Search for: All records

Abstract Models that are both spatially and temporally dynamic are needed to forecast where and when non-native pests and pathogens are likely to spread, to provide advance information for natural resource managers. The potential US range of the invasive spotted lanternfly (SLF, Lycorma delicatula ) has been modeled, but until now, when it could reach the West Coast’s multi-billion-dollar fruit industry has been unknown. We used process-based modeling to forecast the spread of SLF assuming no treatments to control populations occur. We found that SLF has a low probability of first reaching the grape-producing counties of California by 2027 and a high probability by 2033. Our study demonstrates the importance of spatio-temporal modeling for predicting the spread of invasive species to serve as an early alert for growers and other decision makers to prepare for impending risks of SLF invasion. It also provides a baseline for comparing future control options. 

Increasing population and rural to urban migration are accelerating urbanization globally, permanently transforming natural systems over large extents. Modelling landscape change over large regions, however, presents particular challenges due to local-scale variations in social and environmental factors that drive land change. We simulated urban development across the South Atlantic States (SAS), a region experiencing rapid population growth and urbanization, using FUTURES—an open source land change model that uses demand for development, local development suitability factors, and a stochastic patch growing algorithm for projecting alternative futures of urban form and landscape change. New advances to the FUTURES modelling framework allow for high resolution projections over large spatial extents by leveraging parallel computing. We simulated the adoption of different urban growth strategies that encourage settlement densification in the SAS as alternatives to the region’s increasing sprawl. Evaluation of projected patterns indicate a 15% increase in urban lands by 2050 given a status quo development scenario compared to a 14.8% increase for the Infill strategy. Status quo development resulted in a 3.72% loss of total forests, 2.97% loss of highly suitable agricultural land, and 3.69% loss of ecologically significant lands. An alternative Infill scenario resulted in similar losses of total forest (3.62%) and ecologically significant lands (3.63%) yet consumed less agricultural lands (1.23% loss). Moreover, infill development patterns differed qualitatively from the status quo and resulted in less fragmentation of the landscape. 

Abstract. While there are numerical landscape evolution modelsthat simulate how steady-state flows of water and sedimentreshape topography over long periods of time,r.sim.terrain is the first tosimulate short-term topographic changefor both steady-state and dynamic flow regimesacross a range of spatial scales.This free and open-sourceGeographic Information Systems (GIS)-based topographic evolution modeluses empirical models for soil erosionand a physics-based modelfor shallow overland water flow and soil erosionto compute short-term topographic change.This model uses either a steady-stateor unsteady representation of overland flowto simulate how overland sediment mass flows reshape topographyfor a range of hydrologic soil erosion regimesbased on topographic, land cover, soil, and rainfall parameters.As demonstrated by a case studyfor the Patterson Branch subwatershedon the Fort Bragg military installation in North Carolina,r.sim.terrain simulates the development offine-scale morphological features includingephemeral gullies, rills, and hillslopes.Applications include land management, erosion control,landscape planning, and landscape restoration.