The carbon isotope composition (δ13C) of terrestrial vegetation and soils is required for a diverse set of research, including carbon cycle studies that utilize global atmospheric CO2 and δ13C data, as well as studies of animal migration and food web dynamics where the δ13C of plants and soils is imparted to animal tissues. We present δ13C maps for South America that correspond roughly to the year 2000, based upon predictions of the abundance and distribution of C3 and C4 vegetation, along with empirical measures of the δ13C of plant leaf and soil endmembers. Our approach relies upon the near‐universal restriction of C4 photosynthesis to the herbaceous growth form and the differing performance of C3 and C4 grasses in various climates, along with land‐cover and crop‐type distributions. Specifically, we predict the percentage cover of C3 and C4 vegetation in each 5‐minute grid cell (∼10 km) based on input gridded layers of vegetation growth form fractional cover, crop‐area/crop‐type distributions, and a high spatial resolution climate data. We develop a consistent set of rules to harmonize the different data layers. The δ13C of vegetation in South America is then estimated based on the C3/C4 composition in each land grid cell, assuming constant mean values for closed C3 tropical forest (−32.3‰), open C3 forest ecosystems (−29.0‰), C3 herbaceous cover (−26.7‰) and C4 herbaceous cover (−12.5‰). In addition to using the mean isotope values, we also incorporate the measured standard deviation for each category. Soil δ13C is estimated for the C4‐favored climate regions of South America using two, largely independent approaches: one that is derived from our vegetation δ13C prediction and one that is based on a previously published relationship between fractional woody cover and the δ13C of soil organic carbon. Finally, we present preliminary maps of relative uncertainty in the estimates of vegetation growth form, generated by integrating global measures of accuracy with local measures of neighborhood variability. These maps demonstrate that the highest uncertainty is found in savanna ecosystems, which contain the most heterogeneous vegetation cover and structure along with a high percentage of C4 grass cover.