepidoptera
Bibliographic record
Abstract
Abstract Delimitation of allopatric populations into species remains subjective and largely arbitrary. Many cold-adapted species from the sub-Arctic and Central and Southern European Mountain systems provide excellent models to study allopatry problem due to their patchy distributions. The same concerns many Holarctic species, which frequently show varying degrees of differentiation between continents. In this study, we analyze high-throughput target enrichment data for 10 groups of Arctic-alpine and Holarctic lepidopteran species sampled from different regions across the Holarctic realm, i.e., Fennoscandia, European Alps, Altai Mountains, and North America. We first aimed to assess whether the genetic differences in the nuclear genome reflected observed DNA barcode divergences and, secondly, whether the gap between population and species-level differences can be reliably dissected using genomic data. We compared the phylogenetic trees and uncorrected pairwise genetic distances obtained from target enrichment and mitochondrial COI barcodes and performed a suite of population genetic and species delimitation analyses to further explore patterns of intraspecific variation in our study species. We observed that in about one-half of the cases, DNA barcodes showed phylogenetic relationships similar to the target enrichment markers. Nuclear genetic differentiation varied among the populations analyzed, from low differentiation of geographically separated populations to the deeper separation of some Nearctic populations and Arctic-alpine disjunction in the populations from Fennoscandia and Southern European mountains. Our results highlight the need for consistent delimitation of allopatric populations, especially given the prevalence of distributional discontinuities across species. Large sets of standard genetic markers provide a very promising avenue towards this goal.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".