In this chapter we introduce developing and interpreting multilevel models. We first define multilevel models and explore how this approach is an improvement on disaggregation and aggregation of data across multiple levels. We then work through four different multilevel models. We provide examples of what kinds of questions can be answered by each model and how to interpret the statistical output. We then explore some additional issues in fitting multilevel models in Stata and consider additional applications of multilevel models.

This concluding chapter discusses the strengths and limitations of Hierarchical Modelling of Species Communities (HMSC) in light of the results presented in this book. Concerning the strengths, the chapter notes that HMSC is a unifying framework that encompasses classic approaches such as single-species distribution models and model-based ordinations as special cases, which hence provides simultaneous inferences at the species and community levels. As another key strength, the chapter notes that HMSC can be applied to many kinds of study designs (including hierarchical, temporal or spatial) and many types of data (such as presence–absence, counts and continuous measurements). The chapter further emphasises that HMSC offers the general advantages of model-based approaches, such as tools for model validation and prediction, and that it is especially well suited for predictive modelling of communities with sparse data. Concerning the limitations, the chapter discusses three areas where future development is needed: a broader set of data models, a broader array of model structures related to various ecological and evolutionary processes, and improved computational efficiency.