Hi,

I have a set of telemetry data, which provides the foraging distances
travelled by birds at different breeding stage.  My aim is to find out if
birds are travelling different distances at different stages in the breeding
season. Telemetry readings were taken from 30 birds over a period of 3
months, producing around 100 records of distance travelled for each bird. I
have included Bird_ID as a random factor in my mixed model. My model so far
is as follows:

Mlmer1<-lmer(Distance~BreedingStatus +(Status|Bird_id), data=Distance)

I have a few of queries -

1) For one breeding stage eg. incubating female, the same bird, eg female
807, appears repeatedly. In addition, this bird will also be present
in other breeding stage groups, eg provisioning and chicks fledged groups.
Is it enough to have Bird_id in as a simple random effect in the model, or
is it important to allow for the fact that the same bird also occurs in some
(but not all) of the other breeding stage groups? I have been reading about
crossed random effects but am struggling to find relevant ecology examples.
I also don't think this is quite the same thing.

2) Some of the birds were in pairs, so I am thinking of adding 'Pair' in as
a second random effect. However, I'm not sure if I have a big enough sample
size to create a valid model. And would this be crossed or nested??

3) I have been trying mixed models in both R and SPSS. SPSS makes a point of
asking the user to choose an induced correlation structure for the model. Is
this something that should always be checked for? A lot of texts on this
topic for R seem to gloss over this. As I have repeated measures on the same
birds, it is likely that measurements for one bird will be more similar to
each other than those for another bird. Also measurements taken in June for
one bird may all be similar, compared to those in August for the same bird.
Any advice would be really useful.

Thanks for your time and apologies in advance for any glaring errors,
Best wishes,
Katrina

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