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本土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养分循环、生物多样性和恢复资源中发挥着重要作用。美国中部剩余的原生草原的健康和季节动态受降雨量和降雨时间的控制。预计整个美国中部的降雨变异性将增加，极端潮湿和干燥年份的频率将增加。草原可能对这种变化特别敏感，有证据表明，这些系统可能对湿润年份和干燥年份更敏感。然而，研究很少包括对极端年份的评估。本博士论文改进基金（DDIG）将实验评估完整半干旱草地系统中草地组成、碳循环和养分循环对极端降雨的敏感性。研究结果对自然资源和土地管理者管理或恢复原生草原具有重要的参考价值。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如何响应预测的环境变异性和极端增加的基本理解。草地生态系统对极端降水的响应是非线性的、双模态的。提出了一种新的实验处理方法，将半干旱草地暴露在生长季节降水的大梯度下，这将能够检测到响应变量的非线性，否则在低因素实验设计中无法检测到。这项研究将允许评估关键碳循环组分对干湿年的相对敏感性，以及当降水从中等到极端偏离平均水平时，这种敏感性如何变化。

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.