CAREER: Global change and the functional ecology of grasses

禾本科植物是最重要的生态和经济植物之一。它们包括许多主要作物，如小麦、玉米和水稻，是整个生物群落的定义特征。因此，草对全球变化的反应将强烈影响人类福祉。与此同时，植物可以通过反馈回路强烈影响全球变化的速度。因此，准确预测气候需要了解这些反馈。然而，这些反馈是目前气候模型中最大的不确定性来源之一。物种的物理和化学特征，称为功能性状，在预测植物如何应对和影响全球变化方面发挥着核心作用。经典生态学理论预测了功能性状与气候和其他环境特征的关系。该项目将测试和扩展这些预测，并对特质-环境关系的可预测性进行强有力的评估。这项工作将采用长期监测、区域调查和全球数据库分析相结合的办法。这些结果将提高我们对植物对全球变化的反应的理解，阐明可预测性的局限性，并产生可用于改进全球气候预测的有价值的数据。该项目整合了多个层面的教育机会，包括高级本科生的实地研究经验，以及由当地高中教师开发和部署的数据科学模块。该研究包括四个组成项目，从密集的本地研究到全球宏观生态分析。第一个项目将通过在旧金山弗朗西斯科湾区建立一个为期10年的草地性状记录，测试降水对跨空间和跨时间、物种内和物种间功能性状的影响。在第二个项目中，研究人员将在西部各州的降水和大气氮沉积速率不同的地方取样，以研究它们对叶片化学的相互作用。项目三将开发一个基于异速生长的资源分配模型，该模型可以预测草的性状随环境梯度的沿着变化，并利用五大洲合作者收集的新数据来检验这些预测。最后，项目四将使用机器学习工具和1000多种草的高分辨率范围图，研究草性状的大范围物种内变异。该奖项反映了NSF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。

Grasses are one of the most ecologically and economically important plant families. They include many staple crops such as wheat, corn, and rice, and are defining features of an entire biome. Thus, the responses of grasses to global change will strongly impact human well-being. At the same time, plants can strongly influence the rates of global change through feedback loops. For this reason, accurately forecasting climate requires understanding these feedbacks. However, these feedbacks are currently one of the largest sources of uncertainty in climate models. Physical and chemical characteristics of species, called functional traits, play a central role in predicting both how plants will respond to and influence global change. Classic ecological theory makes predictions regarding how functional traits should relate to climate and other environmental features. This project will test and extend these predictions and perform strong assessments of the predictability of trait-environment relationships. This work will use a combination of long-term monitoring, regional surveys, and analysis of global databases. The results will improve our understanding of plant responses to global change, illuminate the limits of predictability, and produce valuable data that can be used to refine global climate predictions. The project integrates educational opportunities at multiple levels, including field research experience for advanced undergraduates and data science modules developed with and deployed by local high school teachers.The research includes four component projects that range from intensive local studies to global macroecological analyses. The first will test predictions for the influence of precipitation on functional traits across space and through time, and within and among species, by building a ten-year record of grass traits in sites across the San Francisco Bay Area. In project two, the researchers will sample sites across the Western states that differ in precipitation and rate of atmospheric nitrogen deposition to examine their interacting effects on leaf chemistry. Project three will develop an allometry-based model of resource allocation that can predict grass trait variation along environmental gradients and test these predictions with new data collected by collaborators on five continents. Finally, project four will examine broad-scale within-species variation in grass traits, using machine learning tools and high-resolution range maps for more than 1000 grass species.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.