Doctoral Dissertation Research: Collaborative Social-Ecological Models for Conservation

这一博士论文研究项目将通过研究当人与环境之间的相互作用模型被用于支持协同环境决策时发生的学习类型，来促进对协同管理以及概念和计算机模拟建模的好处的理解。该项目将提供关于地方和科学知识如何提高管理人员了解、了解和应对环境变化的能力的新见解和信息。通过定量衡量公众参与者参与不同类型的科学模型时发生的文化和认知变化，该项目将通过进一步将学习成果与适应能力和集体行动的概念联系起来，为弹性社会生态系统的分析和管理提供信息。项目成果将通过学术和非学术渠道传播，以支持科学素养和决策。项目方法和结果将适用于具有共享资源的相互关联的人类和自然系统正在经历迅速的社会和生态变化的广泛环境。作为博士论文研究改进奖，该奖项还将提供支持，使有前途的学生能够建立强大的独立研究生涯。建模越来越多地被学者和发展专家用来鼓励资源使用者、政策制定者、研究人员和保护实践者之间的协作管理，但很少有研究衡量这一过程对集体行动结果、当地生态知识以及考虑到围绕人与环境关系的信念、规范和价值观的相关文化模型的影响。博士生将寻求以下几组问题的答案：(1)资源使用者、决策者和研究人员如何理解研究区域中入侵灌木的原因和后果，以及这些类型的知识如何相互关联？(2)协作社会生态模型在多大程度上可以促进社会学习，它如何影响个人知识和共享的文化知识？(3)在协作建模过程中产生了哪些建议的管理策略，这些策略与不同利益相关者群体最初确定的策略如何比较？学生将使用三种日益复杂的建模范例来理解系统的社会文化维度(概念建模)，入侵灌木的生态位和入侵过程(物种分布建模)，以及该地区潜在的未来情景和管理选择(基于代理的建模)。学习过程将通过访谈和文化共识分析进行评估。这一奖项反映了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 doctoral dissertation research project will advance understanding of collaborative management and the benefits of conceptual and computer simulation modeling by examining the kinds of learning that occur when models of interactions between humans and the environment are used to support collaborative environmental decision making. This project will provide new insights and information regarding the ways in which local and scientific knowledge can improve the capacity of managers to understand, learn about, and respond to environmental change. By quantitatively measuring the cultural and cognitive changes that occur when public participants engage with different kinds of scientific models, this project will inform analysis and management of resilient socioecological systems by further connecting learning outcomes to concepts of adaptive capacity and collective action. Project results will be disseminated through academic and non-academic outlets to support science literacy and decision making. Project methods and findings will be adaptable for a broad range of settings in which linked human and natural systems with shared resources are experiencing rapid social and ecological change. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.Modeling increasingly is used by academics and development experts to encourage collaborative management among resource users, policy makers, researchers, and conservation practitioners, yet little research has been conducted to measure the impacts of this process on collective action outcomes, local ecological knowledge, and related cultural models that consider the beliefs, norms, and values surrounding human-environment relationships. The doctoral student will seek answers to the following sets of questions: (1) How do resource users, decision makers, and researchers understand the causes and consequences of invasive shrubs in the study region, and how do those types of knowledge relate to one another? (2) To what degree can collaborative social-ecological models facilitate social learning, and how does it impact both individual knowledge and shared cultural knowledge? (3) What proposed management strategies arise during the collaborative modeling process, and how do these compare to strategies originally identified by different stakeholder groups? The student will use three modeling paradigms of increasing complexity to understand the sociocultural dimensions of the system (conceptual modeling), the ecological niche and invasion process of the invasive shrub (species distribution modeling), and the potential future scenarios and management options for the area (agent-based modeling). The learning process will be assessed using interviews and cultural consensus analysis.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.