Topographic variables improve climatic models of forage species abundance in the northeastern United States

Question: Species distribution modelling has most commonly been applied to presence-only data and to woody species. Can similar methods be used to create detailed predicted abundance maps for forage species? These predictions would be of great value for agricultural management and land-use planning. Location: Northeastern USA. Methods: We used field data from 31 grazed farms to model abundances for six forage species with three statistical methods: GLM, GAM and Random Forest models. A hierarchical ecological framework encompassing climatic, edaphic and topographic variables related to the plant species requirements for water, light and temperature was used to guide variable selection. Results: Although many species distribution modelling studies have used only climatic variables, the inclusion of topography greatly improved explanatory power. Edaphic variables contributed little more beyond the information already provided by climate and topography. Random Forest models had higher overall predictive capability, and were used to produce the final potential abundance maps for the six forage species. Conclusions: Climate-only predictions may be suitable for state or regional planning, but topographic variables must be included in species distribution models used to support decision-making at the farm and field scales.