Assessments of spatial and temporal congruency across taxa from genetic data provide insights into the extent to which similar processes structure communities. However, for coastal regions that are affected continuously by cyclical sea-level changes over the Pleistocene, congruent interspecific response will not only depend upon co-distributions, but also on similar dispersal histories among taxa. Here, we use SNPs to test for concordant genetic structure among four co-distributed taxa of freshwater fishes (Teleostei: Characidae) along the Brazilian Atlantic coastal drainages. Based on population relationships and hierarchical genetic structure analyses, we identify all taxa share the same geographic structure suggesting the fish utilized common passages in the past to move between river basins. In contrast to this strong spatial concordance, model-based estimates of divergence times indicate that despite common routes for dispersal, these passages were traversed by each of the taxa at different times resulting in varying degrees of genetic differentiation across barriers with most divergences dating to the Upper Pleistocene, even when accounting for divergence with gene flow. Interestingly, when this temporal dissonance is viewed through the lens of the species-specific ecologies, it suggests that an ecological sieve influenced whether species dispersed readily, with an ecological generalist showing the highest propensity for historical dispersal among the isolated rivers of the Brazilian coast (i.e., the most recent divergence times and frequent gene flow estimated for barriers). We discuss how our findings, and in particular what the temporal dissonance, despite common geographic passages, suggest about past dispersal structuring coastal communities as a function of ecological and paleo-landscape sieves.

Six double digest Restriction-site Associated DNA (ddRAD) libraries were constructed: three libraries contained 118 individuals of Mimagoniates microlepis for this study, two libraries containing 136 individuals of Hyphessobrycon boulengeri, and one library with 87 individuals of Bryconamericus. In addition, two libraries with 182 individuals of Hollandichthys were re-analyzed for this study (Thomaz et al., 2017). For all the libraries prepared specifically for this study, we followed the protocol of Peterson, Weber, Kay, Fisher, & Hoekstra (2012); the two previously sequenced libraries of Hollandichthys followed the Parchman et al. (2012) protocol (see Thomaz et al., 2017 for preparation details). Genomic data were demultiplexed and processed separately for each taxon with the STACKS version 1.41 pipeline (Catchen, Hohenlohe, Bassham, Amores, & Cresko, 2013). Because of the various requirements of different analyses used to characterize the geographic structuring of genomic variation, three datasets were generated per taxon varying the amount of missing data and the numbers of individuals. One dataset was comprised by one random single SNP per locus with maximum of 50% missing data, which was used for estimates of population trees using SVSquartets on PAUP. The other dataset included loci with maximum 25% missing data after filtering - note that for M. microlepis we allowed 35% missing data - and was used with a random single SNP per locus in the STRUCTURE analysis. Separate datasets were used in FASTSIMCOAL2 analyses and were generated, when possible, from 20 individuals with the smallest amount of missing data from all the populations separated by each geographic barrier for each taxon (40 individuals in total), and a single variable SNP per RADtag with less than 10% missing data. For all these datasets, individuals with considerably fewer SNPs in comparison to other individuals of the same population were excluded. All filtering steps were performed using the toolset PLINK v.1.90 (Purcell et al., 2007) - see Thomaz and Knowles (2020) for details on methodology.

Three folders:
(1) SVDquartets: contain all input and output files from SVSquartets in PAUP.
(2) Structure: contain all input and output files from structure per taxon and break (central, north and south)
(3) Fastsimcoal2: four folders with files and scripts used to (1) estimation of the SFS, (2) point estimates per model tested for each taxon and break, (3) parametric bootstrap, including the simulated SFS, and (4) generate Figure 3 in Thomaz and Knowles (2020).