ROCKY MOUNTAIN LANDSCAPE SIMULATOR (RMLANDS)

Model Overview

This section provides a brief overview of RMLANDS sufficient for minimally understanding its application in the HRV analyses. A more complete, detailed description of RMLANDS is beyond the scope of this report, but may be necessary to fully comprehend the model parameterization and analyses.

      The Rocky Mountain Landscape Simulator (RMLANDS) is a spatially-explicit disturbance-succession simulation model linked to a landscape pattern analysis program (FRAGSTATS) and wildlife habitat capability model (HABIT@). These models are being developed as an aid to ecological assessments and resource planning, and to aid land managers in addressing the following specific questions:

•    What is the range and pattern of variability in landscape structure under “natural” and anthropogenic disturbance regimes? RMLANDS provides a mechanistic approach for evaluating disturbance and succession processes over broad spatial (100,000's of ha) and temporal scales (100's of years) not possible or practical with empirical field studies. Moreover, RMLANDS allows us to ask “what if” questions associated with altered disturbance regimes (e.g., climate change, modified fire regimes, various timber management scenarios) and explore the potential affects on landscape structure dynamics. RMLANDS used in conjunction with FRAGSTATS provides a quantitative framework for understanding the ecological significance of landscape structure measured at one point in time (e.g., current landscape condition).

•    How does the range and pattern of variability in landscape structure vary in relation to spatial and temporal scale? RMLANDS used in conjunction with FRAGSTATS provides a method for quantitatively assessing these scaling relationships and identifying potential threshold behavior. For example, at what spatial scale (i.e., landscape extent) does the landscape exhibit shifting-mosaic, steady-state equilibrium conditions, if at all? Answers to this question will help determine the appropriate scale(s) for monitoring landscape structure change over time.

•    How does the range and pattern of variability in landscape structure affect landscape function (e.g., ability to support viable wildlife populations) under various disturbance regimes? RMLANDS used in conjunction with HABIT@ provides a method for evaluating the differential impacts of landscape changes on wildlife species with different habitat associations and life history characteristics, and identifying potential source habitats important to the long-term persistence of each species. In addition, these models provide a framework for evaluating the biological significance of short-term wildlife population/habitat trends derived from monitoring.

Answers to these and other questions will provide land managers a quantitative understanding of landscape dynamics that can serve as the basis for designing land management strategies, provide a scale-dependent context for interpreting landscape structure and wildlife habitat patterns, and provide a means to assess the impacts of alternative land management scenarios.

Conceptual Overview of RMLANDS

      RMLANDS is a grid-based, spatially-explicit, stochastic landscape simulation model designed to simulate disturbance and succession processes affecting the structure and dynamics of Rocky Mountain landscapes. RMLANDS simulates two key processes: succession and disturbance. These processes are fully specified by the user (i.e., via model parameterization) and are implemented sequentially within 10-year time steps for a user-specified period of time. Succession occurs at the beginning of each time step in the simulation and represents the gradual growth and/or development of vegetative communities over time. Succession is implemented using a stochastic state-based transition approach in which vegetation cover types transition probabilistically between discrete states (conditions). Transition pathways and rates of transition between states are defined uniquely for each cover type and are conditional on several attributes of a vegetation patch. These patches, as defined for succession, represent spatially contiguous cells having the same cell attributes (e.g., identical disturbance history, age). Most cover types progress through a series of stand conditions (states) over time as a result of successional processes (albeit at different rates due to the stochastic nature of succession). In some cases, these transitions are affected by the occurrence of certain disturbances (e.g., low mortality fire) or are regulated by management (e.g., silviculture). Other cover types (e.g., meadows, barren, water) are treated as having a single, static condition and are not affected over time by the interplay of disturbance and succession.

      RMLANDS simulates a variety of natural and anthropogenic disturbances. Natural disturbances include wildfire, a variety of insects/pathogens (pinyon decline [pinyon ips beetle and black stain root rot], mountain pine beetle, Douglas-fir beetle, spruce beetle, and spruce budworm), and prairie dogs. Each natural disturbance process is implemented separately, but affects and is affected by other disturbance processes operating concurrently to effect changes in landscape conditions. For example, the occurrence of beetle-killed trees derived from the spruce beetle disturbance process can affect the local probability of ignition and spread of wildfire.

      Each natural disturbance is modeled as a stochastic process; that is, there is an element of chance (or uncertainty) associated with the initiation, spread, and ecological effects of the disturbance. The disturbance algorithm is common among all natural disturbance processes, however, it is parameterized differently for each process, and consists of the following key components:

•    Climate.–Climate plays a significant role in determining the temporal and spatial characteristics of the disturbance regime. Climate is specified as a global parameter that optionally effects initiation, spread, and mortality of all disturbances within a time step. Climate can be specified as constant with a user-specified level of temporal variability, a trend over time (with variability), or as a user-defined trajectory - perhaps reflecting the climate conditions during a specific reference period.

•    Initiation.–Disturbance events are initiated at the cell level. Each cell has a probability of initiation in each time step that is a function of its susceptibility to disturbance and, optionally, its proximity to other disturbance events or landscape features (e.g., roads). Susceptibility to wildfire, for example, is a function of cover type, stand condition (or, alternatively, stand age or time since last fire), time since last insect outbreak (optionally by specific insect agent and severity), aspect, topographic position, and road proximity - factors that influence fuel mass and moisture and risk of human-caused ignition.

•    Spread.–Once initiated, disturbance spreads to adjacent cells in a probabilistic fashion. Each cell has a probability of spread that is a function of its susceptibility to disturbance (as above), which is modified by its relative position (e.g., relative elevation or wind direction) and the influence of potential barriers (e.g., roads and streams). The probability of spread is further modified to reflect variable weather conditions associated with the disturbance event. This event modifier effects the final size of the disturbance and is specified as a user-defined size distribution. In addition, there is an optional provision for the ‘spotting’ of disturbances during spread so that disturbances are not limited to contiguous spread only.

•    Mortality.–Following spread, each cell is evaluated to determine the magnitude of ecological effect of the disturbance. Each cell can exhibit high or low mortality of the dominant plants. High mortality occurs when all or nearly all (>75%) of the dominant plant individuals are killed. Cells are aggregated into vegetation patches for purposes of determining mortality response, where patches are defined as spatially contiguous cells having the same cell attributes (e.g., identical disturbance history, age).

•    Transition.–Following mortality determination, each high mortality vegetation patch is evaluated for potential immediate transition to a new stand condition (state). Transition pathways and rates of transition between states are defined uniquely for each cover type and are conditional on several attributes at the patch level. Note, these disturbance-induced transitions are differentiated from the successional transitions that occur at the beginning of each time step in response to gradual growth and development of vegetation over time.

      RMLANDS simulates a variety of vegetation treatments. Treatments are implemented via management regimes defined by the user. Management regimes are uniquely specified within management zones, or user-defined geographic units (e.g., urban-wildland interface verus interior). Management zones are further divided into one or more management types based on cover type. Each cover type can be treated separately or it can be combined with other cover types to form aggregate management types. Each management type is then given a unique management regime, which consists of one or more treatment types and associated spatial and temporal constraints. Specifically, each management regime is defined by the following components:

•    Treatment intensity and constraints.–Treatment intensity within a management regime is controlled by treatment area; that is, within each time step treatments are implemented subject to the availability of suitable area and a maximum treatment area constraint. In addition, treatment intensity is subject to restriction based on specified watershed constraints. Specifically, once a watershed exceeds a specified disturbance threshold, all further treatments are prohibited in that watershed. The watershed disturbance threshold is defined in terms of clearcut equivalence and is designed to reflect the impact of disturbances (both natural and anthropogenic) on water quality. Each disturbance type is given a clearcut equivalent coefficient and recovery trajectory over time.

•    Treatment types.–Treatments available for inclusion in a management regime include a variety of vegetation management practices, including silvicultural systems associated with commercial timber harvest and regeneration (cleacut, shelterwood, group selection, individual tree selection), restoration treatments associated with ponderosa pine forest restoration, mechanical and chemical control of woody vegetation in shrubland and pinyon-juniper cover types, and prescribed fire.

•    Treatment allocation.–Treatments included in a management regime are implemented according to an allocation scheme in which a specified proportion of the total treatment area is allocated to each treatment type.

•    Static Spatial constraints and priorities.–For each treatment type in a management regime, static spatial constraints limit where treatment is allowed. Spatial constraints can be defined on the basis of suitability (e.g., timberland suitability), road proximity, ownership, slope, riparian buffer zones, and other factors. Each of these factors alone or in combination can restrict the potential treatment area. In addition, the initiation of treatment units within the potential treatment area can be prioritized based on these factors. These spatial constraints and priorities are static; that is, they do not change over the course of the simulation.

•    Dynamic suitability constraints.–For each treatment type in a management regime, dynamic constraints limit where treatment is allowed in any particular time step based on vegetation characteristics that change over time. Dynamic constraints can be defined on the basis of stand condition class (i.e., seral stage), stand age, or any other age-related attribute (e.g., age since last low mortality fire). These constraints are dynamic because they vary over the course of the simulation.

•    Treatment regime.–Each treatment type in a management regime is implemented according to a treatment regime (or prescription) that includes a treatment schedule, including a rotation period and/or treatment cycle, and spatial and temporal constraints on treatment unit size, adjacency considerations, and treatment unit dispersion. Treatment units can be dispersed in a random, aggregated, or dispersed fashion within user-defined management units (e.g., watersheds).

      Given the management regime specified above, anthropogenic treatments are implemented much the same way as natural disturbances; that is, treatments are initiated at the cell level. Once initiated, treatment units are created by spreading outward from the initiation cell according to rules that seek to create logical treatment units based on land cover and terrain. Following spread, each vegetation patch is evaluated for potential immediate transition to a new stand condition (state). Transition pathways and rates of transition between states are defined uniquely for each cover type and are conditional on several attributes at the patch level. Treatment units remain under management for one full “rotation” (i.e., schedule of individual treatments), after which they are returned to the pool of unmanaged land.

Technical Overview of RMLANDS

      RMLANDS is stand-alone program written entirely in Visual C++ for use in a Microsoft Windows Operating System environment. RMLANDS expects input grids in Arc Grid (ESRI) format and requires libraries from either ArcGIS or ArcView Spatial Analyst. RMLANDS was designed to make full use of required Forest Service GIS data, supported by systems such as NRIS, INFRA, and FACTS; RMLANDS does not require detailed stand inventory data. Consequently, all required input grids can be derived from Forest Service GIS data, although other data sources are acceptable. RMLANDS can be classified as a hybrid statistical/probabilistic model with the following distinguishing characteristics:

•    Grid-based.--RMLANDS utilizes a grid-based data model in which the landscape is represented in a regular grid lattice structure. The grid structure allows for efficient and powerful spatial processing. Each grid cell (pixel), representing a fixed geographic area, possesses a number of ecological attributes (e.g., cover type, age). Attributes possess multiple states (i.e., unique values), many of which change over time in response to succession and disturbance.

•    Spatially explicit.--Consistent with the grid structure, RMLANDS is a spatially-explicit model; grid cells are geographically explicit and topological relationships are important in all processes (e.g., disturbance initiation and spread).

•    Stochastic.--RMLANDS is a stochastic model; that is, there is an element of chance (or uncertainty) associated with the outcome of each process. For example, each cell has a probability of initiation for each disturbance process that is contingent on several cell attributes. A probability less than 1 means that there is only a chance of a disturbance initiating. Thus, given the same cell attributes, some cells will initiate while others will not. There is a stochastic element to nearly all processes in RMLANDS.

•    Spatial scale.--The grid can be defined at any spatial resolution, although current applications utilize a high resolution (25 m cell size) grid that allows for detailed representation of landscape patterns. In addition, while RMLANDS does not limit the extent of the landscape, it is most applicable to large landscapes, say >40,000 ha.

•    Temporal scale.–RMLANDS currently operates on a 10-yr time step and is most applicable to simulating landscape dynamics over 100's of years.