Unravelling the peculiarities of island life: vicariance, dispersal and the diversification of the extinct and extant giant Galápagos tortoises
Bibliographic record
Abstract
In isolated oceanic islands, colonization patterns are often interpreted as resulting from dispersal rather than vicariant events. Such inferences may not be appropriate when island associations change over time and new islands do not form in a simple linear trend. Further complexity in the phylogeography of ocean islands arises when dealing with endangered taxa as extinctions, uncertainty on the number of evolutionary 'units', and human activities can obscure the progression of colonization events. Here, we address these issues through a reconstruction of the evolutionary history of giant Galápagos tortoises, integrating DNA data from extinct and extant species with information on recent human activities and newly available geological data. Our results show that only three of the five extinct or nearly extinct species should be considered independent evolutionary units. Dispersal from mainland South America started at approximately 3.2 Ma after the emergence of the two oldest islands of San Cristobal and Española. Dispersal from older to younger islands began approximately 1.74 Ma and was followed by multiple colonizations from different sources within the archipelago. Vicariant events, spurred by island formation, coalescence, and separation, contributed to lineage diversifications on Pinzón and Floreana dating from 1.26 and 0.85 Ma. This work provides an example of how to reconstruct the history of endangered taxa in spite of extinctions and human-mediated dispersal events and highlights the need to take into account both vicariance and dispersal when dealing with organisms from islands whose associations are not simply explained by a linear emergence model.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".