R help - Jan 2015 - Best method of analysis for negatively skewed longitudinal environmental data?

I have a dataset composed of a dependent variable (species percent cover) and
a range of abiotic variables (salinity, temperature, pH, water movement
etc). It is a longitudinal study, in which species percent cover was
measured once a month for five months. The abiotic variables were measured
using data-loggers every five minutes for the entire duration of the study.
I have organised those data as monthly means in order to compare them with
the species percent cover values. I have 13 study sites, at each of which I
used three settlement collectors (each of the collectors is composed of
three settlement plates). The goal is to determine the extent of influence
of the abiotic variables on the species growth.

I was initially planning on carrying out partial least square regression,
but I have been unable to account for the repeated measures aspect of the
study. I have very limited R experience and have been unable to write script
to carry out an appropriate analysis with PLS, so have been primarily been
using SPSS and JMP.

I am now trying to use Linear Mixed Models. The issue here is that the data
are heavily negatively skewed, given the large number of zero percent cover
points, especially as every single site I worked at started as zero percent
cover. There is also a smaller peak on the positive side where there are a
reasonable number of 100% values. In SPSS -> Analyze -> Mixed Models ->
Linear I use the settlement collectors as the Subject and Months as the
Repeated Measure. Then I use % cover as the Dependent Variable, Site as the
Fixed Factor and the abiotic variables of interest as covariates. Fixed
effects of abiotic variables and a random effect of the intercept and the
subjects. I am not sure if this is appropriate given the non-normality of
those data.

Any suggestions of alternative methods or enhancements of the ones I have
mentioned would be greatly appreciated.

Thanks!



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