I/UCRC FRP: Collaborative Research: Understanding and Modeling Competition Effects on Tree Growth and Stand Development Across Varying Forest Types and Management Intensities

植物群落竞争是一个基本的生态学过程，已经通过实验和模型进行了研究。考虑到树木种群相互作用的时间周期很长，以及具有树木生长和死亡长期测量的广泛数据库的可用性不断增加，该项目旨在使用先进的建模方法来开发更好的竞争量化表达。需要改进的森林种群竞争模型，以进一步提高我们对一系列森林类型的管理选择的预测能力。了解竞争动态对于评估管理活动至关重要，例如部署遗传物质、规定整地处理和种植密度、预测对植被控制和间伐导致的不同程度的种间和种内竞争的反应，以及通过施肥改善营养缺乏。增进对竞争过程的了解和制定量化竞争影响的改进模型，将加强对各种森林管理备选办法的评价。这些结果将产生深远的影响，不仅对于森林管理决策，而且对于森林养护和恢复，以及理解和模拟环境影响，包括气候变化对森林的影响。其他更广泛的影响包括，由于行业科学家和大学教师参与这项研究，为研究生和博士后研究员提供了丰富的教育经验。这一研究项目的总体目标是利用当代建模和统计技术以及计算机技术来利用林业实地研究中的信息，以努力制定改进的树木和林分一级竞争的量化措施。模型是整合和综合现有理论和经验证据的有力工具，可以确定知识差距，并提出有关潜在植物竞争的相关假设，这些假设可能通过实验进行验证。将资源可获得性和竞争强度与生长和生存联系起来，是理解和预测森林林分动态的核心。一个关键的挑战是确定物种、遗传变异、微站点异质性和局部邻域效应对树木大小随时间变化的影响。这些影响是令人困惑的，高度交互的，特别是对于长时间相互作用的林木来说，尤其难以分离。此外，树木间的竞争不能直接测量，而是根据总体林分密度、相对树木大小和/或包括邻居大小和距离的点密度测量来推断。将把先进的统计分析和建模技术应用于数据库，以处理森林生态系统动态在较长时期内的复杂性。将查明知识差距，并提出检验有关竞争过程的相关假设的实验方法。

Competition in plant communities is a fundamental ecological process that has been studied through experimentation and by modeling. Given the long time periods over which tree populations interact and the increasing availability of extensive data bases with long-term measurements of tree growth and mortality, this project aims to use advanced modeling methodology to develop enhanced quantitative expressions of competition. Improved models of competition in forest populations are required to further advance our predictive ability for management options in a range of forest types. Understanding competition dynamics is paramount for evaluating management activities such as deploying genetic material, prescribing site preparation treatments and planting density, predicting response to varying levels of inter-and intra-specific competition resulting from vegetation control and thinning, and ameliorating nutrient deficiencies through fertilizer applications. Increased understanding of competition processes and formulation of improved models for quantifying competition effects will enhance evaluation of a wide array of forest management options. These results will have far-reaching implications, not only for forest management decision-making but also for forest conservation and restoration, and for understanding and modeling environmental influences, including climate change, on forests. Additional broader impacts include an enriched educational experience for graduate students and postdoctoral fellows due to the involvement of industry scientists as well as university faculty in the conduct of this research. The overall goal of this research project is to use contemporary modeling and statistical techniques and computing technology to exploit information in forestry field studies in an effort to develop improved quantitative measures of tree- and stand-level competition. Modeling is a powerful tool for integrating and synthesizing existing theory and empirical evidence, identifying knowledge gaps, and suggesting relevant hypotheses regarding underlying plant competition that might be tested through experimentation. Relating resource availability and competition intensity to growth and survival is central to understanding and projecting forest stand dynamics. A key challenge is determining effects of species, genetic variation, microsite heterogeneity, and local neighborhood effects on tree-size variation through time. These effects are confounded, highly interactive, and especially difficult to separate for stands of trees, which interact over long time periods. Furthermore, tree-to-tree competition cannot be measured directly, but rather is inferred based on measurements of overall stand density, relative tree size, and/or point density measures that include sizes of and distances to neighbors. Advanced statistical analyses and modeling techniques will be applied to data bases to deal with the complexity of forest ecosystem dynamics over extended time periods. Gaps in knowledge will be identified and experimental approaches for testing relevant hypotheses regarding competition process will be advanced.