Thanks for your suggestions. It looks like the R Borg is continuing to assimilate procedures that once required specialty software. Time to learn some new packages!

>You are right, I didn't read it that closely 1st time around. My point

>was that all of it can be done in R, and there are geospatial specific

>packages that have all the tests one might want. The bare minimum

>interaction is via rgdal or spgrass to pull data over from existing

>GRASS data sets. If the data isn't already in GRASS then rgdal one can

>read the original files directly. No need to pass csv around. Of course

>if it is in GRASS then you should have it all in the same projection

>already anyways if you put it all into the same mapset/location.

>

>The other book likely to have exactly what you want (field sampling

>design) is Ch 5.

>

http://www.amazon.com/Spatial-Analysis-Ecology-Agriculture-Using/dp/1439819130/ref=la_B001K6MGR8_1_1?s=books&ie=UTF8&qid=1394557436&sr=1-1>

>Enjoy,

>Alex

>

>On 03/11/2014 09:58 AM, Thomas Adams wrote:

>> Alex,

>>

>> I believe Tyler does plan on using R for the statistical analyses,

>but

>> using GRASS GIS in combination with R is the easiest path, I think.

>>

>> Tom

>>

>> On Tuesday, March 11, 2014, Alex Mandel <

[hidden email]>

>wrote:

>>

>>> Use R. It includes Moran's I and Geary's C tests for

>>> spatial-autocorrelation. Look like it has mantel too.

>>>

>>> You'll probably need the sp, spdep and rgdal packages. You might

>also

>>> want to use the Raster package to extract the sampling data, or you

>can

>>> use spGRASS to tie the R and Grass together.

>>>

>>> See chapter 9 (1st ed) of Applied Spatial Data Analysis with R.

>>>

http://www.asdar-book.org/>>>

>>> Enjoy,

>>> Alex

>>>

>>> On 03/11/2014 09:18 AM, Tyler Smith wrote:

>>>> Hello,

>>>>

>>>> We're preparing a field sampling program, and would like to

>determine

>>>> a minimum distance between samples to reduce/eliminate spatial

>>>> autocorrelation. I think a good approach would be to calculate a

>>>> mantel correlogram, and use the range of the correlogram as our

>>>> minimum sampling distance.

>>>>

>>>> * Questions

>>>>

>>>> 1) is this a reasonable approach

>>>> 2) if so, how best to do this?

>>>>

>>>> * Details

>>>> We have a vector map with the point coordinates of several hundred

>>>> potential sampling sites, and ~ 10 raster layers with appropriate

>data

>>>> to test for spatial autocorrelation (WORLDCLIM, soils). I could do

>>>> something like the following, but I'm not sure if there's a simpler

>or

>>>> more appropriate approach:

>>>>

>>>> 1) extract the raster data for each point

>>>> 2) save the data to csv; import into R

>>>> 3) calculate the spatial distances between points, after projecting

>>>> the lat-long data into an appropriate scale (?)

>>>> 4) calculate the climate distance using the WORLDCLIM data

>>>> 5) use the 'mgram' function in the 'ecodist' package to calculate

>the

>>>> actual correlogram between the spatial distance and climate

>distance

>>>>

>>>> Any suggestions on the approach or the methods would be welcome!

>>>>

>>>> Thanks,

>>>>

>>>> Tyler

>

>>

>

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