On Thu 13 Dec 2012 09:24:41 AM CST, claire della vedova wrote:
Dear all,
I’m a biostatistician working for a French institute involved in
environmental risk assessment, and I would need help to understand the
results I obtained from several ordination analyses.
I have a dataset of 25 sites. For these 25 sites I have abundance data
of 38
species and also the measurement of 5 environmental variables.
Here an extract of my abundance data for the 5 first sites:
Anguinidae.ditylenchus Aphelenchidae Aphelenchoididae Aporcelaimidae
12 18 184 0
0 14 154 0
45 0 101 6
20 0 148 0
0 0 118 0
Here the environmental data for the 5 first sites:
ExtPond moist Corg pH DV50
0.946 9.086 4.269 5.24 171.33
0.682 27.139 23.813 3.82 75.45
2.480 14.322 7.191 4.48 230.90
3.069 18.380 11.404 3.58 211.19
2.615 16.693 7.128 4.12 224.45
My aim was to study how the distribution of species is linked with
environmental data.
Firstly, I did a PCA (with vegan library), using a Hellinger
transformation,
with commands like this :
acp1<-rda(decostand(myDataSpec[,c(25:62)], "hellinger"))
Is the Hellinger transform done on relative proportions?
The first axe represent 19.5% the second one 16.3%. A colleague of me said
it is not so bad with abundance data, but it seems to me quite poor.
What do
you think about ?
You could use something like the broken stick model or others to access
how many axes are necessary, but two axes explaining <40% of the
variation seems low.
Then, I fitted environmental vectors with the envfit function (of vegan
library), with commands like this :
physCInd.fit3<-envfit(acp1,MyDataEnv[,c(13,18,20,21,23)], permut=4999,
na.rm=T)
It appeared that pH variable is significantly linked with the ordination,
and the pval of ExtPond is 0.1.
Next I did a RDA which is not significant.
To finish I did two NMDS. For the first one I used the Hellinger
normalization and the Bray-Curtis distance. The stress obtained value is
0.22, Non metric fit R² is 0.952 and Linear fit R2 =0.777. When I
fitted the
environmental vectors , ExtPond was correlated with the ordination (pval
=0.02) and p-val of pH = 0.23
But then I read in “numerical ecology” page 449 that it’s better to
standardize the data by dividing each value by maximum abundance for
species
and then use Kulcynski distance. The stress value was 0.23 , Non
metric fit
R² was 0.948 and Linear fit R2 =0.69. These values are a little less good
than those of the first NMDS, but the stressplot seems to me more
homogenous.
Nevertheless, the results I obtained are very different... When I fitted
the environmental data it appeared that ExtPond was not correlated
with this
ordination (p-val=0.82) and p-val of pH=0.06. And obviously ExtPond is the
most important variable for us ;-)
With all these results, I’m quite confused, and I don’t know what to
think.
So, if someone can help me, I would appreciate it very much. Be sure that
all comments will be welcome.
To summarize my questions are :
a) Which ordination method would be better for my data : PCA knowing
that the represented inertia is 35.62% or NMDS with a stress value about
0.22?
My opinion is PCA on hellinger transformed relative proportions "means"
more than an NMDS
b) If NMDS is more adapted which one is the better? with Hellinger
normalization and Bray-Curtis distance, or with the normalization
recommended by Legendre and Legendre and Kulcynski distance ?
I sounds like the normalization you are referring to is relative
proportion which is si/sum(s); s is a vector of taxon at a site.
c) Is there other method to apply? I’m going to try co-inertia with
ade4 package
I am reading about co-inertia analysis now as it may be useful for some
of the things that I am planning on doing. This method looks promising.
You are going to have to decide on what type of ordination to use with
COIA...
HTH,
Stephen
Thanks in advance.
Cheers.
Claire Della Vedova
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Stephen Sefick
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Auburn University
Biological Sciences
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