Evolutionary processes driving spatial patterns of intraspecific genetic diversity in river ecosystems

Abstract

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.

Keywords: approximate Bayesian computation; asymmetric gene flow; colonization; dendritic ecological networks; genetic diversity; meta-analysis; random forest; river network; simulated genetic data; spatial patterns of biodiversity.