Hello,
When should a variable be specified as both a fixed effect AND as a random effect?  If a single variable is defined as both fixed and random, how does one interpret the coefficients?  Is it 'sloppy' practice to include it as both?  Should one be cautious specifying a variable twice, or is it actually more conservative to do so?

I am interested in your thoughts in general, but here is a simplified example if helpful:
I am interested in the date of arrival of house wren, a migratory bird, to each state in the US as a response to year, degree days (climate), latitude, longitude, state.  I primarily want to test if there is a temporal trend in earlier arrival in more recent years, as a result of recent climatic warming.

I specified the model as follows, treating state as a random group.  Because I am interested in testing a linear trend across years, I specified Year as a fixed (and continuous) effect.  But I am unsure as to whether I should ALSO specify it as a random factor, because I am interested in making inferences beyond the 10 years of data that I have and I don't have any specific interest in these 10 years over any other period.

lmer(Arrival ~ DegreeDays + Year + Longitude + Latitude + (1|State) )
OR
lmer(Arrival ~ DegreeDays + Year + Longitude + Latitude + (1|State) + (1|Year))

Thanks in advance for any help or suggestions,
Stephen.


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