Date of Award


Level of Access Assigned by Author

Campus-Only Thesis

Degree Name

Master of Science (MS)


Ecology and Environmental Sciences


Jessica Leahy

Second Committee Member

Kathleen Bell

Third Committee Member

Jeremy Wilson


Family forests are an excellent example of coupled social-ecological systems (SES). SES involves human and biophysical subsystems with complex two-way feedback interactions. In addition, the dynamic nature of these systems proves a challenge to study, model, and manage. Further complicating family forest research is a variety of landowner issues such as harvesting, estate transfer, subdivision, and public access. The multifaceted nature of landowner decisions drives a significant need to better understand decision making processes, reactions to policy and combined impacts on ecosystems in a comprehensive manner. As an SES, family forests require an integrated approach to modeling the social and biophysical components comprehensively. One such method is the use of generative social science through agent-based modeling (ABM). Generative social science involves modeling individualistic behavior and interpreting patterns that arise from the bottom up. The interaction between agents and their environments makes ABM a valuable tool to assess repeated decisions of individual landowners responding to changing environmental conditions. ABM can be utilized to determine potential ecological, economic and social outcomes of landowner decisions and reactions to changing conditions. In addition, ABM can be used to make future projections and act as a learning tool for policy makers. This paper presents the Forest Landowner Agent-based Model (FLAME). This research first presents results from social learning activities throughout the FLAME creation with key family forest stakeholders. These activities occurred in the form of three focus groups and surveys at the beginning, middle and end of the FLAME modeling process. The purpose of the social learning activities were to 1) identify factors that influence model acceptance; 2) discover how social learning activities affect stakeholder knowledge and attitudes; and 3) determine how stakeholder involvement affects the modeling process and end results. Our research revealed four stakeholder model acceptance factors: interest, knowledge, trust, and beliefs. Furthermore, we found social learning activities increased stakeholder knowledge, improved attitudes and beliefs, and, ultimately, led to an improved model. The second chapter of this thesis presents FLAME baseline simulation results and compares the effect of a social change (an increased tax rate) and a biophysical change (a pest outbreak resulting in increased tree mortality) on the system. These three scenarios were analyzed using ANOVA and MANOVA tests on harvested acres and landowner goal scores to assess landowner behavior and priorities by action. Finally, we review implications for policy makers, family forest owners and other stakeholders.