Agent-Based Modeling of Harvest Decisions by Small Scale Forest Landowners in Maine, USA

Abstract

Small-scale forests are an excellent example of coupled social-ecological systems, which involve human and biophysical subsystems with complex two-way feedback interactions. 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. Small-scale forests require an integrated approach to modeling the social and biophysical components comprehensively. Agent-based modeling involves modeling individualistic behavior and interpreting patterns that emerge. The interaction between agents and their environments makes this a valuable tool to assess repeated decisions of individual landowners responding to changing environmental conditions. Agent-based models can be used to determine potential ecological, economic, and social outcomes of landowner decisions and reactions to changing conditions. A forest landowner agent-based model experiment was developed to model timber harvesting in Maine, USA. We present baseline simulation results and compare 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 hectares and landowner goal scores to assess landowner behavior and priorities by action. We conclude by reviewing implications for future modeling efforts.

An Agent-Based Model of Private Woodland Owner Management Behavior Using Social Interactions, Information Flow, and Peer-To-Peer Networks

“…Criterion, concurrent, and convergent validation checks were performed against similar models and available empirical data. We found that the percent of forest harvested was higher than harvest percentages reported in Massachusetts and Maine [ 15 ]. On average, PWOs harvest between 0.01–1.48% of the forested landscape annually, however these values are derived from landscapes that contain a mix of ownership types.…”

“…The number of foresters, number of peer leaders, and opinion type were statistically different regarding percentage of the landscape harvested and percentage of landowners that trust foresters. Similar ABMs include FLAME [ 15 ], ForestSim [ 13 ], and a model of spatial interaction and information flow [ 12 ]. Our model configuration and outputs are consistent with these models.…”

“…There is also a growing literature that explores wood markets using ABMs [ 14 ]. Finally, Leahy et al [ 15 ] modeled timber harvesting behavior in Maine and demonstrated potential impacts of forest pests and economic disturbances on harvesting levels of PWOs. These studies demonstrate the potential for ABMs to incorporate PWO demographics and social network concepts to better understand the ramifications of individual decision-making on a forested landscape.…”

Implications of U.S. biofuels policy for sustainable transportation energy in Maine and the Northeast

“…Unlike other companies and government agencies that have a hierarchical structure for decision-making, the first stage of biomass production involves many decision-makersi.e., landowners. The multi-faceted nature of landowner involvement suggests the need to better understand decision-making processes and reactions to policy (Leahy et al 2013). In Maine, non-industrial private forest landownersalso known as NIPFs or family forest ownerscomprise an estimated 100,000 individuals and own about 30% of forest land (Butler 2008) while they produce 50-60% of all harvested timber (Adams et al 2006).…”

ForestSim Model of Impacts of Smallholder Dynamics: Forested Landscapes of the Upper Peninsula of Michigan

“…Models combining GIS with agent-based models have been used to manage recreational opportunities in North American parks [70,71], to understand forest owners' decision-making in response to changes in tax structures and threats from invasive species in Maine [72], and to measure the impact of fuel wood collection on giant panda habitat in China [12] and the ecological impacts of enrollment patterns in the Conservation Reserve Program for agricultural areas in Illinois [73], among other uses [74,75]. However, given the static nature of most GIS data layers [42], incorporating parcelization when a parcel shapefile defines the agents is problematic.…”