part.mvr.beta {multirich}

Partition multivariate richness Beta Component

Description

Function to calculate dissimilarity between a pair of communities, and partition it into nestedness-related & turnover components Either the Sorensen index or the Jaccard index can be used to calculate dissimilarity

Usage

part.mvr.beta(in.mat, in.com, index.rows, index.type = "Sorensen")

Arguments

in.mat

A record x trait matrix. Needs to be in matrix format, not as a dataframe

in.com

A community x record matrix

index.rows

A vector with 2 elements, that gives the row number for the pair of communities of interest.

index.type

specifies which index to use. Options are Sorensen (default) & Jaccard

Value

• aa Overlap between the two communities

• dissimilarity Dissimilarity between the two communities

• turnover This gives the turnover between the two communities. To get the percent of dissimilarity due to turnover, divide by total dissimilarity.

• Bnes Nestedness-related dissimilarity between the two communities. To get the percent of dissimilarity due to this, divide by total dissimilarity.

Author(s)

A.C. Keyel

References

Baselga, A. 2010. Partitioning the turnover and nestedness components of beta diversity. Global Ecology and Biogeography 19: 134-143
Baselga, A. 2012. The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Global Ecology and Biogeography 21: 1223-1232
Villeger, S. Grenouillet, G., and Brosse, S. 2013. Decomposing functional Beta-diversity reveals that low functional Beta-diversity is driven by low functional turnover in European fish assemblages. Global Ecology and Biogeography 22: 671-681.

Examples

## Partition beta diversity for two species in the iris dataset

# Set up record x trait matrix
ind.mat = iris
ind.mat$Species = NULL
ind.lbl = sprintf("Ind_%s",seq(1,nrow(iris)))
ind.mat = as.matrix(ind.mat) #Needs to be in matrix format
rownames(ind.mat) = ind.lbl

# Set up community matrix
com.base = iris$Species
pool = rep(1,nrow(iris))
com1 = sapply(com.base, function(x){ifelse(x == "setosa",1,0)})
com2 = sapply(com.base, function(x){ifelse(x == "versicolor",1,0)})
com3 = sapply(com.base, function(x){ifelse(x == "virginica",1,0)})
com.vec = c(pool,com1,com2,com3)
com.lbl = c("pool","com1","com2","com3") 
com.mat = matrix(com.vec, nrow = 4,byrow = TRUE,dimnames = list(com.lbl,ind.lbl))

# Specify the communities to compare
index.rows = c(2,4) #compare species 1 & 3 (+1 due to the pool being the first community)

# Do the diversity partitioning
part.out = part.mvr.beta(ind.mat,com.mat,index.rows,index.type = "Sorensen")
  com.overlap = part.out[[1]]
  #0: no overlap
  com.dis = part.out[[2]]
  #1: complete dissimilarity
  com.turn = part.out[[3]]
  #1: This gives the absolute amount of dissimilarity due to turnover.
  # For percent dissimilarity due to turnover, you need to divide by overall dissimilarity
  com.nest = part.out[[4]]
  #0: This gives the absolute amount of dissimilarity due to nestedness.
  # For percent, divide by total dissimilarity