Demographic history and patterns of molecular evolution from whole genome sequencing in the radiation of Galapagos giant tortoises
Bibliographic record
Abstract
Whole genome sequencing provides deep insights into the evolutionary history of a species, including patterns of diversity, signals of selection, and historical demography. When applied to closely related taxa with a wealth of background knowledge, population genomics provides a comparative context for interpreting population genetic summary statistics and comparing empirical results with the expectations of population genetic theory. The Galapagos giant tortoises (Chelonoidis spp.), an iconic rapid and recent radiation, offer such an opportunity. Here, we sequenced whole genomes from three individuals of the 12 extant lineages of Galapagos giant tortoise and estimate diversity measures and reconstruct changes in coalescent rate over time. We also compare the number of derived alleles in each lineage to infer how synonymous and nonsynonymous mutation accumulation rates correlate with population size and life history traits. Remarkably, we find that patterns of molecular evolution are similar within individuals of the same lineage, but can differ significantly among lineages, reinforcing the evolutionary distinctiveness of the Galapagos giant tortoise species. Notably, differences in mutation accumulation among lineages do not align with simple population genetic predictions, suggesting that the drivers of purifying selection are more complex than is currently appreciated. By integrating results from earlier population genetic and phylogeographic studies with new findings from the analysis of whole genomes, we provide the most in-depth insights to date on the evolution of Galapagos giant tortoises, and identify discrepancies between expectation from population genetic theory and empirical data that warrant further scrutiny.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".