Hello list members,

I'm planning to model species presence / absence data (N = 523) that was
collected over a geographic area. Samples come from preferential sites (sea
level > 1200 m, obligatory presence of permanent waterbodies, etc). I want
to infere on environmental factors determining the occurrence rate of
several amphibian species.

I want to use partykit::ctree because the method is intuitive and the
output is very illustrative.  I want to use the resulting plots as decision
trees for practitioners. Generally, I need to keep the analyses as simple
as possible.

May only problem is that I'm afraid that there is some amount of
autocorrelation in my variables. I'm thinking about simply using a
conservative minimal criterion - say, p > 0.9 or 0.8 and live with it -
what do you think? (edit: sorry, of course this should say p > 0.99 or
0.999)

Which tests for detecting spatial autocorrelation within dichotomous
responses do you suggest?

Or, can I consider the spatial correlation being irrelevant if I assume
that it is allowed for by the covariates?

Any ideas would be greatly appreciated!

Thanks,
Kay

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