1st International Congress on Environmental Modelling and Software - Lugano, Switzerland - June 2002
Keywords
forest landscape, spatial interaction, succession, piece-wise deterministic markov processes
Start Date
1-7-2002 12:00 AM
Abstract
This paper describes a model for incorporating both spatial interactions and succession dynamicswithin a MOSAIC forest landscape model. MOSAIC uses semi-Markov processes to model the successiondynamics of a forest landscape. A landscape is partitioned into cells and the parameters defining the semi-Markov process associated with each cell are derived from individual tree-based models that run in gap-sizedplots within the cell. Parameters vary from cell to cell based on the environmental and terrain conditions ofthe cells. Ecologically, cells and plots influence their neighbors’ development through processes such as seeddispersal and disease transmission. Neighborhood interactions are modeled through influence arrays that areflexible enough to capture a variety of processes operating at the landscape scale. The influence arrays aresimilar to the transition functions used in cellular automata models. Integrating tree-based and landscapescale models, as well as ideas from cellular automata and semi-Markov processes, further unifies techniquesused in forest landscape models. Simulation examples derived from the H. J. Andrews Forest in Oregon arepresented.
Cell Interaction in Semi-Markov Forest Landscape Models
This paper describes a model for incorporating both spatial interactions and succession dynamicswithin a MOSAIC forest landscape model. MOSAIC uses semi-Markov processes to model the successiondynamics of a forest landscape. A landscape is partitioned into cells and the parameters defining the semi-Markov process associated with each cell are derived from individual tree-based models that run in gap-sizedplots within the cell. Parameters vary from cell to cell based on the environmental and terrain conditions ofthe cells. Ecologically, cells and plots influence their neighbors’ development through processes such as seeddispersal and disease transmission. Neighborhood interactions are modeled through influence arrays that areflexible enough to capture a variety of processes operating at the landscape scale. The influence arrays aresimilar to the transition functions used in cellular automata models. Integrating tree-based and landscapescale models, as well as ideas from cellular automata and semi-Markov processes, further unifies techniquesused in forest landscape models. Simulation examples derived from the H. J. Andrews Forest in Oregon arepresented.
