I began using GIS-enabled analyses to increase the spatial extent of my work (from plot-scale to continental), and took advantage of remote sensing and public data repositories to address how regional processes govern plant diversity.  I used statistical forecast models (OLS regression and Neural Networks) to draw linkages between the distribution of wetland habitats and the prevailing climate, and used these linkages to forecast shifts in habitat distribution for the Midwestern United States over the next 100 years (Garris et al., 2014).  In addition to identifying northern regions as ‘at risk’ for passive degradation (owing to reductions in spring flooding from snowmelt), our models predicted significant increases in wetland habitat throughout the central “Corn Belt”, indicating action is needed to mitigate increasing flooding in these regions to preserve both physical and food security.

Our lab has expanded on this approach to include additional modeling frameworks. Nathan Hawkins (Covenant 2020 Alum) used MaxEnt in R to simulate possible invasion dynamics for Asian Carp in the great lakes watershed under different climate scenarios.