Partitioning spatial effects using trend surface analysis or PCNM

Hi "trichter"

<snip />

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)

Don't think last analysis makes much sense; if you have a cubic polynomials
plus interactions you should only consider the interactions first for
removal, then decide if quadratic rather than cubic are needed

#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)

Again, as above, you have to be very careful with this. Just because you
made a matrix with 9 "covariates" it doesn't mean it makes sense to cherry
pick from these terms.

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.

You are supposed to choose from among the set of PCNMs which explain the
species data best, not use them all in the model. The problem appears to be
that you have a model that is far too complex with lots of redundant axes
(or more likely too few constraints).

One suggestion is to use only those PCNMs that have positive spatial
correlation. Compute that using Moran's I of which there are a few
implementations around in various R packages. You can do CCA analysis with
the positive spatial correlation PCNMs separately from the negatively
correlated PCNMs if you wish.

You will probably need to do some type of forward selection but the
preferred method seems to be limited to RDA (because the adjusted R2
measure used in the global significance test isn't worked out for CCA). If
you skip the global test, you could just do forward selection on the
positive PCNMs, but you probably want to try to control for accepting too
many PCNMs by having low entry threshold for significance.

HTH

G

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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Gavin Simpson, PhD

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