Lehmann lovegrass (Eragrostis lehmanniana) is an invasive non-native perennial grass introduced at multiple locations in the American Southwest in the 1930s to restore ground cover and forage production on overgrazed, eroded rangelands. Since that time it has aggressively expanded throughout the Sonoran Desert where now dominates many landscapes. The Chihuahuan Desert, in contrast, has been resistant to its expansion, with local E. lehmanniana patches being dynamic without a clear trend. Climate and soils are known to influence broad-scale distributions of E. lehmanniana. We hypothesize that at the landscape-scale, expansion will be affected by seed availability. Accordingly, we sought to identify the “hot spots” and “hot moments” of spread across a landscape with spatially heterogeneous soils, climate, and current patch locations that affect seed availability. Long-term climate and spatially-resolved soils data were used to parameterize and test a numerical ecosystems model at the Jornada LTER-USDA site in the northern Chihuahuan Desert. We simulated E. lehmanniana recruitment and spread either assuming (i) seeds were always available or (ii) seed availability was a function of the distance from an existing patch.
Results/Conclusions
When seed availability was not constrained, E. lehmanniana recruitment was largely confined to years with above-average annual temperature and variable precipitation. Soils with higher silt content intensified the climate effects on recruitment. When seed availability was constrained, distance from existing patches was the primary driver of E. lehmanniana expansion and it interacted with climatic and edaphic parameters and generate non-linear rates and patterns of spread. Areas downwind from established patches and areas near large patches had the highest E. lehmanniana establishment probabilities. This approach can be used to strategically (i) identify hot spots and hot moments of potential E. lehmanniana expansion into native Chihuahuan Desert ecosystems under dynamic and changing climatic conditions and (ii) prioritize the location of monitoring sites and the timing of their sampling.