[R] variable scale and transform confusion with glmm

[Sharada Ramadass]

Hello,
  I am a complete newbie to GLMM and R. I do understand some bit of
statistics though I am in no-way a core statistician. So, here are my
doubts and I would really appreciate if someone can provide some
inputs.
I have looked up for prior responses on various lists and could not
come up with satisfactory results that clear my confusion.
1. My problem is an ecological problem and I am trying to model growth
rate in trees as a response to various predictors (fixed and random).
So far, so good.
2. Literature tells me that people use RGR (relative growth rate) to
look at growth to account for girth size classes.
3. My AGR or RGR are very small values (mathemetically in terms of
numbers) since my timeline for the data is very short. That is my
limitation.
4. Some predictors have large values (orders of magnitude,
mathematically) while some other others have smaller values.
5. So I have very small values for my growth rate, very large values
for some predictors and all the other predictors are in a similar
range of values, mathematically.

Here are my questions:
1. Does using AGR (absolute growth rate) introduce any bias or
inflation in the model if we use AGR instead of RGR? One paper (stoll
1990) did mention the use of AGR over RGR to avoid skewness.
2. I get 'large variance' errors when running lmer on the model with
the raw data (both response and predictors). Is that a problem?
3.If I had to transform the data, should I transform it for all
predictors and response (independent of which ones are extreme in
their values in orders of magnitude)?
4. If I did apply some kind of transformation, how do you interpret
the parameter estimates? Do you need to undo the transformation to get
correct values? Some posts seem to indicate you need to un-transform
the results.
5. For transformation/scaling, I am confused as to what should be
done. Some posts suggested simply scaling the variables up/down my
multiplicative factors. Again should this be done for all predictors?
If done for only select few, do we need to interpret their parameter
estimates differently?
6. The scale function in R has also been suggested as a way to do the
scaling. This seems to center the mean and not necessarily have just a
multiplicative effect? Is this the function to use for transform?
Again, only for some variables or for all?
7. Can the response alone be transformed (log or scale) and results
interpreted as-is?
8. Is there a certain log transform only that should be applied (to
which base)? Again, some posts indicate you can transform to base 10
or natural log while others indicate log transform is natural log
only.

Thanks and Regards,
Sharada