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

非技术描述氮素是所有生命所必需的元素，在我们的环境中以非常紧凑的周期运行。人类活动使我们环境中循环的氮量增加了一倍多，但我们对全球氮循环的理解落后于我们对全球碳循环的理解大约50年。虽然完整的大片森林有能力储存大量多余的氮，但由于城市和郊区的发展，美国的大多数温带落叶林都是小片森林。小森林更容易受到人类活动的后果的影响，例如过量的N输入和非本地入侵植物的传播。这项研究试图了解嵌入在发达景观中的小森林的N汇潜力，这些景观从人类活动中获得过量的N，并经历了非本地植物入侵改变了N的有效性。该奖学金使特拉华大学职业生涯早期的PI和威斯康星大学麦迪逊分校的一名资深科学家之间有可能进行新的合作，利用新颖的遥感技术，在面临多种全球变化因素(如过量N投入和非本地植物入侵)的情况下，对森林N的动态进行大规模研究。这项工作的结果将提供对一种重要的空气/水污染物(氮)的源/汇潜力的新的理解。技术描述上个世纪以来，由于人类活动导致世界范围内活性氮的增加，全球氮循环发生了巨大的变化。温带落叶林是反应氮沉积的重要汇，除非N输入超过N需求，并且森林成为N的来源。全球变化的多个方面，如入侵物种扩散和改变的营养循环，可以相互作用和反馈，改变森林的结构和功能，最终决定森林充当氮汇的能力。该项目试图确定多种共同发生的全球变化如何改变森林中的氮循环，方法是利用创新的遥感和机器学习技术，将森林冠层化学和高光谱成像结合起来，以确定不同地貌的森林健康和营养状况。这项研究将利用遥感技术评估城市化和入侵对森林冠层N含量和再吸收的影响，这是森林N有效性的指标。将在城市化梯度上评估森林冠层N的动态，以确定在高N沉积环境中可供树木吸收的N。此外，还将比较入侵和未入侵森林中的林冠N，以确定植物入侵者是否比本地树木竞争土壤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。该奖学金的研究将增强我们估计温带落叶森林氮源与汇潜力的能力，并将改善我们对森林中全球活性氮命运的预测能力。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.