Resolving evolutionary relationships in closely related nonmodel organisms: a case study using chromosomally distinct members of a black fly species complex
Bibliographic record
Abstract
Abstract Coalescent‐based analyses have been recommended for species delimitation and tree reconstruction. Yet, despite recent advances in molecular methods, acquiring sufficient genetic data often precludes coalescent‐based analyses in nonmodel organisms. We show that these methods are accessible to questions in nonmodel systems. Specifically, we use coalescent‐based analyses to investigate evolutionary independence and relationships among chromosomally distinct members (sibling species) of a black fly (Diptera: Simuliidae) species complex. Our dataset consists of nuclear DNA (nuDNA) and mitochondrial DNA (mtDNA) markers – the former developed using the black fly transcriptome and available genomes from model relatives. We show that individual gene trees exhibit considerable discordance among one another and reveal little about potential species limits. Consistent species tree topologies are obtained from analyses of nuDNA, both with and without the inclusion of mtDNA data. Nodes on the tree receive robust support with the addition of mtDNA. Coalescent‐based species validation approaches recognize all sibling species. However, unlike species tree analyses, the addition of mtDNA data does not improve or alter the results. Thus, analyses of nuDNA alone can validate sibling species. Contrary to these results, population genetic patterns of neutral diversity, within‐locus recombination, and linkage disequilibrium suggest that sibling species represent a single species with high levels of nucleotide polymorphism, large effective population sizes, and extensive gene flow. Demographic patterns inferred from neutrality tests integrate seemingly disparate results by showing signatures of gene flow among structured populations that are expanding in size. Thus, admixture associated with geographic range expansions may be assimilating sibling species into a single genetic lineage.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".