COLLABORATIVE RESEARCH: Defining Stream Biomes to Better Understand and Forecast Stream Ecosystem Change

生物学家使用一个公认的分类系统来确定主要或主要植被生物群落的区域。这一主要基于陆地的分类系统在进行研究和了解这些地区的环境、地质和生物特征方面非常有用。这些因素影响着生物群落内生态系统的结构和功能。分类方案为在更大的区域范围内理解特定地点的研究提供了一个框架，并将结果扩展到更大的区域。这个框架的一个弱点或缺失部分是溪流和河流。世界上大多数生物群落的地图或列表都表明，流动的沃茨彼此非常相似，所有的河流都可以归为一类。它们通常被集中在一起，而不考虑区域地质、流域植被或气候因素。这项研究将为河流开发一个生物群落分类系统，以更好地了解河流的功能，并提供预测河流如何因人类和环境因素而变化的能力。这个大陆规模的项目将解决看似简单的问题：是否存在河流生物群落？从生态系统的角度来看，我们现在知道内陆沃茨在全球碳（C）和氮（N）循环中发挥着关键作用。与陆地系统相比，海洋沃茨的物理多样性及其趋势更具时间动态性。最终，河流生态系统能量学的物候学将是能量供应（光和固定陆地碳）和固定碳去除（通过水文扰动）的函数。流域结构决定了水进入河道的路线和速率，而流域植被决定了固定碳输入的大小和时间以及光照可用性的程度和时间模式。这项研究工作将使年度新陈代谢的测量增加近两个数量级。目前，只有两条河流的年代谢率是使用连续溶解氧测量计算的。到本项目结束时，将为总共35个流生成55年的高质量代谢数据，项目PI将获得（通过杠杆资金和合作）至少196个额外流的代谢数据。所有这些流的代谢指标将被用来建立流生态系统的基础上，其季节性和年度模式的初级生产力和生态系统呼吸的第一个层次分类。河流生物群系的划分将有助于在从河段到河流网络的空间尺度上以天到年的时间尺度估计河流代谢率。模拟模型是根据基本原则开发的，并根据每个生物群落的具体经验数据加以完善，将预测新陈代谢率的变化，以应对可能的气候和土地利用变化情景。数据管理计划是与USGS综合数据分析中心的信息学工作人员合作设计的，USGS已同意托管并帮助开发一个公共数据存储库，建模和数据可视化平台，专门用于整理长期或高分辨率的代谢和溶解氧数据集。通过建立，完善和激活一个社区数据平台，该研究计划将改变研究单个流的方式，并将促进和鼓励近即时的跨站点合成。除了能力建设外，该项目还将在资助期内直接支持7名研究生和7名博士后。

Biologists have used a well accepted classification system to identify regional areas by the major or predominant vegetation biomes. This largely land-based classification system has been very useful in conducting research and understanding the environmental, geological, and biological features of those regions. These factors influence how ecological systems within the biome are structured and how they function. The classification scheme provides a framework for site- specific research to be understood in a larger regional context and scale the results to the larger region. A weakness or missing part of this framework is streams and rivers. Most maps or lists of biomes of the world would suggest that flowing waters are so similar to one another that all streams can be lumped into a single category. They are generally lumped together regardless of the regional geology, watershed vegetation, or climatic factors. This research will develop a biome classification system for streams to better understand how streams function and provide an ability to predict how streams will change from human and environmental factors.This continental scale project will address the deceptively simple question: is there such a thing as a stream biome? From an ecosystem perspective we now know that inland waters play critical roles in both global carbon (C) and nitrogen (N) cycling. The physical diversity of lotic waters as well as their tendency is more temporally dynamic than terrestrial systems. Ultimately the phenology of stream ecosystem energetics will be a function of energy supply (light and fixed terrestrial carbon) and fixed carbon removal (via hydrologic disturbance). Watershed structure determines the route and rate at which water enters stream channels while watershed vegetation determines the magnitude and timing of fixed carbon inputs and the degree and temporal patterning of light availability. This research effort will increase the measurements of annual metabolism by nearly two orders of magnitude. At the present time there exist only two streams for which annual metabolic rates have been calculated using continuous dissolved oxygen measurements. By the conclusion of this project 55 years of high quality metabolism data will have been generated for a total of 35 streams, and the project PIs will have acquired (via leveraged funds and collaborations) metabolism data for at least 196 additional streams. Metabolism metrics from all of these streams will be used to build the first hierarchical classification of stream ecosystems based on their seasonal and annual patterns of primary productivity and ecosystem respiration. Stream biome delineation will facilitate estimation of stream metabolic rates at timescales of days to years for spatial scales from reaches to river networks. Simulation models, developed from first principles and refined with empirical data specific to each biome, will forecast changes in metabolic rates in response to likely climate and land use change scenarios. The data management plan has been designed in collaboration with informatics staff of the USGS Center for Integrated Data Analytics and USGS has agreed to host and help develop a public data repository, modeling, and data visualization platform specifically designed to collate long-term or high-resolution metabolism and dissolved oxygen datasets for streams. By building, refining and activating a community data platform this research program will change the way individual streams are studied and will facilitate and encourage near instantaneous cross-site synthesis. In addition to capacity building, this project will directly support seven graduate students and 7 postdoctoral associates over the funding period.