Reconciling genomic and ecological species delimitation using a confusing group of butterflies
Bibliographic record
Abstract
Species delimitation is essential to understanding and categorizing our planet’s biodiversity, particularly amidst rapid changes to environmental conditions and natural landscapes. However, the process of speciation is heterogenous and often complex, and robust characterization of species boundaries has remained a challenge for many taxa. Recent advances in both genomics and ecological modelling have been a boon for research focused on population dynamics, and present new, multidisciplinary opportunities for clarifying species boundaries in taxa that have been difficult to classify otherwise. Here, we present an approach to combining ecological niche models with next-generation sequence data to aid in integrated species delimitation. We apply this approach to the Speyeria atlantis-hesperis (Lepidoptera: Nymphalidae) species complex, which is notorious for its muddled species delimitations, morphological variation and mito-nuclear discordance. Using genomic SNPs, we recovered substantial divergence, not only between S. hesperis and S. atlantis, but also within S. hesperis, which may be attributed to a combination of past introgression with another species, S. zerene, and post-glacial range expansion. We then applied niche modelling to assess ecological divergence and barriers to gene flow among the recovered genomic lineages. Results of these analyses suggest that adaptation to ecological conditions is hindering contemporary gene flow between northern and southern populations of S. hesperis, contributing to and reinforcing their genetic integrity. We suggest that the current species delimitation of S. hesperis should be revised, and demonstrate the utility of an approach to integrated species delimitation that combines ecological and genomic data and reconciles related species concepts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".