RUI: Canopy Tree Mortality and Regeneration in Southern Appalachian Gaps Sampled Thrice

RUI：阿巴拉契亚山脉南部间隙的树冠死亡和再生三次采样

• 批准号: 9006779
• 负责人: James Runkle
• 金额: $ 4万
• 依托单位: Wright State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1990
• 资助国家: 美国
• 起止时间: 1990-07-01 至 1992-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9006779&HistoricalAwards=false
• 关键词: RUI; Canopy; Tree; Mortality; Regeneration

This research will improve understanding of the dynamics of tree death replacement in old-growth forests. Specific goals are to investigate the species-and size-specific growth and death rates of canopy trees, gap characteristics associated with canopy tree death, vegetation development in gaps, and the dynamics of dead trees. These goals will be met by resampling canopy trees and gap vegetation originally sampled in old-growth forests of the southern Appalachians. The combinations of data sets on growth and mortality will allow turnover in the forests to be studied to a degree impossible elsewhere. These forests are the best example in the eastern United States of forests likely to be in long-term equilibrium in terms of species composition and ecosystem properties. They will serve as a valuable control for interpreting changes in other eastern forests; e.g., determining whether human activity or changing climate is causing more or different changes than usual. This study also will demonstrate how different aspects of species life histories (e.g., canopy longevity versus gap-capturing ability) can result in their coexistence in diverse forests. Findings from the analysis will contribute directly to the knowledge base needed to predict the effects of global change.

这项研究将提高对动态的理解， 在古老的森林中树木死亡的替代。 具体目标是 来研究特定物种和特定大小的生长和死亡 林冠树比率，与林冠有关的林隙特征 树木死亡，植被发展的差距，和动态 死树 这些目标将通过重新种植树冠树木和gap来实现 最初在原始森林中取样的植被 南阿巴拉契亚山脉 关于增长和死亡率的数据集的组合将 允许在一定程度上研究森林的周转 在别处是不可能的。 这些森林是最好的例子 美国东部的森林可能会长期 物种组成和生态系统的平衡 特性. 他们将作为一个有价值的控制， 解释其他东部森林的变化;例如，确定 是人类活动还是气候变化导致了 与往常不同的变化。 这项研究还将证明 物种生活史的不同方面（例如，冠层 寿命与间隙捕获能力）可能会导致 在不同的森林中共存。 分析结果将 直接有助于预测所需的知识库， 全球变化的影响。