Data-driven identification of grassland types
First, a total of 49 independent grassland locations with active prairie dog colonies within GPCA El Tokio were identified with the use of previous literature (Ceballos et al. 1993; Trevino-Villarreal et al. 1998; Scott-Morales et al. 2004; Estrada-Castillon et al. 2010), up-to-date Google Earth imagery and historical and present delimitations of colonies (provided by the Mexican organizations PROFAUNA and Organizacion Vida Silvestre A.C.-OVIS). In order to select a representative sample of sites covering the varying environmental conditions from these locations, a data-driven clustering approach was used. We used a self-organizing map (SOM), a type of artificial neural network that is trained using competitive learning and well suited to finding clusters within data, as implemented in the package kohonen (Wehrens and Buydens, 2007; R version 4.0, R Development Core Team 2020). Using geospatial data on mean annual precipitation, elevation, slope, soil type and mean annual temperature (see Appendix S1 for the specific data sets used), this analysis clustered all grassland locations into eight groups, four of which occupy most of GPCA El Tokio and are therefore here considered as distinct grassland types (Fig. 1a): 1) Agricultural (Agri): characterized by agricultural land use, xerosol haplic soils, total annual precipitation between 300 to 400 mm and temperature between 14-16 °C; 2) Arid: characterized by solonchak orthic soils, low elevation and total annual precipitation from 200-400 mm; 3) Calcareous (Calc): characterized by xerosol calcic soils, total annual precipitation between 300 to 400 mm, low elevation and temperatures between 14-16 °C and 4) Mountain (Mount): characterized by litosol, high precipitation ranging from 400 to 500 mm, temperature between 14-16 °C and high elevation.