Doctoral Dissertation Research: Assessing Connectivity Among Grizzly Bear Populations Near the U.S.-Canada Border

University Of Utah, Salt Lake City UT

Investigators

Abstract

The long-term maintenance and survival of the endangered species like the grizzly bear depends on the capabilities of small groups of animals to move and interact with other groups of the same species, thereby reducing the negative effects of an increasingly narrow gene pool over time. Grizzly populations inhabiting the Selkirk and Cabinet-Yaak ecosystems along the U.S.-Canada border are is dependent upon periodic interactions with other Canadian grizzly populations. Although researchers have studied many facets of grizzly activity, the have not yet determined how dispersed populations stay connected and what demographic and genetic effects current levels of connectivity have individual populations. Previous studies addressing connectivity between various populations of a species have used field studies to collect and analyze data or have taken a theoretical landscape ecology modeling approach. Though a landscape modeling approach allows examination of the issue from the correct spatial scale, it falls short of fully capturing the spatial dynamics of bear movement. Field methods and fine scale studies can capture the dynamics of bear movement, but because of low population densities and life history traits of bears in settings like the those along the U.S.-Canada border, the collection of data on bears at the appropriate spatial and temporal scales is very difficult. This doctoral dissertation research project will use an autonomous agent methodology, object-oriented design principles, and remote sensing and geographic information system (GIS) technologies to develop a spatially and behaviorally explicit, individual-based model replicating the movements of grizzly bears. Movements within and between local populations will be generated and analyzed by means of a computer simulation. More specifically, the project will examine the theoretical foundations of bear behavior across geographic space and develop a simulation model that evaluates the current level of connectivity between the different populations of grizzly bears. The latter task will be done by defining population boundaries between local populations, by determining the probability and frequency of bears in one population successfully dispersing and breeding in another population, and by determining the demographic and genetic effects that dispersals have on each of the individual local populations. The project also will identify any connectivity barriers between local populations. The integration of GIS and remote sensing technologies with theoretical models should provide a valuable new tool for inquiry and understanding. This integration will provide a level of spatial realism that cannot be achieved by theoretical modeling alone. Use of an autonomous agent approach should allows the explicit inclusion of the interaction between individuals and between individuals and their habitat. The inclusion will produce a more accurate model of wildlife spatial dynamics. Because of the increased spatial realism and dynamics, this project will advances the current understanding of how spatial structure influences animal movement. This is critical to the investigation of the importance of considering spatial structure when exploring the affects of human factors, such as human-induced landscape fragmentation and global warming, on endangered species persistence. In addition, this research will advance knowledge about the importance of including local interactions in the regional level assessment of ecological connectivity between geographically disjunct populations. This research will offer an advancement in bear conservation, because it will go beyond discerning potential corridors based on habitat to assessing the contribution of current corridors to the long-term genetic and demographic viability of each of the local populations. 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.