Data from: Sun skink landscape genomics: assessing how microevolutionary processes shape genetic and phenotypic diversity across a heterogeneous and fragmented landscape
Barley, Anthony J., University of Kansas
Monnahan, Patrick J., University of Kansas
Thomson, Robert C., University of Kansas
Grismer, L. Lee, La Sierra University
Brown, Rafe M., University of Kansas
Published Mar 12, 2015 on Dryad.
Cite this dataset
Barley, Anthony J. et al. (2015). Data from: Sun skink landscape genomics: assessing how microevolutionary processes shape genetic and phenotypic diversity across a heterogeneous and fragmented landscape [Dataset]. Dryad. https://doi.org/10.5061/dryad.v6726
Incorporating genomic data sets into landscape genetic analyses allows for powerful insights into population genetics, explicitly geographical correlates of selection, and morphological diversification of organisms across the geographical template. Here, we utilize an integrative approach to examine gene flow and detect selection, and we relate these processes to genetic and phenotypic population differentiation across South-East Asia in the common sun skink, Eutropis multifasciata. We quantify the relative effects of geographic and ecological isolation in this system and find elevated genetic differentiation between populations from island archipelagos compared to those on the adjacent South-East Asian continent, which is consistent with expectations concerning landscape fragmentation in island archipelagos. We also identify a pattern of isolation by distance, but find no substantial effect of ecological/environmental variables on genetic differentiation. To assess whether morphological conservatism in skinks may result from stabilizing selection on morphological traits, we perform FST–PST comparisons, but observe that results are highly dependent on the method of comparison. Taken together, this work provides novel insights into the manner by which micro-evolutionary processes may impact macro-evolutionary scale biodiversity patterns across diverse landscapes, and provide genomewide confirmation of classic predictions from biogeographical and landscape ecological theory.
Allele Frequency Data
This file contains allele frequency data for each of the 14 populations used in the fst-pst analyses. The format of the file format for each population is as follows, for each line: locus number, the total number of genes in the population for the locus, the number of alleles for the locus, the number of copies of each allele in the population.
This file contains information on the barcode sequence assigned to each individual in our study.
This is a structure formatted file containing genotypic data for each individual used in the study.
This is an R object file that was used to run the BEDASSLE analyses.
This R script contains the code for the bootstrapping procedure for obtaining confidence intervals for the global pst data as described in the manuscript.
This python script contains the code for calculating global Fst's for each locus as described in the manuscript.
This archive contains the files used to run fastsimcoal2 to obtain the isolation-migration parameter estimates as in the manuscript.
This R script contains the code for calculating bootstrapped confidence intervals for global Fst as described in the manuscript.
This text file contains the distance matrix information used for testing for isolation by distance using the Isolation by distance web server.
This is the habitat suitability output file from Maxent.
This file contains the morphological data used for calculating Psts.
This file contains the geographic coordinates (WGS84) for each population used in the landscape genetic analyses.