CAREER: Predicting plant functional trait variation across spatial, temporal and biological scales

多变性是地球上生命的固有属性。因此，理解变异的原因和后果是理解生命本质的核心。随着世界各地平均气温的上升，许多地区同时经历着越来越多的气候变异。然而，没有理论可以预测自然系统的可变性。这项研究将开发一个模型，用于预测从生物体到生态系统的生物组织尺度上植物功能的变异性。这项研究将侧重于植物功能性状（形态，生理和物候特征）和环境变异性，因为功能性状将植物性能与生态系统过程联系起来。此外，环境变化的梯度在自然界的所有空间和时间尺度上都是普遍存在的，并且是突出的生态和进化假设的基础。除了开发新的理论和生成预测物种对日益增加的气候变化的反应所必需的新数据外，该项目还将解决数据素养，数据科学以及与不同科学学科合作的国家培训需求。为此，本科课程内容和培训模块将按照开放科学原则设计。课程内容将利用NSF在研究和基础设施方面的投资所产生的开放生物和环境数据，包括国家生态观测站网络（氖），长期生态研究（LTER）网络，综合数字化生物收集（iDigBio）和全球生物多样性信息设施（GBIF）。此外，该项目将支持来自历史上代表性不足的科学群体的学生的培训和专业发展，为多元化，熟练和创新的STEM劳动力做出贡献。具体而言，本研究将量化的功能性状变异的新兴属性分解性状性状和性状与环境的关系;测试环境异质性和气候变异性假设;并在生物体规模的性状变异物种分布的模式。在大陆范围内，该项目将利用来自包括美国东部和波多黎各在内的整个纬度范围内的氖站点的数据，量化空间、时间和生物尺度上的植物性状变异。该项目还将描述温带和热带山区植物功能性状变异、环境异质性和气候变异之间的比例关系，这些山区的非生物温度和降水梯度在空间和时间上都很陡。植物丰度，生长，功能性状的测量，以及植被指数的卫星观测将形成跨时空尺度的环境异质性和气候变异性假设的少数明确的测试之一。最终，这项研究将解决功能性状变异的测量、量化和建模中的主要差异。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

Variability is an inherent property of life on Earth. As a result, understanding the causes and consequences of variability is central for understanding the nature of life itself. As mean temperatures increase around the world, many areas are simultaneously experiencing increasing climatic variability. Yet, there is no theory that predicts the variability of natural systems. This research will develop a model for predicting the variability of plant function across scales of biological organization, from organisms to ecosystems. This research will focus on plant functional traits (morphological, physiological, and phenological characteristics) and environmental variability because functional traits link plant performance to ecosystem processes. Further, gradients of environmental variability are ubiquitous in nature across all spatial and temporal scales, and underlie prominent ecological and evolutionary hypotheses. In addition to developing new theories and generating new data essential for predicting species responses to increasing climatic variability, this project will address a national training need in data literacy, data science, and working with different scientific disciplines. To do so, undergraduate course content and training modules will be designed following open science principles. Course content will leverage open biological and environmental data produced by NSF investments in research and infrastructure, including the National Ecological Observatory Network (NEON), the Long-Term Ecological Research (LTER) network, Integrated Digitized Biocollections (iDigBio), and the Global Biodiversity Information Facility (GBIF). Additionally, this project will support the training and professional development of students from historically underrepresented groups in science, contributing to a diverse, skilled, and innovative STEM workforce. Specifically, this research will quantify emergent properties of functional trait variation by decomposing trait-trait and trait-environment relationships; testing the environmental heterogeneity and climatic variability hypotheses; and linking trait variation at the organismal scale to patterns of species distributions. At a continental scale, this project will leverage data from NEON sites across latitude encompassing the eastern United States and Puerto Rico to quantify plant trait variation across spatial, temporal and biological scales. This project will also characterize scaling between plant functional trait variation, environmental heterogeneity, and climatic variability across temperate and tropical mountains, where abiotic gradients in temperature and precipitation are steep in space and time. Measurements of plant abundance, growth, functional traits, and satellite observations of vegetation indices will form one of the few explicit tests of the environmental heterogeneity and the climatic variability hypotheses across spatiotemporal scales. Ultimately, this research will resolve major disparities in the measurement, quantification, and modeling of functional trait variation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.