Dear list,
I am seeking some thoughts/advice on whether my approach to this problem
(below) makes sense.

We have compiled a rather large dataset (n> 25,000 for most species of
interest) on the levels of a contaminant in fish covering 40 years and a
continental scale. We would like to investigate broad temporal changes
across a large geographic region. Because the data comes from a variety of
sources, with different resources and mandates for sampling fish, we do not
consider this dataset to be a "true random sample", but in the absence of
such, this is the best possible approximation to one.

Sites that are sampled over time are generally not sampled frequently
enough and with sufficient constraints (sample sizes, sizes of fish) to do
more focused analysis of temporal trends.

Having spent some time perusing the resources available on mixed models, I
think this offers the best choice for making some sense of this messy
dataset. I'm less inclined to try and estimate site specific slopes
(regressed over year) for sites that have low sampling effort.

Rather, I split the dataset into time periods (A,B and C) of ~ 15 year
blocks. (Note: the levels of this particular contaminant are known to
change very slowly over time), and assigned each site  to an ecoregion
based on geographic location. Thus, I am aiming to assess (if possible)
whether levels of contaminant in each ecoregion change over the time blocks
(A,B and C), where sites are assumed to represent a random selection of
possible locations within an ecoregion.

The variables of interest are as follows;
CONT=contaminant Conc.
WB_TYPE = waterbody type (lake, river)
PORT = portion (fillet, whole fish)
len=mean centered length of fish
REGION=Ecoregion (37 unique types)
TIME= time block (A, B or C)
wb_id=unique id of site

My initial thought was to specify the model with time and region as fixed
effects.

lmer(log(CONT) ~ TIME*REGION + WB_TYPE + PORT*len + (1|wb_id))

comparison of this model with one with only additive time and region terms
suggests that this improves the model fit and the interaction is probably
important.

I can test TIME and REGION interaction contrasts specifically using the
multcomp package and the results indeed suggest some regions have
significant changes between time blocks.

Or,

would it make more sense to specify the time and region effects as part of
the random terms with site nested within region, nested within time period?

lmer(log(CONT) ~ WB_TYPE + PORT*len + (1|TIME/REGION/wb_id))

I'm assuming (perhaps wrongly) that the conditional means and 95% CI could
be extracted and compared to assess changes within a region?

I'm aware that there are arguments that can be made to treat TIME and
REGION as either fixed or random, depending on the objective of the
analysis. I'm mainly seeking some clarification if a) my interpretation of
the specified model is correct, and b) if this makes sense with respect to
the initial problem.

Any thoughts or advice would be much appreciated.

thanks



-- 
David Depew
Postdoctoral Fellow
School of Environmental Studies
Queen's University
Kingston, Ontario
K7L 3N6

david.depew@queensu.ca

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