SESAM - a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages

Abstract

Explaining the spatial and temporal distribution of biological diversity on Earth has been a research focus since the days of Alexander von Humboldt, Augustin Pyramus de Candolle, Alfred Russel Wallace and Charles Darwin, and it remains one of the major focuses in biogeography and macroecology. Understanding the processes governing the distribution and assembly of biological communities has become a prerequisite for successfully predicting how the world will look in the wake of global environmental changes. Currently, two distinct predictive spatial modelling approaches prevail (Ferrier & Guisan, 2006), which rely on two theoretical paradigms. The first paradigm focuses directly on realized properties of species assemblages (e.g. Brown, 1995), such as richness, and the methods used include macroecological modelling (MEM; see Gotelli et al., 2009). The second paradigm focuses on aggregate properties of individual constituent species, used to reveal the properties of assemblages (e.g. Lortie et al., 2004; Ackerly & Cornwell, 2007) and applies species distribution modelling (SDM; see Guisan & Thuiller, 2005; Elith & Leathwick, 2009) to a spatial stack of species. The properties of species assemblages include the number of co-occurring species (richness), inter-specifc abundance patterns, and compositional (e.g. community types), functional and structural characteristics. Hereafter, all of our examples use species richness, the simplest measure of biodiversity and the most commonly considered property of species assemblages (Whittaker et al., 2001). In MEM, species richness is predicted directly, either based on theoretical expectations or from various factors thought to control the number of species able to coexist in a geographical unit (Fig. 1, top). The main controlling factors are typically hypothesized to be available energy, environmental heterogeneity, disturbance or history, with scale effects and some level of stochasticity (Whittaker et al., 2001; Currie et al., 2004; Mittelbach et al., 2007; Field et al., 2009; Gotelli et al., 2009). The same approach can be used to model any other property of communities, although different hypotheses and explanatory variables are likely to apply to each property. MEM is typically Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland, Center for Macroecology, Evolution and Climate, University of Copenhagen, Copenhagen, Denmark