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

合作研究：定义河流生物群落以更好地理解和预测河流生态系统变化

• 批准号: 1442444
• 负责人: William McDowell
• 金额: $ 59.46万
• 依托单位: University of New Hampshire
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1442444&HistoricalAwards=false
• 关键词: COLLABORATIVE; RESEARCH; Defining; Stream; Biomes

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.

生物学家已经使用了一个公认的分类系统来识别主要或主要植被生物群落区域。这种基于土地的分类系统在进行研究和理解这些地区的环境，地质和生物学特征方面非常有用。这些因素会影响生物群落内部的生态系统结构以及它们的功能。该分类方案为网站研究提供了一个框架，可以在更大的区域环境中理解，并将结果扩展到较大的区域。该框架的弱点或缺少的部分是流和河流。大多数世界生物群落的地图或列表都表明，流动的水是如此相似，以至于所有流都可以分为一个类别。无论区域地质学，流域植被或气候因素如何，它们通常都会结合在一起。这项研究将开发出流媒体的生物群体分类系统，以更好地了解流如何从人类和环境因素中变化流的能力。该大陆规模项目将解决一个看似简单的问题：是否有诸如流生物群落之类的东西？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）骑行中都起着关键作用。与地面系统相比，底水的物理多样性及其趋势在时间上更具动态性。最终，流生态系统能量学的物候学将是能源供应（光和固定陆碳）和固定碳去除（通过水文干扰）的函数。分水岭结构确定水进入溪流通道的路线和速率，而流域植被决定固定碳输入的大小和时机以及光的程度和时间模式。这项研究工作将使年度新陈代谢的测量值近两个数量级。目前，只有两条流使用连续溶解的氧测量值计算了每年的代谢率。根据该项目的结论，将生成55年的高质量新陈代谢数据，总共35个流，PIS将获得（通过杠杆基金和协作）代谢数据至少为至少196个流提供。来自所有这些流的代谢指标将根据其主要生产力和生态系统呼吸的季节性和年度模式来建立流生态系统的第一个分层分类。流生物群落描述将有助于估计几天到几年的时间尺度上的河流代谢率，从触及到河网络。仿真模型是从第一原理开发的，并用特定于每个生物群落的经验数据进行了改进，将预测代谢率的变化，以应对可能的气候和土地使用变化方案。数据管理计划是与USGS集成数据分析中心的信息学人员合作设计的，USGS已同意托管并帮助开发公共数据存储库，建模和数据可视化平台，专门旨在融合了旨在整理长期或高分辨率的代谢和溶解的氧气数据集的流。通过构建，完善和激活社区数据平台，该研究计划将改变研究单个流的研究方式，并促进并鼓励接近瞬时的跨站点合成。除了能力建设外，该项目还将在资金期间直接支持七名研究生和7个博士后同事。