Evolutionary diversification in the marine realm: a global case study with marine mammals
Bibliographic record
Abstract
Speciation is thought to be predominantly driven by the geographical separation of populations of the ancestral species. Yet, in the marine realm, there is substantial biological diversity despite a lack of pronounced geographical barriers. Here, we investigate this paradox by considering the biogeography of marine mammals: cetaceans (whales and dolphins) and pinnipeds (seals and sea lions). We test for associations between past evolutionary diversification and current geographical distributions, after accounting for the potential effects of current environmental conditions. In general, cetacean lineages are widely dispersed and show few signs of geographically driven speciation, albeit with some notable exceptions. Pinnipeds, by contrast, show a more mixed pattern, with true seals (phocids) tending to be dispersed, whereas eared seals (otariids) are more geographically clustered. Both cetaceans and pinnipeds show strong evidence for environmental clustering of their phylogenetic lineages in relation to factors such as sea temperature, the extent of sea ice, and nitrate concentrations. Overall, current marine mammal biogeography is not indicative of geographical speciation mechanisms, with environmental factors being more important determinants of current species distributions. However, geographical isolation appears to have played a role in some important taxa, with evidence from the fossil record showing good support for these cases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".