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Grasslands, rangelands and beyond: Predicting landscape conditions with ST-Sim

Status: 
Action
Dates: 
July, 2019

A photo of a grassland dotted with juniper. A river runs through it, curving to the left around a rock formation sitting on a red dirt plateau.
ST-Sim projected many areas where grasslands may transition to juniper-dominated ecosystems. (Photo by Jack Triepke, U.S. Forest Service.)

Unforeseen Events and Climate Variability

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. With this tool, the scientists are able to provide land managers with worst-case and best-case scenarios under different conditions.

Asking “What-if” Questions

Although ST-Sim has been available since 2013, it was recently deployed in Region 3 of the National Forest Service 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. This study was described earlier this year in Rangelands, in an article entitled, “A tool for projecting rangeland vegetation response to management and climate.” The article describes a study that applies to nearly 10 million acres across the New Mexico Rocky Mountain region. Using ST-Sim, scientists were able to project a variety of conditions, including vegetative state transitions, net primary production, drought likelihood, and forage use and grazing targets. One prediction was that, in grazing areas, drier conditions may quickly cause perennial grass cover to be replaced by ruderal, annual and sparse grasses. Another prediction was that forage grazing targets for some ecological systems would drop significantly below 35 percent of historic annual production.

ST-Sim is based on an Apex-designed framework called SyncroSim which manages “big data” scenario inputs and outputs for any kind of simulation model. Scientists are working to quantify uncertainties and communicate their significance to natural resource managers with applications that include various land cover types and managing for species of interest.

Generating the Best Available Data

ST-Sim can be downloaded from the Apex website, where tutorials and a host of other resources can be found: http://www.apexrms.com/state-and-transition-simulation-models/

 

A map showing the potential vegitation types in New Mexico's Rocky Mountains region, represented by different colors.
ST-Sim simulation models were used to identify potential vegetation types in New Mexico’s Rocky Mountains region. Image credit: Matt Reeves

 

Key Findings

  • Based on ST-Sim models, increased drought may lead to shrub encroachment and transitions between vegetative states, especially if current grazing levels continue.ST-Sim can be used to prioritize sites and vegetation types that are candidates for restoration or resilience-building management regimes.
  • This ST-Sim software, which can be used on any National Forest System district and beyond, can be downloaded from: http://www.apexrms.com/state-and-transition-simulation-models/
  • Video tutorials and other resources can also be found from this webpage.
  • Additional information can be obtained by contacting Paulette Ford or Matt Reeves.

Publications

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


Principal Investigators:
Co-Investigators:
Leonardo Frid - Apex Resource Management Solutions