We constructed ENMs based on all parasite occurrences (all-occurrence model) and subsets of occurrences associated with a particular host. ENMs were built using MaxEnt (Phillips et al. 2006), a machine learning based method for modeling distributions based on presence-only data. Models were built using ENMeval (Muscarella et al. 2014) to test a variety of tuning parameters. Eight environmental variables were considered, including several associated with differential parasitism on host species (Wilson-Jones 1955, Mohamed et al. 2001), host exudation and production of hormones required for parasite germination (Yoneyama et al. 2013), or parasitism severity (annual rainfall; (Wilson-Jones 1955). Bioclimatic and topographic variables (annual rainfall, mean temperature of the wettest quarter, isothermality, potential evapotranspiration, and topographic wetness index) were obtained from CHELSA (Karger et al. 2017) and ENVIREM (Title and Bemmels 2018) datasets. Soil variables (clay content, nitrogen, and phosphorus) were obtained from SoilGRIDs250m or AfSoilGrids250m (Hengl et al. 2015, 2017). For AfSoilGrids and SoilGrids250m products, we downsampled to 1-km resolution using bilinear interpolation with the ‘resample’ function of the raster package (Hijmans 2020).  After downsampling, edaphic variables represent ‘average’ conditions experienced by a parasite and its close relatives, rather than local heterogeneity associated with a single individual.  To characterize the background of the study, we randomly sampled 10,000 points from within a 500 km radius of all occurrences; the same background points were used to build each of the host-specific models and the all-occurrence model. Habitat suitability values from an ENM of S. hermonthica are associated with a large effect resistance allele in sorghum landraces, supporting both the accuracy of the model and some degree of local host adaptation to S. hermonthica (Bellis et al. 2020).