Erika, This article: http://dx.doi.org/10.1007/s11258-005-3495-x Behavior of Vegetation Sampling Methods in the Presence of Spatial Autocorrelation Plant Ecology Volume 187, Number 2 / December, 2006 used the gstat package to generate spatial patterns, and then sampled them using different quadrat layouts.
The basic workflow: Use gstat to generate fake vegetation patterns. Sample the data using your quadrat layout. Analyze as if it were real data. You need to decide in advance how big a pixel is, so how many pixels per quadrat your samples are: that will depend on the size of the plants you are working on. More detailed help would require knowing more about what you are trying to do. Sarah On Thu, Nov 20, 2008 at 2:34 PM, Erika Mudrak <[EMAIL PROTECTED]> wrote: > Hi everyone, > > I am trying to investigate how well a certain (quirky) quadrat layout will > uncover underlying spatial phenomenon of plant locatons. I am using the geoR > package to generate variograms, but I am noticing some interesting trends > that may be artifacts of the sampling design rather than the natural > phenomenon. > > I would like to conduct simulations where I generate a known spatial pattern, > superimpose the quadrat layout, sample from that, and analyze the resulting > variogram. I see that geoR has a sample.geodata() function, but I don't want > to pick random samples, I want to easily determine what my data would look > like if I could only see it through the quadrat layout. Is there any > function where I can easily do this? > > [I realize that there is a lot of literature about preferred quadrat layouts, > but the data was taken before I arrived at school, handed to me, and if I can > figure out how to work with it, then I get a prize - a degree!] > > Thanks > Erika > -- Sarah Goslee http://www.functionaldiversity.org _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
