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Course: State-and-transition simulation modeling of landscape dynamics using ST-Sim

This two-day course provides a foundation for developing and running state-and-transition simulation models of landscape change using the free ST-Sim software. The course covers state-and-transition simulation modeling concepts, how to use ST-Sim to create simple models of landscape change and terrestrial carbon dynamics, and how to run those models and interpret the results. Examples of state-and-transition simulation models in a range of ecosystems will be presented, including models of both vegetation dynamics and land use/land cover change. 


The course is designed for those who are new to ST-Sim and/or new to state-and-transition simulation modeling. It is also recommended for those who have been introduced to state-and-transition simulation models through workshops or projects, but who now wish to build and run models themselves. While no particular software knowledge or disciplinary expertise is required, the course is computer exercise-based, fast-paced, and ventures into advanced modeling topics. 

Course Prerequisites

Participants will be required to install a desktop version of the SyncroSim software on a computer running a 64-bit version of Windows. 

Learning Objectives

  • State-and-transition simulation modeling concepts, as they have been applied to both vegetation dynamics and land use/land cover change
  • How to create and run a state-and-transition simulation model in ST-Sim, and how to interpret model results
  • Approaches for incorporating uncertainty and variability, in both space and time, into state-and-transition simulation models
  • How to integrate additional variables, such as wildlife habitat and carbon, into state-and-transition simulation models
  • How to run ST-Sim from the command line and using the R programming language 

Topics Covered

  • Theory behind state-and-transition simulation models
  • Conceptualizing spatially-explicit landscape change models in ST-Sim: defining ecological strata, state classes and transition pathways
  • Parameterizing ST-Sim models: setting transition probabilities, initial conditions, and targets for management
  • Running ST-Sim models: comparing alternative scenarios, viewing model output, and exporting results
  • Incorporating variability and uncertainty into ST-Sim models: sampling from input distributions; defining spatial and temporal transition multipliers
  • Tracking derived variables, such as timber volume and wildlife habitat, using the attributes feature of ST-Sim
  • Integrating spatially-explicit carbon budget models into ST-Sim
  • Introduction to scripting workflows with ST-Sim, including both pre- and post-processing, with examples provided using the rsyncrosim R package