Data from: Seascape continuity plays an important role in determining patterns of spatial genetic structure in a coral reef fish

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.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.tx