The phylogenetic history of ecosystem structure and function

现代生态学的一个关键前提是，生物过程通常以可预测的方式与特征或环境条件相匹配。生物结垢的规律性形成了许多宏观生态学理论的基础，最突出的是生态学的代谢理论。然而，在宏观生态模型中，这些比例关系通常被假设为是静态的，通过时间和跨类群，即使这个假设从来没有明确。然而，越来越多的证据表明，许多生态尺度关系似乎在不同的分类群体之间存在差异，这表明它们是进化过程的结果，而不仅仅是物理上的必然性。 我的研究目标是开发新的数学，统计，计算和概念工具，以揭示生态系统结构和功能的宏观进化动力学，通过生态和生理尺度关系捕获。这项工作将为生物多样性的产生者提供一个新的视角，并将帮助我们利用进化历史的信息来更好地预测生态系统将如何应对全球变化。从更理论的角度来看，这项研究将导致对生物多样性的更综合的理解，弥合宏观进化和宏观生态学之间的概念分歧。在这五年的发现补助金，我将致力于我的目标，专注于四个不同的，但相互关联的项目： 1)关键的生态和生理尺度关系是如何随着时间的推移而演变的？[PhD第1页] 2)当我们认为尺度关系是进化的（和进化的）特征时，宏观生态模型的预测是如何变化的？[PhD二、 3)限制和权衡如何塑造了C3植物光合能力的宏观进化，以及这种“性状”的进化动力学如何影响结构和功能？[PhD三、 4)大型性状数据库中的偏见如何影响宏观生态和宏观进化推论，以及当前处理这一问题的策略如何改善这些问题？[PhD四、 我相信这项工作将为在进化、生态学、生理学和信息科学的界面上进行全新的研究项目奠定基础。从应用的角度来看，了解关键尺度特征的宏观进化动力学将有助于我们更好地预测全球变化将如何影响生物多样性。

One key premise of modern ecology is that biological processes often scale with traits or with environmental conditions in predictable ways. The regularity of biological scaling forms the basis of a number of macroecological theoriesmost prominently the Metabolic Theory of Ecology. However, in macroecological models, these scaling relationships are usually assumed to be static through time and across taxa, even if this assumption is never made explicit. However, there is growing evidence that many ecological scaling relationships appear to vary across taxonomic groups, suggesting that they are the result of evolutionary processes and not simply a physical inevitability. My research goal is to develop new mathematical, statistical, computational, and conceptual tools to uncover the macroevolutionary dynamics of ecosystem structure and function, as captured by ecological and physiological scaling relationships. This work will provide a novel perspective on the generators of biodiversity, and reciprocally, will help us leverage information on evolutionary history to better forecast how ecosystems will respond to global change. And from a more theoretical angle, this research will result in a more synthetic understanding of biodiversity, one that bridges the conceptual divides between macroevolution and macroecology. Over the five years of this Discovery Grant, I will work towards my aim by focusing on four distinct, but interrelated projects: 1) How have key ecological and physiological scaling relationships evolved over deep time? [PhD 1] 2) How do predictions from macroecological models change when we consider scaling relationships to be evolved (and evolving) traits? [PhD 2] 3) How have constraints and trade-offs shaped the macroevolution of photosynthetic capacity across C3 plants and how has the evolutionary dynamics of this “trait” influenced structure and function? [PhD 3] 4) How do biases in large trait database trickle down to affect macroecological and macroevolutionary inferences and how well do current strategies for dealing with this ameliorate the problems? [PhD 4] I believe this work will lay the foundation for entirely new research programs at the interface of evolution, ecology, physiology, and information science. And from an applied perspective, understanding the macroevolutionary dynamics of key scaling traits will help us better forecast how global change will affect biodiversity.