My colleagues and I conducted a study of the effects of an experimental translocation on the movement and activity patterns of common brushtail possums in New Zealand. This involved first capturing 12 individuals (6 males and 6 females), fitting them with GPS collars, and releasing each study animal within its home range. After seven days, we re-captured the animals, fitted them with new GPS collars, and then moved them to a common release site that was well outside of all of their home ranges. For both deployments the GPS collars recorded location data at 5-min intervals for an 11-h period during the night when possums are active. Our response variables were duration of nightly active periods, total distance moved per night, mean nightly speed and several other metrics that were descriptive of variation in movement behaviour. Our data are a bit messy (unbalanced) because we didn’t get observations from all animals each night of the two 7-d sampling periods (for various reasons), but basically look like this for each animal, with multiple response variables:
- Up to seven nights of movement data prior to translocation;
- Translocation event;
- Up to seven nights of movement data after translocation.
Our major research questions were: 1. Do males and females differ in the responses to translocation? 2. How do responses to translocation (as measured by activity, movement, etc) change with respect to the time (day) since the translocation event? 3. The interaction between 1 and 2 above.
As far as I can tell, these data are best analysed with a mixed-effects model with REML, because of their repeated-measures nature (both before and after translocation), missing values, and combination of fixed (sex) and random factors. From what I’ve read (Zuur’s book), classical repeated-measures ANOVA is inappropriate for several reasons.
My question is thus: what is an appropriate mixed-effects model formulation for these data in R using the ‘lme4’ package? There is a before/after effect (with respect to translocation), a repeated-measures effect (seven sequential days of data for each GPS collar deployment), and again the fixed effect of sex. I am confused on how to nest the data properly to incorporate the two different levels of temporal autocorrelation (i.e., before/after translocation and then the time series of each GPS deployment).
Any help with what is the correct model code for analysis in R would be hugely appreciated!!