Collaborative Research: MRA: A lineage-based framework to advance grassland macroecology and Earth System Modeling

合作研究：MRA：推进草原宏观生态学和地球系统建模的基于谱系的框架

• 批准号: 1926345
• 负责人: Jesse Nippert
• 金额: $ 29.86万
• 依托单位: Kansas State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1926345&HistoricalAwards=false
• 关键词: Collaborative; Research; MRA; lineage; based

Humanity depends on grasses like corn, wheat, and switchgrass for food, fuel, and fiber. Grasslands and savannas play an important role in carbon and water cycling. They are heavily impacted by humans. They are widespread ground cover for many other ecosystems. Despite their importance, grasses are often omitted from plant databases and studies so have less attention than trees and other woody plants. This project will advance predictability of grassy ecosystem responses to global change by measuring many grass species traits. The new data will be incorporated into new modeling approaches. The project will enhance understanding of grass ecology, with many applications in agriculture and natural resource management. It will also provide important training opportunities for young scientists and will share results with K-12 teachers for use into their classrooms.In this work, a novel, integrative framework that reorganizes grass vegetation types around phylogeny-driven functional diversity will be developed. Lineage-based trait coordination and distribution will be investigated along environmental gradients in North America at select NEON and LTER sites by collecting an unprecedented suite of trait and leaf spectral data and integrating the information with existing databases. The method is fundamentally different from previous approaches, as it uses phylogenetic relatedness to create lineage-based functional types (LFTs), which anchors trait data in an evolutionary context. Developing and implementing LFTs will increase the accuracy of site-, regional-, and Earth-System-Model-scale predictions, and provide a synthesis of grass functional ecology that is critical for forecasting how grassy biomes will respond to increasing CO2, climate change, and disturbance. This project will provide training and career development opportunities for interdisciplinary research to 3 graduate students and 1 postdoctoral researcher. The education and training centerpiece will be an immersive graduate student workshop that will train dozens of graduate students in state-of-the-art ecophysiological and spectroscopic techniques that underpin a broad swath of plant ecology and precision agriculture.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.

人类依赖玉米、小麦和柳枝稷等草类来获取食物、燃料和纤维。草原和稀树草原在碳和水循环中发挥着重要作用。它们受到人类的严重影响。它们是许多其他生态系统广泛的地面覆盖物。尽管草很重要，但植物数据库和研究中经常忽略草，因此与树木和其他木本植物相比，草受到的关注较少。该项目将通过测量许多草种特征来提高草生态系统对全球变化响应的可预测性。新数据将被纳入新的建模方法中。该项目将增进对草生态学的了解，并在农业和自然资源管理方面有许多应用。它还将为年轻科学家提供重要的培训机会，并将与 K-12 教师分享成果，以便在课堂上使用。在这项工作中，将开发一个新颖的综合框架，围绕系统发育驱动的功能多样性重新组织草地植被类型。通过收集一套前所未有的性状和叶片光谱数据并将信息与现有数据库集成，将在北美选定的 NEON 和 LTER 地点沿着环境梯度研究基于谱系的性状协调和分布。该方法与以前的方法有根本的不同，因为它使用系统发育相关性来创建基于谱系的功能类型（LFT），将性状数据锚定在进化背景中。开发和实施 LFT 将提高场地、区域和地球系统模型规模预测的准确性，并提供草地功能生态学的综合，这对于预测草地生物群落如何应对二氧化碳增加、气候变化和干扰至关重要。该项目将为3名研究生和1名博士后研究员提供跨学科研究的培训和职业发展机会。教育和培训的核心将是一个身临其境的研究生研讨会，将培训数十名研究生掌握最先进的生态生理学和光谱技术，这些技术支撑着广泛的植物生态学和精准农业。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。