Species delimitation using genomic data to resolve taxonomic uncertainties in a speciation continuum of pelagic seabirds
Bibliographic record
Abstract
Speciation is a continuous and complex process shaped by the interaction of numerous evolutionary forces. Despite the continuous nature of the speciation process, the implementation of conservation policies relies on the delimitation of species and evolutionary significant units (ESUs). Puffinus shearwaters are globally distributed and threatened pelagic seabirds. Due to remarkable morphological status the group has been under intense taxonomic debate for the past three decades. Here, we use double digest Restriction-Site Associated DNA sequencing (ddRAD-Seq) to genotype species and subspecies of North Atlantic and Mediterranean Puffinus shearwaters across their entire geographical range. We assess the phylogenetic relationships and population structure among and within the group, evaluate species boundaries, and characterise the genomic landscape of divergence. We find that current taxonomies are not supported by genomic data and propose a more accurate taxonomy by integrating genomic information with other sources of evidence. Our results show that several taxon pairs are at different stages of a speciation continuum. Our study emphasises the potential of genomic data to resolve taxonomic uncertainties, which can help to focus management actions on relevant taxa, even if they do not necessarily coincide with the taxonomic rank of species.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".