Collaborative Research: Grassland Sensitivity to Climate Change at Local to Regional Scales: Assessing the Role of Ecosystem Attributes vs. Environmental Context

合作研究：地方到区域尺度上草地对气候变化的敏感性：评估生态系统属性与环境背景的作用

• 批准号: 1137378
• 负责人: Alan Knapp
• 金额: $ 106.02万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-01-15 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1137378&HistoricalAwards=false
• 关键词: Collaborative; Research; Grassland; Sensitivity; Climate

Although scientists have learned much in the past 20 years about how individual ecosystems are likely to respond to climate change, extending this knowledge to regional and continental scales has been a far greater challenge. At these larger spatial scales, both the environment and the attributes of ecosystems vary dramatically. For example, in the central US, there are strong temperature and rainfall gradients from Texas to North Dakota and the dry plains of Colorado to eastern Kansas, and the types of grasslands differ as well (from short grasslands in the west to tall grasslands in the east). In order to better forecast how entire regions will respond to expected climatic changes, there is a pressing need to understand why ecosystems differ in their sensitivity to changes in climate. This project includes research designed to answer a question of fundamental importance for advancing knowledge of biological processes at large scales: How important are the attributes of ecosystems per se vs. the environmental context in which climate is changing in determining ecological responses to climate change at regional scales? To answer this question, a geographically distributed field experiment will be conducted at six sites in NM, CO, WY and KS and the results from this experiment will be used to strengthen an existing process-based terrestrial ecosystem model. With this model, the relative importance of ecosystem attributes versus the environment for determining responses to climate change will be evaluated and then scaling rules for extending site-based knowledge to regional scales will be developed. The experiment imposed will be a severe multi-year drought in grasslands arrayed along a rainfall gradient (from desert grassland to mesic tallgrass prairie). Key responses measured will include many related to carbon cycling and budgets and plant biodiversity. The project has a number of broader impacts including enhancing existing terrestrial ecosystem modeling approaches, and conducting a field experiment that will provide a platform for others in the scientific community to conduct additional research activities. A project website will make products (datasets, key results, papers, models) available to the scientific community and general public. Research will occur on federal lands and at a number of other research stations where outreach programs are in place, allowing results to be shared with a wide range of stakeholders. The project is also designed to provide training opportunities for numerous graduate and undergraduate students. Because this study is unique in its spatial extent and combination of field and modeling approaches, the collaborative training opportunities available to students differs substantially from traditional degree programs.

尽管科学家在过去20年中对单个生态系统可能如何应对气候变化有了很多了解，但将这一知识扩展到区域和大陆规模一直是一个更大的挑战。在这些更大的空间尺度上，环境和生态系统的属性都有很大的差异。例如，在美国中部，从得克萨斯州到北达科他州，从科罗拉多的干燥平原到堪萨斯东部，有很强的温度和降雨梯度，草地的类型也不同（从西部的矮草地到东部的高草地）。为了更好地预测整个区域将如何应对预期的气候变化，迫切需要了解为什么生态系统对气候变化的敏感性不同。 该项目包括旨在回答一个对推进大尺度生物过程知识具有根本重要性的问题的研究：生态系统本身的属性与气候变化的环境背景在确定区域尺度上对气候变化的生态反应方面有多重要？ 为了回答这个问题，将在新墨西哥州、科罗拉多州、怀俄明州和堪萨斯州的六个地点进行一个地理分布的实地实验，实验结果将用于加强现有的基于过程的陆地生态系统模型。利用这一模式，将评估生态系统属性与环境在确定气候变化应对措施方面的相对重要性，然后将制定尺度规则，以便将基于地点的知识扩展到区域尺度。 实验将是一个严重的多年干旱的草原排列沿着降雨梯度（从沙漠草原到金属草草原）。 衡量的关键应对措施将包括许多与碳循环和预算以及植物生物多样性相关的措施。该项目具有一些更广泛的影响，包括加强现有的陆地生态系统建模方法，并进行实地实验，为科学界的其他人提供一个平台，以开展更多的研究活动。 项目网站将向科学界和公众提供产品（数据集、关键成果、论文、模型）。研究将在联邦土地和其他一些研究站进行，这些研究站都有推广计划，可以与广泛的利益相关者分享研究结果。 该项目还旨在为众多研究生和本科生提供培训机会。由于这项研究在其空间范围和领域和建模方法的结合方面是独一无二的，因此学生可以获得的合作培训机会与传统的学位课程有很大不同。