Incorporating Uncertainty into Projections of Landscape Change

A wide range of spatially-explicit simulation models have been developed to forecast landscape dynamics, including models for projecting changes in both vegetation and land use. A key challenge facing these models is how to incorporate uncertainty, including the future effects of climate change, into their projections. I present here a general framework, called a state-and-transition simulation model (STSM), for incorporating uncertainty into projections of landscape change. The STSM method divides a landscape into a set of spatial units, and then simulates the discrete state of each spatial unit forward in time as a stochastic process, in response to discrete transitions, using a Monte Carlo approach. The method also allows for any number of continuous stocks to be defined as stochastic processes for each spatial unit, along with continuous flows to move material between these stocks over time. I demonstrate the STSM method using two different applications. The first is a model of land use/land cover (LULC) change for the State of Hawaii. This model explores interactions between possible future changes in LULC, combined with projected shifts in moisture zones due to climate change, in order to generate projections for the future spatial and temporal distribution of LULC across the State. Through the integration of a carbon stock-flow model with the STSM, I further demonstrate how spatially-explicit projections for terrestrial carbon can incorporate uncertainties in projections of LULC change. The second application presents a new approach for incorporating uncertainty into forest management planning, which I demonstrate using two landscapes in the boreal forest of Ontario (Canada). Here I explore the implications of incorporating uncertainties regarding the spatial and temporal variability of wildfire, including the potential effects of climate change, into spatially-explicit projections of timber supply and habitat for woodland caribou. STSMs can be applied to a wide range of landscapes, including questions of both land use change and vegetation dynamics. Because the method is both spatially-explicit and stochastic, it is well suited for characterizing uncertainty in landscape dynamics. STSMs offer a simple yet powerful means for incorporating uncertainty into projections of landscape change.Ph.D