DOI: 10.5061/dryad.kh18932jm
title: Data from: Inferring riverscape dispersal processes from fish biodiversity patterns

description: 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 river directions; iii) an outlet as a source of individuals that
 affects diversity even at distant communities; and finally iv) a strong
 effect of dams, indicating that fish diversity at the riverscape level is
 now affected by dam constraints on individual movement. Both observed
 alpha (r=0.58) and beta (r=0.54) diversity were well predicted by the
 selected model, with a positive and a U-shaped association with
 communities centrality. Process stochasticity—variation in diversity
 metrics along realizations of the same assembly process— in alpha and beta
 diversity were negatively and hump-shaped associated with communities
 centrality. The ongoing fragmentation of rivers worldwide demands a
 mechanistic understanding of the role of large-scale dispersal on
 ecosystem diversity and stability. Selecting models and combining evidence
 from different models allows the present research to advance on the
 relative support for hypotheses with a comparatively limited amount of
 empirical information, thus highlighting the potential of
 theoretical-empirical approaches for identifying the mechanisms that shape
 biodiversity.
description: # 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 second one called
 “Coalescent.and.lottery.hill.all.J” is the “master” function that makes up
 the metacommunity assembly, considering an explicit graph (G) and directed
 dispersal kernels. In this case, since the graph is a river, three kernels
 of dispersal are considered: upstream dispersal, downstream dispersal and
 dispersal from the outlet, and are represented in the code by the
 parameters d50.range.in, d50.range.out, d50.range.outlet (d50 refers to
 the dispersal ability). In addition, the size of the communities (Ji), a
 regional pool and the potential effect of fragmentation by dams are
 considered. Briefly, communities are filled with Ji individuals according
 to the relative strength of the different dispersal sources. When all
 communities are filled, the lottery dynamic begins with a deletion and
 replacement of individuals in each community, through it iterations. A
 final metacommunity matrix N with elements representing the abundance of
 each species in each community is obtained; from this matrix the alfa and
 beta diversity is estimated. Then, a Generalized Linear Model (GLM) is
 fitted to the observed and expected species richness from the different
 simulated scenario (dispersal sources). The observed diversity is taken
 from
 [https://doi.org/10.6084/m9.figshare.12476393](https://doi.org/10.6084/m9.figshare.12476393). The model with the largest weight of evidence is identified and the difference in AIC between the best models was assessed. #### 2. iterations\_best\_models.R This code allows to account for the stochastic nature of lottery model results. Thus, once the best model was identified, a number of replicates (“replicas”) were performed with the same parameters, retaining the median richness and beta diversity expected in each community. #### 3. Data\_JAE2024.RData This file contains the R space with the graph used that represents the riverscape, and the vector with the empirical records of fish diversity used in this study, representing in the argument “Obs”. ## Access information Other publicly accessible locations of the data: * Not apply Data was derived from the following sources: * All data is generated from the codes presented above. Only, the empirical records of fish diversity used in this study (called "Obs") as well as the position of communities in the riverscape are taken from [https://doi.org/10.6084/m9.figshare.12476393](https://doi.org/10.6084/m9.figshare.12476393). This information is also in the R space load herein (Data_JAE2024.RData).