Jamie Adkins Stoll | Geology Reports

USGS2026

Population structure and genetic stock identification in southeastern United States loggerhead sea turtles (Caretta caretta) using genome-wide SNPs

Characterizing the genetic structure and connectivity between populations of endangered species can be used to inform management actions. In vagile species with high gene flow or recently established populations, such characterizations can be difficult to undertake using traditional genetic markers, and genetic stock identification (GSI) may be confounded by allele-sharing between populations. Loggerhead sea turtles ( Caretta caretta ) in the southeastern United States comprise seven management units (MUs) based on female philopatry inferred via mitochondrial DNA sequences, yet nuclear microsatellite data do not reflect divergence. Further, loci for accurate GSI are not currently known. To address this, we generated genome-wide single nucleotide polymorphism (SNP) data from 146 females nesting at individual sites representative of each southeastern United States MU. We found weak (F ST =0.001–0.003) but significant divergence among all MUs, with more notable divergence between the Gulf Coast and Atlantic Ocean MUs, and amongst the Atlantic Ocean MUs. We then used an iterative leave-one-out approach to identify candidate loci for GSI. This approach identified loci that could assign individuals to natal ocean basins (i.e., to the Gulf Coast or to the Atlantic Ocean), and to individual MUs within the Atlantic Ocean, with high (≥90%) success and accuracy. Analyses of genome-wide SNPs refined our understanding of the magnitude and scale of population connectivity in loggerhead turtles in the southeastern United States, and provided a foundation for the development of SNP panels for accurate, fine-scale GSI in sea turtles.

Alabama, Florida, Georgia↗

USGS2021

Species and population specific gene expression in blood transcriptomes of marine turtles

Background Transcriptomic data has demonstrated utility to advance the study of physiological diversity and organisms’ responses to environmental stressors. However, a lack of genomic resources and challenges associated with collecting high-quality RNA can limit its application for many wild populations. Minimally invasive blood sampling combined with de novo transcriptomic approaches has great potential to alleviate these barriers. Here, we advance these goals for marine turtles by generating high quality de novo blood transcriptome assemblies to characterize functional diversity and compare global transcriptional profiles between tissues, species, and foraging aggregations. Results We generated high quality blood transcriptome assemblies for hawksbill ( Eretmochelys imbricata ) , loggerhead ( Caretta caretta ), green ( Chelonia mydas ), and leatherback ( Dermochelys coriacea ) turtles. The functional diversity in assembled blood transcriptomes was comparable to those from more traditionally sampled tissues. A total of 31.3% of orthogroups identified were present in all four species, representing a core set of conserved genes expressed in blood and shared across marine turtle species. We observed strong species-specific expression of these genes, as well as distinct transcriptomic profiles between green turtle foraging aggregations that inhabit areas of greater or lesser anthropogenic disturbance. Conclusions Obtaining global gene expression data through non-lethal, minimally invasive sampling can greatly expand the applications of RNA-sequencing in protected long-lived species such as marine turtles. The distinct differences in gene expression signatures between species and foraging aggregations provide insight into the functional genomics underlying the diversity in this ancient vertebrate lineage. The transcriptomic resources generated here can be used in further studies examining the evolutionary ecology and anthropogenic impacts on marine turtles.

BMC Genomics↗