Progressive colonization and restricted gene flow shape island-dependent population structure in Galápagos marine iguanas (Amblyrhynchus cristatus)
Bibliographic record
Abstract
BACKGROUND: Marine iguanas (Amblyrhynchus cristatus) inhabit the coastlines of large and small islands throughout the Galápagos archipelago, providing a rich system to study the spatial and temporal factors influencing the phylogeographic distribution and population structure of a species. Here, we analyze the microevolution of marine iguanas using the complete mitochondrial control region (CR) as well as 13 microsatellite loci representing more than 1200 individuals from 13 islands. RESULTS: CR data show that marine iguanas occupy three general clades: one that is widely distributed across the northern archipelago, and likely spread from east to west by way of the South Equatorial current, a second that is found mostly on the older eastern and central islands, and a third that is limited to the younger northern and western islands. Generally, the CR haplotype distribution pattern supports the colonization of the archipelago from the older, eastern islands to the younger, western islands. However, there are also signatures of recurrent, historical gene flow between islands after population establishment. Bayesian cluster analysis of microsatellite genotypes indicates the existence of twenty distinct genetic clusters generally following a one-cluster-per-island pattern. However, two well-differentiated clusters were found on the easternmost island of San Cristóbal, while nine distinct and highly intermixed clusters were found on youngest, westernmost islands of Isabela and Fernandina. High mtDNA and microsatellite genetic diversity were observed for populations on Isabela and Fernandina that may be the result of a recent population expansion and founder events from multiple sources. CONCLUSIONS: While a past genetic study based on pure FST analysis suggested that marine iguana populations display high levels of nuclear (but not mitochondrial) gene flow due to male-biased dispersal, the results of our sex-biased dispersal tests and the finding of strong genetic differentiation between islands do not support this view. Therefore, our study is a nice example of how recently developed analytical tools such as Bayesian clustering analysis and DNA sequence-based demographic analyses can overcome potential biases introduced by simply relying on FST estimates from markers with different inheritance patterns.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".