I am trying to run some analysis to determine the percentage contribution
of various factors (minimum temp in lgm, npp, etc) on determining
species richness of 171 species throughout Southeast Asia (on a km by km
basis-so over 4 million rows of cells, and about 14 columns).
I have read about variou
Hi,
How is represented your data: a regular grid: raster, irregular samples:
points, or slots/parcels: areas?
If you want to determine species richness, you can use simpson and shannon
indexes with diversity package.
On a point pattern object, use spatstat::marktable function as mentionned in
h
Hi
Thanks.
I have species richness projections (made using Maxent projections of
species distribution of 171 species, reclassified to give binary estimates
of species distributions {10 percentile training threshold) before combining
all 171 projections).
So I have a raster of species richness in a
So, it seems you have one raster for your dependent variable: species
richness and one for each explanatory variable.
I have never accomplished this but I think you should make a raster stack of
all these variables using raster package: the dependent one + the
explanatory ones then fit a model, fo
