The terrestrial ecosystems of southern California are subject to periodic wildfires, but fire regimes have been altered historically by fire suppression, and a shift from natural lightning ignitions in summer to human-caused ignition sources and catastrophic fires during extreme fire weather. An altered fire regime has a profound influence on vegetation structure, composition and landscape pattern, and wildfire hazards for humans. In the proposed research, a computer model that simulates landscape dynamics (LANDIS, developed by David Mladenoff and others) will be used to predict the effects of changes in fire frequency, intensity and size on plant community succession, and on the resulting patterns of vegetation composition and structure at a regional scale in southern California. These changes to the fire regime may result from fire suppression, anthropogenic ignition, land use change, climate change, or interactions among these. They could result in local loss of plant species or communities (that are otherwise thought of as widespread, not rare), changes in habitat pattern with potentially negative effects on animals with limited dispersal ability or specific habitat requirements, and feedbacks to the fire regime (a more, or less, flammable landscape).
Simulation modeling is used to predict changes over spatial and temporal scales for which experimentation is impossible and monitoring data are sparse in space and time. The following will be addressed through modeling experiments: Do increased intervals between fires lead to large-scale changes in forest and chaparral species dominance and landscape age structure? Where do the predicted changes occur? The model results will be evaluated by a) testing for the significance of the difference in landscape patterns metrics resulting from replicated model runs; b) comparison with fire history maps; and c) comparison with site-specific patterns of vegetation dynamics reported in the literature and determined through re-survey of vegetation plots first surveyed in ca. 1930. Model predictions can yield recommendations for geographicallystratified long-term monitoring of future ecosystem dynamics.