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State-and-transition simulation model (ST-Sim): Predicting landscape conditions

Abstract

How does a land manager make a decision today that will impact landscapes decades into the future? With the uncertain influence of climate change and its associated stressors, it’s an increasingly thorny question. 

To help answer this question, Rocky Mountain Research Station scientists partnered with a company called Apex Resource Management Solutions (commonly known as “Apex”) to use a software-based ecological simulation tool called ST-Sim, which is short for state-and-transition simulation model. Using computer-aided modeling, land management teams can use ST-Sim to document or justify management actions in forthcoming forest plans and NEPA documentation. ST-Sim allows managers to ask landscape-wide “what-if” questions based on different management regimes and land treatments while estimating interactions with expected climate changes. This tool provides land managers with worst-case and best-case scenarios under different conditions. 

ST-Sim simulation models were used to identify potential vegetation types in New Mexico’s Rocky Mountains region. (Image credit: Matt Reeves, U.S. Forest Service.)
ST-Sim simulation models were used to identify potential vegetation types in New Mexico’s Rocky Mountains region. (Image credit: Matt Reeves, U.S. Forest Service.)

Purpose

Although ST-Sim has been available since 2013, it was recently deployed in Region 3 of the NFS to predict the ecological response of rangelands to livestock grazing across numerous vegetation types. Net annual primary production and ecological response to herbivory were calibrated for 19 potential vegetation types covering nearly 10 million acres. ST-Sim can be used to prioritize sites and vegetation types that are candidates for restoration or resilience-building management regimes. 

ST-Sim can be downloaded from the Apex website, where tutorials and a host of other resources can be found. This ST-Sim software can be used on any National Forest System district and beyond.

Publications

Ford, Paulette L. ; Reeves, Matthew C. ; Frid, Leonardo , 2019

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