[R-sig-eco] Partitioning spatial effects using trend surface analysis or PCNM

[trichter]

Hi,

i am working on a bacterial dataset on hundreds of plots, distributed over a
10x10 meter grid.

I was using the VarPart function of vegan to identify the pure spatial
effect, with the classical approach of calculating CCAs using orthogonal
polynomes of my x,y coordinates. I have a significant community shift in
some of the plots, and CCA was better depicting this general behaviour in
the ordination space than RDA. Therefore, i am using CCA.

My main way to deal with it is a general additive model using spatial
coordinates, but i would like to use varpart in CCA as a supporting
analysis, as well.

I wanted to compare that to the PCNM approach, but i guess i have messed it
up.
I followed tutorials with the mite dataset, as given in the vegan vignette
and as found on the web (written by  Benoit Gendreau-Berthiaume).

Usually i am using my relative bacteria counts, as CCA is supposed to do its
own data transformation, anyway. I am also limiting my columns (OTUs) to
obtain a matrix balanced between observations and samples, so rare species
are usually not a problem.

Here is what i do:

spat <- as.data.frame(poly(as.matrix(spatxy), degree=3))  

cca1_s <- cca(OTU~., data=spat)
#significances
anova(cca1_s)
anova(cca1_s, by="term", perm=999)

#forward selection for most parsimonious model
cca1_s.f <- ordistep(cca(OTI~1, data=spat), scope=formula(cca1_s),
direction="forward", pstep=1000)
sig1_s.f <- anova(cca1_s.f, by="term", perm=999)

The result is a significant CCA object. Spat is usuable in VarPart and
yields a low but significant value for overall autocorrelation.

For PCNM i do 

rs <- rowSums(OTU)/sum(OTU)
pcnmw <- pcnm(dist(spatxy), w = rs)
cca1_pcnm <- cca(acido1 ~ scores(pcnmw))

pcnmw consists of 250 vectors, and the result is a non-significant CCA
object, where i expected a "finer" spatial decomposition.

The same is true if i am using total count data (hellinger transformed or
not).

I am sure i am doing it wrong, so if you have advise to properly do the
calculation, please let me know. Thank you for the help.





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