Data from: Riverscape genetics identifies replicated ecological divergence across an Amazonian ecotone

Ecological speciation involves the evolution of reproductive isolation and niche divergence in the absence of a physical barrier to gene flow. The process is one of the most controversial topics of the speciation debate, particularly in tropical regions. Here, we investigate ecologically based divergence across an Amazonian ecotone in the electric fish, Steatogenys elegans. We combine phylogenetics, genome scans and population genetics with a recently developed individual-based evolutionary landscape genetics approach that incorporates selection. This framework is used to assess the relative contributions of geography and divergent natural selection between environments as biodiversity drivers. We report on two closely related and sympatric lineages that exemplify how divergent selection across a major Amazonian aquatic ecotone (i.e., between rivers with markedly different hydrochemical properties) may result in replicated ecologically mediated speciation. The results link selection across an ecological gradient with reproductive isolation and we propose that assortative mating based on water color may be driving the divergence. Divergence resulting from ecologically driven selection highlights the importance of considering environmental heterogeneity in studies of speciation in tropical regions. Furthermore, we show that framing ecological speciation in a spatially-explicit evolutionary landscape genetics framework provides an important first step in exploring a wide range of the potential effects of spatial dependence in natural selection.

Usage Notes

UniformSelection_CDPOP1) No spatial selection gradient (‘uniform’): In this scenario, the three genotypes (AA, Aa, and aa) were being selected against, but uniformly across the ‘water color’ riverscape scenario, thus having no spatial dependency and allowing us to test for type I statistical errors.GentleSelection_CDPOP2) Gentle spatial selection gradient (‘gentle’): Here, we used a ‘gentle’ spatial selection gradient corresponding to the three river color locations. For AA, we used the relative fitness coefficients of 0.4, 0.3, and 0.2 for black, mixed, and white waters, respectively. For Aa, we implemented an opposite spatial selection gradient (relative fitness coefficients of 0.2, 0.3, and 0.4 for black, mixed, and white waters, respectively).SteepSelection_CDPOP3) Steep spatial selection gradient (‘steep’): For this scenario, stronger spatial selection gradients were assigned to each genotype, with the relative fitness coefficients for AA of 1.0, 0.6, and 0.2 for black, mixed, and white waters, respectively. An opposite spatial selection gradient was implemented for aa (0.2, 0.6, and 1.0 for black, mixed, and white waters, respectively). Aa received a uniform selection gradient of 0.2 in all three scenarios.BarrierRemove_HighMigration_CDPOPSimulating Secondary Contact 1) We initially placed complete barriers on the riverscape to restrict movement of individuals to and from the three subgroups (Negro, Maderia, and Amazon). We then conducted an isolation-by-riverine distance simulation modeling experiment within each subgroup using a movement distance that considered the maximum extent of the riverscape (i.e., mimicking the spatial selection simulation parameters or high migration).barrierremoveS3_long_1381430149.tar.gzBarrierRemove_LowMigration_CDPOPSimulating Secondary Contact 1) We initially placed complete barriers on the riverscape to restrict movement of individuals to and from the three subgroups (Negro, Maderia, and Amazon). We then conducted an isolation-by-riverine distance simulation modeling experiment within each subgroup using a movement distance that considered nearest neighbor extent of the riverscape (i.e., low migration). For the low migration scenario, offspring dispersed to locations nearer to their birth location with probability based on the inverse-square of distance and a restricted threshold of 200 km, which reduced the occasional long-range dispersers as in the high migration scenarios.barrierremove_2ALL_NN_1377816908.tar.gz