Our team is assembling a meta-model to provide strategic guidance for sustainable forest management (SFM) in forest planning District 19A in Labrador. Here we overview the modeling “tools” that contribute the District 19A meta-model summarized briefly in Table 1. Each tool summary includes a brief illustration of how the tool contributes to the larger meta-model (Fig. 5). Following the iterative collaborative modeling approach to support SFM planning (Fall et al. 2001, Fig. 2), the meta-model presented here is a work in progress that will continue to be refined through repeated feedback from local domain experts, planners, and stakeholders.
SELES (Spatially Explicit Landscape Event Simulator; Fall and Fall 2001) is a raster-based tool for constructing, running and visualizing spatial landscape models that integrate natural and anthropogenic processes (e.g., fire, insects, logging, and succession). It can also perform spatial analysis (e.g., habitat connectivity), and track indicators (e.g., age class, habitat supply, growing stock) over long time-frames and large spatial areas. SELES is a research tool as well as a decision-support tool for problems related to conservation and resource management. It combines a declarative language for specifying spatial and spatio-temporal models, a text editor for creating or adapting and parsing models, and a simulation engine for running models and visualizing outputs. The SELES modeling language can be used to specify key landscape processes and link to other models such as SORTIE and LANDIS. SELES captures landscape dynamics using “landscape events” and “landscape agents.” The former are used for processes that return to the landscape periodically, initiate in one or more locations, spread to adjacent areas, and cause some change to the state of the system. The latter are used to for individual-based models, where dynamic components retain their identity as they move around and change the landscape state. SELES uses a discrete-event simulation engine to process events or agents during a simulation, allowing for complex interactions to be captured. As a flexible modeling tool, SELES supports collaborative modeling frameworks (Fall et al. 2001), while the open nature of the language allows re-use and adaptation of model components from other projects. More information and a free downloadable version are available at http://www.gowlland.ca.
Fig. 5 D19aLM (SELES)
The District 19a Landscape Model (D19aLM) is a forest dynamics model implemented in SELES. The underlying basis is a spatial forest estate model that captures stand aging and timber harvesting, and projects growing stock (based on input growth and yield information) and timber supply indicators such as volume harvested, mean age harvested, and roads built. The D19aLM was designed to support timber supply analysis, in which sustainable harvest levels are identified by sets of experimental simulations in which maximum sustainable levels are identified based on an ability to meet harvest targets, and on non-declining growing stock. The main components of the D19aLM are (i) stand aging, (ii) calculation of growing stock based on site type, stand age and growth and yield tables (Fig. A1a), (iii) a planning step that identifies stands available for harvest based on minimum harvest ages, road access, etc., (iv) harvesting, and (v) road building. The harvesting component selects available stands to start cutblocks, spreading out to adjacent available stands to reach a target block size (selected from an input distribution), and continuing to place blocks until either the harvest target for the time step is met or there are no more available stands (Fig. A1b). Selection preferences are controlled by parameters (e.g., increasing preference with age and decreasing preference with distance to nearest road). The road building component uses the mapped existing road network to constraint harvest access. As harvesting proceeds, road segments are created by adding segments to connect blocks to the current road network (Fig. A1b). The D19aLM “base model” includes an empirical fire model defined by fire rotation and size distribution parameters. This base model was designed for extension – for example it can support state-based tree species succession and more complex fire processes. The D19aLM was also designed to link with other models by producing spatial-temporal output (time sequences of spatial maps) that can be used as input to other models (e.g. indicators). To date the model has been used to contrast the road-building and timber supply consequences of the current 20 year plan in comparison with some simple alternatives (e.g., Fig. A1c), to elicit stakeholder feedback, and to train local foresters in spatial timber supply techniques in Labrador.
Figure A1. a. Empirical growth and yield curves for Labrador, used as input for the D19aLM model to project timber supply. Individual yield curves represent species and “site types” combinations for “high boreal” ecoregion, where the first two letters represent the species (Bf = balsam fir; Bs = black spruce), the third letter is the site quality (G = good; M = medium; P = poor), and the last number is the crown closure class (1 = > 75%; 2 = 51-75%; 3 = 26-50%). b. Simulated forest “cutblocks” (yellow) dependent on an expanding road network (magenta), overlaid on a digital elevation model. c. D19aLM output showing maximum annual allowable cut in response to different combinations of constraints: No plan versus the 20-year plan (i.e., “Plan”); spatial constraints (i.e., within 2km of roads, including new road placement) versus no spatial constraints on harvesting (“Aspatial”); no minimum rotation period versus 120 and 150 year minimum stand rotations.
Fig. 5 LINKAGES
LINKAGES is an ecosystem process model that simulates individual tree establishment, growth, competition, and mortality as a function of soil water, nutrient dynamics, and monthly average temperature and precipitation (Post and Pastor 1996). The model is a direct descendent of the original forest gap models (Botkin et al. 1972, Shugart and West 1977). LINKAGES was used to estimate the probability of tree establishment for tree species in two different ecozones of the District 19A landscape that are defined primarily by elevation (i.e., high-boreal, sub-artic), following the methods of Scheller et al. (2005). Resulting species establishment probabilities (Table A1) were consistent with the experience of local foresters in Labrador. An exception was balsam fir (Abies balsamea), for which establishment was low relative to local expectations. The discrepancy was resolved when it became clear that balsam fir was sensitive to local soil conditions, i.e., likelihood of establishment should be less on poor-quality soils and greater on high-quality soils. A more detailed “land type” map based on stand-scale soil conditions will therefore improve the successional patterns observed in LANDIS-II (see LANDIS-II below).
Table A1. Establishment probabilities required as input for LANDIS-II for eight tree species in Labrador planning District 19A, as estimated using LINKAGES, a forest gap-scaled ecosystem model. Discrepancies with the establishment probabilities for balsam fir (in bold) identified by provincial foresters led to recognition that finer-scaled soils data are required to accurately simulate successional trends in LANDIS-II.
Forestry Canada Fire Danger Group. 1992. Development and structure of the Canadian Forest Fire Behavior Prediction System. Forestry Canada, Science and Sustainable Development Directorate, Information Report ST-X-3, Ottawa, Ontario, Canada.
Fig. 5 Canadian FBP
The Canadian Forest Fire Behavior Prediction (FBP) System uses data from both experimental fires and wildfires across Canada to provide quantitative estimates of potential head fire spread rate, fuel consumption, and fire intensity, as well as qualitative descriptions of forest fire behavior (e.g. surface fire, crown fire) (Forestry Canada Fire Danger Group 1992). Key inputs include fuel type, weather, topography, and foliar moisture content (typically estimated using geographic location). The system is often combined with an elliptical fire growth model to provide tactical support for fire-fighting personnel. More information on the Canadian FBP can be found at http://cwfis.cfs.nrcan.gc.ca/en/background/bi_FBP_summary_e.php. Fire disturbance and fuel extensions based on the Canadian FBP were implemented in LANDIS-II (Fig. A2) to investigate interactions between fire, harvesting, and succession in Labrador District 19A (see LANDIS-II below).
Figure A2. a. Forest cover types from LANDIS-II translated into fuel types the Canadian Forest Fire Behavior Prediction (FBP) System. b. A simulated fire event and resulting fire severity pattern responding to the landscape configuration of fuel types, using fire spread equations from the Canadian FBP.
Fig. 5 LANDIS-II
LANDIS-II (Scheller et al. 2007; http://landis.forest.wisc.edu) is a recent elaboration of previous LANDIS models (from LANDscape DIsturbance and Succession; Mladenoff et al. 1996). LANDIS models in general simulate broad-scale (>105 ha) landscape dynamics, including succession, disturbance, seed dispersal, forest management, and climate change effects (Mladenoff 2004). Landscapes are represented as a grid of interacting cells with user-defined spatial resolution (cell size) generally ranging from 0.1 ha – 100 ha in size. Individual cells have homogeneous light environments, and are aggregated into ecoregions with homogeneous climate and soils. Forest composition at the cell level is represented as age cohorts of individual tree species that interact via a suite of vital attributes (i.e., shade tolerance, fire tolerance, seed dispersal, ability to sprout vegetatively, and longevity) to produce nondeterministic successional pathways sensitive to disturbance type and severity. LANDIS-II was re-engineered as an integrated modeling environment that allows the creation of custom forest landscape disturbance and succession extensions while maintaining and building upon the scientific rigor of the original LANDIS model (Scheller et al. 2007). Strengths of LANDIS-II include the new flexibility introduced through multiple inter-woven time steps, a library of published succession and disturbance extensions (He and Mladenoff 1999, Gustafson et al. 2000, Sturtevant et al. 2004), and the optional integration of additional cohort data and biomass dynamics (Scheller and Mladenoff 2004).
We are using LANDIS-II to investigate the strength of interactions between forest succession, harvesting, and fire disturbance processes (Simon et al. 2006), using both functions and input from other models in the toolkit. For example, the harvest module of LANDIS-II was designed for managed landscapes of the United States where abundant roads rarely limit harvest patterns (Gustafson et al. 2000), an assumption that did not capture the road-limited harvest pattern of Labrador well. We circumvented this limitation by parameterized the existing harvest extension to match the harvest patterns output by the D19aLM. In contrast, new fire disturbance and fuel extensions were created in LANDIS-II based on the Canadian FBP. The fuel extension translates the species age-list present on each cell into one of the 17 Canadian fuel types using a look-up table (Fig. A2a). We applied a duration-based approach to simulate the Labrador fire regime, where a fire duration distribution was calibrated to generate the fire size distribution observed in regional fire records, and fire duration for a given event was then selected from the calibrated distribution (Penannen and Kuuluvainen 2002). This approach allows the fire regime to change in response to changing fuel conditions and patterns (Didion et al. 2007). Fires spread to adjacent cells at rates defined by the fire event weather, wind direction, and fuel type based on the rate functions defined by the Canadian FBP (Forestry Canada Fire Danger Group 1992; Fig. A2b). Fire severity is a based on the estimated crown fraction burned; a fuel-specific function of the fire spread rates (Forestry Canada Fire Danger Group 1992). Species composition changes in response to fire and harvest (Fig. A3) that in turn influence the fire regime by modifying landscape fuel patterns.
Figure A3. a. Initial forest composition in Labrador’s forest planning District 19a; b. LANDIS-II projection of forest condition following 200 years of succession in response to fire and harvest (no plan scenario).
Fig. 5 SORTIE
SORTIE is an individual-tree model of forest dynamics at the stand scale originally developed for hardwood forests in the northeastern US to forecast long-term changes in the abundance and spatial distribution of tree species as a function of the competitive dynamics of individuals in a stand (Pacala et al., 1993, 1996). The model has since been adapted to study the effects on forest dynamics of spatial patterns of forest management (Beaudet et al. 2002, Coates et al. 2003), wind storms (Papaik and Canham 2006a, Uriarte and Papaik in press) and pathogens (Papaik et al. 2005). It uses empirically supported relationships in four basic submodels: seedling recruitment, light availability, growth, and mortality, as well as submodels for disturbance that include: wind, biotic agents, and harvest. The harvest submodel can simulate any type of silvicultural strategy that removes or retains trees by species, size and location. Thus, SORTIE is a flexible and well-tested complex stand model that can be used to support a wide range of SFM applications.
SORTIE has recently been re-engineered as a general neighborhood dynamics model that can incorporate variation in ecosystem characteristics and disturbance (SORTIE-ND). SORTIE-ND is a scalable model that has been designed to quickly incorporate key site specific relationships across a region to improve inferences above the stand scale. For our Labrador case study, field efforts first targeted data required to estimate juvenile and adult tree growth functions for use with growth and yield models as these are the most important submodels for estimating the short-term effects of silviculture on stand dynamics. SORTIE will allow us to “scale-up” the alternative silviculture treatments to the landscape to help address some scaling issues confronting forest management. More information on the SORTIE-ND model can be found at http://www.sortie-nd.org).
Fig. 5 Patchworks
Patchworks (Spatial Planning Systems, Inc) is a spatially explicit harvest scheduling model that uses optimization techniques to analyze trade-offs between competing sustainability goals (Lockwood and Moore 1993). Different objectives such as timber supply, habitat and old-growth retention, and patch distributions are evaluated with user-defined weighting factors that rank the importance and contribution of each factor into a multi-objective function. This design allows planners to explore the interactions between stakeholder interests in order to derive a trade-off function. Patchworks integrates operational-scale decision-making within a strategic-analysis environment, such that spatially explicit harvest allocations can be developed over different planning horizons, compatible with 5-year and 20-year operational plans as well as long-term sustainability. The model is fully integrated with an interactive GIS interface. The real-time, interactive nature of the Patchworks model allows planners to visualize solutions over time, and to test the abilities of management actions to achieve a range of management goals. A variety of realistic long-term spatial allocation criteria can be applied simultaneously, such as patch size targets, adjacency constraints, sub-regional targets, zonal constraints (e.g. landscape management, visual quality objectives). Patchworks is used in this project to assess optimal solutions of multi-objective forest management problems in order to derive trade-off functions between stakeholder interests. Forest dynamics from stand-level models are summarized in the form of yield tables for input to Patchworks (e.g., volume of merchantable timber, number of snags). The harvest schedules output from Patchworks can be used as input to indicator models or to guide logging in the D19aLM. More information on the Patchworks model can be found at http://www.spatial.ca/products/index.html.
Fig. 5 Real Options
Real options is an analysis method used for financial decision making that considers risk and uncertainty (Dixit and Pindyck 1994). A real option is characterized as “the value of being able to choose some characteristic (e.g., the timing) of a decision with irreversible consequences, which affects a real asset (as opposed to a financial asset)” (Saphores and Carr 2000). Under real options, problems are formulated so that they can be solved by numerical methods. We applied this technique to the problem of the negative impacts that timber harvesting may have on the viability of a woodland caribou (Rangifer tarandus caribou) population (Morgan et al. unpublished). Wildfire, forest harvesting, and forest age were used as the defining processes of the system. These processes were modeled using the D19aLM to estimate the amount and variability of old forest over time. To apply the real options methodology, these estimates were used to represent the expected supply and variability of caribou habitat using a mean-reverting numerical equation (Dixit and Pindyck 1994) Fig. A4). Included in the formulation is a stopping rule, which in our case reflects the timing of closing down harvesting when the amount of caribou habitat approaches a critical threshold. The rule represents the trade-off between maintaining an adequate amount of habitat to ensure the survival of the caribou, and providing socio-economic opportunity by harvesting timber. The timing is sensitive to the level of risk that society is willing to tolerate and the amount of uncertainty associated with the system, such as, long term natural disturbance or how caribou population dynamics would be impacted by commercial forestry activities.
Figure A4. Example of the amount H (i.e., the natural log of caribou habitat in hectares, over 200 years) where the supply of habitat falls below a critical threshold of minimum habitat after 160 years of landscape dynamics, including forest growth, harvesting and fire (Morgan et al. unpublished).
Fig. 5 BAP Toolbox
The Biodiversity Assessment Project (BAP) toolbox is a suite of indicator models used to assess diverse forest management strategies at three levels of biodiversity: landscape patterns, ecosystem diversity, and habitat supply for specific vertebrate species (Doyon and Duinker 2003). The approach was inspired by the coarse- and fine-filter approach from conservation biology (Hunter 1990) where landscape pattern and ecosystem diversity indicators serve as coarse filters while habitat supply models (HSMs) serve as fine filters. The BAP Toolbox translates a time series of landscape conditions output from landscape models (e.g., D19aLM) into habitat types that serve as spatial units for ecosystem and the landscape biodiversity (i.e., coarse-filter) assessment. The HSMs are based on up-to-date literature on the wildlife species, where the envirogram technique (Andrewartha and Birch 1984), proposed by Van Horne and Wiens (1991), is used to conceptualize the models. Habitat suitability is defined using stand-level habitat elements (including the spatial arrangement of elements) required for species crucial life activities. Many of these stand-level habitat elements such as snags, downed woody debris or understory vegetation are typically not available as output from forest projection tools or standard forest inventory. In these cases the BAP Toolbox uses Stand-level Habitat Element (SHE) models to characterize their changes through forest succession, vegetation manipulation, and disturbances based on empirical relationships between different forest conditions (e.g., forest age) and the habitat elements. In the Labrador case study, some SHE models will be replaced by output from SORTIE to address element responses to novel silvicultural treatments.
The BAP Toolbox is coded into the Arc-GIS environment (ESRI Inc.) and each of the three levels of biodiversity forms an independent analytical module that can be parameterized to express the regional forest conditions. The BAP Toolbox also includes some analysis and interpretation tools that allow comparison of bioindicator performance among model scenarios over long (century-scale) time scales. A forest planner can use BAP Toolbox output to design novel management strategies, and also to provide guidance on the implementation of a biodiversity monitoring plan. More information on the BAP Toolbox can be found at http://giant.lakeheadu.ca/carisweb/hsm/bap_reports/bap_reports_main.htm.
The models comprising the specific Labrador District 19a toolkit were selected for a variety of reasons, including the relative overlap between model domains and local questions/conditions; the availability, data requirements, and adaptability of existing tools; and the collective experience of the modeling team with those tools. Some of the tools used in the case study may be useful in other applications, but our focus is on a general approach to assembling and building a toolkit that can address specific problems and locations while leveraging research and investment in existing models. The effectiveness of such a toolkit in guiding the SFM planning process is dependent not only on quality tools, but also the degree to which their application and integration promotes information exchange between the key participants in the planning process.
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