New sea urchin phylogeography reveals latitudinal shifts associated with speciation
Bibliographic record
Abstract
Where do new species arise? When do they form and how do they diverge from a common ancestor? A new comprehensive study of Arbacia sea urchins provides surprising answers to these questions. By combining mtDNA phylogeographic markers with a nuclear locus(encoding the sperm acrosomal protein bindin) known to be susceptible to high rates of adaptive codon evolution, Lessios et al. (2012) show that new species and lineages arose relatively recently, most often in association with latitudinal shifts between the temperate zones and the tropics, and in one case, in association with a significant geological barrier to gene flow (the rise of the Isthmus of Panama). In addition to the 'where' and 'when' of Arbacia speciation, these new data resolve an important question about 'who' Arbacia species are by revealing extensive allele sharing at both loci between a pair of broadly sympatric nominal species (that should perhaps be considered a single taxon). 'How' Arbacia diverge from each other is less easily resolved: there is no evidence for reinforcement (via selection on bindin) as an important source of divergence between nominal species, and there are few other data to decide among the alternative hypotheses to explain Arbacia speciation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".