I D E A A N D P E R S P E C T I V E Patterns and causes of species richness: a general simulation model for macroecology

Nicholas J. Gotelli,* Marti J. Anderson, Hector T. Arita, Anne Chao, Robert K. Colwell, Sean R. Connolly, David J. Currie, Robert R. Dunn, Gary R. Graves, Jessica L. Green, John-Arvid Grytnes, Yi-Huei Jiang, Walter Jetz, S. Kathleen Lyons, Christy M. McCain, Anne E. Magurran, Carsten Rahbek, Thiago F.L.V.B. Rangel, Jorge Soberón, Campbell O. Webb and Michael R. Willig Abstract Understanding the causes of spatial variation in species richness is a major research focus of biogeography and macroecology. Gridded environmental data and species richness maps have been used in increasingly sophisticated curve-fitting analyses, but these methods have not brought us much closer to a mechanistic understanding of the patterns. During the past two decades, macroecologists have successfully addressed technical problems posed by spatial autocorrelation, intercorrelation of predictor variables and non-linearity. However, curve-fitting approaches are problematic because most theoretical models in macroecology do not make quantitative predictions, and they do not incorporate interactions among multiple forces. As an alternative, we propose a mechanistic modelling approach. We describe computer simulation models of the stochastic origin, spread, and extinction of species geographical ranges in an environmentally heterogeneous, gridded domain and describe progress to date regarding their implementation. The output from such a general simulation model (GSM) would, at a minimum, consist of the simulated distribution of species ranges on a map, yielding the predicted number of species in each grid cell of the domain. In contrast to curve-