Hi,

I am trying to run a repeated measures RDA in vegan but am unsure about how
to implement this, and whether I also need to use the permute package to run
permutations.

I have time series data  - one data frame with community composition data
(aquatic invertebrates) for 9 sites sampled on 16 dates (not all site/dates
combinations have data), then another data frame with environmental
constraints (water quality data) for the same 9 sites and 16 dates. At first
I ran the analysis as a fairly simple RDA using
JInvRDA <- rda(JInvHel ~ ., JEnvT.stand, na=na.omit, subset =
complete.cases(JInvHel), scale=TRUE)

where JInvHel - Hellinger-transformed invertebrate data
JEnvT.stand - transformed and standardised environmental data

I then ran permutation tests on the output using
anova.cca (JInvRDA, step=1000)

However, I have since realised that since my data are time series and likely
to be temporally autocorrelated, that I should be using a repeated measures
type RDA e.g. a split plot design in which permutations are performed within
each block of plots (sites). In my searching I have seen lots of mention of
the two references below, which outline examples of this analysis using
CANOCO, but am not sure how to do this in R.

Lepš, J. 1999. Nutrient status, disturbance and composition: an experimental
test of relationships in a wet meadow. J. Veg. Sci. 10: 219–230.
Leps, J. & Smilauer, P. 2003. Multivariate analysis of ecological data using
CANOCO. Cambridge University Press, Cambridge, UK.

I am unsure about whether I need to reorganise my data frames (currently
samples (first column) are coded with a combined site/date code - does this
need to be split so that sites and dates are separate - into two columns?)
and also about how to structure the analysis so that the time component is
incorporated correctly. Finally, when I want to run permutation tests,
should I be using the permute package, and how should this be structured?

Many thanks,
Lien

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