Re: Time series and GLS

Hi,

Just a few quick thoughts.

*) your success.gls model contains a linear effect for year.  Is this 
really likely over the time period you mention?  I would highly doubt it 
(but this is really just a guess).  If this is not the case then your 
residuals are likely to show falsely high autocorrelation, not because 
it is there but because the residuals come from an inappropriate mean model.
*) With the previous point in mind: have you considered using GAM 
models?  It seems like a perfect application as you can specify 
different smooth functions for each of the populations and then see if 
they really are all that different (through LRTs).
*) The GLS function will assume normality (albeit correlated).  Is this 
really all that believable?  In the GAM framework you could specify 
binomial data, an assumption that is much more likely to make sense.  Of 
course, your data may contain enough nests sampled and a favourable 
probability of success, to make the normality assumption very plausible.
*) The GAM model, when viewed as a random effects model, does specify a 
correlation structure amongst the outcomes.  It may not be the most 
appropriate correlation structure, nor even *an* appropriate structure 
but it may be a suitable starting place.  Most analysts would consider 
it a useful finishing place too (but you can extend the GAM model -- 
Richard Morton has done some work in this line although I can't find the 
reference right now). 

Be careful taking acf of residuals in GAM models -- the residuals from 
the model conditional on the random effects may not tell much about the 
correlation structure (need the marginal distribution for this).