RAPID: Breaking drought as an opportunity to examine regional vs. local constraints on microbial community responses to environmental change

土壤中储存的碳量是大气中二氧化碳的两倍多。微生物消耗土壤有机物质以获取碳和养分，因此它们的活动决定了土壤保留了多少碳，释放了多少碳。目前用于预测微生物将如何影响土壤碳的模型假设，无论它们位于哪里，它们对水分和温度的反应都是完全相同的。相比之下，PI最近的研究发现，来自较干燥地区的土壤对水分的反应能力与来自较潮湿地区的土壤不同。这些地方性的“历史偶然性事件”增加了准确预测未来土壤碳储量和损失的难度。让情况更加复杂的是，PI之前的研究是在较长的区域干旱时期进行的，因此可能反映了干旱压力造成的临时状况。今年，她在德克萨斯州的样本点经历了过度降雨，甚至洪水，这可能会影响地区移民和休眠/复苏的动态，可能会克服当地的历史限制，让土壤微生物更好地适应湿度的变化。这一快速获奖将使她能够检验这些想法。她的团队将在2015年利用这个罕见的多雨年，这是自2007年以来德克萨斯州的第一个多雨年。这些研究将改善我们对土壤微生物群落如何发挥作用和对降水变化做出反应的理解，这些知识对农业、牧场和林业管理非常重要。一名技术人员、一名研究生和几名本科生将通过这个项目接受培训。这些本科生将从PI为新生授课的班级中招募，这是德克萨斯大学奥斯汀分校新生研究倡议的一部分，该倡议实施创新的研究型教育，目标是STEM领域中代表性不足的群体。研究生和本科生还将被鼓励开发相关的独立研究项目。这个快速项目的目标是确定局部和区域过程的平衡如何影响微生物对环境变化反应的历史偶然性。当前的多雨年提供了一个机会，将当地和地区的驱动因素分开。这将通过(1)测试释放土壤呼吸对水分的响应的历史偶然事件，(2)记录田间群落组成的变化是否平行发生，以及(3)检查可能支撑先前偶然事件观察的两个机制：休眠/复活和迁入动态。这项研究将结合PI和她的实验室工作了9年的陡峭降雨梯度，以及实验室微观世界实验来生成水分响应曲线和操纵扩散。研究结果有可能改善气候变化情景下土壤生物地球化学过程的预测方式。如果尽管今年是多雨的一年，历史上的意外情况仍然存在，那么过程响应中的局部空间差异可能是准确预测所必需的。或者，如果区域过程挽救了以前的历史偶发事件，并允许土壤适应新的降雨制度，那么模型可能会通过纳入过程响应中的滞后而得到改进。

More than twice the amount of carbon is stored in soils than is found as carbon dioxide in the atmosphere. Microbes consume soil organic material for both carbon and nutrients, and so their activities dictate how much carbon is retained by soils, and how much is released. Current models used to forecast how microbes will affect soil carbon assume that they all behave exactly the same in response to moisture and temperature no matter where they are located. By contrast, recent research by the PI has found that soils from drier regions did not have the same capacity to respond to moisture as soils from wetter sites. These local 'historical contingencies' make it more difficult to accurately predict future soil carbon storage and loss. Also complicating the picture is that the PI's prior studies have taken place during an extended regional drought period, and therefore could reflect a temporary condition created by drought stress. This year, her sample sites in Texas are experiencing excessive rainfall, and even flooding, which could affect both regional immigration and dormancy/resuscitation dynamics that might overcome local historical constraints and allow for soil microbes to better acclimate to a change in moisture. This RAPID award will enable her to test these ideas. Her team will take advantage of this rare wet year in 2015, the first in Texas since 2007. These studies will result in improvements in our understanding of how soil microbial communities function and respond to changes in precipitation, knowledge that is important to agriculture, range land and forestry management. One technician, one graduate student, and several undergraduates will receive training through this project. The undergraduates will be recruited from a class taught by the PI for freshman as part of the Freshman Research Initiative at the University of Texas, Austin, which implements innovative research-based education and targets underrepresented groups in STEM fields. The graduate and undergraduate students also will be encouraged to develop a related independent research projects.The goal of this RAPID project is to determine how the balance of local vs. regional processes affect historical contingencies in microbial responses to environmental change. The current wet year provides an opportunity to separate local vs. regional drivers. This will be addressed by (1) testing for release of historical contingencies on soil respiration responses to moisture, (2) documenting whether community composition shifts occur in parallel in the field, and (3) examining two mechanisms that likely underlie the previous observations of contingencies: dormancy/resuscitation and immigration dynamics. This research will be accomplished using a combination of a steep rainfall gradient where the PI and her lab have worked for 9 years, as well as lab microcosm experiments to generate moisture response curves and manipulate dispersal. Results have the potential to improve how soil biogeochemical processes are forecast in climate change scenarios. If historical contingencies persist despite this wet year, then local spatial variation in process responses may be necessary for accurate forecasting. Alternatively, if regional processes rescue prior historical contingencies and allow soils to acclimate to a new rainfall regime, then models might be improved by incorporating lags in process responses.