Jari,

Thank you for the quick reply.  Maybe I should use something like PCNM
first with the lat/long data to then use in the rda?  I really appreciate
all of your help.  Are there anyother/better ways to account for spatial
autocorrelation.  I guess I need to show that spatial autocorellation
exists and then if it does account for it?  Any reading etc. would be
greatly appreciated.  I appreciate all of the help.
kindest regards,

Stephen

P.S.  I will let you know about the stepwise selection and scope argument


On Wed, Jul 10, 2013 at 2:28 PM, Jari Oksanen <jari.oksanen@oulu.fi> wrote:

>
> On 10/07/2013, at 21:00 PM, Stephen Sefick wrote:
>
> > Hello all,
> >
> > I would like to run this by everyone and maybe get some hints as to what
> R functions I could use for this.  Ok, so I have macroinvertebrate
> assemblage data from across the SE.  I would like to control for geographic
> distance (lat/long), Watershed area, and year before submitting these data
> to an RDA with the rest of the environmental data using a variable
> selection technique.
> >
> > Does it make sense to detrend the data using a mlm on hellinger
> transfomed abundances with the above env variables as regressors and then
> submit the residuals to rda with the rest of the env variables I am
> interested in?
>
>
> Stephen,
>
> If you happen to use vegan functions for forward selection, please note
> that they all (should) take a scope argument that can (should) be a list of
> lower and upper scopes. Put your controlled variables (distance???,
> watershed area, year) in the lower scope and these plus other candidate
> variables in the upper scope, and there you go. I have used "should",
> because I have rarely used these functions myself, and I'm not sure if
> lower scope really is implemented in all, but is *should* be: file a bug
> report if this fails.
>
> I have no idea how to have distance RDA. Well, I have ideas, but none that
> I have are very good.
>
> Using separate mlm and modelling residuals will not work quite correctly,
> because that ignores correlations between groups of variables. Vegan
> functions do not ignore those.
>
> Cheers, Jari Oksanen
> --
> Jari Oksanen, Dept Biology, Univ Oulu, 90014 Finland
> jari.oksanen@oulu.fi, Ph. +358 400 408593, http://cc.oulu.fi/~jarioksa
>
>
>
>
>
>

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