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数量的变化如何影响美国西南部旱地生态系统。该奖项将改善这些生态系统将如何应对未来气候变化的预测。在全球范围内，这样的生态系统在全球碳循环中发挥着重要作用，并支持40%的人口。提高对旱地应对气候变化的预测性认识是一项重大挑战，将给社会带来惠益，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，并将提供对全球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利亚长期生态研究（LTER）站点全年降雨事件的数量和强度。研究将在四种明显不同的生态系统类型中进行，包括两个草地和两个灌木丛。将安装数码相机，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。这些照片将立即不受限制地公开提供，用于研究和教育。对实验处理的反应将通过自动定量分析每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中招募的研究生和本科生将通过该项目接受培训。公众参与科学研究将通过与Sevilleta LTER站点的“校园”计划的持续合作来实现，并将该项目的科学知识带给K-12教师及其学生。未来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温度升高和平均降水量的变化，还取决于降水量方差的增加。研究人员将测试总体假设，即水资源可用性的平均值和方差的变化将对植被物候产生独立和相互作用的影响，然后通过生长季节长度的变化来驱动生产力。根据环境响应函数理论，预测了四种不同类型旱地生态系统的独特响应。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，总活跃季节长度，以及通过缩放Sevilleta LTER现有CO2通量测量量化的总初级生产力来估计初级生产率和年度总初级生产力。项目数据将被纳入一个已经开发的生态系统模型，该模型将用于预测和基于模型的假设检验。该项目的成果将包括同行评审的出版物以及与全世界旱地研究人员有关的图像和数据集，该奖项反映了国家科学基金会的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

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.