The influence of vegetative changes due to livestock grazing on grassland birds is well-recognized because these birds are heavily influenced by vegetative structure. Traditionally, species distribution models (SDMs) use direct variables, resources that the animal consumes or requires to persist in an area (e.g., water) to define and project a species’ niche and distribution. Indirect variables, which are features the animal does not consume or require for persistence but with which it may still interact, are often excluded. Our objective was to improve the traditional SDMs projecting the distribution of three summer resident South Texas grassland birds (Northern Bobwhite Colinus virginianus, Eastern Meadowlark Sturnella magna, and Cassin’s Sparrow Peucaea cassinii) by incorporating livestock grazing pressure, an indirect variable, into five SDM algorithms: BioClim, generalized linear model, MaxEnt, boosted regression tree, and random forest. We collected data from the Coloraditas Grazing Research and Demonstration Area (CGRDA), a 7684-ha area located on the San Antonio Viejo Ranch (SAV) in South Texas. We used several relevant environmental characteristics to build SDMs and compared model performance (AUC and TSS) with and without grazing pressure as an indirect variable.