Are communities composed of non-interacting (i.e., neutral) mixtures of species that reflect chance and contingency or do they represent tightly co-evolved deterministic systems that reflect the environment and species interactions? To address this general question, we have developed and tested stochastic and deterministic models of community assembly using meta-analyses and field data we collect. We found that a simple stochastic sampling processes may be a reasonable approximation of a tallgrass prairie species-area time relationships (STARs) suggesting caution when interpreting the STAR as a signature of deterministic processes (McGlinn and Palmer 2009). We also found that the spatial structure of environmental variation in grasslands and savannas influences the rate of spatial species accumulation supporting a deterministic rather than stochastic model of community assembly (McGlinn and Palmer 2011). We used an individual-based model to demonstrate that neutral competition for space and demographic stochasticity can result in realistic diversity-productivity relationships (McGlinn and Palmer 2010).
We have also extended and tested the Maximum Entropy Theory of Ecology (METE), a constraint based neutral model, to predict patterns of plant species turnover and stem diameter using only input on measured total number of species, individuals, and biomass from a large collection of mapped tree stands (McGlinn et al. 2013, McGlinn et al. 2015, Xiao et al. 2015). METE accurately predicted patterns of species occupancy but not spatial turnover which suggests the need to incorporate information on the environment and dispersal limitation when modeling turnover but not occupancy. We successfully scaled-up METE to a continental scale to predict patterns of relative abundance and occupancy by predicting its input parameters using spatial machine learning algorithms (McGlinn and White in prep). In the future, we think ecologist will find that these kinds of multi-scale models will increase the accuracy diversity forecasts. Ultimately, if we are going to predict future trajectories in biodiversity we need to understand which processes are the most important in shaping the dynamics of the community.