Disturbance Models

Natural disturbances play an instrumental role in the distribution, composition, and productivity of the Earth's vegetation. Although disturbances are, by definition, infrequent events, their effects can be diverse and long lasting (stress/disturbances). A suite of statistical measures are commonly used to describe the frequency, intensity (physical energy), severity (impact on biotic components), and spatial dimensions of disturbances. These descriptors define a disturbance regime and provide a synoptic method for comparing different disturbance types in different landscapes. For instance, the fire regime of dry western coniferous forests has a short return interval, high intensity, and high severity resulting in communities dominated by fire-adapted species. Altering this regime (e.g., by suppressing fire) will also alter the age structure and composition of the vegetation.

Disturbance models that provide a spatially explicit description of pattern-process dependencies are true landscape models. Simple models based on cellular automata have been an effective tool for representing the heterogeneity of landscape patterns and the process of disturbance spread (cellular automata). Although these simple models have limited management applications, they have been effective for simulating change over large areas and long timescales. Consequently, models based on cellular automata have helped define the dependency between disturbance regimes and resulting changes in vegetation patterns. Simple models are also ideally suited for characterizing the consequence of climate-induced changes in disturbance regimes that ultimately affect the evolution of landscape pattern over timescales ranging from centuries to millennia.

Fire, like other disturbances, requires detailed models for assessing complex disturbance effects on carbon sequestration, changes in species composition, hydrologic impacts and climate change, and the alteration of biogeochemical pathways (fire). Unfortunately, a coherent and consistent approach has not yet been developed that will guide disturbance model formulation and testing. In the absence of a well-defined theoretical foundation, complex disturbance issues have been forced to depend on approximations based on experience and local conditions. This lack of rigor is compounded by data limitations which increase as the scale of the problem expands from meters to many kilometers. For instance, highly accurate fire models developed by carefully controlled laboratory experiments require detailed information regarding fuel moisture and structure for accurate predictions. Unfortunately, these details cannot be obtained at landscape scales. In addition, the downwind dispersal of fire brands, which are a major component of fire spread at landscape scales, are variable events dependent on local topography, fuels, and weather conditions.

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