DISSERTATION RESEARCH: Mechanisms of asymmetrical ecosystem responses to precipitation extremes in xeric vs. mesic grasslands

曾经覆盖美国数百万英亩的原生草原已经减少到以前分布的一小部分。这些剩余的草原在碳和营养物质的循环、生物多样性以及恢复资源方面发挥着重要作用。美国中部剩余的天然草原的健康和季节动态受到降雨量和降雨时间的控制。整个美国中部的降雨量变化预计将增加，预计极端潮湿和干燥年份的频率将增加。草地可能对这种变化特别敏感，有证据表明，这些系统可能对潮湿年份比干旱年份更敏感。然而，研究很少包括对极端年份的评估。这个博士论文改进补助金（DDIG）奖，将实验性地评估草原组成，碳循环和养分循环的敏感性，降雨极端在一个完整的半干旱草原系统。研究结果将对自然资源和土地管理者管理或恢复天然草地有价值。研究生和本科生将接受培训并参与研究。该研究项目旨在促进对生态系统将如何应对环境变化和极端预测增加的基本理解。草地生态系统对极端降雨的响应被假设为非线性和双峰的。提出了一种新的实验处理暴露半干旱草原生长季降水的大梯度，将能够检测响应变量的非线性，否则无法检测在少因子实验设计。这项研究将允许评估关键碳循环组成部分对干旱和湿润年份的相对敏感性，以及当降水量从中等偏离平均水平过渡到极端偏离时，这种敏感性可能如何变化。

Native grasslands once covering millions of acres across the US have been reduced to a tiny fraction of their former distribution. These remaining grasslands play important roles in the cycling of carbon and nutrients, biodiversity, and as sources for restoration. The health and seasonal dynamics of the remaining native grasslands in the central US are controlled by the amount and timing of rainfall. Across the central US rainfall variability is predicted to increase, with greater frequencies of extreme wet and dry years expected. Grasslands are likely to be particularly sensitive to such changes, with evidence that these systems may be more sensitive to wet versus dry years. However, studies rarely include assessment of extreme years. This Doctoral Dissertation Improvement Grant (DDIG) award, will experimentally assess the sensitivity of the grassland composition, carbon cycle, and nutrient cycling to rainfall extremes within an intact semi-arid grassland system. The research results will be valuable to natural resource and land managers in managing or restoring native grasslands. Graduate and undergraduate students will be trained and participate in the research. This research project aims to advance a fundamental understanding of how ecosystems will respond to predicted increases in environmental variability and extremity. The grassland ecosystem's response to rainfall extremes is hypothesized to be non-linear and bi-modal. A novel experimental treatment is proposed to expose semi-arid grassland to a large gradient of growing season precipitation that will be able to detect nonlinearities in response variables that are otherwise not detectable in few-factor experimental design. The research will allow assessment of the comparative sensitivities of key carbon cycle components to dry versus wet years, and how this sensitivity may change when precipitation transitions from moderate to extreme deviations from mean levels.