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

职业：预测植物功能性状在空间、时间和生物尺度上的变化

• 批准号: 2042453
• 负责人: Catherine Hulshof
• 金额: $ 106.43万
• 依托单位: Virginia Commonwealth University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2042453&HistoricalAwards=false
• 关键词: CAREER; Predicting; plant; functional; trait

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.

可变性是地球上生命的一种固有属性。因此，理解可变性的原因和后果对于理解生命本身的本质是至关重要的。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化。然而，没有任何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，用于预测从生物到生态系统的生物组织尺度上植物功能的可变性。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表现与生态系统过程联系在一起。此外，环境变异性的梯度在自然界的所有空间和时间尺度上都是无处不在的，并且是突出的生态和进化假说的基础。除了开发新的理论和生成对预测物种对日益增加的气候变异性的反应至关重要的新数据外，该项目还将满足国家在数据素养、数据科学和与不同科学学科合作方面的培训需求。为此，将遵循开放科学原则设计本科课程内容和培训模块。课程内容将利用NSF在研究和基础设施方面的投资产生的公开生物和环境数据，包括国家生态观测网络(NEON)、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基金(GBIF)。此外，该项目将支持来自科学界历史上代表性不足的群体的学生的培训和专业发展，为培养一支多样化、有技能和创新的STEM劳动力做出贡献。具体地说，这项研究将通过分解性状-性状和性状-环境关系，检验环境异质性和气候变异性假说，并将生物尺度上的性状变异与物种分布模式联系起来，来量化功能性状变异的出现特性。在大陆尺度上，该项目将利用横跨美国东部和波多黎各的纬度地区霓虹灯站点的数据，以量化空间、时间和生物尺度上的植物特征变化。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性之间的比例关系，这些山脉的温度和降水的非生物梯度在空间和时间上都很陡峭。对植物丰度、生长、功能特征的测量和植被指数的卫星观测将形成对环境异质性和气候变异性假设的为数不多的跨时空尺度的明确测试之一。最终，这项研究将解决在功能特征变化的测量、量化和建模方面的主要差距。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Specific leaf area is lower on ultramafic than on neighbouring non-ultramafic soils

• DOI: 10.1080/17550874.2022.2160673
• 发表时间: 2022-12
• 期刊: Plant Ecology & Diversity
• 影响因子: 1.5
• 作者: Thomas J. Samojedny;Claudia Garnica-Díaz;Dena L. Grossenbacher;G. Adamidis;P. Dimitrakopoulos;S. Siebert;M. Spasojevic;C. Hulshof;N. Rajakaruna