I have percentage data and would like to see if these different variables have an affect on certain factors;
i.e., I have different habitats of an area e.g., improved grassland: 40%, arable: 15%, urban: 15%, woodland: 30% (these may not add up to 100% as I have removed certain habitats which I am not interested in). I want to see if any of these habitats have an effect on a) the density of bird species with in an area and b) the bird species richness.
So, my data looks something like this:
My question is: what is the best way to analyse this data? I have tried running a generalised linear model, but as many of the habitats come out as significant, it seems that I am almost picking and choosing my result. Also, and more significantly, there seems to be an issue with Simpson's paradox (e.g., there is a significant main effect of improved grassland, but a negative interaction when I look at two different types of sites (urban VS countryside sites); please see here). I then decided to run separate GLMs for each habitat, but this doesn't seem the most efficient way.
Would it be sensible to run a PCA? Or would this not be suitable due to a) them being percentages, and b) the fact that the habitats are already linked in some way?