State‐and‐transition simulation models: a framework for forecasting landscape change

Abstract

Summary1. A wide range of spatially explicit simulation models have been developed to forecast landscape dynamics, including models for projecting changes in both vegetation and land use. While these models have generally been developed as separate applications, each with a separate purpose and audience, they share many common features.2. We present a general framework, called a state-and-transition simulation model (STSM), which captures a number of these common features, accompanied by a software product, called ST-Sim, to build and run such models. The STSM method divides a landscape into a set of discrete spatial units and simulates the discrete state of each cell forward as a discrete-time-inhomogeneous stochastic process. The method differs from a spatially interacting Markov chain in several important ways, including the ability to add discrete counters such as age and time-since-transition as state variables, to specify one-step transition rates as either probabilities or target areas, and to represent multiple types of transitions between pairs of states. 3. We demonstrate the STSM method using a model of land-use/land-cover (LULC) change for the state of Hawai'i, USA. Processes represented in this example include expansion/contraction of agricultural lands, urbanization, wildfire, shrub encroachment into grassland and harvest of tree plantations; the model also projects shifts in moisture zones due to climate change. Key model output includes projections of the future spatial and temporal distribution of LULC classes and moisture zones across the landscape over the next 50 years. 4. State-and-transition simulation models can be applied to a wide range of landscapes, including questions of both land-use change and vegetation dynamics. Because the method is inherently stochastic, it is well suited for characterizing uncertainty in model projections. When combined with the ST-Sim software, STSMs offer a simple yet powerful means for developing a wide range of models of landscape dynamics.

Distribution Pattern of Red Fox (Vulpes vulpes) Dens and Spatial Relationships with Sea Turtle Nests, Recreation, and Environmental Characteristics

“…Simulation models are possible when there is spatial data that is related to the predicted species at a spatial scale relevant to the species' habitat use [47]. ST-Sim (State-and-Transition Simulation) is open-source software that has been used to develop models that integrate existing data and knowledge about species behavior, can identify gaps or missing data, and can explore "what if" scenarios such as management strategies and habitat changes [48,49]. Future work on Masonboro Island will include the development of habitat suitability models and state-and-transition simulation models to estimate the spatial patterns of the red fox species given various management scenarios.…”

Integrating continuous stocks and flows into state‐and‐transition simulation models of landscape change

“…Note that this model is an extension of the STSM presented in Daniel et al. (); readers are thus referred to this paper for additional details regarding the LULC change portion of the model.…”

“…As in Daniel et al. (), a total of 21 possible state types were included here, consisting of all possible combinations of seven LULC classes (Agriculture, Grassland, Shrubland, Forest, Eucalyptus Plantation, Developed and Barren), crossed with three moisture zones (Wet, Mesic, Dry). In addition to these discrete state variables, we also defined eight carbon (C) pools as continuous state variables for each cell, with each pool defined as a stock type using the new STSM‐SF approach.…”

“…In the STSM approach, space is represented as a set of discrete spatial units, time is represented in discrete steps and the change in discrete state of each spatial unit over time is modelled as a stochastic process. Examples of questions for which STSMs have been developed include forest management (Costanza, Terando, McKerrow, & Collazo, ; Daniel, Ter‐Mikaelian, Wotton, Rayfield, & Fortin, ), rangeland management (Provencher, Frid, Czembor, & Morisette, ) and LULC change (Daniel et al., ; Wilson, Sleeter, & Cameron, ).…”

“…A current limitation of STSMs, however, is that all of the state variables must be discrete, making it challenging to model processes that do not lend themselves well to discretization (Daniel et al., ). Examples of situations in which spatially explicit continuous state variables are often required include landscape projections of ecosystem carbon (Boisvenue, Smiley, White, Kurz, & Wulder, ), tracking of continuous tree attributes such as density or biomass across forested systems (Scheller et al., ; Wang et al., ), and spatially explicit population models (Turner et al., ).…”