Hello everyone,
I need to analyse the role of spatial vs environmental effect (through variation partitioning) on Notonecta species distribution among fishless ponds. I have been using adespatial package to do that. My question is that after I calculate the MEMs how can I evaluate the scale of each one of them (fine versus large) to form the submodels which can be used in variation partitioning? I have read and tried most of the adespatial functions but I could not find the function which can help me calculate the scale of each MEM.
I really appreciate if anyone can help.
Mitra
Dear Victor,
Thanks a lot for your notes and the article. That was really helpful.
I have 11 sites and I got 10 MEMs by running dbmem on my sites` coordinates. Based on the explanation provided by you and the paper, I think MEM 1 and the few first ones have broader scale compare to the MEM 10 which is the last one, for example.
Also, the forward selection analyses showed only MEM 9, and Mem 10 as significant ones, so I am going to use only these two MEMs in my spatial model in variation partitioning.
Thank you again!
Mitra
Dear all,
I have posted another question I had on the website, I am copying the same question here too and I really appreciate if anyone has any comments that can help me with it.
I want to analyze the role of spatial vs environmental effect (through variation partitioning) on Notonecta species distribution among fishless ponds. I have been using adespatial package to do that.
After calculating the MEMs, I need to estimate the Moran`s I spatial autocorrelation values, in addition, to the positive and negative part of this index for the ten MEMs I have.
In adespatial package, in the dbMEM example of mite data, Moran`s I is calculated using moran.randtest, without using the listw function as follows:
test
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