Modeling spatial dimensions of Bison preferences on the Konza Prairie landscape ecology: An overview

ABSTRACT

In order to facilitate quantitative measures of the tallgrass prairie landscape ecology, particularly as it relates to impact of bison (Bos bison), we have used remote sensing and geographic information systems (GIS) for capturing, manipulating, processing and analyzing spatial data. In our paper we provide an overview of selected examples of the use of remote sensing and GIS in an attempt to characterize bison grazing spatial patterns.

Since 1991, bison on KPRNA have been observed twice per week from March through November with one observation during the week in the morning and one in the afternoon to insure accurate and timely sampling. Large-scale aerial photographs overlaid with a 30 meter grid system (rectified using a global positioning system (GPS)) were used for recording bison location during each field observation session. Each 30 meter cell has a unique field within ARCINFO GIS software that corresponds to the grid system on the field sheets.

The collected field data were then entered into ARCINFO via a custom macro (SML) developed by the researchers. Once the bison data were entered into the GIS, the data were then easily summarized for various time periods and analyzed relative to other GIS spatial data attributes (e.g. soil quality or vegetation condition based upon NDVI measurements). Although the complete range of findings from our research are not complete, the following section provides a selected sample review of our analysis results to date.

From earlier research based on data collected 1989-1991 (Nellis et al., 1992) using the GIS modeling approach, we were able to determine that bison preferred burned watersheds during April, May, and June, but showed less interest in these watersheds later in the growing season. This is probably explained in relation to the dynamics of watershed green-up. Burned watersheds (normal burning is late March/early April) have a more rapid green-up process in contrast to unburned watersheds. Therefore bison move to the tender, green grass as watershed rapidly responds to the field burning process. In contrast, soil, which influenced the potential productivity of vegetation, was a more significant component in bison grazing pattern from July through autumn based on GIS overlaying techniques. Generally, deeper, more productive soils become targets of bison grazing later in the summer. As grasses and forbs on burned and unburned watersheds develop toward senescence, soil factors, which created variability in the overall growth and vigor of the vegetation, tended to be more important in bison preferences. In addition, according to our GIS overlay procedures, access to water had no regular impact on bison grazing behavior.

In comparison with the pre-1992 data, we analyzed a similar series of GIS data sets for April through August of 1993. Although results were consistent with early analysis, bison appeared to stay on burned watersheds for a longer period of time (through August), and to follow watersheds that exhibited the highest NDVI values. Such NDVI values relative to bison grazing pattern are also consistent with findings of Harnett, et al. (1995), who found that bison grazing tended to increase plant species richness, evenness, and diversity on sites grazed by bison over a 4-year study period. In addition, increases in plant diversity components associated with bison grazing were generally greater in watersheds annually burned (as part of a control fire experimental treatment) than in watersheds burned on a 4-year rotation (Hartnett et al. 1996). Part of the longer bison lag on burned watersheds may be related to record rainfall amounts during the summer of 1993.

In order to further verify the relationship between bison grazing and tallgrass prairie heterogeneity we also used the fractal dimension. A test of the fractal dimension of patches using satellite data from the GIS database for multiple years by the researchers also supported these findings. The fractal dimension measurement suggested that bison grazing increased spatial heterogeneity in the tallgrass prairie landscape.

As we look to the future it will be important to refine our approach. To understand landscape dynamics associated with bison grazing will require more accurate spatial information associated with GIS approaches. Further, to adequately scale up beyond Konza Prairie to the Flint Hills to measure the impact of grazing on this ecosystem, it is imperative that researchers understand the impact of scale in their measurements. At sensor spatial resolutions of greater than 100 meters, drought and fire affect multiple contiguous pixels while grazing occurs on a smaller scale and occurs at sub-pixel scales. While proposed finer spatial resolution sensors could offer a potential solution to largescale grazing analysis, the associated costs seem prohibitive in the near future for use on the bison project. Therefore field observation data and its manual input into the GIS, as well as linkages to Landsat TM datasets (30 meter spatial resolution) will continue to offer important information on the grazing-landscape interface. Overall our research approach demonstrates some examples of how bison seasonal movements and general location in relation to watersheds and sub-watered units relate the watershed burning treatments, soil variability, and grassland species diversity.