Transition zones or ecotones between biomes are predicted to be particularly sensitive areas to directional changes in climate. However, for many ecotones, there is little understanding of the key processes that allow dominant species from adjacent biomes to coexist at transition zones and how differences in these processes affect species responses to changes in environmental conditions. The objective of this study was to examine the relationship between plant life history traits and patterns in dominance and composition at a grassland–shrubland transition zone in order to predict shifts in dominance with directional changes in climate. It was hypothesized that differences in life history traits allow species from adjacent biomes to coexist at this transition zone, and that these dominance patterns are dynamic through time as a result of species-specific responses to changes in climate. A mixed lifeform individual plant-based gap dynamics model (ECOTONE) was developed to examine consequences of differences in recruitment, resource acquisition, and mortality to patterns in species dominance and composition under a variety of soils and climatic conditions. This model is unique because it represents interactions among multiple potential dominant species that include congeneric species of one lifeform as well as herbaceous and woody lifeforms across multiple spatial scales. Similar to other gap models, ECOTONE simulates the recruitment, growth, and mortality of individual plants on a small plot through time at an annual timestep. ECOTONE differs from other gap models in the degree of detail involved in determining successful recruitment by each species and in the simulation of belowground resources. Individual plant root distributions and resource availability by depth are dynamic. Soil water content is simulated on a daily timestep and nitrogen is simulated monthly. Multiple spatial scales can be simulated using a grid of plots connected by seed dispersal. ECOTONE was parameterized for two soil types at the Sevilleta National Wildlife Refuge (SEV), a site located within the transition zone between two major biomes in North America. Shortgrass steppe communities are dominated by the perennial grass Bouteloua gracilis (blue grama) and Chihuahuan desert communities are dominated by the perennial grass Bouteloua eriopoda (black grama) or the shrub Larrea tridentata (creosotebush). Experiments were conducted to provide key parameters related to recruitment and growth that were supplemented with information from the literature for remaining parameters. Model output was verified using field estimates of cover and biomass for the three dominant species as well as other groups of species. Simulation analyses were conducted under current climate and for a directional change in climate. Nitrogen was assumed constant for all runs to allow a focus on water availability constraints as affected by climate. Under current climatic conditions, simulated biomass on sandy loam soils was dominated by B. eriopoda with smaller biomass of B. gracilis and other species groups. By contrast, simulated biomass on a loamy sand soil was codominated by B. eriopoda and L. tridentata with very small biomass attributed to other species groups. Under a GFDL climate change scenario of increased year-round temperatures and increased summer precipitation, vegetation patterns shifted to a clear dominance of biomass by B. eriopoda on both soil types. These results show that temporal partitioning of soil water is important to codominance by the two Bouteloua species, and that spatial and temporal partitioning of soil water is important for grass–shrub interactions. The results also suggest that global climate change may provide a mechanism for the recovery of B. eriopoda following shrub invasion in the Southwestern U.S. Thus, an individual-based modeling approach is capable of representing complex interactions among herbaceous and woody species as well as between congeneric species with different life history traits at a biome transition zone. This modeling approach is useful in improving our understanding of key processes driving these vegetation dynamics as well in predicting shifts in dominance as environmental conditions change in the future.