Filters: partyWithName: Leonardo Frid (X) > Types: Citation (X)
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This is a spatially-explicit state-and-transition simulation model of rangeland vegetation dynamics in southwest South Dakota. It was co-designed with resource management partners to support scenario planning for climate change adaptation. The study site encompasses part of multiple jurisdictions, including Badlands National Park, Buffalo Gap National Grasslands, and Pine Ridge Indian Reservation. The model represents key vegetation types, grazing, exotic plants, fire, and the effects of climate and management on rangeland productivity and composition (i.e., distribution of ecological community phases). See Miller et al. (2017) for further details. The model was built using the ST-Sim software platform (www.apexrms.com/stsm)....
Categories: Data;
Types: Citation;
Tags: Badlands National Park,
Bison,
Buffalo Gap National Grassland,
Cattle,
Climate change,
Context Agent-based models (ABMs) and state-and-transition simulation models (STSMs) have proven useful for understanding processes underlying social-ecological systems and evaluating practical questions about how systems might respond to different scenarios. ABMs can simulate a variety of agents (autonomous units, such as wildlife or people); agent characteristics, decision-making, adaptive behavior, and mobility; and agent-environment interactions. STSMs are flexible and intuitive stochastic landscape models that can track scenarios and integrate diverse data. Both can be run spatially and track metrics of management success. Objectives Due to the complementarity of these approaches, we sought to couple them through...
Categories: Publication;
Types: Citation
Abstract (from http://www.aimspress.com/article/10.3934/environsci.2015.2.400): State-and-transition simulation models (STSMs) are known for their ability to explore the combined effects of multiple disturbances, ecological dynamics, and management actions on vegetation. However, integrating the additional impacts of climate change into STSMs remains a challenge. We address this challenge by combining an STSM with species distribution modeling (SDM). SDMs estimate the probability of occurrence of a given species based on observed presence and absence locations as well as environmental and climatic covariates. Thus, in order to account for changes in habitat suitability due to climate change, we used SDM to generate...
Categories: Publication;
Types: Citation;
Tags: Grasslands and Plains,
Landscapes,
Mountain pine beetle,
North Central CASC,
Other Landscapes,
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