Doctoral Dissertation Research: Collaborative Social-Ecological Models for Conservation

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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.