Data from: Conservation genomics of the threatened Trispot Darter (Etheostoma trisella)
Data files
Sep 26, 2023 version files 18.89 MB
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Etheostoma_trisella_SNP_report_2023.csv
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README.md
Abstract
The southeastern United States is a hotspot of biodiversity, but aquatic habitats are fragmented by anthropogenic activities such as hydrologic alteration. Small-stream-inhabiting fish can suffer population declines, loss of genetic diversity, and migration impediment from riverine impoundment. The Trispot Darter (Etheostoma trisella) is a small, freshwater fish endemic to the southeastern United States. E. trisella was previously believed to be extinct and has now been listed as threatened under the U.S. Endangered Species Act, due to destruction of its limited habitat and a restricted range. We used mitochondrial DNA (NADH dehydrogenase subunit 2 gene), seven microsatellite loci, and 9732 single nucleotide polymorphisms (SNPs) to evaluate population structure and diversity in E. trisella. Mitochondrial data provided evidence of historical connectivity between populations, with haplotype sharing across populations and weak support for population structure. Microsatellite and SNP data, however, indicate that populations have more recently become isolated. Furthermore, we detect three distinct management units (i.e., genetic groups) which reflect isolated geographic localities (i.e., Little Canoe Creek, Ballplay Creek, and a system including the Conasauga and Coosawattee rivers). We also detected a recent bottleneck event in the Ballplay Creek population as well as a low effective population size. Tests of isolation by distance further suggest that populations are structured by riverine isolation rather than geographic distance. A better understanding about the distribution, abundance, and habitat occupancy of all E. trisella populations will be important for informing future decisions for conservation of the species.
README: Etheostoma_trisella_population_genomics_2023
https://doi.org/10.5061/dryad.jq2bvq8g6
This repository contains the data used to perform population genomic and phylogenetic analyses for Etheostoma trisella collected in the United States. The data are the result of DArTSeq: a combination of DArT complexity reduction with sequencing on the Next Generation Sequencing platforms. It represents combined marker discovery and genotyping for SNPs. Approximately 50,000 DNA fragments from DArT representations were assayed for polymorphism. We detect three distinct management units (i.e., genetic groups) which reflect isolated geographic localities (i.e., Little Canoe Creek, Ballplay Creek, and a system including the Conasauga and Coosawattee rivers). We also detected a recent bottleneck event in the Ballplay Creek population as well as a low effective population size. Tests of isolation by distance further suggest that populations are structured by riverine isolation rather than geographic distance.
Description of the data and file structure
The SNP report file in .csv format includes data for 93 Etheostoma trisella specimens. The file includes columns for AlleleID, CloneID, AlleleSequence, TrimmedSequence, SNP, SnpPosition, CallRate, OneRatioRef, OneRatioSnp, FreqHomRe, FreqHomSnp, FreqHets, PICRef, PICSnp, AvgPIC, AvgCountRef, AvgCountSnp, RepAvg. Binary states for loci are also indicated for each specimen. Specimens are named with a unique number identifier followed by collection locality.
Sharing/Access information
Not applicable.
Code/Software
Not applicable.
Methods
Genomic DNA was isolated from tissue using the DNeasy Blood and Tissue Kit following the manufacturer’s instructions (QIAGEN). The quality of DNA was verified by electrophoresis using a 1.5% agarose gel stained with ethidium bromide. DNA quantity was determined using a NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific). Purified DNA was stored at -4 °C until further analysis. Genotyping by DArTseq™ analysis was performed at Diversity Arrays Technology Pty, Ltd. (DArT; Canberra, ACT, Australia) utilizing DArT complexity reduction methods and next generation sequencing (Kilian et al. 2012).