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

像灰熊这样的濒危物种的长期维持和生存取决于小动物群体的移动能力和与同一物种的其他群体互动的能力，从而随着时间的推移减少日益狭窄的基因库的负面影响。生活在美国-加拿大边境塞尔柯克和雅克生态系统的灰熊种群依赖于与其他加拿大灰熊种群的周期性互动。尽管研究人员已经研究了灰熊活动的许多方面，但他们还没有确定分散的种群如何保持联系，以及目前的联系水平对个体种群的人口统计和遗传影响。以前关于物种不同种群之间的联系的研究使用了实地研究来收集和分析数据，或者采取了理论上的景观生态学建模方法。虽然景观建模方法允许从正确的空间尺度检查问题，但它不能完全捕捉熊运动的空间动态。野外方法和精细尺度的研究可以捕捉到熊运动的动态，但由于在美加边境等地熊的种群密度和生活史特征较低，在适当的时空尺度上收集熊的数据非常困难。这项博士论文研究项目将使用自主代理方法、面向对象的设计原则以及遥感和地理信息系统(GIS)技术来开发一个在空间和行为上明确的、基于个体的复制灰熊行动的模型。将通过计算机模拟产生和分析当地人口内部和之间的流动。更具体地说，该项目将研究熊在地理空间中行为的理论基础，并开发一个模拟模型，评估不同种群灰熊之间目前的联系水平。后一项任务将通过确定当地种群之间的种群边界，通过确定一个种群中的熊成功地在另一个种群中扩散和繁殖的概率和频率，以及通过确定扩散对每个单独的当地种群的人口和遗传影响来完成。该项目还将查明当地人口之间的任何连接障碍。地理信息系统和遥感技术与理论模型的结合应为查询和理解提供宝贵的新工具。这种整合将提供仅靠理论建模无法实现的空间现实主义水平。使用自主代理人办法应允许明确包括个人之间以及个人与其栖息地之间的互动。这一纳入将产生一个更准确的野生动物空间动态模型。由于增加的空间现实主义和动力学，这个项目将推进目前对空间结构如何影响动物运动的理解。这对于研究在探索人为因素对濒危物种持久性的影响时考虑空间结构的重要性至关重要，这些因素包括人为导致的景观破碎化和全球变暖。此外，这项研究将增进人们对在区域层面评估地理上分离的种群之间的生态连通性时包括地方互动的重要性的认识。这项研究将在熊保护方面取得进展，因为它将超越基于栖息地的识别潜在走廊，以评估当前走廊对每个当地种群的长期遗传和人口生存能力的贡献。作为博士论文研究改进奖，该奖项还将提供支持，使有前途的学生建立一个强大的独立研究生涯。

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.