NRT-HDR: Convergent Research in Integrative and Public Land Management

这项授予西科罗拉多大学的国家科学基金会研究培训(NRT)奖将培养不同的未来科学家，帮助土地管理机构应对美国公共土地面临的戏剧性挑战。这些挑战包括全球环境变化、快速的政治和经济变化、日益增长的娱乐需求以及不同的资源开发模式。土地管理者面临着与这些加速变化相关的无数困境，这些变化既需要新的生态知识，也需要在复杂的社会系统中有效利用这些知识的能力。越来越多的大数据和强大的计算平台从根本上改变了生态学家可以就此类土地管理挑战提出和回答的问题。该项目预计将培训120多名学生，其中包括16名资助的实习生，这些学生来自生态学硕士和环境管理硕士项目。受训者将制定、实施和评估适应快速变化的环境条件的适应性管理方法。这一培训将培养新一代科学家，以应对重塑地球的生态变化所需的规模、速度和方向，为生态和土地管理项目提供信息和管理。通过将大数据、传感技术和机器学习与野外生物学家传统的现场级工作相结合，NRT学员将进行的研究将产生关于生态过程的变革性知识。学员将使用最先进的数据工具来评估管理困境，为实地决策提供信息，并评估应对战略对物种和生态系统的影响。学生将通过与工程学和计算机科学的同事合作来开发大数据技能，以构建机器学习算法或定制数据生成工具。该计划将推进与生态研究人员、土地管理机构和公众的合作研究。将在试验性框架内设计和实施干预战略，以促进对效果和效力的严格评估。除了发展尖端的研究能力外，NRT的学员还将获得必要的领导技能，以分析和应用科学知识来规划、政策制定和公共土地管理的复杂现实。通过将生态数据科学的有针对性的培训与专业领导力相结合，实习生将在科学和专业硕士学位课程之间培养高回报、跨学科的协同效应。NSF研究培训(NRT)计划旨在鼓励开发和实施大胆的、具有潜在变革意义的STEM研究生教育培训模式。该计划致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求保持一致的综合实习生模式，在高度优先的跨学科或趋同研究领域对STEM研究生进行有效培训。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This National Science Foundation Research Traineeship (NRT) award to Western Colorado University will train diverse future scientists to help land management institutions respond to the dramatic challenges faced by American public lands. These challenges include global environmental change, rapid political and economic shifts, increased recreation demand, and varying patterns of resource development. Land managers face innumerable dilemmas associated with these accelerating changes that demand both new ecological knowledge and the capacity to effectively utilize that knowledge within complex social systems. The increasing public availability of big data and powerful computing platforms fundamentally change the questions ecologists can ask and answer regarding such land management challenges. The project anticipates training more than 120 students, including 16 funded trainees, from the Master in Ecology and Master in Environmental Management programs. Trainees will develop, implement and assess adaptive management approaches aligned with rapidly changing environmental conditions. This traineeship will cultivate a new generation of scientists to inform and manage ecological and and land management projects at the scale, speed, and direction needed to respond to ecological changes reshaping our planet. By converging big data, sensing technology, and machine learning with the traditional site-level work of field biologists, the research to be conducted by the NRT trainees will generate transformative knowledge of ecological processes. Trainees will employ state-of-the-art data tools to evaluate management dilemmas, inform on-the-ground decision-making, and evaluate the effects of response strategies upon species and ecosystems. Students will develop big-data skills through collaborations with engineering and computer science colleagues to build machine learning algorithms or customize data generation tools. The program will advance research collaborations undertaken in partnership with ecological researchers, land management agencies, and the public. Intervention strategies will be designed and implemented within an experimental framework to facilitate the rigorous assessment of effects and effectiveness. In addition to developing cutting-edge research capabilities, NRT trainees will attain the leadership skills necessary to analyze and apply scientific knowledge to the complex realities of planning, policy development, and management of public lands. By integrating targeted training in ecological data science with professional leadership, the traineeship will foster a high-return, interdisciplinary synergy between science and professional master’s degree programs. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.