The spatial characteristics of paleoecological disturbances are critical to understanding the natural variability of environmental processes. However, many areas of paleoecological inquiry, particularly those relying on data collected at discrete points, such as pollen and charcoal studies, emphasize temporal variability and minimize or neglect the spatial dimension. This is particularly true of fire history research that is based on fire-scar data collected from individual trees. Many such studies result in a fire return interval that estimates how frequently a portion of the landscape burned. These fire-return intervals are spatially ambiguous and sensitive to the extent of the area for which they are calculated, making interpretation challenging. Furthermore, past studies that have addressed spatial variability have used discrete representations of fires, principally mapped outlines of the estimated fire extent. This format is difficult to evaluate because presents spatial and temporal variability separately, and it precludes independent interpretation. The purpose of this Doctoral Dissertation Research Improvement project is to develop an integrated spatial-temporal representation system for paleo-fire. The project will incorporate fire-history science, landscape ecology, and geographic information science, particularly space-time and spatial cognition theory. The doctoral candidate will use georeferenced fire-scarred tree data in a geographic information system. The representation will integrate field and object models of past fires and incorporate an iconic visualization language that will illustrate the change in state of individual trees in space and through time. This system will facilitate novel approaches to analyzing paleo-fire. A spatially explicit approach for estimating fire return interval will be presented. Spatial metrics extracted from the representation system will be used to evaluate the influence of top-down climatic factors and bottom-up topographic factors on spatial-temporal variability of past fires.
Current explosive wildfires can only be understood within their historical ecological context. This project will improve basic understanding of natural wildfire regimes by contributing new analytical methods and a cognitively sensitive approach to data representation. These new approaches to data representation, including maps of paleoecological fires, will be accessible to a wide range of interested parties, including scientists, managers, and concerned members of the public. This will improve the understanding of paleo-fire and present educational opportunities using complex paleoecological data. Finally, this project can benefit other types of paleoecological research where deep time point data are used. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.
