Phylogenomics and Pervasive Genome-Wide Phylogenetic Discordance Among Fin Whales (<i>Balaenoptera physalus</i>)
Bibliographic record
Abstract
Phylogenomics has the power to uncover complex phylogenetic scenarios across the genome. In most cases, no single topology is reflected across the entire genome as the phylogenetic signal differs among genomic regions due to processes, such as introgression and incomplete lineage sorting. Baleen whales are among the largest vertebrates on Earth with a high dispersal potential in a relatively unrestricted habitat, the oceans. The fin whale (Balaenoptera physalus) is one of the most enigmatic baleen whale species, currently divided into four subspecies. It has been a matter of debate whether phylogeographic patterns explain taxonomic variation in fin whales. Here we present a chromosome-level whole genome analysis of the phylogenetic relationships among fin whales from multiple ocean basins. First, we estimated concatenated and consensus phylogenies for both the mitochondrial and nuclear genomes. The consensus phylogenies based upon the autosomal genome uncovered monophyletic clades associated with each ocean basin, aligning with the current understanding of subspecies division. Nevertheless, discordances were detected in the phylogenies based on the Y chromosome, mitochondrial genome, autosomal genome and X chromosome. Furthermore, we detected signs of introgression and pervasive phylogenetic discordance across the autosomal genome. This complex phylogenetic scenario could be explained by a puzzle of introgressive events, not yet documented in fin whales. Similarly, incomplete lineage sorting and low phylogenetic signal could lead to such phylogenetic discordances. Our study reinforces the pitfalls of relying on concatenated or single locus phylogenies to determine taxonomic relationships below the species level by illustrating the underlying nuances that some phylogenetic approaches may fail to capture. We emphasize the significance of accurate taxonomic delineation in fin whales by exploring crucial information revealed through genome-wide assessments.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".