In December 2017, Colin Daniel and others published a study in the journal Methods in Ecology and Evolution describing a new approach for integrating continuous stocks and flows into a state-and-transition simulation model (STSM). This is the method behind the Stock-Flow Add-On to the ST-Sim software, which allows users to extend their models developed in ST-Sim to include interactions between continuous state variables and STSMs. For example, a model in ST-Sim can now track forest carbon in any number of continuous carbon pools (i.e., live biomass, deadwood and soil), with fluxes between these carbon pools triggered by wildfire transitions in the STSM. The paper illustrates the approach by extending the original Hawaii STSM case study in Daniel et al. 2016 to integrated a spatially-explicit carbon budget model with a STSM of land use/land cover change.
Benjamin Sleeter and others at the U.S. Geological Survey and the State of California have recently completed a study published in Earths Future. This research demonstrates a new approach for projecting changes in Land Use and Land Cover Change (LULC) based on land use histories and demographic trends. This study suggests that estimates based on demographic trends alone will likely under represent the impacts of future LULC transitions when compared to a scenario representing business as usual projections. Simulations conducted as part of this study project that the greatest impacts of LULC change in California will occur in rangeland ecosystems.
SyncroSim Version 2, including the new rsyncrosim package for R, is now available for free download. This new version of SyncroSim represents a major milestone in the development of our scenario-based, stochastic modeling toolkit. With the release of Version 2, it is now possible to develop of any number of plug-in “modules” for SyncroSim – such as our flagship ST-Sim module for developing state-and-transition simulation models of landscape change – each representing one or more interconnected processes in a modeling project. Key new features of the SyncroSim software framework include: (1) the ability to connect multiple simulation models; (2) to script a modeling workflow from start to finish in programming languages such as R and Python; (3) to publish models for use by a non-technical audience using the new SyncroSim Lite user interface.
Early registration is now open for the ST-Sim Training & User Group Meeting.
This meeting brings together current and future users of ST-Sim in order to learn more about the software and to share experiences regarding its application to a wide range of questions. The event begins with two days of training – including both introductory and advanced streams – followed by a third day of case study presentations by scientists demonstrating the latest ST-Sim tools and techniques. To close the event there will be a panel discussion regarding possible future directions for ST-Sim.
Audience: Scientists, managers and analysts from any and all land management agencies, including experienced, new and undecided users
Register, or find out more at www.syncrosim.com.
ApexRMS associate, Dr. Bronwyn Rayfield, recently coauthored a paper in Science on the effect of modularity on the ability of a network to withstand perturbations. This study is the first that experimentally demonstrates this theoretically predicted property of modular networks. The results have implications for network design across disciplines from ecology and conservation to financial systems.
A newly published article in Forest Ecology and Management by Colin Daniel and others demonstrates the use of ST-Sim as a tool to incorporate uncertainty into forest management planning. The approach is applied to two boreal forest landscapes in Ontario and quantifies the risk of shortfalls in future timber harvest due to uncertainties in wildfire. The article is available as a free download for a limited time. The ST-Sim model library used for this study is also available.
ApexRMS recently presented a webinar in collaboration with Dr. Matt Reeves and Dr. Paulette Ford at the U.S. Forest Service Rocky Mountain Research Station. The webinar demonstrates how a state-and-transition simulation modeling approach can be used to account for future uncertainties regarding climate change when evaluating alternative rangeland management strategies. This project focuses on the impacts of drought and grazing on rangeland productivity and composition. A recording of the webinar is available online.
A recent USGS led publication in the International Journal of Disaster Risk Reduction used ST-Sim to project land use change and associated population growth in tsunami hazard zones along the US Pacific Northwest coast. The study demonstrates how land change simulation modeling can be used by local governments to incorporate the hazard exposure implications of community growth in land use policy and risk reduction planning.
A recent publication in the journal Carbon Balance and Management, entitled A carbon balance model for the great dismal swamp ecosystem, uses ST-Sim to assess the historical changes in the net ecosystem carbon balance for a critical 54,000 ha wetland in North Carolina and Virginia. The study, conducted by Rachel Sleeter and others at the U.S. Geological Survey, concludes that changes in the wetland’s carbon balance over the past 30 years, as a result of emissions due to recent fire and storm events, are essentially irreversible over a management timeframe. Future applications of this model will explore alternative land management scenarios for sequestering additional ecosystem carbon, such as the rewetting of large portions of the wetland.
LANDFIRE recently conducted an interview with landscape ecologist, Jen Costanza, a professor at North Carolina State University. Jen has been using ST-Sim in combination with LANDFIRE models to explore the impacts of biofuel production on landscapes and wildlife habitat in the southeastern United States. A news story this month in Science Magazine features Jen’s collaborator Robert Abt, and mentions their recent article in Global Change Biology Bioenergy.