Function to calculate the dissimilarity between ecological communities taking into account both their composition and the size of organisms.
1 2 
x 
A stratified vegetation data set (see function 
y 
A second stratified vegetation data set (see function 
paired 
Only relevant when 
type 
Whether dissimilarities between pairs of sites should be calculated from differences in cummulative abundance ( 
method 
The dissimilarity coefficient to calculate (see details). 
transform 
A function or the name of a function to be applied to each cumulative abundance value. 
classWeights 
A numerical vector or a matrix containing the weight of each size class or combination of size classes (see functions 
The six different coefficients available are described in De Caceres et al. (2013): (1) method="bray"
for percentage difference (alias BrayCurtis dissimilarity); (2) method="ruzicka"
for Ruzicka index (a generalization of Jaccard); (3) method="kulczynski"
for the Kulczynski dissimilarity index; (4) method="ochiai"
for the complement of a quantitative generalization of Ochiai index of similarity; (5) method="canberra"
for the Canberra index (Adkins form); (6) method="relman"
for the relativized Manhattan coefficient (Whittaker's index of association). Currently, the function also supports (7) method="manhattan"
for the city block metric.
Returns an object of class 'dist
'.
Miquel De Cáceres, Forest Science Center of Catalonia.
De Cáceres, M., Legendre, P. & He, F. (2013) Dissimilarity measurements and the size structure of ecological communities. Methods in Ecology and Evolution 4: 11671177.
stratifyvegdata
, vegdist
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  ## Load stratified data
data(medreg)
## Check that 'medreg' has correct class
class(medreg)
## Create cumulative abundance profile (CAP) for each plot
medreg.CAP = CAP(medreg)
## Create dissimilarity (percentage difference) matrix using profiles
medreg.D = vegdiststruct(medreg, method="bray")
## Create dissimilarity (percentage difference) matrix using abundances
medreg.D2 = vegdiststruct(medreg, method="bray", type="total")
## Calculate correlation
cor(as.vector(medreg.D), as.vector(medreg.D2))

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