Hey Graeme,

Check out the ecology section at the bottom of http://cran.r-project.org/web/views/Spatial.html and especially "vegan." Theres also a book http://www.amazon.com/Numerical-Ecology-R-Use/dp/1441979751/ref=sr_1_4?ie=UTF8&qid=1384379994&sr=8-4&keywords=ecology+r

Hope I could help! I'm not sure exactly what metrics you speak of. R is a blast though.

Alex

Http://www.r-bloggers.com/landscape-metrics-with-r-sdmtools-imagej-and-bio7/

Alex, thanks... I use vegan a lot, but what I mean is code to calculate things like connectivity, landscape diversity, mean nearest neighbour distance, etc, etc from land cover maps (the dark art of patch metrics - see, for instance, Monica Turner's book on landscape ecology or Gustafson's 1999 paper in Ecosystems [Gustafson, E. J. (1998). Quantifying landscape spatial pattern: what is the state of the art?. Ecosystems, 1(2), 143-156.] if I'm still not being clear!)

Giorgio, that looks like a potentially useful link - I'll have a look :)

Hi Graeme. There is a QGIS plugin called LecoS by Martin Jung that calculates landscape ecological metrics. QGIS also has plugins to link it to R (e.g. ATOR).

Thanks Klaus, I'll have a look. I've still found nothing that fits what I really need, unfortunately, which is a tool with a wide range of functions that I can use to bootstrap in R without needing to go via a GIS. SDMTools is a good start but offers limited numbers of metrics.

Hi Graeme,

I liked the Geospatial Modeling Environment which is a scripting interface that can also call R functions. It might not have all the tools you need but it might be worth a look.

Check "dismo" in R , it has function to calculate the methics in Fragstat

Hi, did you find a solution for you question or still looking ?

Fragstats could be used, and it has the option to batch process data.

Hi Paulo, SDM tools still seems the best option, but what I would really like is Fragstats capability in an R environment. Jorge, Fragstats is great but it has no bootstrap function, I need the ability to randomly move shapes around to derive null models for autocorrelated, non-normal variables. And Li, dismo is handy too, but doesn't do most traditional metrics...