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. Results
Machine learning models (MaxEnt and random forest) had the highest predictive performance for all species, with random forest being the most consistent for each analysis. BioClim and generalized linear model remained constant or only marginally improved with the addition of the grazing pressure.
Our findings suggest that model selection for SDM should include consideration of species prevalence, and machine-learning algorithms should be preferred when the target species is of low or unknown prevalence. Further, livestock grazing has measurable influence on grassland bird species’ distributions and should be included in SDMs as an indirect variable in addition to associated vegetative changes.