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MS Biology Thesis Defense - Matt Clark
June 14 @ 1:00 pm - 2:00 pm MDT
Methodological Advances for Understanding Social Connectivity and Environmental Implications in Multi-Use Landscapes
Advisor: Dr. Vicken Hillis, Human-Environment Systems
Committee Members: Dr. Trevor Caughlin, Biological Sciences, Dr. Marie-Anne de Graaff, Biological Sciences
Integrated social-ecological systems research is challenging; complicated feedbacks and interactions across scales in multi-use landscapes are difficult to decouple. Novel methods and innovative data sources are needed to advance social-ecological systems research. In this thesis we use network science as a means of explicitly assessing feedbacks between social and ecological systems, and internet search data to better predict visitation in protected areas.
This thesis seeks to provide empirical examples of emerging social-ecological systems science methods as a precedent for resource managers on-the-ground, as well as extending the line of scientific inquiry on the subject.
In the first chapter of this thesis, we used an online survey to gather information on the collaborative network and current projects of 169 wetland management organizations in the state of Montana. We used this information along with geographic analyses to delineate the flow of information between managers and ecological connectivity of projects, characterizing the social-ecological network of wetlands and wetland management within the state. We demonstrate that just 2 key organizations facilitate landscape scale information sharing, while most stakeholders collaborate on the basis of project difficulty and proximity <10km. This chapter contributes to an emerging body of literature on social-ecological networks, a promising frontier for integrating social and environmental sciences, specifically addressing feedbacks within and between the two systems.
For the second part of this thesis, we apply novel data to a classic natural resource management problem. In recent years, visitation to U.S. National Parks has been increasing, with the majority of this increase occurring in a subset of parks. Improved visitation forecasting would allow park managers to more proactively plan for such increases and subsequent visitor-related challenges. In this study, we leverage internet search data that is freely available through Google Trends to create a forecasting model. We compare this Google Trends model to a traditional autoregressive forecasting model. Overall, our Google Trends model accurately predicted 97% of the total visitation variation to all parks one year in advance from 2013-2017 and outperformed the autoregressive model by all metrics. While our Google Trends model performs better overall, this was not the case for each park unit individually; the accuracy of this model varied significantly from park to park. This project applies a contemporary social science data set to a traditional natural resource management problem, demonstrating the potential for social-ecological systems research to provide real-world solutions in multi-use landscapes. Both chapters of this thesis explicitly address feedbacks between social and ecological systems, a key advance for social-ecological systems science.