Whole-genome sequencing confirms multiple species of Galapagos giant tortoises
Bibliographic record
Abstract
Galapagos giant tortoises are endemic to the Galapagos Archipelago, where they are found in isolated populations. While these populations are widely considered distinguishable in morphology, behavior, and genetics, the recent divergence of these taxa has made their status as species controversial. Here, we apply multispecies coalescent methods for species delimitation to whole-genome resequencing data from 38 tortoises across all 13 extant taxa to assess support for delimiting these taxa as species. In contrast to previous studies based solely on divergence time, we find strong evidence to reject the hypothesis that all Galapagos giant tortoises belong to a single species. Instead, a conservative interpretation of model-based and divergence-based results indicates that these taxa form a species complex consisting of a minimum of 9 species, with most analyses supporting 13 species. There is mixed support for the species status of taxa living on the same island, with some methods suggesting multiple populations of a single species per island. These results make clear that Galapagos giant tortoise taxa represent different stages in the process of speciation, with some taxa further along in that evolutionary process than others. Our study provides insight into the complex process of speciation on islands, which is urgently needed given the threatened status of island species around the world.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".