part.mvr.beta: Partition multivariate richness Beta Component

Description Usage Arguments Value Author(s) References Examples

View source: R/MVR.r

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

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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

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

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## 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

akeyel/multirich documentation built on May 11, 2017, 2:16 a.m.