Genetic structure of loggerhead turtle (Caretta caretta) and green turtle (Chelonia mydas) populations nesting in the northwest Atlantic inferred from mitochondrial DNA
Shamblin, Brian Michael
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Mitochondrial DNA polymorphisms have been widely utilized to assess demographic connectivity of marine turtle rookeries, including those of the southeastern United States of America (USA). This region hosts the largest nesting aggregation of loggerhead turtles in the Atlantic and one of two globally significant nesting assemblages for the species. Determining the stock structure of the nesting aggregation is important for defining demographically independent nesting populations (management units) and providing appropriate baseline data for mixed stock analyses of foraging aggregations and stranded turtles. Previous studies based on a 390 base pair fragment of the mitochondrial control region partitioned the southeastern USA loggerhead turtle nesting aggregation into four management units. I reassessed the population structure of the nesting aggregation using 834 novel samples collected from North Carolina through the Florida panhandle as well as published haplotype frequency data. Pairwise FST comparisons, exact tests of population differentiation, and analysis of molecular variance (AMOVA) supported the presence of six management units corresponding to beaches from 1) North Carolina through northeastern Florida, 2) central eastern Florida, 3) southern Florida (southeastern and southwestern), 4) Dry Tortugas, Florida, 5) central western Florida (Sarasota County), and 6) northwest Florida. Despite the increased resolution gained from expanded sampling and larger sample sizes, the relationship of southernmost rookeries on the Gulf and Atlantic coasts of Florida remained unresolved. To address this question and assess the utility of an expanded control region fragment (817 bp) to refine stock structure, I analyzed 2,260 samples representing twelve rookeries of the southeastern USA aggregation as well as the Cay Sal, Bahamas rookery. This analysis supported the six management units suggested by the earlier study and additionally differentiated southeastern and southwestern Florida as distinct management units. The Cay Sal and Dry Tortugas rookeries were not genetically differentiated and were grouped as a single management unit, although it is probable that these distant rookeries are demographically isolated. As previously demonstrated by a published study, use of the larger control region fragment significantly increased population structure detected between western and eastern Atlantic loggerhead turtle rookeries. However, expanded sequences did not significantly improve resolution of structure among rookeries comprising the southeastern USA nesting aggregation in most comparisons, which were dominated by two common control region haplotypes. The single exception was southeastern Florida rookeries compared to all others because of the high percentage of CC-A1.3 relative to the common CC-A1.1. Given the ability of marine turtles to colonize sites far from their natal regions and the slow evolutionary rate of the mitochondrial genome relative to many other vertebrates, haplotype sharing is a common phenomenon among marine turtle rookeries regionally and in some cases across ocean basins. This haplotype sharing can confound detection of demographic independence of rookeries as well as introduce uncertainty into rookery contribution estimates to mixed foraging aggregations. I explored the utility of mitogenomic sequencing to differentiate green turtle lineages nesting at southern Greater Caribbean rookeries and carrying 490 bp control region haplotype CM-A5. Mitogenomic sequencing revealed four variants of CM-A5 and suggested demographic independence of eastern Caribbean rookeries that were not differentiated based on the 490 bp haplotypes: Buck Island (St. Croix), United States Virgin Islands; Aves Island, Venezuela; and Galibi, Suriname. Mitogenomic sequencing may resolve several cases of haplotype overlap among marine turtle rookeries and thus improve the resolution of stock structure and mixed stock analyses.