A Theoretical and Computational Framework for Linking Tree form and Function to Forest Diversity and Productivity

怀俄明州大学获得一笔赠款，用于开发一个用于了解森林多样性和生产力的缩放框架。这项研究解决了三个问题，这是至关重要的发展这一框架。(1)与树的形成相关的性状是如何形成的（例如，异速生长，形态学）和功能（例如，生理学、生长、分配、生存）在物种之间存在差异，以及进化与环境驱动因素如何影响性状变异？(2)是一个物种特有的形式和功能的表示，必须准确地描述社区和生态系统的属性（例如，多样性、演替、生产力、碳循环）？(3)我们如何开发一个通用的尺度框架来预测大规模的森林动态，包括物种特有的性状变异和关键的生理机制？为了解决这些问题，本研究开发和应用数据模型集成方法，包括：（i）动态过程模型，链接树的形式和功能，纳入关键植物功能性状，并适用于广泛的空间和时间尺度;（ii）分析大量物种文献资料的新荟萃分析方法-包含系统发育关系并克服经典2元分析方法常见的限制的特定性状;以及（iii）用于用大的和不同的数据源通知过程模型的严格的统计和计算方法（即，文献、森林清查和树木年轮宽度数据库）。这种高度集成的方法将为构建和测试通用扩展框架迈出重要一步。 这项工作的更广泛的影响包括通过研究生科学家为本科生提供数据模型集成方法的多种培训机会。在这项研究中开发的方法将部分通过每年一次，为期一天的研讨会在生态学贝叶斯分析的美国生态学会年会上传播。在数据模型集成，特别是贝叶斯方法的培训，是缺乏在许多大学的课程和两个新的研究生课程贝叶斯数据分析和高级/计算贝叶斯将进一步开发和整合，提供应用统计建模和计算在怀俄明州大学（UW）的现代课程。在现代统计建模培训将提供一个独特的教育机会，这些博士生在华盛顿大学的新的和充满活力的研究生课程生态学。这项研究还将为西澳大学的本科生创造独立的研究机会，并将为一名博士后和两名博士生提供独特的跨学科教学，指导和生态学，统计学，数学和计算科学的研究培训。

The University of Wyoming is awarded a grant to develop a scaling framework for understanding forest diversity and productivity. This study addresses three questions that are paramount to developing this framework. (1) How do traits related to tree form (e.g., allometries, morphology) and function (e.g., physiology, growth, allocation, survival) vary between species, and how do evolutionary versus environmental drivers affect trait variability?(2) Is a species-specific representation of form and function necessary to accurately describe community and ecosystem properties (e.g., diversity, succession, productivity, carbon cycling)? (3) How do we develop a general scaling framework for predicting large-scale forest dynamics that includes species-specific trait variability and key physiological mechanisms? Towards addressing these questions, this study develops and applies data-model integration methodologies, including: (i) dynamic process models that link tree form and function, incorporate key plant functional traits, and are applicable to broad spatial and temporal scales; (ii) new meta-analysis methods for analyzing vast amounts of literature information on species-specific traits that incorporate phylogenetic relationships and overcome limitations common to ³classical² meta-analytic approaches; and(iii) rigorous statistical and computational methods for informing the process model with large and disparate data sources (i.e., literature, forest inventory, and tree-ring width databases). This highly integrative approach will provide a major step towards building and testing a general scaling framework. The broader impacts of this work include multiple training opportunities in data-model integration methods for undergraduates through post-graduate scientists. Methods developed in this study will be partly disseminated through an annual, daylong workshop on Bayesian analysis in ecology for the Ecological Society of America annual meetings. Training in data-model integration, and specifically Bayesian methods, is lacking in many university curriculums and two new graduate-level courses in Bayesian data analysis and advanced/computational Bayesian will be further developed and integrated, providing a modern curriculum in applied statistical modeling and computing at the University of Wyoming (UW). Training in modern statistical modeling will offer a unique educational opportunity for those PhD students in UW¹s new and vibrant graduate Program in Ecology. This study will also create independent research opportunities for UW undergraduates, and it will provide one post-doctoral and two PhD students with unique interdisciplinary teaching, mentoring, and research training in ecology, statistics, mathematics, and computational science.