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.

This chapter applies Hierarchical Modelling of Species Communities (HMSC) to a real dataset on Finnish birds, with the aim of using the case study to simultaneously demonstrate the many uses of HMSC. Specifically, it illustrates the full workflow of a typical HMSC analysis, shows how the researcher can access the full posterior distribution to go beyond the default outputs of HMSC analyses, shows how predictions of HMSC can be used as a starting point for further analyses as well as compares HMSC outputs to results obtained by other statistical methods in community ecology. The chapter starts by outlining the five steps of the HMSC workflow, and then shows how the researcher can access the entire posterior distribution of model parameters or predictions, e.g. for examining the level of statistical support related to either of these. Next the chapter illustrates how one may use HMSC predictions as a starting point for applied research, such as spatial conservation prioritisation or bioregionalisation. Finally, the chapter applies other widely used methods in statistical community ecology such as ordination methods and co-occurrence analysis to the same data, with the aim of comparing how their results relate to those obtained by HMSC.

This chapter covers the basics of generalised linear mixed models in the univariate context of single-species distribution modelling. The chapter starts by discussing how species distribution models relate to the theory on environmental species niches. The modelling part of the chapter first introduces the linear model, then moves to generalised models, then to mixed models with both fixed and random effects, and finally describes how the explained variance can be partitioned among the explanatory variables. The applied part of the chapter uses both simulated and real data to illustrate how the R-package HMSC-R can be used to analyse generalised linear mixed models. While these analyses are rather standard and could also be conducted with many other packages, the reader is encouraged to go through them, as they provide the simplest way of becoming familiar with the syntax of HMSC-R.