MEM : Spatial structures detection issues

My sampled plots are represented by x-y coordinates corresponding to the
central point of the plots. The euclidean distance matrix is computed from
this dataframe.

Plot    x    y
01a    112.5    12.5
02a    112.5    62.5
03c    187.5    12.5
...

The permanent forest plot is a rectangular area of 200 x 500 m. All this
area has been divided in 25x25 m plots, from which I sampled 24 more or
less regularly dispersed over the forest.

What I would like to know is how can I give to R the necessary information
so that those plots are considered as squared areas of 25x25 m, and not as
simple points. And when this is done, how do I compute an euclidean
distance matrix between my 24 plots ?

Which packages/functions allow to do this ?

Thank you


2013/11/30 Roger Bivand <Roger.Bivand at nhh.no>

On Sat, 30 Nov 2013, David Bauman wrote:

 Hi everyone,

I am facing a problem I really do not know how to resolve about
detecting significant spatial structures in a region I am studying.

The study is about the Miombo forest (wooded savanna). I am working in
a 10 ha permanent forest plot, where all trees are mapped and
identified. I sampled 24 more or less regulately scattered plots of 25
x 25 m over the 10 ha. In each plot of 25 x 25 m I got 5 soil samples
on which I will measure a battery of variables (the explanatory
variables).
What I want to study is the b?ta-diversity of the ectomycorrhizal
community, so that in each plot I got about 15 pieces of root
containing some ectomycorrhizal fungus on it (response variables).

My purpose is to explain the spatial variation of the ectomycorrhizal
diversity thanks to the soil variables I will have measured.

To do so, I want to use a PCNM (or MEM).

My problem is that :

All the 24 plots are considered as points, so that the euclidean
distance between them is overestimated. Two adjacent, contiguous,
plots are considered to be 25 m far from each other, while the
distance should be 0, since they "touch" each other.

Well, how are you representing the support of the plots? If you are only
providing a central point rather than a polygon, you are getting what you
asked for. If you are providing polygon boundaries, but using the
coordinates() method to return the central points, the same follows. If tou
need distances between polygon objects, use gDistance() in rgeos (if your
positional data are projected).

It always helps if questioners show their reasoning by including a small
example with code, as the problem may result from an unfortunate choice of
functions in the workflow. Also always include the output of sessionInfo()
as functions may behave differently in different versions of R and packages.

Hope this clarifies,

Roger

 This leads me to the problem that the truncation threshold distance is

to high to allow the RDA of the response dataframe on the spatial PCNM
variables to detect a significant linear link between both matrices
(function anova.cca()).

So the question is : How can I do so that the area of my plots are
taken into account, and they are not considered as points anymore ?
This would lower the truncation distance and probably allow me to
detect spatial structures that, I really think, do exist.

I hope someone with some experience and good ideas will be able to help.

If you want some more concrete information about the study, do not
hesitate to ask it to me.

Thanks a lot,

David Bauman

--
Roger Bivand
Department of Economics, Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: Roger.Bivand at nhh.no