I have an ecological experiment for which I need to analyze bird count data. Here is the set up:
2 treatments (open/control), 3 regions. Not quite a full 3x2 factorial because in 2 regions there are 3 plots (250m x 250m) for both open & control (6 plots). For “reference” region there are just 3 plots (more similar to open plots in the upstream region).
Birds were counted weekly (can be considered independent) in each plot over 4 years. Bird type & densities are highly dependent on water depth (have depth for each survey date) – would like to use as covariate (note: the response isn’t linear, it’s unimodal, and it differs by species).
The goal is to examine treatment vs control differences in numbers of types of birds, & examine differences/interactions of region & year (as open plots mature & vegetation grows in). The closed plots contain many zeros (I may need zero-inflated model).
The literature is pointing me towards negative binomial or zero-inflated Poisson GLM. If I choose to do this, though, I have following questions:
- Can I nest plots within region for this type of model? I wanted unit of replication to be the week for each treatment, without sacrificing replication for each treatment x region plot.
- Also, any suggestions on what to do with a covariate like depth? It could really dominate the terms of a GLM.
I'm still learning R, but assuming based on this forum & speaking with others that I will need to do this in R or SAS. Advice is welcome on the software front, including what R packages I'll need to load.