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

生物学家使用了一种公认的分类系统，通过主要或主要的植被生物群系来识别区域。这种主要以陆地为基础的分类系统在开展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系内生态系统的结构和功能。该分类方案为特定地点的研究提供了一个框架，以便在更大的区域背景下理解，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，流动的水彼此如此相似，以至于所有的溪流都可以归为一类。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一个看似简单的问题：是否存在河流生物群落？从生态系统的角度来看，我们现在知道内陆水域在全球碳(C)和氮(N)循环中发挥着关键作用。与陆地系统相比，陆地水域的物理多样性及其趋势在时间上更具动态性。最终，河流生态系统能量学的物候将是能量供应（光和固定陆地碳）和固定碳去除（通过水文干扰）的函数。流域结构决定了水进入河道的路径和速率，而流域植被决定了固定碳输入的大小和时间，以及光可用性的程度和时间模式。这项研究工作将使年代谢的测量增加近两个数量级。目前，使用连续溶解氧测量方法计算了年代谢率的只有两条河流。到该项目结束时，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（通过杠杆基金和合作）至少196个其他流的代谢数据。所有这些河流的代谢指标将被用于建立河流生态系统的第一个分层分类，基于它们的季节性和年度初级生产力和生态系统呼吸模式。从河段到河网的空间尺度上，河流生物群系的描绘将有助于在天到年的时间尺度上估计河流代谢率。模拟模型根据基本原理开发，并根据每个生物群系的具体经验数据进行改进，将预测代谢率的变化，以响应可能的气候和土地利用变化情景。数据管理计划是与美国地质调查局综合数据分析中心的信息学人员合作设计的，美国地质调查局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，该研究项目将改变单个数据流的研究方式，并将促进和鼓励近乎即时的跨站点综合。除能力建设外，本项目将在资助期内直接资助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.