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I am struggling conceptually with how I can best model my dataset. My data is the abundance of spiders collected from 20 permanent traps in a forested area. These traps have been repeatedly re-sampled every summer for ~15 years.
I have two sets of explanatory variables that I wish to model. The first is climatic data, namely rainfall and temperature. These have been obtained for each sampling year, so they are the same for all permanent traps. The second is geophysical data, including slope, elevation, aspect etc. These have been obtained through an online terrain model, so are unique to each permanent trap, but are obviously the same for each sampling year.

I'm trying to understand conceptually how to take these different scales of explanatory variables into account. Climate is regional, top-down whereas geophysical is local and bottom-up. I am after a relatively simple procedure if possible.

Any suggestions would be greatly appreciated.

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  • $\begingroup$ When you say "abundance" do you mean that you have a count of spiders collected in each trap for each of the ~ 15 summers? $\endgroup$ – StatsStudent Oct 30 '15 at 2:48
  • $\begingroup$ Yes that's correct, abundance is the number of individuals caught per trap. $\endgroup$ – slam200 Oct 30 '15 at 22:13

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