Inferring riverscape dispersal processes from fish biodiversity patterns

Dispersal patterns are recognized as a main determinant of biodiversity structure, particularly in rivers. The dendritic organization of rivers, waterflow direction, large distance immigrants from the outlet, and fragmentation by dams combine to produce a complex dispersal scenario. Unraveling the role, magnitude and spatial scale at which these different dispersal sources determine metacommunity diversity is challenging and demands a large amount of spatiotemporal information, which is rarely available. Here, we incorporated alternative dispersal hypotheses in coalescent and lottery models, contrasting their predictions with observed fish diversity in the Negro River basin of Uruguay, South America. Evidence from different models but supporting the same hypothesis was combined, finding support for i) a dispersal constrained by the riverscape—with no role for direct immigration from the metacommunity; ii) an asymmetric neighbor dispersal, sharply decaying in upward but not in downward r..., , , # Data from: Inferring riverscape dispersal processes from fish biodiversity patterns

Access this dataset on Dryad: DOI:10.5061/dryad.kh18932jm

## Description of the data and file structure

These files contain R-codes to reproduce metacommunity assembly combining a coalescent-based approach and a lottery dynamic run on a spatially explicit landscape to estimate alfa and beta diversity, considering different hypothesis of dispersal that could take place in a riverscape.  That is, upstream dispersal, downstream dispersal, and dispersal from the outlet. It also considers the size of the communities, a regional pool, and the potential effect of fragmentation by dams.

#### 1. hill.kernel.to.all.J.2nd.model.migration.corrected.R

This file contains the code to estimate metacommunity diversity based on a coalescent assembly and a lottery simulation. Note that the first function called “fit.Coalescent.and.lottery.hill.all.J” corresponds to a parallelisation to optimise the simulation. The s...,