Whole‐genome sequencing reveals forgotten lineages and recurrent hybridizations within the kelp genus <i>Alaria</i> (Phaeophyceae)
Bibliographic record
Abstract
The genomic era continues to revolutionize our understanding of the evolution of biodiversity. In phycology, emphasis remains on assembling nuclear and organellar genomes, leaving the full potential of genomic datasets to answer long-standing questions about the evolution of biodiversity largely unexplored. Here, we used whole-genome sequencing (WGS) datasets to survey species diversity in the kelp genus Alaria, compare phylogenetic signals across organellar and nuclear genomes, and specifically test whether phylogenies behave like trees or networks. Genomes were sequenced from across the global distribution of Alaria (including Alaria crassifolia, A. praelonga, A. crispa, A. marginata, and A. esculenta), representing over 550 GB of data and over 2.2 billion paired reads. Genomic datasets retrieved 3,814 and 4,536 single-nucleotide polymorphisms (SNPs) for mitochondrial and chloroplast genomes, respectively, and upwards of 148,542 high-quality nuclear SNPs. WGS revealed an Arctic lineage of Alaria, which we hypothesize represents the synonymized taxon A. grandifolia. The SNP datasets also revealed inconsistent topologies across genomic compartments, and hybridization (i.e., phylogenetic networks) between Pacific A. praelonga, A. crispa, and putative A. grandifolia, and between some lineages of the A. marginata complex. Our analysis demonstrates the potential for WGS data to advance our understanding of evolution and biodiversity beyond amplicon sequencing, and that hybridization is potentially an important mechanism contributing to novel lineages within Alaria. We also emphasize the importance of surveying phylogenetic signals across organellar and nuclear genomes, such that models of mixed ancestry become integrated into our evolutionary and taxonomic understanding.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".