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, for instance using a linear regression
(lm function)

With the model, you'll get coefficients for each exp. variables and
p-values. You'll trhen see which ones are significant and contribute the
most to explain species richness.

Hope this helps..

Mathieu

2011/7/4 ah3881 <ah3881@bristol.ac.uk>

> 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 addition to all the possible
> correlates I am examining (npp, intra-annual variability of npp,
> inter-annual variability of npp, distance from coast, distance from karsts,
> latitude, lgm minimum temperature, lgm mean temperature, temperature change
> in minimum temperature since lgm, t change in mean temp since lgm, actual
> evapotranspiration)
> None of the correlates were used in species distribution projections and I
> have a gis raster of each variable, in addition to a database denoting the
> value of each for every km2 throughout Southeast Asia.
>
> Now I want to determine how much variability in species richness can be
> explained by variation in each of the other variables, in addition to the
> relationships. But I don't know which test would be best to do this, or
> software (GIS or database) which could best analyse this-especially given
> that the database is over 4520000 rows, and a large number of columns.....
> Advice would be greatly appreciated
>
> --
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