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 bêta-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 since I only give the central
coordinates of each plot, so that the euclidean distance between them is
overestimated. Two adjacent, contiguous, plots are considered to be 25 m
apart, while the distance should be 0, since they "touch" each other.
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 to make my plots polygons instead of
point ? And then how do I get a Euclidean distance matrix that I will be
able to use for the PCNM ?
I hope someone with some experience and good ideas will be able to help.
Thanks a lot,
David Bauman
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