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: 2307196
• 负责人: Katherine Todd-Brown
• 金额: $ 59.16万
• 依托单位: University of Florida
• 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=2307196&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）覆盖了世界近一半的陆地表面，对社会至关重要，养育着全球三分之一的人口和一半以上的牲畜。旱地在全球养分和碳循环中也发挥着主导作用。死亡植物材料（如叶子和树枝）的分解是一个关键的生物过程，影响植物的养分利用率以及生物圈和大气之间的碳循环，相对于湿润的生态系统，对旱地分解的科学理解是有限的。并且似乎受到对这些系统特别重要的机制的影响，例如太阳辐射和短期水分可用性。此外，旱地的特点是环境条件随空间和时间的极端变化，但目前的知识不足以描述这种变化。该项目将揭示对从小到大空间尺度的旱地分解的定量理解，最终建立一个模型来预测跨尺度的分解。它将利用国家生态观测站网络的数据和资源来实现这一目标。 （NEON）。这将大大提高北美广大旱地营养物和碳循环的预测能力。该项目将支持多项教育举措，包括艺术和科学专业合作开发科学视觉传达技能的课程模块。一个成功的教育推广平台，即叶子分解互动模型，将扩展到包括数十亿人口的旱地，并代表着未来碳循环和气候预测的巨大未知数。这项工作将增进对旱地生态过程的理解。 ，这很关键面对环境变化时做出明智的土地管理决策。对旱地生态系统功能进行改进的预测性理解的一个主要挑战是，通常在不能代表腐烂有机物质所在位置的位置测量分解，旱地的极端空间异质性加剧了规模化。量化这种微生物控制的宏观系统过程的挑战可能无法捕获调节分解的关键小规模模式和过程。为了解决这一知识差距，该项目将通过将实地、遥感和空间嵌套方法中的分层连续模型结合起来，对从微型站点到北美旱地区域的旱地分解进行定量理解。该项目将利用 NEON 的力量，通过现场和受控环境研究来开发对跨微站点分解的环境控制的过程，以制定微生物显式分解模型。旱地 NEON 地点的腐烂有机物质分布、环境条件和分解这些数据将验证微生物显式模型，并为在更大空间尺度上运行的降低复杂性的模型提供信息。腐烂有机物质池的区域尺度将基于分层嵌套的空间。遥感图像的尺度来表征从四个 NEON 焦点站点到北美旱地地区的微站点分布，这种明确的分层嵌套模型将能够将驱动程序的精细尺度分布传播到。这项综合的、面向系统的研究将显着提高对从微型站点到北美旱地地区的空间尺度上的垃圾分解的理解和预测。 -为不同群体的本科生、研究生和博士后科学家提供学科职业发展机会。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。