ABI INNOVATION: PHYLOGENETIC METHODS FOR IMPUTING MISSING PLANT TRAIT VALUES IN GLOBAL DATABASES

密歇根州立大学被授予一个奖项，以开发利用已知的物种共同进化史来估计全球数据库中缺失的特征值的方法。生态学家已经证明了植物功能的多样性与我们生态系统的健康和稳定之间的密切联系。因此，这种联系可以用来生成从地方到全球范围内植被如何对气候作出反应的预测模型。然而，由于缺乏大陆尺度上植物功能多样性的详细地图，这种全球尺度上的模拟从根本上受到了限制。这一限制通常是由于全球植物特性数据库中的大漏洞在不久的将来不会通过额外的田间运动得到充分填补的结果。因此，这个项目将通过开发派生计算工具来解决的一个关键挑战是估计全球数据库中缺失的特性值。特别是，以前的研究表明，由于共同的血统，关系密切的物种往往具有相似的特征。这种已经收集的性状数据在进化上的非独立性可以被利用来对近期无法收集的性状数据做出可靠的估计。这个项目在真实和模拟的数据集上测试估计方法的适用性，以确定方法何时、何地和为什么是健壮的。本研究的最终目标是确定和测试一系列方法，这些方法可以快速可靠地填补全球性状数据库的空白，便于在大陆尺度上绘制植物功能图。预计这种地图将在未来用于生成更精细的植被结构和动态模型。该项目包括对一名博士后研究员和研究生的培训和指导，以及与丹麦同事的合作研究。该项目还包括为研究生和早期职业研究人员举办的系统发育和功能特征培训讲习班。这项研究将使几个以前未发表的数据集公开可用，并将导致开发和传播新的R代码，以使用大型系统发育树在全球数据库中归因于功能特征值。有关该项目的更多信息，请访问：https://www.msu.edu/~swensonn

An award is made to Michigan State University to develop methods to use the known shared evolutionary history of species to estimate the trait values missing in global databases. Ecologists have demonstrated a strong linkage between the diversity of how plants function and the health and stability of our ecosystems. This linkage can therefore be utilized to generate predictive models of how vegetation responds to climate from local to global scales. However such modeling on global scales is fundamentally limited by a lack of detailed maps of the diversity of plant function on continental scales. This limitation is generally the result of large holes in global plant trait databases that will not be adequately filled through additional field campaigns in the near future. Thus a key challenge this project will address by developing derive computational tools is the estimation of missing trait values in global databases. In particular, previous work has shown that closely related species tend to have similar traits due to common descent. This evolutionary non-independence in trait data already collected can be harnessed to make reliable estimations of trait data that cannot be collected in the near future. This project tests the suitability of estimation methods on real and simulated datasets to determine when, where and why the methods are robust. The ultimate goal of this research is to identify and test a series of methods that can rapidly and reliably fill in the gaps of global trait databases to facilitate the mapping of plant function on continental scales. It is expected that such maps will then be utilized to generate more refined models of vegetation structure and dynamics in the future.The project includes training and mentoring of a postdoctoral researcher and graduate student and collaborative research with colleagues in Denmark. The project also includes phylogenetic and functional trait training workshops for graduate students and early career researchers. The research will make several previously unpublished datasets publicly available and will lead to the development and and dissemination of novel R code to impute functional trait values in global databases using large phylogenetic trees. More information on the project can be found at: https://www.msu.edu/~swensonn