Spatial and spatial-temporal change in the natural environment

自然环境的变化可能会产生严重的生态、社会、经济和政治后果，因此，人们对自然环境的变化越来越感兴趣。环境变化与空间和时间有关。例如，气候变化正在改变许多物种的空间范围（例如，北极熊），并增加了森林火灾的时间频率和严重程度。目前在不列颠哥伦比亚省流行的山地松甲虫被认为是气候变化和长期森林管理做法的结果，是环境变化如何产生破坏性后果的一个尖锐例子。数据收集和储存方面的技术改进（即，成像卫星、全球定位系统和地理信息系统）增加了空间数据的可用性。这些绘图技术与监测工作相结合，正在生成当前山区松甲虫爆发的大量空间和多时相数据集。诸如此类的数据为探索自然环境中的空间和时空变化提供了前所未有的科学机会;然而，需要有效的科学方法来调查这些数据，以应对这些新数据集所带来的独特挑战（例如，大面积覆盖、多个空间尺度、多个时间段、动态现象）。这项研究的重点是克服与这些大型，多时相变化数据集相关的方法局限性。将开发新的分析方法，以调查空间的变化，并通过空间和时间的变化，在森林景观上的山松甲虫侵扰的位置，程度和规模。这些调查将提高对甲虫动态的空间理解（例如，分散和扩散）在大面积上。开发的方法将广泛适用于其他大型空间和多时相数据集，并将成为检查流行病的宝贵工具（例如，禽流感），以及与环境变化相关的空间现象（例如，生物活动范围扩大或缩小，积雪覆盖变化）。

Change in the natural environment can have severe ecological, social, economic, and political consequences and as result, is of growing scientific interest. Environmental change is linked to both space and time. For instance, climate change is altering the spatial range of many species (e.g., polar bears), and increasing the temporal frequency and severity of forest fires. The current mountain pine beetle epidemic in British Columbia, believed to be the result of both climate change and long-term forest management practices, is a poignant example of how environmental change can have devastating consequences. Technological improvements in data collection and storage (i.e., imaging satellites, global positioning systems (GPS), and geographic information systems (GIS)) have increased the availability of spatial data. These mapping technologies, combined with monitoring efforts, are generating large spatial and multi-temporal data sets of the current mountain pine beetle outbreak. Data such as these provide unprecedented scientific opportunities to explore spatial and spatial-temporal change in natural environments; however, effective scientific methods for investigating these data are required to address the unique challenges these novel data sets present (e.g., large area coverage, multiple spatial scales, multiple time periods, dynamic phenomena). This research focuses on overcoming methodological limitations associated with these large, multi-temporal change datasets. Novel analysis methods will be developed to investigate changes in space, and changes through space and time, in the location, extent, and magnitude of mountain pine beetle infestations on the forested landscape. These investigations will improve the spatial understanding of beetle dynamics (e.g., dispersal and spread) over large areas. Methods developed will have widespread applicability to other large spatial and multi-temporal datasets, and will be valuable tools for examining epidemics (e.g., Avian Influenza), and spatial phenomena associated with environmental change (e.g., biological range expansion or contraction, snow cover change).