18.5 Next steps

This chapter has provided an overview of GLMs that we can use for count data in Bayesian inference, and demonstrates one way to handle cases of skewed counts or (more extreme case) zero-inflated counts. These are common “problems” in biological and ecological data that are easily resolved within the flexible framework of GLM, which includes all of the other models we’ve looked at since Chapter 6. These tools have been extended in both MLE and Bayesian frameworks within the mark-recapture and occupancy realms that I strongly encourage you to check out if you think these data are representative of the kinds of data you collect - this should get you started though! In Chapter 19 and Chapter 20 we’ll look at how to extend this framework even further (another umbrella) to include repeated observations and relatedness between groups when we introduce Bayesian hierarchical models.