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

怀俄明大学获得了一笔赠款，用于开发一个了解森林多样性和生产力的规模框架。这项研究解决了对开发这一框架至关重要的三个问题。(1)与树形(例如，异速生长、形态)和功能(例如，生理、生长、分配、存活)相关的性状在物种之间是如何变化的，以及进化和环境驱动因素如何影响性状的变异性？(2)对于准确地描述群落和生态系统的属性(例如，多样性、演替、生产力、碳循环)，特定物种的形态和功能的表示是必要的吗？(3)我们如何开发一个通用的尺度框架来预测大规模森林动态，其中包括物种特定的性状变异性和关键的生理机制？为了解决这些问题，这项研究开发和应用了数据模型集成方法，包括：(I)将树的形状和功能联系在一起的动态过程模型，纳入了关键的植物功能特征，并适用于广泛的空间和时间尺度；(Ii)新的荟萃分析方法，用于分析关于物种特有特征的大量文献信息，纳入了系统发育关系，并克服了经典元分析方法的共同限制；以及(Iii)严格的统计和计算方法，用于向过程模型提供大量和不同的数据来源(即文献、森林调查和树轮宽度数据库)。这种高度一体化的方法将为建立和测试一个通用的扩展框架迈出重要的一步。这项工作的更广泛影响包括通过研究生科学家为本科生提供多种数据模型集成方法的培训机会。这项研究开发的方法将通过为美国生态学会年会举办的为期一天的关于生态学中的贝叶斯分析的年度研讨会部分传播。许多大学课程缺乏数据模型集成方面的培训，特别是贝叶斯方法，将进一步开发和整合贝叶斯数据分析和高级/计算贝叶斯两门新的研究生水平课程，在怀俄明大学提供应用统计建模和计算的现代课程。现代统计建模培训将为威斯康星大学S生态学研究生项目的博士生提供一个独特的教育机会。这项研究还将为华盛顿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后提供独特的跨学科教学、指导和研究培训，涉及生态学、统计学、数学和计算科学。

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.