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

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，在社会经济方面至关重要，养活了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（如树叶和树枝）的分解是一个关键的生物过程，影响植物营养物质的可用性以及生物圈和大气之间的碳循环。相对于较湿润的生态系统而言，对旱地分解的科学认识有限，而且似乎受到对这些系统特别重要的机制的影响，如太阳辐射和短期的水分供应。此外，旱地的特点是环境条件随时间和空间的变化极大，但目前的知识不足以充分描述这种变化，以开发预测性分解模型。该项目将揭示从小到大的空间尺度对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。它将通过利用国家生态观测网的数据和资源来做到这一点。这将大大提高对北美广大旱地的养分和碳循环的预测能力。该项目将支持多项教育举措，包括一个课程模块，艺术和科学专业的学生合作开发科学思想的视觉传达技能。一个成功的教育推广平台-树叶分解互动模式-将扩大到包括旱地。旱地养活着数十亿人，在对未来碳循环和气候的预测中，旱地是一个巨大的未知数。这项工作将增进对旱地生态过程的了解，这对于在环境变化的情况下作出知情的土地管理决定至关重要。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规模挑战。环境控制的粗尺度平均可能无法捕获关键的小尺度模式和过程调节分解。现有的分解模型通常不能捕获跨尺度驱动因素和环境异质性。为了弥补这一知识差距，本项目将通过利用氖的力量，将实地、遥感和一个空间嵌套方法中的模型层次连续体结合起来，对从微型站点到北美旱地区域的旱地分解进行定量了解。该项目将开发一个过程中的理解，环境控制的分解跨微型网站使用现场和受控环境研究，制定一个明确的微生物模型的分解。该项目将捕捉旱地氖现场腐烂有机物质分布、环境条件和分解的空间变化。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复杂性降低的模型提供信息。腐烂有机物质库的区域尺度将根据遥感图像的分层嵌套空间尺度来确定从四个氖焦点地点到北美旱地地区的微地点分布特征。这种显式的层次-下一层次嵌套模型将能够通过对系统的过程级理解将驱动因素的细尺度分布传播到粗尺度紧急行为。这一综合性的、面向系统的研究将大大改善对从微型地点到北美旱地区域的空间尺度上的凋落物分解的理解和预测。该项目还将为本科生、研究生和博士后科学家提供跨学科的职业发展机会。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。

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.