Revisiting the species problem in Northeast Pacific ribbon kelp lineages (genus <i>Alaria</i>): Lessons learned using whole genome data
Bibliographic record
Abstract
The transition from interbreeding populations to species continues to represent difficult terrain for phylogenetic investigations. Genotyping entire genomes holds promise for enhancing insights into the process of speciation and evolutionary relationships among recently speciated taxa. Northeast Pacific ribbon kelp was once recognized as four species before they were folded into Alaria marginata based on DNA barcodes, although several lineages continue to be recognized. We used whole genome sequencing to determine whether these lineages represente species. Whole genomes of 69 individuals from five genetically distinctive lineages in the Gulf of Alaska (United States) and Salish Sea (Canada) were analyzed, along with 63 genomes from three other species of Alaria. Our analysis of >3.4 million single nucleotide polymorphisms reaffirmed that organellar and nuclear phylogenetic signals are incongruent in Alaria, producing different topologies among five organellar and six nuclear A. marginata lineages. Lineages appeared to be reproductively isolated, as evidenced by strong clustering and lack of recent admixture across nuclear genomes. Genetic divergence between A. marginata lineages also exceeded intra-lineage divergence, proxied by A. esculenta populations, but fell short of distances observed across other species of Alaria. Despite the genomic data supporting predictions of the biological and genetic species concepts, we encountered inherent limitations in declaring species status. While our work shifts taxonomic conversations toward a genome-scale framework that provides a more comprehensive picture of divergence and connectivity, our work also highlights that philosophical challenges inherent to defining species persist and that integrative approaches continue to be necessary in the genomic era.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".