Interacting Impacts of Changes in Mean and Variance of Water Availability on Vegetation Phenology and Productivity in Dryland Ecosystems

该奖项调查降雨时间和降雨量的变化如何影响美国西南部的旱地生态系统。这一奖项将改善对这些生态系统将如何应对未来气候变化的预测。在世界范围内，这样的生态系统在全球碳循环中发挥着重要作用，养活着40%的人类人口。改善对旱地应对气候变化的预测性理解是一项重大挑战，它将对社会产生益处，并与政策决策相关。该奖项的结果直接解决了一个重要的知识差距，并将提供对世界各地旱地生态系统的管理和保护具有重要意义的见解。该奖项将利用已经建成的实验基础设施，改变新墨西哥州塞维莱塔长期生态研究(LTER)站点全年的降雨数量和强度。研究将在四个明显不同的生态系统类型中进行，包括两个草原和两个灌木丛。安装的数码相机将俯瞰每种生态系统类型的试验田，并将全年记录数千张照片。这些照片将立即公开，不受限制，用于研究和教育。对试验性治疗的反应将通过自动、定量的分析每一张图片的颜色来量化。该奖项的更广泛影响将包括正式和非正式的科学教育。从传统上代表性不足的群体招聘的研究生和本科生将通过该项目接受培训。公众对科学研究的参与将通过与Sevilleta LTER站点的“校园”计划的持续合作实现，并将把这个项目的科学带给K-12教师和他们的学生。未来的气候变化将如何影响旱地生态系统，不仅取决于这些系统如何应对气温上升和平均降水量的变化，而且还取决于这些系统如何应对降水变化的增加。研究人员将测试最重要的假设，即可用水的平均值和方差的变化将对植被物候产生独立和交互影响，然后植被物候将通过生长季长度的变化来推动生产力。基于环境响应函数理论，预期了四种不同旱地生态系统类型的独特响应。来自数码相机图像的数据产品将用于量化活跃(生长)季节的开始和结束、活跃季节的总长度，以及通过对Sevilleta LTER现有的二氧化碳通量测量结果量化的毛初级生产力来估计初级生产率和年度初级生产力总量。项目数据将被同化到已经开发的生态系统模型中，该模型将用于预测和基于模型的假说测试。该项目的成果将包括同行评议的出版物以及与世界各地的旱地研究人员相关的图像和数据集。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。

This award investigates how changes in the timing and amount of rainfall impact dryland ecosystems of the southwestern United States. This award will improve predictions of how these ecosystems will respond to future climate change. Worldwide, ecosystems like these play an important role in the global carbon cycle and support 40% of the human population. Improving predictive understanding of dryland responses to climate change is a critical challenge that will have benefits to society and also relevance to policy decision-making. Results from this award directly address an important knowledge gap, and will provide insights that have significance for management and conservation of dryland ecosystems worldwide. The award will leverage experimental infrastructure, already constructed, that alters the amount and the intensity of rainfall events throughout the year at the Sevilleta Long-Term Ecological Research (LTER) site in New Mexico. Research will be conducted in four distinctly different ecosystem types, including two grasslands and two shrublands. Digital cameras will be installed overlooking experimental plots in each ecosystem type and will record thousands of photographs year-round. These photographs will be made publicly available, immediately and without restriction, for research and education. Responses to the experimental treatments will be quantified through automated, quantitative analysis the color of each picture. Broader impacts from this award will include both formal and informal science education. Graduate and undergraduate students recruited from traditionally under-represented groups, will receive training through this project. Public participation in scientific research will be achieved through ongoing collaboration with the Sevilleta LTER site’s “Schoolyard” program, and will bring the science of this project to K-12 teachers and their students.How future climate change will impact dryland ecosystems depends on how these systems respond not only to increases in temperature and changes in mean precipitation, but also to increases in precipitation variance. Researchers will test the overarching hypothesis that changes in the mean and variance of water availability will have independent as well as interacting effects on vegetation phenology, which then will then drive productivity via changes in growing season length. Based on environmental response function theory, unique responses of four distinct dryland ecosystem types are expected. Data products derived from digital camera imagery will be used to quantify the start and end of the active (growing) season, total active season length, as well as to estimate rates of primary production and total annual primary productivity by scaling gross primary productivity quantified from existing CO2 flux measurements at Sevilleta LTER. Project data will be assimilated into an already-developed ecosystem model, which will be used for forecasting and model-based tests of hypotheses. Outcomes from this project will include peer-reviewed publications as well as imagery and datasets that will be relevant to dryland researchers worldwide.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.