Collaborative Research: MRA: Resolving and scaling litter decomposition controls from leaf to landscape in North American drylands

合作研究：MRA：解决和扩展北美旱地从树叶到景观的垃圾分解控制

• 批准号: 2307195
• 负责人: Heather Throop
• 金额: $ 175.81万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2028-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2307195&HistoricalAwards=false
• 关键词: Collaborative; Research; MRA; Resolving; scaling

Drylands (arid and semi-arid ecosystems) cover nearly half the world’s land surface and are socioeconomically critical, globally supporting a third of the human population and more than half the livestock. Drylands also play a dominant role in global cycles of nutrients and carbon. Decomposition of dead plant material such as leaves and branches is a key biological process that affects the availability of nutrients to plants and the cycling of carbon between the biosphere and the atmosphere. Scientific understanding of decomposition in drylands is limited relative to wetter ecosystems, and appears to be affected by mechanisms uniquely important to these systems, such as solar radiation and short periods of moisture availability. In addition, drylands are characterized by extreme variation in environmental conditions through space and time, but knowledge is currently insufficient to characterize this variability sufficiently to develop predictive decomposition models. This project will reveal a quantitative understanding of dryland decomposition from small to large spatial scales, ultimately building a model to predict decomposition across scales. It will do so by leveraging data and resources of the National Ecological Observatory Network (NEON). This will substantially advance predictive capability for cycling of nutrients and carbon over the vast drylands of North America. This project will support several educational initiatives, including a course module where art and science majors collaborate to develop skills for visual communication of scientific ideas. A successful educational outreach platform, the Interactive Model of Leaf Decomposition, will be expanded to encompass drylands. Drylands support billions of people and represent large unknowns in forecasts of future carbon cycling and climate. This work will advance understanding of ecological processes in drylands, which is critical for informed land management decisions in the face of environmental change.A central challenge to developing an improved predictive understanding of dryland ecosystem function is that decomposition is often measured in locations not representative of where decaying organic material resides. Extreme spatial heterogeneity in drylands exacerbates the scaling challenges of quantifying such a microbial-controlled, macrosystem process. Coarse-scale averaging of environmental controls may fail to capture critical small-scale patterns and processes regulating decomposition. Available decomposition models typically do not capture cross-scale drivers and environmental heterogeneity. To address this knowledge gap, this project will develop a quantitative understanding of dryland decomposition that scales from the microsite to the North American dryland region, by joining field, remote sensing, and a hierarchical continuum of models in a spatially-nested approach that leverages the power of NEON. The project will develop a process understanding of the environmental controls over decomposition across microsites using field and controlled environment studies to formulate a microbial explicit model of decomposition. The project will capture the spatial variation of decaying organic material distribution, environmental conditions, and decomposition at dryland NEON sites. These data will validate a microbial explicit model and inform a reduced complexity model operating at larger spatial scales. Regional scaling of decaying organic material pools will be based on hierarchically-nested spatial scales of remotely-sensed imagery to characterize microsite distributions from four NEON focal sites to the North American dryland region. This explicit hierarchically-next hierarchical-nested model will be able to propagate the fine scale distribution of drivers to coarse scale emergent behavior via a process level understanding of the system. This integrated, system-orientated research that will significantly improve understanding and prediction of litter decomposition at spatial scales ranging from the microsite to the North American drylands region. The project will also provide cross-disciplinary career development opportunities for a diverse group of undergraduate, graduate, and postdoctoral scientists.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.

旱地（干旱和半干旱生态系统）几乎覆盖了世界上一半的土地表面，并且在社会经济上至关重要，在全球范围内支持三分之一的人口，超过一半的牲畜。旱地在养分和碳的全球周期中也起着主要作用。死亡植物材料（如叶子和树枝）的分解是一个关键的生物学过程，它影响植物的养分以及生物圈和大气之间的碳循环。对旱地分解的科学理解相对于湿生态系统是有限的，并且似乎受到对这些系统至关重要的机制的影响，例如太阳辐射和较短的水分可用性。此外，旱地的特征是环境条件通过空间和时间的极端变化，但是当前知识不足以表征这种可变性以开发预测分解模型。该项目将揭示对旱地分解的定量理解，从小到大空间尺度，最终建立了一个模型，以预测跨尺度的分解。它将通过利用国家生态观测网络（NEON）的数据和资源来做到这一点。这将大大提高北美广阔旱地的养分和碳循环的预测能力。该项目将支持几项教育计划，包括一个课程模块，其中艺术和科学专业协作以发展科学思想的视觉交流技能。成功的教育外展平台，即叶子分解的互动模型，将扩展到包含旱地。旱地支持数十亿人，并在未来的碳循环和气候的预测中代表了大量未知数。这项工作将提高人们对旱地生态过程的了解，这对于面对环境变化的知情土地管理决策至关重要。对发展对旱地生态系统功能的预测性理解的核心挑战是，分解通常是在腐烂的有机材料住宅中未代表的位置中衡量的。旱地的极端空间异质性加剧了量化这种微生物控制的宏观系统过程的缩放挑战。环境控制的平均粗尺度可能无法捕获确定分解的关键小规模模式和过程。可用的分解模型通常不会捕获跨尺度驱动因素和环境异质性。为了解决这一知识差距，该项目将通过连接田地，遥远灵敏度和模型的层次连续体，以一种利用霓虹灯的力量的方法来发展对旱地分解的定量理解，该方法从微型站点到北美旱地地区扩展到北美旱地地区。该项目将使用现场和受控环境研究对环境控制对环境控制的分解进行理解，以形成微生物的显式分解模型。该项目将捕获腐烂的有机材料分布，环境条件和分解的空间变化。这些数据将验证微生物的显式模型，并在较大的空间尺度下为降低的复杂性模型提供信息。腐烂的有机材料池的区域尺度将基于远程感应图像的层次结构空间尺度，以表征从四个霓虹灯焦点站点到北美旱地地区的微型材料分布。这种明确的层次结构层次结构 - 纳斯特模型将能够通过对系统的过程水平理解来传播驱动程序到粗略的新兴行为的细节分布。这项集成的，面向系统的研究将显着改善从微型矿石到北美旱地地区的空间尺度上对垃圾分解的理解和预测。该项目还将为跨学科的职业发展机会为一组的本科，研究生和博士后科学家提供。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估NSF的法定任务。