divPartition | R Documentation |
This function computes alpha, beta and gamma diversity of a list of networks. It measures either group, links, or probability of links diversity.
divPartition(gList, groups, eta=1, framework=c('RLC','Chao'), type=c('P','L','Pi'), abTable=NULL)
gList |
A |
groups |
A named vector of class |
eta |
A positive number that controls the weight given to abundant groups/links. Default value is 1. |
framework |
The framework used to partitionate diversity, either Reeve Leinster Cobbold ('RLC') or Chao ('Chao') |
type |
The type of diversity to measure and partitionate. It can be groups diversity ('P'), link diversity ('L') or probability of link diversity ('Pi'). |
abTable |
A matrix of size the number of nodes of the metanetwork times the number of networks. The rownames of this matrix must be the node names of metanetwork and the columns must
be in an order corresponding to gList. The element (i,j) of this matrix is the abundance of species i in network j. Importantly, the non-nul elements in each column of |
Returns a list
the following components:
mAlpha |
The mean value of alpha-diversity accross all networks. |
Alphas |
A vector of |
Beta |
The value of the overall beta-diversity |
Gamma |
The value of the gamma-diversity |
Authors: Stephane Dray, Vincent Miele, Marc Ohlmann, Wilfried Thuiller Maintainer: Wilfried Thuiller <wilfried.thuiller@univ-grenoble-alpes.fr>
Marc Ohlmann, Vincent Miele, Stephane Dray, Loic Chalmandrier, Louise O'Connor & Wilfried Thuiller, Diversity indices for ecological networks: a unifying framework using Hill numbers. Ecology Letters (2019) <doi:10.1111/ele.13221>
# Generating a set of Erdos-Renyi graphs and give node names. library(econetwork) library(igraph) nbGraph <- 3 gList <- c() n <- 57 # number of nodes of each graph C <- 0.1 # connectance of each graph for(i in 1:nbGraph){ graphLocal <- erdos.renyi.game(n, type='gnp', p.or.m=C, directed=TRUE) V(graphLocal)$name <- as.character(1:57) gList = c(gList,list(graphLocal)) } # vector that gives the group of each node groups <- c(rep("a",23),rep("b",34)) names(groups) <- as.character(1:57) # generating random (non-nul) abundances data abTable <- sapply(1:nbGraph,function(x) rpois(n,1)+1) rownames(abTable) = unlist(unique(lapply(gList,function(g) V(g)$name))) # Diversities in link abundances # at a node level divPartition(gList, framework='Chao', type = 'L') # at a node level while taking into account node abundances divPartition(gList, framework='Chao', type = 'L', abTable = abTable) # at a group level divPartition(gList, framework='Chao', groups, type = 'L') # at a group level while taking into account node abundances divPartition(gList, framework='Chao', groups, type = 'L', abTable = abTable)
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