RII TRACK-4: Multiple Global Change Factors Control Forest Nitrogen Cycling - Remote Sensing and Machine Learning Identify Forest Function Across Developed Landscapes

非技术描述氮(N)是所有生命所需的基本元素，在我们的环境中以一个非常紧密的循环运动。人类活动使在我们的环境中循环的氮量增加了一倍多，然而我们对全球氮循环的理解比我们对全球碳循环的理解晚了大约50年。虽然大型完整的森林有能力大量储存多余的氮，但由于城市和郊区发展的扩大，美国大多数温带落叶林都是小森林斑块。小森林更容易受到人类活动的影响，如过量的氮输入和非本地入侵植物的扩散。本研究旨在了解嵌入在发达景观中的小森林的氮汇潜力，这些景观从人类活动中获得过量的氮，并经历了非本地植物入侵改变的氮可用性。该奖学金使特拉华大学早期职业PI与威斯康星大学麦迪逊分校的一位高级科学家之间的新合作成为可能，利用新型遥感技术，在面临多种全球变化因素（如过量的N输入和非本地植物入侵）的情况下，对森林N动态进行大规模研究。这项工作的结果将为一种重要的空气/水污染物（氮）的源/汇潜力提供新的理解。技术描述在过去的一个世纪中，由于人类活动导致全球活性氮的增加，全球氮(N)循环发生了巨大变化。温带落叶林是活性氮沉降的重要汇，除非N输入超过N需求，森林成为N源。全球变化的多个方面，如入侵物种的扩散和养分循环的改变，可以相互作用和反馈，改变森林的结构和功能，最终决定森林作为氮汇的能力。该项目旨在通过利用创新的遥感和机器学习技术，整合森林冠层化学和高光谱成像，确定多种共同发生的全球变化如何改变森林中的N循环，以确定异质性景观中的森林健康和营养状况。该研究将利用遥感技术来评估城市化和入侵如何影响森林冠层氮含量和再吸收，这是森林氮有效性的指标。森林冠层氮动态将在城市化梯度中进行评估，以确定高氮沉降环境中树木吸收的可用氮。此外，将比较入侵和未入侵森林的森林冠层N，以确定植物入侵是否比本地树木更能获取土壤N。我们将使用宏观生态学方法将这项工作的结果扩展到美国不同地区的森林。本研究将增强我们估算温带落叶森林氮源汇潜力的能力，并将提高我们对全球森林中活性氮命运的预测能力。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。

Nontechnical DescriptionNitrogen (N), an essential element required by all life, moves through our environment in a very tight cycle. Human activity has more than doubled the amount of nitrogen that cycles through our environment, and yet our understanding of the global nitrogen cycle is about 50 years behind our understanding of the global carbon cycle. While large intact forests have the capacity to store excess N in substantial quantities, the majority of temperate deciduous forests in the U.S. are small forest patches due to expanding urban and suburban development. Small forests are more susceptible to consequences from human activities, such as excess N inputs and non-native invasive plant spread. This research seeks to understand the N sink potential of small forests embedded across developed landscapes that receive excess N from human activities and that experience altered N availability from non-native plant invasion. The fellowship makes new collaborations possible between the early-career PI at the University of Delaware and a senior scientist at the University of Wisconsin-Madison to utilize novel remote sensing techniques that enable large-scale study of forest N dynamics in the face of multiple global change factors, such as excess N inputs and non-native plant invasion. The outcome of this work will provide novel understanding of the source/sink potential of an important air/water pollutant (nitrogen). Technical DescriptionThe global nitrogen (N) cycle has changed dramatically over the last century through increases in reactive N worldwide due to anthropogenic activities. Temperate deciduous forests are an important sink for reactive N deposition unless N inputs exceed N demand and forests become an N source. Multiple facets of global change, such as invasive species spread and altered nutrient cycling can interact and feedback to alter forest structure and function, ultimately determining the ability of forests to act as an N sink. This project seeks to ascertain how multiple, co-occurring global changes alter N cycling in forests by utilizing innovative remote sensing and machine learning techniques to integrate forest canopy chemistry and hyperspectral imaging to determine forest health and nutrient status across heterogeneous landscapes. The research will leverage remote sensing techniques to assess how urbanization and invasion impact forest canopy N content and resorption, which are indicators of forest N availability. Forest canopy N dynamics will be evaluated across an urbanization gradient to determine N available for tree uptake in high N-deposition environments. Additionally, forest canopy N will be compared in invaded and uninvaded forests to determine whether plant invaders outcompete native trees for soil N. We will extend beyond the results of this work to forests in different regions across the US using a macroecology approach. The research from this fellowship will enhance our capability to estimate the N source vs sink potential of temperate deciduous forests, and will improve our predictive ability on the fate of global reactive N in forests.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.