Ecological Impacts of the Cerro Grande Fire: Predicting Elk Movement and Distribution Patterns in Response to Vegetative Recovery through Simulation Modeling October 2005

In May 2000, the Cerro Grande Fire burned approximately 17,200 ha in north-central New Mexico as the result of an escaped prescribed burn initiated by Bandelier National Monument. The interaction of large-scale fires, vegetation, and elk is an important management issue, but few studies have addressed the ecological implications of vegetative succession and landscape heterogeneity on ungulate populations following large-scale disturbance events. Primary objectives of this research were to identify elk movement pathways on local and landscape scales, to determine environmental factors that influence elk movement, and to evaluate movement and distribution patterns in relation to spatial and temporal aspects of the Cerro Grande Fire. Data collection and assimilation reflect the collaborative efforts of National Park Service, U.S. Forest Service, and Department of Energy (Los Alamos National Laboratory) personnel. Geographic positioning system (GPS) collars were used to track 54 elk over a period of 3+ years and locational data were incorporated into a multi-layered geographic information system (GIS) for analysis. Preliminary tests of GPS collar accuracy indicated a strong effect of 2D fixes on position acquisition rates (PARs) depending on time of day and season of year. Slope, aspect, elevation, and land cover type affected dilution of precision (DOP) values for both 2D and 3D fixes, although significant relationships varied from positive to negative making it difficult to delineate the mechanism behind significant responses. Two-dimensional fixes accounted for 34% of all successfully acquired locations and may affect results in which those data were used. Overall position acquisition rate was 93.3% and mean DOP values were consistently in the range of 4.0 to 6.0 leading to the conclusion collar accuracy was acceptable for modeling purposes. SAVANNA, a spatially explicit, process-oriented ecosystem model, was used to simulate successional dynamics. Inputs to the SAVANNA included a land cover map, long-term weather data, soil maps, and a digital elevation model. Parameterization and calibration were conducted using field plots. Model predictions of herbaceous biomass production and weather were consistent with available data and spatial interpolations of snow were considered reasonable for this study. Dynamic outputs generated by SAVANNA were integrated with static variables, movement rules, and parameters developed for the individual-based model through the application of a habitat suitability index. Model validation indicated reasonable model fit when compared to an independent test set. The finished model was applied to 2 realistic management scenarios for the Jemez Mountains and management implications were discussed. Ongoing validation of the individual-based model presented in this dissertation provides an adaptive management tool that integrates interdisciplinary experience and scientific information, which allows users to make predictions about the impact of alternative management policies