DOI: 10.5061/dryad.td28r
title: Data from: Seascape continuity plays an important role in determining patterns of spatial genetic structure in a coral reef fish

description: Detecting patterns of spatial genetic structure (SGS) can help identify
 intrinsic and extrinsic barriers to gene flow within metapopulations. For
 marine organisms such as coral reef fishes, identifying these barriers is
 critical to predicting evolutionary dynamics and demarcating
 evolutionarily significant units for conservation. In this study, we
 adopted an alternative hypothesis-testing framework to identify the
 patterns and predictors of SGS in the Caribbean reef fish Elacatinus lori.
 First, genetic structure was estimated using nuclear microsatellites and
 mitochondrial cytochrome b sequences. Next, clustering and network
 analyses were applied to visualize patterns of SGS. Finally, logistic
 regressions and linear mixed models were used to identify the predictors
 of SGS. Both sets of markers revealed low global structure: mitochondrial
 ΦST = 0.12, microsatellite FST = 0.0056. However, there was high
 variability among pairwise estimates, ranging from no differentiation
 between sites on contiguous reef (ΦST = 0) to strong differentiation
 between sites separated by ocean expanses ≥ 20 km (maximum ΦST = 0.65).
 Genetic clustering and statistical analyses provided additional support
 for the hypothesis that seascape discontinuity, represented by oceanic
 breaks between patches of reef habitat, is a key predictor of SGS in E.
 lori. Notably, the estimated patterns and predictors of SGS were
 consistent between both sets of markers. Combined with previous studies of
 dispersal in E. lori, these results suggest that the interaction between
 seascape continuity and the dispersal kernel plays an important role in
 determining genetic connectivity within metapopulations.
description: Sampling
 LocationsThis Excel sheet
 provides the coordinates (latitude and longitude) for all sampling
 locations, as well as the number of individuals that were successfully
 genotyped (microsatellitess) and sequenced (mtDNA cytb) at each
 site.sampling_locations.xlsxHaplotype FrequenciesThis spreadsheet contains mtDNA haplotype frequencies across all ten sampling locations.mtdna_haplotype frequencies.xlsxMicrosatellite GenotypesThis spreadsheet contains all individual genotypes for the 14 microsatellite loci used in the study. Individuals are grouped by sampling location.microsatellite_genotypes.xlsxAligned mtDNA sequencesThis fasta file includes all aligned sequences for the mtDNA cytb haplotypes.haplotypes_fasta.fasR CodeComplete R code for all statistical analyses including Firth penalized likelihood logistic regressions, linear mixed models, and mantel tests.Finalized_RCODE.RLogistic Regression Input FileInput file required to run R code. Contains all data needed for Firth logits.input_mixedmodel_logit.csvLinear Mixed Models (mtDNA) Input FileInput file to run linear mixed models in R code for mtDNA-based metrics of pairwise structure.input_nonzeros.csvLinear Mixed Models (micros) Input FileInput file needed for R code to run linear mixed models for microsatellite-based metrics of pairwise structure.input_nonzeros_micros.csvGeographic Distance MatrixGeographic distance matrix (km) used for mantel test in R code.distance_matrix.txtPhiSt Matrix (mtDNA)Pairwise PhiST (mtdna) matrix used for mantel tests in R code.PhiST_matrix.txtFST Matrix (mtDNA)Pairwise FST matrix (mtDNA) used for mantel tests in R code.FST_mtdna_matrix.txtFST Matrix (micros)Pairwise FST matrix, based on microsatellites, used for mantel tests in R code.micros_matrix.txtG'ST Matrix (micros)Hedrick's G'ST pairwise matrix, based on microsatellites, used for mantel tests in R code.HedricksGST_micros_matrix.txt