US-IALE 2009: Abstracts

The two abstracts I submitted to US-IALE 2009 have been accepted for (oral) presentation at the meeting. I’ll be presenting both on the work I’ve been doing here at CSIS and from my PhD. I’ve copied the initial abstracts below (these may change slightly) and I’ll post a full list of what everyone in CSIS is up to at the conference nearer the time. See you in Snowbird!

Modeling Interactions of Human and Natural Disturbances in a Managed Forest Landscape

James D.A. Millington, Michael B. Walters, Megan S. Matonis, Frank Lupi, Susan Chen, Kimberly R. Hall, Edward J. Laurent, Jianguo (Jack) Liu

As is often the case for coupled human and natural systems, the interactions between human and natural forest disturbances have the potential to produce complex system behavior. Spatially-explicit ecological-economic modeling provides a useful tool to investigate these phenomena in an integrated manner, revealing patterns and processes not observable by investigating the social and natural components separately. We present the development and initial results from such a model that examines the complex interactions among timber harvest, white-tailed deer browse and vegetation dynamics in a managed forest landscape in Michigan’s Upper Peninsula. This landscape has been experiencing low tree regeneration due to overabundant white-tailed deer, and changes in habitat for songbirds of conservation concern due to deer impacts and timber harvesting.

The multi-scale model uses input data on deer population, forest stand structure, tree regeneration, forest cover, habitat type and land ownership data collected at plot, stand, and landscape levels. Vegetation establishment, regeneration and growth are simulated using the USFS Forest Vegetation Simulator (FVS). Deer browse impacts are represented in FVS and parameterized by data we have collected on deer density and forest gap regeneration. As is common for many studies, our stand-level data for model initialization are incomplete across the 4,000 km2 study area. We show how we impute our stand-level data across the remainder of the study area using auxiliary variables including topography and remotely-sensed land cover.

Results show that distance to nearest lowland conifer stand, mean stand tree diameter-at-breast-height and the proportion of hardwood species in the surrounding local area are statistically significant predictors of deer density across the landscape (p < 0.01). These variables alone explain 40% of variance in deer density. Our initial model simulation results indicate complex spatial interactions between deer densities, stand structure and timber values across the managed forest landscape.

Investigating the Interaction of Land Use/Cover Change and Wildfire using Agent-Based Modelling
(Global Land Project symposium on agent-based modelling of land use effects on ecosystem processes and services)

James D.A. Millington, John Wainwright, Raul Romero-Calcerrada, George L.W. Perry and David Demeritt

Humans have a long history of activity in Mediterranean Basin landscapes. Spatial heterogeneity in these landscapes hinders understanding about the impacts of changes in human activity on ecological processes, such as wildfire. We present an Agent-Based Model (ABM) of agricultural land-use decision-making. This model is integrated with a spatially-explicit, state-and-transition Landscape Fire-Succession Model (LFSM) to investigate the relative importance of anthropic and ecological drivers of the wildfire regime.

The ABM considers two ‘types’ of land-use decision-making agent with differing perspectives; ‘commercial’ agents that are perfectly economically rational, and ‘traditional’ agents that represent part-time or farmers that manage their land because of its cultural, rather than economic, value. Results from the ABM indicate that land tenure configuration influences trajectories of land use change. However, simulations for various initial land-use configurations and compositions converge to similar states when land-tenure structure is held constant. For the scenarios considered, mean wildfire risk increases relative to the observed landscape.

The LFSM uses plant functional types to represent spatial and temporal competition for resources (predominantly water and light) in a rule-based modelling framework. Wildfire behaviour is represented using a cellular-automata approach. Results from the integrated ABM-LFSM indicate that fires ignited by human causes burned greater areas of shrubland than would be expected at random, and modelled lightning fires burned greater areas of forest land-cover types than would be expected at random.

We conclude by discussing our efforts to achieve a form of ‘stakeholder model validation’. This evaluation process involved taking the model and its results back for examination by the agricultural actors and decision-makers that aided our model conceptualization. We put this discussion in the context of recent calls for increased engagement between science and the public, highlighting some of the problems we encountered with this form of model evaluation.

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