I'm trying to analyse some data from a set of bird surveys. My response variable is "bird abundance", which is the number of birds counted over a five-minute period. These five-minute counts were conducted at ~200 sites. Counts were repeated three times at each site, although there are ~20 sites where only two counts were completed. I want to model bird abundance as a function of site-level attributes (habitat quality etc) as well as Count-level attributes (weather conditions at the time of the count etc).
So, I have ~200 sites and ~600 individual counts, but only 2-3 counts per site. My question is: given that I only have 2-3 counts per site, can I include site as a random factor to account for the nonindependence of counts within sites? (note I can drop the sites that have only two counts if necessary).
I've read conflicting information about the number of observations that you need within each level of the random factor. Ben Bolker’s paper on GLMMs in Trends in Ecology and Evolution says "5-6 random effect levels per random effect and 10-20 samples per treatment level or experimental unit", but then I've also read stuff that suggests using mixed models for repeated measures designs that take a pre-treatment, post-treatement, and follow up sample – ie only three observations within each level of the random effect.
Thanks for the help!
Jay
Best Answer
There's nothing about a random or mixed effects model that requires having a certain number of observations per level. In fact, if you have many observations per level then the random effect may not be necessary and you may be able to just include that as a factor variable.
You should be able to just specify the site as a random effect variable and proceed, and it shouldn't negatively affect the quality of your inference. As you note in the comment, the standard GLMM with the lme4 package ought to work fine for this.