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Is adonis the right test for me?

2 messages · Kari Lintulaakso, Eduard Szöcs

Original
Kari Lintulaakso
# 
Dear list,

I'm not so familiar with ecological statistics (though I should be),
so I'm looking for a confirmation and support to my decision to move
on with the adonis path.

I've been trying to find a method to analyse differences between
mammalian communities and I think adonis would do the work for me.

My data consists of different localities (each of assigned into a
vegetation class) and number of species in different ecomorphological
classes (groups) I have generated.
There are nine vegetation classes, 52 localities, six mammalian groups
having some 200-300 species in them.
My idea is to use these mammalian groups as "species" in the data
matrix which would look like this:

L	V	G1	G2	G3	...	G6
1	1	0	1	2	...	8
2	2	1	2	1	...	3
3	2	1	3	2	...	4
4	3	5	2	2	...	9
5	3	5	3	3	...	8
6	3	4	2	3	...	7
...		
52	9	8	8	1	...	0

L=locality id
V = vegetation class
G1...G6 mammalian groups, and in the matrix the counts of species in the group.

First I'm trying to test if there are any differences between the
vegetation groups. For this I'm planning to use a pairwise adonis
comparisons between the different vegetation groups, i.e V1 vs V2, V1
vs V3, ... V8 vs V9. From each of these comparisons I'm taking the Df,
F, P>F etc values into a matrix so that I'm able to see the values
later.

When I get the P-values I suppose I can say that there are differences
between the vegetation classes when the P-value is < 0.05, right?
And if so, I need to find a way to explain which mammalian groups are
responsible for these differences? Does anyone have a good suggestion
for this? I've found one approach which is called SimPer (Similarity
Percentage Analysis), but I'm not sure is it in vegan.

And finally, is there any requirement, how many different data rows I
need for one type of vegetation? In some cases I only have one which
would mean that the within group variation is zero?

Any suggestions are welcome!
-Kari

Kari Lintulaakso, M.Sc.(Biosciences)

Doctoral student
Paleontology and Paleoecology
Department of Geosciences and Geography
University of Helsinki

* Email: kari.lintulaakso at helsinki.fi
* Post: Department of Geology, Gustaf H?llstr?min katu 2a (P.O Box
64), 00014 University of Helsinki
* Web page: http://blogs.helsinki.fi/lintulaa/
Eduard Szöcs
# 
Hai Kari,

as far as i know SimPer is not available in vegan.

I have written some code for myself a few months ago to do this.
I think its doing the job, but coding is poor (3 for-loops...).


- Eduard


############################################################
######         SIMPER      , Clarke (1993)                           
            ########
###### V 0.3   29.04.2011  Sz?cs, Eduard                                 
########
############################################################


###############
#### Code #####
###############

simper <- function(comm, groups, select){
   group.a <- as.matrix(comm[groups == select[1], ])
   group.b <- as.matrix(comm[groups == select[2], ])
   n.a <- nrow(group.a)
   n.b <- nrow(group.b)
   P <- ncol(comm)
   me <- matrix(ncol = P)
   md <- matrix(ncol = P)
   contr <- matrix(ncol = P, nrow = n.a * n.b)
   for(j in 1:n.b) {
       for(k in 1:n.a) {
           for(s in 1:P) {
               md[s] <- abs(group.a[k, s] - group.b[j, s])
               me[s] <- group.a[k, s] + group.b[j, s]
               a <- rowSums(me)
               c <- md / a
               contr[(j-1)*n.a+k, ] <- md / a
   }}}
   av.contr <- apply(contr, 2, mean)*100
   ov.av.dis <- sum(av.contr)
   sdi <- apply(contr, 2, sd)
   sdi.av <- sdi / av.contr
   av.a <- colMeans(group.a)
   av.b <- colMeans(group.b)
   dat <- data.frame(av.contr, sdi, sdi.av, av.a, av.b)
   dat <- dat[order(dat$av.contr, decreasing = TRUE),]
   cum <-  cumsum(dat$av.contr / ov.av.dis)*100
   out <- data.frame(dat, cum)
   out
}


###############
#### Usage ####
###############

#  simper(comm, groups, select)

# Arguments :
############

#   * comm : commumity data. species in columns, observations in rows
#   * groups : vector containing treatment per observation
#   * select : vector with length 2, indicating the two groups to compare


# Output :
##########

#   * av.contr : average contribution to overall similarity
#   * sdi : standard deviation of contribution
#   * sdi.va : ratio mean / sd = sdi / av.contr.
#   * av.a : average abundance in first group
#   * av.b : average abundance in second group
#   * cum : cumulative contribution


#################
#### Example ####
#################

require(vegan)
data(dune)
data(dune.env)
adonis(dune ~ Management, data=dune.env, permutations=99)
levels(dune.env$Management)
simper(dune, dune.env$Management, c("BF", "SF"))




Am 29.04.2011 06:34, schrieb Kari Lintulaakso: