|Title||Simulated geographic distribution of an invasive grass, Eragrostis lehmanniana (Lehmann lovegrass): interactions between soils and climate complicate predictions|
|Publication Type||Journal Article|
|Year of Publication||2021|
|Authors||N. Burruss D, Peters DPC, Huang H, Yao J|
The invasive perennial grass, Eragrostis lehmanniana (Lehmann lovegrass), has expanded rapidly throughout the Sonoran Desert (SD) while remaining sparse and patchily distributed in the neighboring Chihuahuan Desert (CD). As temperatures and patterns in precipitation change, identifying the drivers limiting spread in the CD is needed. Our objectives were: (1) to identify the climatic and edaphic factors limiting recruitment of E. lehmanniana throughout the CD, and (2) to predict the edaphic and climatic locations in the CD where this species is expected to have higher probabilities of recruitment under future climatic conditions. We used a daily, multi-layer soil water model (SOILWAT) was parameterized and tested to develop recruitment parameters for E. lehmanniana using long-term data from the SD and CD. Recruitment was then simulated at 57 locations throughout the CD. Logistic regression was used to predict recruitment across the CD using climate and soil factors to create a map of simulated recruitment under current and alternative climate scenarios. Our results show that simulated recruitment under current climate was low for most of the CD. However, localized areas with high probabilities (> 0.8) occurred along the western transition between the CD and the SD, and in the southern extent of the CD. In general, the CD climate is too cool and dry for rapid and wide-spread invasion. However, increases to temperature and precipitation are likely to increase recruitment success. Interactions among soil, temperature, and precipitation were important to increases in recruitment of E. lehmanniana, and are expected to lead to heterogeneous increases in abundance as climate continues to change. This patchy increase in abundance will result in changes in its geographic distribution that will make predictions in the future challenging.