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

密歇根州立大学获得了一项奖项，用于开发方法，利用已知的物种共享进化历史来估计全球数据库中缺失的性状值。生态学家已经证明，植物功能的多样性与生态系统的健康和稳定之间存在着密切的联系。因此，可以利用这种联系来生成植被如何响应从局部到全球尺度的气候的预测模型。然而，这种全球尺度的建模从根本上受到限制，因为缺乏大陆尺度植物功能多样性的详细地图。这种限制通常是全球植物性状数据库中存在大漏洞的结果，在不久的将来，这些漏洞将无法通过额外的实地活动得到充分填补。因此，该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征值。特别是，之前的研究表明，密切相关的物种由于共同的血统往往具有相似的特征。可以利用已收集的性状数据的这种进化非独立性来对在不久的将来无法收集的性状数据进行可靠的估计。该项目测试估计方法在真实和模拟数据集上的适用性，以确定这些方法何时、何地以及为何稳健。这项研究的最终目标是确定和测试一系列能够快速可靠地填补全球性状数据库空白的方法，以促进在大陆尺度上绘制植物功能图。预计这些地图将来将用于生成更精细的植被结构和动态模型。该项目包括对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系统发育和功能特征培训研讨会。该研究将公开几个以前未发表的数据集，并将导致新型 R 代码的开发和传播，以使用大型系统发育树来估算全球数据库中的功能性状值。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