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，越来越受到科学的关注。环境变化既与空间有关，也与时间有关。例如，气候变化正在改变许多物种(如北极熊)的空间范围，并增加森林火灾的时间频率和严重程度。不列颠哥伦比亚省目前的山松甲虫疫情被认为是气候变化和长期森林管理做法的结果，是环境变化可能产生毁灭性后果的一个令人心酸的例子。数据收集和存储(即成像卫星、全球定位系统和地理信息系统)方面的技术改进增加了空间数据的可用性。这些测绘技术与监测工作相结合，正在生成当前山松甲虫暴发的大量空间和多时间数据集。这样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研究这些数据，以应对这些新的数据集面临的独特挑战(例如，大范围覆盖、多空间尺度、多时间段、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学限制。将开发新的分析方法来调查山松甲虫在森林景观上的位置、范围和规模在空间上的变化，以及在空间和时间上的变化。这些研究将改善对甲虫在大范围内的动态(例如，扩散和扩散)的空间理解。所开发的方法将广泛适用于其他大型空间和多时间数据集，并将成为审查流行病(例如禽流感)和与环境变化相关的空间现象(例如生物范围扩大或缩小、积雪覆盖变化)的宝贵工具。

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).