Dear Colleen.

This paper may help in your quest for spatial partitioning:

Gilbert, B. & Bennett, J.R. (2010) Partitioning variation in ecological 
communities: do the numbers add up? /Journal of Applied Ecology/, *47*, 
1071-1082.

 From the distance of my armchair (figuratively), I fear that the whole 
spatial effects/spatial autocorrelation business is difficult to pin 
down. My own experience is that the statistical model one uses 
dramatically affects the amount of SAC in the residuals, and hence in 
consequence the inference on the importance of spatial effects.
I only know that for univariate data, where the methods vary 
dramatically in their flexibility (or "wiggliness" as Simon Wood puts 
it) and where SAC is often much reduced when using say a GAM over a GLM. 
This indicates (to me) that model misspecification is the key reason for 
SAC in many of my analyses. If so, no wonder that Gilbert and Bennett 
don't find that the numbers add up.
I am largely ignorant of the multivariate approaches and their 
flexibility, but CCA/RDA and friends are IMHO much "stiffer" than, say, 
Boosted Regression Trees for univariate data. So I wouldn't be surprised 
if you find a considerable amount of "variance" explained by space - if 
only because the environmental model is not wiggly enough.

(This is my guess. I would love to hear more competent people adding 
their opinion, particularly for the multivariate case.)

Cheers,

Carsten


P.S.: By the way, your response in an earlier email "Right. The code. 
Sorry about that." reminded me of something. What was that? Zaphod 
Beeblebrox? Sir Graham Forbes (from the Paul Temple mysteries)? The Now 
Show's impersonation of Tony Blair? Or was that genuine confusion? ;-)

On 4/21/11 9:33 PM, ckellogg wrote:
> Hi All,
> I am planning to use varpart in {Vegan} to partition the variance in my
> bacterial community composition between 14 sites in the Arctic Ocean between
> space and environmental variables.  I am noticing that in many instances, a
> PCNM is employed to remove spatial autocorrelation.  But, I think I am not
> quite understanding what this does exactly and how I use this prior to doing
> the variance partitioning.  Maybe i can try and explain what I am trying to
> do.  Basically, I have 14 sites and at each site 3 samples were taken at 2
> to 3 depths to for bacterial community composition analysis.  I also took
> samples for environmental data at each depth.  I have distance calculated
> between sites over water (not over land or 'as the crow flies'), referenced
> to a point on land (i also have the latitude and longitude of each site; but
> i wasn't sure that these coordinates themselves were the best measure of
> distance between sites).  Ultimately, I am trying to figure out how much of
> the variation in the bacterial community between sites is attributable to
> environmental variability as well as to geographic distance between the
> sites.  Surely, the environmental variables also have some spatial component
> to them as well.  So, my plan was to use varpart as follows:
>
> BACvsEnvvsGeo<-varpart(ANbac,ANenv.z,ANdist_MR.z)
> plot(BACvsEnvvsGeo)
>
> ...where ANbac = bacterial community matrix (site x species), ANenv.z =
> log-transformed and standardized environmental data (site x env parameters),
> ANdist_MR.z = log-transformed and standardized distance between cites
> referenced to a point land to the west of all sites, site x distance (1
> column))
>
> I am planning to do this in conjunction with an RDA for space and
> environmental data separately.  This would allow me to calculate how much
> variation is do to environmental variation alone, space alone, and
> environment+space.  So, my questions are: (1) do I need to do a PCNM
> beforehand.  And do I do this just on the distance between sites data. Or do
> this also on the environmental data.  (2) And ultimately, what does doing
> the PCNM get me.  (3) Also, I am wondering if I really should use Lat/Long
> data instead of distance between sites.  I am not really sure it matters,
> but I was not sure if it matter for the analysis.  (4) And, my bacterial
> community data is standardized to total abundance at each size (so, percent
> abundance), and I am wondering if it is necessary to do further
> transformations of the data.  I see that this standardization is an option
> in the decostand algorithm, so I am think that it is okay to not further
> transform my data. (5) Alternatively, can varpart be used with db-RDA, so
> that I can put in my distance matrix instead of the transformed species
> data.  If so, how can I do this?
>
> Sorry for the onslaught of questions.  There are not really any people in my
> department that I can ask these questions too...
>
> Thank you very VERY much for any advice.  I am really having a hard time
> deciding/figuring out which is the best way to proceed.
>
> Colleen
>
> ----
> Colleen Kellogg
> PhD Candidate
> School of Oceanography
> University of Washington
> Box 357940
> Seattle, WA 98195
>
> --
> View this message in context: http://r-sig-ecology.471788.n2.nabble.com/when-to-use-PCNM-and-other-questions-about-varpart-in-Vegan-tp6295323p6295323.html
> Sent from the r-sig-ecology mailing list archive at Nabble.com.
>
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-- 
Dr. Carsten F. Dormann
Department of Computational Landscape Ecology
Helmholtz Centre for Environmental Research-UFZ
Permoserstr. 15
04318 Leipzig
Germany

Tel: ++49(0)341 2351946
Fax: ++49(0)341 2351939
Email: carsten.dormann@ufz.de
internet: http://www.ufz.de/index.php?de=4205


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