Predictive Mapping of Low-Density Juniper Stands in Prairie Landscapes of the Northern Great Plains

Abstract

Expanding distributions of native juniper species have had significant ecological and economic impacts on prairie ecosystems of the Great Plains. Juniper encroachment reduces rangeland production by decreasing herbaceous biomass and affecting natural ecosystem functions as it alters other native plant communities, microclimates, and soils. Juniper distribution maps are needed to support proactive management, but they often underestimate the extent of low-density juniper stands. Our objectives were to extend a previous juniper mapping study by 1) fitting a predictive ecological model for low-density (< 15% fractional cover) juniper stands and assessing the classification accuracy, 2) determining the habitat variables that had the strongest associations with low-density juniper, and 3) applying the model to map low-density juniper stands, where proactive management has the greatest potential for stopping further juniper encroachment. The study area included counties bordering the Missouri River in southeastern South Dakota and northeastern Nebraska covering approximately 23 000 km2. Environmental predictors included seed source distance and density, as well as topography, climate, soils, and land use variables. Areas of low-density juniper were identified by visual interpretation of sample plots from digital aerial photography. We used a machine-learning approach to classify low-density juniper with the random forests algorithm. Model accuracy was high with an area under the receiver operating characteristic curve of 0.884. Variables related to seed sources were the most important predictors, and precipitation, slope angle, and the local intensity of human land use also had substantial influences. A previous map based on Landsat imagery identified 209 968 acres (84 971 ha) as juniper with in the study area, and this study found an additional 430 648 acres (174 277 ha) classified as low-density juniper stands. These results can provide agencies and land managers with more accurate information about the distribution of juniper, and the underlying techniques can be extended to map woody plant encroachment in other areas.