Publication Date


Date of Final Oral Examination (Defense)


Type of Culminating Activity


Degree Title

Master of Science in Biology



Major Advisor

Vicken Hillis, Ph.D.


Trevor T. Caughlin, Ph.D.


Marie-Anne de Graaff, Ph.D.


Integrated social-ecological systems research is challenging; complicated feedback 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 feedback 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

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 mult-iuse landscapes. Both chapters of this thesis explicitly address feedbacks between social and ecological systems, a key advance for social-ecological systems science.


Related publication:

Clark, M.; Wilkins, E.J.; Dagan, D.T.; Powell, R.; Sharp, R.L.; & Hillis, V. (2019). Bringing Forecasting into the Future: Using Google to Predict Visitation in U.S. National Parks. Journal of Environmental Management, 243, pp. 88-94. doi: 10.1016/j.jenvman.2019.05.006