Doctoral Dissertation Research: A Multi-Scale Analysis of Disturbance, Dynamics, and Climate-Growth Relationships in Mountain Longleaf Pine Forests, USA

本研究整合了地理空间和生态学方法，以研究气候，竞争，干扰和人类活动在调节美国东南部的古老和第二次生长的山地长叶松林的组成和结构中的作用。 山地长叶松森林是相对研究不足的危险和高度多样化的长叶松生态系统的组成部分，历史上占主导地位的沿海平原和部分的皮埃蒙特，山脊和山谷，西南阿巴拉契亚山脉。 这些山地森林位于长叶松生态系统的北方纬度和海拔范围边缘。 博士生Arvind Bhuta在弗吉尼亚理工大学的丽莎肯尼迪博士的监督下，将在多个山地长叶松站点对竞争和干扰进行高分辨率（时间和空间）重建。 将对山地长叶松森林中随机放置的研究地块进行森林结构和组成调查。 所有树冠内的树木将被识别，测量，参考全球定位系统，并取芯。 在地理信息系统中对森林计量的分析将记录从单个树种和群落到更广泛的生态系统的多个空间尺度上的森林进程和格局。 每棵树的年轮将被测量、标准化和统计分析。 树木年轮提供了树木生长，森林组成和结构的年度分辨率记录，以及气候，干扰和竞争的代理记录。 历史树木生长将与当前的森林指标，土地使用历史和气候记录相联系，以提供有关长期森林动态和该生态系统变化驱动因素的详细信息。 气候和长叶松径向生长之间的关系的调查将允许在范围边缘和其他网站之间进行比较，在整个范围内，以更好地了解该物种的气候响应的地理变化。山地长叶松林具有经济和生物价值。 它们的生物多样性很高，有丰富的地方性草本和脊椎动物物种，其中一些物种由于土地利用模式密集、支离破碎和自然火灾状况的破坏，现在已经稀少和濒临灭绝。 多个地点的森林动态的长期记录将为这些森林及其脆弱物种（如红冠啄木鸟和坎伯兰杜鹃）的恢复和管理提供合理的决策背景。 这项研究将通过提供有关生态系统和社区过程和模式的生态驱动因素的新信息，填补山区长叶松林知识的关键空白，这些生态驱动因素可能受到气候变化的影响。 山地长叶松森林的土地管理者的管理和恢复实践的基础上的研究，侧重于个人的立场和短的时间尺度，许多在低地网站，可能会有不同的生态功能。 相反，本研究将在大的时间和空间尺度上解释模式和过程。

This research integrates geospatial and ecological approaches to examine the roles of climate, competition, disturbance, and human activities in regulating the composition and structure of old-growth and second-growth mountain longleaf pine forests of the southeastern United States. Mountain longleaf pine forests are relatively understudied components of the imperiled and highly diverse longleaf pine ecosystems that historically dominated the Coastal Plains and parts of the Piedmont, Ridge and Valley, and Southwestern Appalachians. These mountain forests are at the northern latitudinal and altitudinal range margin for longleaf pine ecosystems. Doctoral student Arvind Bhuta, under the supervision of Dr. Lisa Kennedy at Virginia Tech will produce a high-resolution (temporal and spatial) reconstruction of competition and disturbance at multiple mountain longleaf pine sites. Randomly placed study plots in mountain longleaf pine forest sites will be surveyed for forest structure and composition. All canopy trees within the plots will be identified, measured, referenced with a global positioning system, and cored. Analysis of forest metrics in a geographic information system will document forest processes and patterns at multiple spatial scales ranging from individual tree species and communities to the broader ecosystem. Annual rings from each tree will be measured, standardized, and statistically analyzed. Tree rings provide an annual-resolution record of tree growth, forest composition and structure, and proxy records of climate, disturbance, and competition through time. Historical tree growth will be linked with current forest metrics, land-use history, and climate records to provide detailed information on long-term forest dynamics and the drivers of change in this ecosystem. The investigation of the relationship between climate and longleaf pine radial growth will allow for comparisons to be made between sites at the range margin and others throughout its range to better understand the geographic variation in the climate response of this species. Mountain longleaf pine forests are economically and biologically valuable. They are high in biodiversity and rich with endemic herbaceous and vertebrate species some of which are now rare and endangered due to intense land-use patterns, fragmentation, and the disruption of the natural fire regime. Long-term records of forest dynamics across multiple sites will provide context for sound decisions on the restoration and management of these forests and its vulnerable species, such as the red-cockaded woodpecker and the Cumberland azalea. This research will fill a critical gap in the knowledge of mountain longleaf pine forests by providing new information on the ecological drivers of ecosystem and community processes and patterns which could be influenced by climate change. Land managers of mountain longleaf pine forests have based management and restoration practices on studies that focused on individual stands and short time scales, many at lowland sites that may differ in ecological function. In contrast, this research will account for pattern and processes over large temporal and spatial scales.