Data from: Evolutionary processes driving spatial patterns of intra-specific genetic diversity in river ecosystems

Describing, understanding and predicting the spatial distribution of genetic diversity is a central issue in biological sciences. In river landscapes, it is generally predicted that neutral genetic diversity should increase downstream, but there have been few attempts to test and validate this assumption across taxonomic groups. Moreover, it is still unclear what are the evolutionary processes that may generate this apparent spatial pattern of diversity. Here, we quantitatively synthesized published results from diverse taxa living in river ecosystems, and we performed a meta-analysis to show that a downstream increase in intraspecific genetic diversity (DIGD) actually constitutes a general spatial pattern of biodiversity that is repeatable across taxa. We further demonstrated that DIGD was stronger for strictly waterborne dispersing than for overland dispersing species. However, for a restricted data set focusing on fishes, there was no evidence that DIGD was related to particular species traits. We then searched for general processes underlying DIGD by simulating genetic data in dendritic-like river systems. Simulations revealed that the three processes we considered (downstream-biased dispersal, increase in habitat availability downstream and upstream-directed colonization) might generate DIGD. Using random forest models, we identified from simulations a set of highly informative summary statistics allowing discriminating among the processes causing DIGD. Finally, combining these discriminant statistics and approximate Bayesian computations on a set of twelve empirical case studies, we hypothesized that DIGD were most likely due to the interaction of two of these three processes and that contrary to expectation, they were not solely caused by downstream-biased dispersal.Simulated data from the gene-flow modelParameter values and summary statistics for simulations generated under the gene-flow modelData_gene-flow_model.txtSimulated data from the habitat availability modelParameter values and summary statistics for simulations generated under the habitat availability modelData_habitat-availability_model.txtSimulated data from the colonization modelParameter values and summary statistics for simulations generated under the colonization modelData_colonization_model.txtSimulated data from the gene-flow / habitat modelParameter values and summary statistics for simulations generated under the gene-flow / habitat modelData_gene-flow-habitat_model.txtSimulated data from the gene-flow / colonization modelParameter values and summary statistics for simulations generated under the gene-flow / colonization modelData_gene-flow-colonization_model.txtSimulated data from the habitat / colonization modelParameter values and summary statistics for simulations generated under the habitat / colonization modelData_habitat-colonization_model.txtSimulated data from the gene-flow / habitat / colonization modelParameter values and summary statistics for simulations generated under the gene-flow / habitat / colonization modelData_gene-flow-habitat-colonization_model.txtSimulated data from the NULL modelParameter values and summary statistics for simulations generated under the NULL modelData_NULL_model.txtScripts and data for meta-analysesScripts and data we used for performing the meta-analysesScripts_meta-analyses_v2.zipScripts for simulating data under the eight modelsThis file contains the .est and .par input files used for simulating the genetic datasets used in this article. There are eight different couples of .est/.par files, each of them being associated to one of the eight models presented in the article. The event at 40,000 generations before present (i.e. all genes in the network were send back to an unique deme at this date, considering a backwards in time timeframe), was used to uniformize coalescence times across simulations and models. Please, check the readme file for additional guidance.Scripts_par_est_MODELS.zipDemes IDs, and equivalences article vs. scripts(i) Figure representing the ID of demes in function of their spatial positioning in the network. This figure will help readers to assess to which deme corresponds each summary statistic provided in the simulated datasets shared in DRYAD. (ii) Table reporting the equivalences between the name of the parameters we used in the article and those we used in the .est and .par scripts we share in DRYAD (i.e., the scripts that were used to simulate genetic data with ABCSampler and SIMCOAL 2). We also report the description of the parameters and the prior parameter values we used.Demes_and_parameter_equivalences.doc