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

人类依靠像玉米、小麦和柳枝稷这样的草来获取食物、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。它们受到人类的严重影响。它们是许多其他生态系统的广泛覆盖地。尽管草类很重要，但它们经常在植物数据库和研究中被忽略，因此受到的关注不如树木和其他木本植物。该项目将通过测量许多草类物种的特征，提高草地生态系统对全球变化响应的可预测性。新的数据将被纳入新的建模方法。该项目将加强对草地生态学的了解，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教师分享结果，以便在课堂上使用。在这项工作中，将围绕系统发育驱动的功能多样性开发一个新的综合框架来重组草地植被类型。通过收集前所未有的性状和叶片光谱数据，并将信息与现有数据库相结合，研究基于谱系的性状协调和分布在北美的环境梯度。该方法与之前的方法有着根本的不同，因为它使用系统发育相关性来创建基于谱系的功能类型（LFTs），将性状数据锚定在进化背景下。开发和实施LFTs将提高站点、区域和地球系统模型尺度预测的准确性，并提供草功能生态学的综合，这对于预测草类生物群落如何应对不断增加的二氧化碳、气候变化和干扰至关重要。本项目将为3名研究生和1名博士后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沉浸式研究生研讨会，该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，这些技术将为植物生态学和精准农业的广泛发展奠定基础。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.