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.

人类依赖于玉米，小麦和开关草的草，用于食物，燃料和纤维。草原和稀树草原在碳和水循环中起着重要作用。它们受到人类的影响。它们是许多其他生态系统的广泛地面覆盖物。尽管它们的重要性，但通常会从植物数据库中省略草，并且研究的关注少于树木和其他木本植物。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的反应。新数据将纳入新的建模方法中。该项目将通过在农业和自然资源管理中进行许多应用来增强对草生态学的理解。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，以便在他们的课堂上使用。在这项工作中，将开发出一种新颖的，整合的框架，重组围绕系统发育驱动的功能多样性的草植被类型。将通过收集前所未有的性状和叶片频谱数据的套件并将信息与现有数据库集成在一起，从而沿北美的环境梯度在北美的环境梯度沿北美的环境梯度进行研究。该方法从根本上与以前的方法不同，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），该功能类型（LFTS）在进化环境中锚定特征数据。开发和实施LFT将提高现场，区域和地球系统模型尺度的预测的准确性，并提供对草功能生态学的综合，这对于预测草生物群落对增加二氧化碳，气候变化和干扰的响应至关重要。该项目将为跨学科研究提供培训和职业发展机会，为3名研究生和1名博士后研究员。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的生态生物生物学和光谱技术，这些技术基于植物生态学和精确农业的广泛范围，这是NSF的法定任务，并通过评估了企业的支持，并反映了对企业的支持，并反映了概念的支持。