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

这项研究结合了地理空间和生态学的方法，研究了气候、竞争、干扰和人类活动在调节美国东南部古老和次生山地长叶松林的组成和结构中的作用。山地长叶松林是危险和高度多样化的长叶松林生态系统中相对较少被研究的组成部分，该生态系统历史上主导着沿海平原和部分山前、山脊和山谷以及阿巴拉契亚山脉西南部。这些山地森林位于长叶松生态系统的北纬和海拔山脉边缘。博士生阿文德·布塔在弗吉尼亚理工大学丽莎·肯尼迪博士的指导下，将对多个山地长叶松地点的竞争和干扰进行高分辨率(时间和空间)重建。将对山地长叶松林地随机放置的研究样地进行森林结构和组成的调查。将对地块内的所有树冠树进行识别、测量、全球定位系统参考和取芯。地理信息系统中的森林指标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过程和模式。将对每棵树的年轮进行测量、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，以及随时间变化的气候、干扰和竞争的替代记录。树木的历史生长将与当前的森林指标、土地利用历史和气候记录联系起来，以提供关于长期森林动态和该生态系统变化驱动因素的详细信息。对气候和长叶松径向生长之间关系的调查将允许在山脉边缘的地点和整个范围内的其他地点进行比较，以更好地了解该物种在气候响应方面的地理变化。山地长叶松林具有很高的经济价值和生物价值。它们的生物多样性很高，并拥有丰富的特有草本植物和脊椎动物物种，其中一些物种现在由于紧张的土地利用模式、碎片化和自然火灾制度的破坏而变得稀有和濒危。多个地点森林动态的长期记录将为恢复和管理这些森林及其脆弱物种，如红冠啄木鸟和坎伯兰杜鹃等作出合理的决定提供背景。这项研究将通过提供可能受气候变化影响的生态系统和群落过程和格局的生态驱动因素的新信息，填补对山地长叶松林的认识空白。山地长叶松林的土地管理人员基于对个别林分和短时间尺度的研究，其中许多是在可能具有不同生态功能的低地地点进行的。相反，这项研究将解释大时间和空间尺度上的模式和过程。

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.