Spatial Monte Carlo Simulation and Analysis of Climate Change Enhanced Fire and Projected Landscape-Scale Variation in Vegetation Heterogeneity

There is rising management concern regarding future increases in high-severity fire, conversion of conifer forests to shrublands and grasslands, and reduced landscape-scale vegetation heterogeneity. Our aim was to project future changes in landscape heterogeneity and fragmentation indices in mixed conifer forests over 100 years in response to climate change enhanced high-severity wildfire frequency. We developed a novel spatial Monte Carlo simulation (SMCS) model with three high-severity fire frequency scenarios based on historical and climate changed fire probabilities and cover type conversion in the Sierra-Cascade region of California, USA. The spatial Monte Carlo simulation process randomly sampled locations for fire events from the simulation space and randomly sampled fire spatial characteristics from the pool of historical fires. We calculated various landscape metrics using FRAGSTATS. The percentage of landscape covered by conifers declines and shrub cover increases with increased fire frequency. Heterogeneity is projected to increase with increasing fire probability. Conifers are more aggregated than shrubs in all scenarios, but shrubs are most aggregated with the climate changed fire regime. Our analysis demonstrates that areas with concentrated anthropogenic hazards, i.e., powerlines and railroads in our analysis region, already resembled in 2014 the climate changed landscape in 100 years. Spatial dynamics at broader levels of organization, involving a limited set of drivers, and with predictable vegetation responses can be analyzed using spatial Monte Carlo simulation. The SMCS method facilitates comparisons of scenarios, as well as field observations with modeled output to identify variance with regional mean outcomes, to target management actions.