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@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 bęta-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
>>
>> _______________________________________________
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>>
>>
> --
> 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@nhh.no
>

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