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依赖于小群体动物的移动能力和与同一物种的其他群体的相互作用，从而减少随着时间的推移日益狭窄的基因库的负面影响。居住在美国-加拿大边境的塞尔柯克和卡贝纳-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定期互动。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是如何保持联系的，以及目前的连接水平对单个种群有什么人口和遗传影响。以前研究一个物种的不同种群之间的连通性使用实地研究来收集和分析数据或采取理论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熊的运动动态，但由于在美加边境等环境中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是非常困难的。这个博士论文研究项目将使用自主代理方法、面向对象设计原则、遥感和地理信息系统（GIS）技术来开发一个空间和行为明确的、基于个体的复制灰熊运动的模型。当地人口内部和人口之间的流动将通过计算机模拟产生和分析。更具体地说，该项目将研究熊在地理空间中的行为的理论基础，并开发一个模拟模型来评估不同种群之间的联系水平。后一项任务将通过确定地方种群之间的种群边界，通过确定一个种群中的熊成功分散并在另一个种群中繁殖的概率和频率，以及通过确定分散对每个地方个体种群的人口和遗传影响来完成。该项目还将确定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将为探究和理解提供有价值的新工具。这种整合将提供一种仅靠理论建模无法实现的空间真实感。使用自主代理方法应该允许明确地包含个体之间以及个体与其栖息地之间的相互作用。这将产生一个更准确的野生动物空间动态模型。由于增加了空间现实性和动态性，该项目将推进当前对空间结构如何影响动物运动的理解。这对于探索人类活动对濒危物种持久性的影响（如人类活动引起的景观破碎化和全球变暖）时考虑空间结构的重要性至关重要。此外，这项研究将促进对在地理上不相交的种群之间的生态连通性的区域水平评估中包括当地相互作用的重要性的认识。这项研究将为熊的保护提供一个进步，因为它将超越根据栖息地识别潜在的走廊，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.