Buffelgrass, a highly competitive and flammable African bunchgrass, is spreading rapidly across both urban and natural areas in the Sonoran Desert of southern and central Arizona. Damages include increased fire risk, losses in biodiversity, and diminished revenues and quality of life. Feasibility of sustained and successful mitigation will depend heavily on rates of spread, treatment capacity, and cost–benefit analysis. We created a decision support model for the wildland–urban interface north of Tucson, AZ, using a spatial state-and-transition simulation modeling framework, the Tool for Exploratory Landscape Scenario Analyses. We addressed the issues of undetected invasions, identifying potentially suitable habitat and calibrating spread rates, while answering questions about how to allocate resources among inventory, treatment, and maintenance. Inputs to the model include a state-and-transition simulation model to describe the succession and control of buffelgrass, a habitat suitability model, management planning zones, spread vectors, estimated dispersal kernels for buffelgrass, and maps of current distribution. Our spatial simulations showed that without treatment, buffelgrass infestations that started with as little as 80 ha (198 ac) could grow to more than 6,000 ha by the year 2060. In contrast, applying unlimited management resources could limit 2060 infestation levels to approximately 50 ha. The application of sufficient resources toward inventory is important because undetected patches of buffelgrass will tend to grow exponentially. In our simulations, areas affected by buffelgrass may increase substantially over the next 50 yr, but a large, upfront investment in buffelgrass control could reduce the infested area and overall management costs.
Nomenclature: Buffelgrass, Pennisetum ciliare (L.) Link
Management Implications: Knowledge of where invasive species occur is often slim to nonexistent. In the face of this imperfect knowledge, land managers are still required to determine where to allocate their limited resources. Using a decision support model such as TELSA allows land managers to make a more informed decision on where to allocate funding. We addressed this imperfect knowledge in three ways. First, we acknowledged that there were many undetected buffelgrass plants on the landscape by stochastically adding and growing patches across the landscape throughout a 50-yr simulation. This is a way to see how populations that are not detected grow over time. We also developed a map of potentially suitable habitat to predict the future spread of buffelgrass patches. Finally, we calibrated spread rates by comparing past and current aerial photographs with simulation outputs. We found that areas invaded by buffelgrass may increase substantially over the next 50 yr, but that a large, upfront investment in buffelgrass control could reduce that area and overall management costs. The application of sufficient resources toward inventory is important because patches that remain undetected will tend to grow exponentially and, when eventually detected, will require substantially higher treatment efforts to control.