Dear Etienne,

Thanks for your response, and yes I see clearly now the nested structure.
However, I think this is not getting at our question.  I believe this method
would address the question of "how different are communities given different
land use treatments (and given that fact that treatments are nested within
regions)?"

We are not interested in describing the community itself, but rather changes
in the community composition, and the independent variable we are interested
in is indeed *region* (we are interested in beta diversity).  We'd
specifically like to know, in a sense, how beta diversity partitions
differently to within and between regions, given treatment.  Here's a little
more information:

There are four regions.  Within each region, three different land use
"treatments" (say A, B, C) were replicated between 3 and 4 times.  When we
plot these data in an ordination space, we see that for treatment C the
regions for diffuse clouds that overlap, but their centroids are still far
apart.  For the other two treatments, the regions form tight clouds, also
with well spaced centroids.  We'd basically like to be able to say whether
the relative "diffuseness" of these region clouds are different across
treatments, and whether the relative distance between the region centroids
is different across treatments.

I get the sense that perhaps we want region nested within treatment, but I
feel like we also need an interaction term?

Thanks for your help--
Andy


On Thu, Dec 16, 2010 at 5:28 PM, Etienne Laliberte <
etiennelaliberte@gmail.com> wrote:

> Dear Andy,
>
> For testing the null hypothesis of no land use effect, what you suggest
> does
> not take into account the nested structure of your design (with land use
> nested in location).
>
> In ?adonis:
>
> "If the experimental design has nestedness, then use strata to test
> hypotheses. For instance, imagine we are testing the whether a plant
> community is influenced by nitrate amendments, and we have two replicate
> plots at each of two levels of nitrate (0, 10 ppm). We have replicated the
> experiment in three fields with (perhaps) different average productivity.
> In
> this design, we would need to specify strata = field so that randomizations
> occur only within each field and not across all fields . See example
> below."
>
> Which in your case translates to
>
> adonis(bird.com  ~ land.use, strata = site.data$region, data = site.data)
>
> Hope that helps,
>
> Etienne
>
>
> -----Original Message-----
> From: r-sig-ecology-bounces@r-project.org
> [mailto:r-sig-ecology-bounces@r-project.org] On Behalf Of Andy Rominger
> Sent: Friday, 17 December 2010 5:17 AM
> To: r-sig-ecology@r-project.org
> Subject: [R-sig-eco] adonis to test difference in sums of squares
> partitioning across treatments?
>
> Dear list,
>
> We are working with a dataset of bird community composition across four
> regions, each of which contain three land use treatments.  We are
> interesting in understanding if the community dissimilarity within regions
> and between regions differs according to land use treatment.  I understand
> that multivariate analysis of variance (as implemented in adonis{vegan})
> can
> partition sums of squares between main effects and residuals, which I
> understand would, for example, test the ratio (F-statistic) of between
> versus within region dissimilarity.
>
> What we'd like to know is effectively if this partitioning of sums of
> squares within (residual) and between (main effects) regions changes
> significantly under the different land use treatments.  So would the
> following model to the trick?
>
> adonis(bird.com~region*land.
> use,data=site.data)
>
> where...
> bird.com is a sites by species matrix, and
> site.data is a data.frame containing columns that indicate to which region
> (column 'region') and land use (column 'land.use') each site belongs.
>
> I appoligize for this simple question (and probably wrong model) I'm very
> naive about these multivariate analyses.  We assumed that this approach was
> probably wrong and so we are also working on a bootstrap method, which I
> could post for your consideration if this use of adonis is incorrect, or if
> no perMANOVA-based method would answer our question.
>
> Thanks in advance for your help--
> Andy Rominger
>
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