Pacific Southwest Research Station

Predictive modeling of cheatgrass invasion risk for the Lake Tahoe Basin

Researchers are developing and applying a spatially explicit model of cheatgrass (Bromus tectorum L.) invasion risk in montane meadows of the Tahoe Basin by examining regional and local moisture gradients and other factors that affect plant invasion in meadows. The research will predict future impacts and suggest management solutions to cheatgrass invasion under projected climate scenarios.

Lead Researchers: Brian Anacker, and Susan Harrison, UC Davis; Hugh Safford, USFS Region 5

Final Report [pdf]

Project Summary

Cheatgrass (Bromus tectorum) is an exotic species of major concern that is increasingly detected in the Sierra Nevada. This notorious annual grass has been a major driver of ecosystem change in the intermountain west due to its tendency to dramatically increase the frequency of early season fires via the highly flammable nature of its early-drying herbage. While cheatgrass is present in some sites in the Lake Tahoe Basin (LTB), especially in drier, disturbed sites in the Carson Range, it is at a relatively early stage of invasion. Continued cheatgrass invasion and subsequent changes in fire regimes could result in increased frequencies of fire in forest and meadow habitats, loss of biodiversity and hydrologic function, and a lowering of habitat quality.  This project examined the relationship between climate and the invasion of cheatgrass. In an effort to forecast if climate change and disturbance will trigger further cheatgrass establishment and spread, the investigators modeled the environmental factors related to current cheatgrass distribution and abundance in the LTB. Examining the distribution of cheatgrass along environmental gradients allows them to develop a spatially explicit predictive risk model of cheatgrass invasion and to apply it under both the current climate and future climate scenarios. In addition, they incorporated parameters for disturbance to improve model accuracy and predictive value. The resulting information will be useful for creating management scenarios to resist species invasion, restore natural communities, and sustain biodiversity and ecosystem function in the face of changing climate.