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Copyright © 2007 by the author(s). Published here under license by The Resilience Alliance.
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Sturtevant, B. R., A. Fall, D. D. Kneeshaw, N. P. P. Simon, M. J. Papaik, K. Berninger, F. Doyon, D. G. Morgan, and C. Messier. 2007. A toolkit modeling approach for sustainable forest management planning: achieving balance between science and local needs. Ecology and Society 12(2): 7. [online] URL: http://www.ecologyandsociety.org/vol12/iss2/art7/

Insight, part of Special Feature on Crossing Scales and Disciplines to Achieve Forest Sustainability

A Toolkit Modeling Approach for Sustainable Forest Management Planning: Achieving Balance between Science and Local Needs

Brian R. Sturtevant 1, Andrew Fall 2, Daniel D. Kneeshaw 3, Neal P. P. Simon 4, Michael J. Papaik 3, Kati Berninger 3, Frédérik Doyon 5, Don G. Morgan 6 and Christian Messier 3

1Northern Research Station, U.S. Forest Service, 2Gowlland Technologies Ltd, 3Centre d’Étude de la Forêt (CEF), University of Quebec at Montreal, 4Newfoundland and Labrador Department of Natural Resources, 5Institut Québécois d'Aménagement de la Forêt Feuillue, 6British Columbia Ministry of Forests


To assist forest managers in balancing an increasing diversity of resource objectives, we developed a toolkit modeling approach for sustainable forest management (SFM). The approach inserts a meta-modeling strategy into a collaborative modeling framework grounded in adaptive management philosophy that facilitates participation among stakeholders, decision makers, and local domain experts in the meta-model building process. The modeling team works iteratively with each of these groups to define essential questions, identify data resources, and then determine whether available tools can be applied or adapted, or whether new tools can be rapidly created to fit the need. The desired goal of the process is a linked series of domain-specific models (tools) that balances generalized “top-down” models (i.e., scientific models developed without input from the local system) with case-specific customized “bottom-up” models that are driven primarily by local needs. Information flow between models is organized according to vertical (i.e., between scale) and horizontal (i.e., within scale) dimensions. We illustrate our approach within a 2.1 million hectare forest planning district in central Labrador, a forested landscape where social and ecological values receive a higher priority than economic values. However, the focus of this paper is on the process of how SFM modeling tools and concepts can be rapidly assembled and applied in new locations, balancing efficient transfer of science with adaptation to local needs. We use the Labrador case study to illustrate strengths and challenges uniquely associated with a meta-modeling approach to integrated modeling as it fits within the broader collaborative modeling framework. Principle advantages of the approach include the scientific rigor introduced by peer-reviewed models, combined with the adaptability of meta-modeling. A key challenge is the limited transparency of scientific models to different participatory groups. This challenge can be overcome by frequent and substantive two-way communication among different groups at appropriate times in the model-building process, combined with strong leadership that includes strategic choices when assembling the modeling team. The toolkit approach holds promise for extending beyond case studies, without compromising the bottom-up flow of needs and information, to inform SFM planning using the best available science.

Key words: decision support; ecosystem management; forest sustainability; interdisciplinary modeling; land planning; participatory modeling; scaling


Society demands that forest planners balance more diverse resource objectives than ever before (Côté and Bouthillier 1999, Kneeshaw et al. 2000, Schulte et al. 2006). Modern sustainable forest management (SFM) has, therefore, evolved from basic timber supply to more integrated land-use planning with social, economic, and ecological dimensions (Lämås and Eriksson 2003). Despite dramatic advancements in computing power, GIS technology, and simulation modeling, decision support tools for SFM have lagged behind the growing diversity of forest planning objectives (Province of British Columbia 1996, Baker and Landers 2004). A root cause underlying this lag is the sheer complexity of the problem. Multi-scalar ecological and human systems form complex relationships (Gunderson and Holling 2002), making them difficult to understand, let alone model. Nonetheless, forest management requires sound guidance for strategic planning, because choices made today will have lasting effects on future ecosystem services and opportunities (Spies et al. 1994). There is a pressing need for approaches to support strategic landscape planning that can maximize innovation for a particular situation (i.e., address specific questions and use local information) and minimize re-invention (i.e., make use of existing models and techniques).

Forest planners often look to the many existing models for decision support (Messier et al. 2003). The primary limitation with this approach is that all models, as simplifications of reality, are limited to the domains for which they were created. Modeling domains have multiple dimensions, including space and time, traditional scientific disciplines, and type of system or location (Messier et al. 2003, Mladenoff 2004). Forest ecology models designed for research (e.g., Aber et al. 1995, Pacala et al. 1996, He and Mladenoff 1999, Kimmins et al. 1999) lack the social and economic dimensions of SFM and often do not match the scales of interest to planners. Such models also require expertise and specialized data for parameterization and interpretation that is often not available to forest planners or simply irrelevant to them. In contrast, forest optimization models that combine growth and yield with harvest scheduling or timber supply analyses were designed specifically for production-oriented forestry and are the current staple of most forest planning (e.g., Feunekes and Cogswell 1997). Such models are well-suited for production-oriented questions (i.e., their intended domain), but they lack integration with key ecological processes, including succession and natural disturbance, which affects their reliability (Fall et al. 2004).

Using off-the-shelf models is a top-down approach, where information primarily flows from researchers and planners to local communities. It benefits from the expertise and resources that went into model development, but risks being unable to adapt to the unique questions, knowledge, context, and cross-disciplinary integration inherent to any specific SFM planning initiative. An alternative approach is case-specific modeling (Antle et al. 2001, Kruse et al. 2004), where information primarily flows from local sources to researchers and planners to build a model from the bottom up in support of local needs. Case-specific modeling customizes the modeling domain to the specific planning needs, but the time and cost of developing new models can limit their ability to rapidly inform the decision-making process (Fall et al. 2001), and by definition, customized models are not intended for re-use elsewhere. Therefore, planners are handed the “devil’s choice” between top-down and bottom-up modeling approaches. No single model can address the needs of all forest planning situations, and attempts to build such models will likely suffer from over-generality, scale mismatch issues, or endless additions to address new data and questions (Derry 1998, Commission d’étude sur la gestion de la forêt publique québécoise 2004). Given the exploding demand for simulation modeling support in SFM, it is also doubtful that the technical capacity exists to produce customized models for every planning situation.

Managers need a general and flexible framework to support SFM planning, one that answers the questions being asked at the right scale and in a timely and cost-efficient fashion, while still integrating the three dimensions (social, economic, and ecological) that shape managed forest ecosystems. We propose a “toolkit” approach that builds on existing and readily adaptable modeling “tools” that have been developed and applied to previous research and planning initiatives across Canadian boreal forests and similar ecosystems. This approach is a hybrid between selecting a model “off the shelf” and building a customized model. The goal is to keep the scientific and rapid deployment advantages of top-down approaches, as well as the adaptive, shared-ownership advantages of bottom-up approaches. Although our experience comes primarily from North American boreal forests, we believe that such an approach should rapidly inform sustainable forestry in any social, economic, and ecological context because it can adapt to new circumstances while simultaneously taking advantage of cumulative experience to answer planning questions quickly and appropriately.

Our purpose is to outline the process of identifying questions, finding the tools and information to answer them, and then ensuring that the interacting suite of domain specific tools informs the global objectives of the planning process (i.e., the toolkit approach to SFM). We first overview a process that inserts a meta-modeling approach into a collaborative modeling framework that focuses on local planning needs. We then illustrate the process of applying that framework to a case study in central Labrador, an area dominated by pristine forests that is currently managed by a cooperative provincial government–First Nation partnership. In closing, we elaborate on our lessons learned when coupling a suite of models in contrast to using or developing one integrated model in the context of participatory modeling in support of SFM.



Motivation for our toolkit approach arose from a suite of studies across boreal and similar forested ecosystems (Coates et al. 2003, Van Damme et al. 2003, Fall et al. 2004, Pennanen et al. 2004, Sturtevant et al. 2004, Gustafson et al. 2006; Fig. 1). Key ecological processes common to all systems included succession, environmental constraints on vegetation, and natural disturbances (e.g., fire, wind, and insects). Likewise, management activities in each system were determined by social (e.g., hunting, recreation, water flow, and other various ecosystem services) and economic values (e.g., timber production and tourism). Human and ecological dimensions of these managed ecosystems were also interactive. For example, fire suppression and timber salvaging often changed stand-level processes, whereas the loss of or perceived threat to key species often changed the social perception of the ecosystem, which in turn changed harvesting practices. Finally, the scale of forest management had profound effects on ecosystem structure and function. By simply scaling up the expected mean behavior of stands and ignoring fine-scale processes, traditional forestry has created more homogeneous stands and landscapes (Hunter 1990, Cissel et al. 1994, Bergeron et al. 1999). Similarly, broad-scale processes such as disturbance, fragmentation, and long-distance dispersal constrain forest ecosystem behavior (Peterson 2002). Attempts to include all of these processes into a single model are fraught with difficulty because of the persistent boundaries between traditional scientific disciplines and nonlinearities inherent in scaling (Lertzman and Fall 1998). These difficulties begged the question: Could a toolkit of domain-specific modeling tools provide a more adaptable alternative to either a simplistic modeling approach (i.e., one domain-specific forecasting tool) or a fully integrated modeling approach (i.e., an interdisciplinary but case-specific model)?

Collaborative Framework for SFM Modeling

The existence of potential tools, and the capacity to use them, is necessary but not sufficient to support SFM planning. A collaborative process is critical to ensure that appropriate issues are addressed (Fall et al. 2001). Collaborative modeling (e.g., Holling 1978, Grudin 1991, Maxwell and Costanza 1997) is an iterative process that aims to include the appropriate people at the appropriate time in the modeling process (Fig. 2). The first step in the process is to clarify the questions and issues of concern from the stakeholders and decision makers. The next step is to define key ecological processes, and social and economic values, along with their respective scales and interactions. Understanding scale as it relates to these drivers is fundamental to the approach, as it underlies both social perceptions and the strength of interactions among the drivers (Allen and Hoekstra 1992, Gunderson and Holling 2002). Collectively, these processes and stakeholder interests form the conceptual model for the study system that can be formalized through more intensive work with local domain experts. During this stage, available data is identified (or a protocol to collect it is designed). Given data resources, important social values, and essential processes, the modeling team can then create a model that captures the system dynamics and produces the key indicators for scenario assessment. The iterative aspect of the modeling process proceeds from verification that the implemented model captures the essential dynamics of the conceptual model, to scenario design and assessment, to presentation of results (Fig. 2). Key benefits of this process include rapid response, local adaptation, and mutual learning. A typical drawback is that scientific rigor may be restricted by the timeline required to build fully customized models. A toolkit approach has the potential to extend and empower the collaborative process by allowing the rapid assembly of domain-specific modeling tools that, in combination, account for different scales and domains.

Inserting a Model Toolkit into the Collaborative Modeling Framework

A toolkit approach extends the collaborative modeling framework by explicitly incorporating a priori modeling knowledge captured in pre-existing tools to create “meta-models”, defined as “models derived from other models” (Urban et al. 1999). Meta-modeling has been increasingly applied to scale fine-scaled processes and behaviors to broader spatial scales (e.g., Williams et al. 1997, He et al. 1999, Urban 2005), and also to modularize different components of systems that have limited interactive feedback (e.g., forest succession models used to project habitat suitability for wildlife metapopulations; Akçakaya 2001, Larsen et al. 2004). Meta-modeling may be integrated with the collaborative SFM planning process as the model system evolves from the conceptual through the formalized and then the implemented meta-model, where models (i.e., tools) for each component are selected from a model toolkit, or the need for adapting a model or even developing a new one is identified. To be useful in this process, component models must be capable of interacting via a loose-coupling (e.g., Clarke and Gaydos 1998), in which output from one component becomes input to another (e.g., a time series output of raster maps, or a statistical distribution describing a fine-scale process). In this way, the benefit of adaptation to local needs is interlaced with collective experience embedded in the tools applied.

Meta-modeling simplifies a model system by encapsulating processes within their appropriate disciplinary and spatio-temporal domains (i.e., within a single tool), while allowing more limited interactions between domains by means of data exchange between models. For example, a fine-scale forest gap model can statistically parameterize tree species establishment probabilities for a coarser-scaled, rule-based succession model (He et al. 1999). Such one-way flow of information is known as “pipelining”, a term used by computer scientists to describe loose-coupling of independent processes, where the output of one becomes the input to others (Orton and Weick 1990, Salus 1994). Semi-dependent components can also interact through two-way information flow. In the above example, long-distance seed dispersal simulated by the coarser-scaled model could provide a higher-order context (i.e., seed rain) for fine-scaled gap dynamics. In this way, questions centered at specific scales can still be informed by processes occurring at different scales. The assumption of limited cross-scale interactions is consistent with a hierarchical view of ecological systems, where processes occurring at vastly different rates have limited interactions (O’Neil et al. 1986). We characterize such cross-scale meta-modeling as “vertical data exchange”, to separate it conceptually from within-scale meta-modeling or “horizontal data exchange” (e.g., forest dynamics affecting habitat suitability). Sensitivity analyses applied to transferred data can then evaluate the degree to which the pipelining strategy influences modeling results.

A meta-modeling strategy can be embedded within a collaborative modeling framework to foster local participation. Questions and issues raised during the conceptual model stage (Fig. 2) bound the suite of modeling tools applicable to the “system of interest”, defined as forest ecosystem to be managed and the social, ecological, and economic drivers affecting SFM decisions. The modeling team and domain experts then elaborate on this initial conceptual model, separating key processes and relationships into the three main components of the formal conceptual model: (1) interactive model system, (2) indicators of values, and (3) management scenarios. The interactive model system is defined as those processes of the system that interact dynamically. Indicators are measurable characteristics of stakeholder interests output from the interactive model system, including simple outputs (e.g., harvest flow, age-class structure), translation of outputs (e.g., patch size distribution), and results of domain-specific indicator models applied to those outputs (e.g., wildlife habitat or population models). Management scenarios simulate human activities that control specific components of the interactive meta-model, with the indicators acting as the interface between meta-model behavior and human interpretation to evaluate alternative management scenarios.

Once the key processes and relationships are defined, the formal conceptual model can be decomposed along logical boundaries between processes and then modeled using domain-specific tools (e.g., forest landscape change, habitat supply, growth and yield stand modeling, etc.). Strong interactions between processes should ideally be modeled within the same tool, whereas weak interactions between processes become logical breaking points between modeling tools. Overlap between the modeling domains of different tools is common, creating redundancy in the model assemblage that can be exploited in different ways. For example, one can compare output from two models where their domains overlap. Agreement between models with different architecture can increase confidence in our understanding of ecosystem dynamics as represented by the models, whereas disagreement between models can point to areas of uncertainty, leading (if time allows) to improved model structure (Rastetter 2003). Furthermore, a model can inform implementation even if it is not part of the final meta-model. For example, a model can evaluate the sensitivity of a system to a process or interaction before it is included in the meta-model design. The following section illustrates the process of applying a toolkit at a specific location.


SFM Issue and Local Needs

District 19A is a 2.1 million ha forest planning district located in south-central Labrador (53° 19’ N, 60° 25’ W; Fig. 3a). The district straddles an ecotone between high boreal and taiga ecosystems and contains most of Labrador’s closed-canopy forests, dominated by black spruce (Picea mariana) and balsam fir (Abies balsamea) (Forsythe et al. 2003). Spruce–fir stands are embedded within a diverse mosaic of open sphagnum forest, lichen woodlands, mixed hardwoods (Betula spp., Populus spp.), black spruce bogs, lakes, and open wetlands. Fire is the dominant natural disturbance; although fire is less prevalent than in more continental regions further south and west (Simard 1973). Commercial harvesting in this district was limited to a few thousand hectares harvested between 1970 and the present day, and the district contains correspondingly few roads. The region is currently under treaty negotiations regarding land title and aboriginal rights between the Innu Nation and the Canadian and provincial governments. The largest communities in the region are Happy Valley – Goose Bay with about 8000 inhabitants, and the Innu community of Sheshatshiu with about 1200 people (Fig. 3a). Two main items of concern to local indigenous and non-indigenous communities have been identified: sufficient timber supply to support a local mill and therefore boost the local economy, and the viability of a threatened woodland caribou (Rangifer tarandus caribou) population (Schaefer 1999, Schmelzer et al. 2004) important to the cultural well-being of the region. Nonetheless, the Innu believe it is the interconnections between all elements of the forest, including the people who achieve their livelihood there, that ultimately ensure forest sustainability.

In 2001, the provincial government formed a cooperative agreement with the Innu Nation to produce a forest management plan for District 19A (Forsyth et al. 2003). The planning process started with extensive public consultations (of both Innu and non-Innu communities) to ensure that key values of all stakeholders were protected, before determining the extent and location of forest harvesting. The plan balances social, economic, and ecological values by first establishing a socioecological network (sensu Kangas and Store 2002) of conservation reserves to protect critical stakeholder interests (e.g., caribou habitat, culturally important travel corridors, viewsheds, etc.) (Fig. 3b). Management areas slated for timber harvesting are set within the remaining land area. The network of reserves is the broadest scale of conservation, but further conservation constraints are planned at the watershed and stand spatial scales. Despite the 5-year effort devoted to the development of the current plan, planners recognized several key areas where modeling could assist with decision making. These included a formal timber supply analysis, exploration of novel silvicultural systems, evaluation of alternative scenarios with different harvest rates or spatial patterning of cuts, more concrete projections of timber harvest impacts on caribou and other key stakeholder interests, and evaluation of how these different scenarios and their tradeoffs would be accepted by the local communities.

Conceptual Model

The team leader was reviewing dynamic forest models applicable to boreal systems (Messier et al. 2003) at the time the District 19A plan was developed and was invited to participate in the above planning process. Questions and issues raised suggested a suite of modeling tools that could improve planning in the district (Table 1, Appendix), and the application of those tools across Canada and other boreal systems (Fig. 1) suggested candidate team members. The final team included scientists with modeling and field expertise in: forest ecology at stand and landscape scales, habitat suitability and wildlife population dynamics, timber supply analyses, forest harvest optimization methods, forest economics, social science, and participatory modeling. Among the team members was a local scientist (N. Simon) who served as a key information conduit between the Labrador participants (stakeholders, planners, and domain experts) and the modeling team.

The formal conceptual model for Labrador planning District 19a included forest succession, tree seed dispersal, fire disturbance, timber supply, silvicultural practices, road building, and harvest patterns within the interactive model system (Fig. 4). Local stakeholders are interested in economic development, but rely heavily on the forests for a variety of non-timber values. We based our indicators on information gleaned from the public consultations of the planning process and through our own surveys and interviews (Berninger et al., unpublished manuscript), including social, economic, and ecological dimensions (Fig. 4, Table 2). Human activities were conceptualized as controls implemented through alternative management scenarios, including the current forest management plan contrasted against unrestricted harvesting (i.e., no plan), a “no harvest” scenario, and an alternative to the plan that emphasized larger patch sizes for both cut-blocks and residual forest.

Implemented Meta-model

The modeling team first organized the available data resources (with the help of local domain experts) and modeling tools applicable to Labrador, creating a standardized data repository and a working document summarizing the models and their required inputs (http://www.lfmi.uqam.ca/home.htm). Among the tools is SELES (Spatially Explicit Landscape Event Simulator; Fall and Fall 2001), a general tool for building spatio-temporal models. SELES is our “glue” that links the assembly of models together by providing building blocks for landscape models and serving as translation engines for the transfer of data between models with different architecture (Table 1, Appendix).

The design of the Labrador District 19A meta-model (Fig. 5), along with some examples of results and links among models (Appendix), illustrates how meta-modeling can rapidly adapt existing models to specific SFM planning needs (See Table 1 and the Appendix for model descriptions). The District 19A landscape model (D19aLM), implemented in SELES, was designed initially as a spatially explicit timber supply model using growth and yield data provided from the province to forecast landscape-scale sustainable harvest levels under a range of scenarios relevant to the current 20-year plan. Explorations of the fire regime using LANDIS-II, informed by fuel-specific fire spread rates from the Canadian forest fire behavior prediction (FBP) system, will be used to help integrate a simplistic forest succession into the D19aLM and inform the current empirical fire module. Results from individual tree modeling using SORTIE will be used to define more complex succession trajectories and yield curves in response to alternative silvicultural treatments. Results from spatial optimization of harvest schedules using Patchworks will be contrasted with the simulation-based timber supply approach used in the D19aLM to better understand how succession and fire disturbance affect harvest scheduling, economic return, and forest patterns. Output from some scenarios of the D19aLM have been used for economic analysis of caribou / timber harvesting interactions using a method known as “real options” (Morgan et al. unpublished). Ecological indicator models from the Biodiversity Assessment Project (BAP) toolbox are being adapted from application in western Newfoundland to the District 19a study area, and use both landscape-scale outputs from D19aLM and stand-scale outputs from SORTIE (Fig. 5).

Iterative Learning and Model Refinement

Modeling activity for the District 19A meta-model (Fig. 5) has focused on some components, yet this activity has still assisted mutual learning at all levels of participation (Fig. 2). For example, application of an economic tool (i.e., real options) to output from an ecological tool (D19aLM) facilitated interdisciplinary learning within the modeling team (i.e., the inner feedback loop of Fig. 2). A review of LANDIS output by local foresters (middle feedback loop, Fig. 2) identified soil conditions as a key driver of succession, specifically as it affects the establishment of balsam fir. In turn the foresters received formal training in spatial timber supply analyses using the current D19aLM. Preliminary output from a prototype D19aLM illustrating three main scenarios (no plan, current plan, and an alternative plan emphasizing larger cut-blocks) was shown to various stakeholder groups (outer feedback loop, Fig. 2). Participants were then asked if they had learned something or changed their opinions on forestry issues during the session (Fig. 6). Several participants had greater confidence in the current plan after viewing the forest projections. An important take-home lesson for the communities was that smaller cut blocks, or a network of small protected areas, require more roads to cut the same amount of wood. Thus, some were ready to accept bigger cut blocks and others were left with a desire to learn more. However, a key point raised by stakeholders was that forest roads do not last forever, and therefore, road accumulation may have been overestimated—these and similar comments were used to refine the model.


In many respects the SFM toolkit approach is the same as any integrated modeling effort, thus previous experience and advice on communication across disciplines (Côté et al. 2001, Kinzig 2001, Bradshaw and Bekoff 2001, Lele and Norgaard 2005) and working as part of integrated teams (Nicolson et al. 2002) all apply. The key difference lies in the assembly of models designed for different purposes into a cohesive system that collectively informs the SFM planning process. This difference poses both unique opportunities and unique challenges to the modeling team. Chief among the advantages is that the cumulative science and experience underlying currently available models can be brought to bear on a specific planning initiative (in our case the D19A Forest Management plan). A primary challenge is the complexity associated with coupling models designed for different domains (see Appendix). The scientific advantages of the approach can be realized as long as the strengths and limitations of the tools are well understood (especially when the number of tools is large), and careful attention is paid to the pipelining strategies used to transfer information from one tool to the next.

A perpetual challenge during the assembly of the meta-model is finding the right balance between re-use or adaptation of existing tools and creation of new ones. When using an existing tool, there is always a risk of a mismatch between the tool and the conceptual model. This risk must be weighed against the time required to create and evaluate (Rykiel 1996) a new custom tool. In our case, most tools were modified versions of pre-existing models. Modern programming practices, such as modular architecture (Maxwell and Costanza 1997, Groenwold and Sonnenschein 1998, Scheller et al. 2007), simplify adaptation of existing models. As a case in point, the interaction between succession, harvesting, and fire disturbance could be realistically simulated in LANDIS-II by creating a new fire extension, but retaining other model components that fit the conceptual model of the case study. Similarly, simulation support tools such as SELES will continue to make customized modeling and meta-model assembly easier and more accessible to a broader audience. In time we envision a more general SFM toolkit applicable to boreal systems that can expand as new tools are added, key parameter ranges are defined, new issues are addressed, and new insights are gained from both individual and comparative modeling initiatives in the region.

The modular architecture of a meta-model allows progress to be made on multiple fronts simultaneously without waiting for results from the entire collection of models. We divided our team into working groups to make efficient use of effort, to ensure a set of elements that address project needs, and to focus attention on appropriate tools for each element. Preliminary, domain-specific modeling is an important form of prototyping that is essential for the iterative, two-way communication at all levels of participation (Fig. 2; Fall et al. 2001, Nicolson et al. 2002). However, there are inherent dependencies built into the modeling process (i.e., project definition, data identification, model selection, indicator development, etc.). If these dependencies are ignored, the process can easily degrade into an uncoordinated set of modeling exercises and the opportunity for true synthesis will be lost. Our experience suggests that strong leadership, in combination with a structured framework, is essential to the success of a toolkit approach.

Team selection is critical when applying an SFM toolkit because the diversity of tools familiar to team members often defines the tools in the toolkit. Both off-the-shelf models and model-building software require knowledge, experience, and training before their use, and learning complex new tools may be at odds with project timelines. Thus, the team leader or leaders must ensure that the right team is assembled to meet a local SFM need. That is, to overcome the “chicken and egg” dilemma, where “until you define the problem, you cannot assemble a team; and until you have a team, you cannot really define the problem” (Nicholson et al. 2002, page 378), team leaders must go through a high-level iteration of the collaborative process and also have at least a cursory understanding of available modeling tools, as was our case in Labrador, before assembling the team. The conceptual model can then be refined by subsequent iterations with the newly assembled team. We also learned that including a local representative on the core modeling team vastly improved communications between the major groups (i.e., modelers, domain experts, planners, and stakeholders).

The need for model transparency in participatory modeling initiatives is well recognized, but can also conflict with the use of research models designed for science rather than transparency. For example, Mendoza and Prabhu (2005, pages 146-147) suggest:

...for participatory modeling to be embraced at the local level, it must be configured in a form that is simple, transparent, and stripped of the typical complexity that often characterizes many models. The modeling paradigm must be such that stakeholders with little or no formal training in modeling can grasp the modeling process, feel comfortable in sharing their input and knowledge, and are able to contribute their expertise with relative ease.

Does this mean that published research models that are generally not transparent to the general public have no place in the collaborative modeling arena? Bypassing such models in favor of simplistic alternatives may restrict the flow of scientific knowledge into the planning process. A key to resolving this dilemma is effective two-way communication between the modeling team and the other participant groups at the appropriate time. For example, we found that stakeholder confidence in modeling results was greatly enhanced through frequent formal and informal communication with their experts. Therefore, the modeling team should work with local experts to ensure that they understand the strengths and weaknesses of tools applied to their domain. As domain experts often have their own tools, they may request model comparisons before they will begin to trust a new tool. Once satisfied that the implemented model is consistent with the formal conceptual model (Fig. 2), local experts can work with the modeling team to develop output that is accessible and easily understood by stakeholders.

Direct two-way communication with local stakeholders is also essential. In Labrador, long-term and large-spatial-scale comparisons of different management scenarios were shared with different stakeholder groups following meetings with outside experts organized by local domain experts. All parties gained important insights from this process (e.g., Fig. 6). Local stakeholders need to have their views heard, and discussed, and incorporated at several stages of the process. The scientists should make clear what the models are capable of doing and what may be unrealistic. This feedback is inherent in our hybrid approach of top-down and bottom-up flow of information through model analysis, workshops, and transparent discussion.


Any attempts to provide analytical support for SFM across different areas must recognize both the commonality and distinctiveness of issues and socioecological dynamics. Integrated models cannot be customized to fit every planning situation because there is a lack of capacity for building and applying complex spatio-temporal models. Conversely, no single model could adequately capture all systems and issues, especially as collaborative input from local stakeholders is important for plan acceptance. The toolkit approach has been developed in recognition of these constraints and opportunities, to use resources efficiently to minimize reinvention yet maximize innovation.

A toolkit approach to SFM analytical support is more about perspectives on information flow than on technical details. Certainly expertise and enabling technology are required to allow a team to apply such a framework. However, the essence of this approach is to seek balance between top-down (off the shelf, science-driven) and bottom-up (case-specific, stakeholder-driven) approaches to SFM decision support. We aim to find a pivot point, with adequate information flow from local experts and stakeholders to scientists, while at the same time avoiding “reinventing the wheel” (e.g., Fig. 1) by making full use of the cumulative experience of scientists and tools they have constructed. The mixture of local experts and stakeholders who understand how the tools work, scientists who are willing and able to communicate their science to stakeholders, and integrated analytical tools that can simulate complex spatial and temporal problems will provide powerful and efficient decision support for SFM. Bi-directional information flow between local experts, stakeholders, scientists, and planners is essential for efficient, timely, reliable, and adequate SFM meta-models. We have applied the toolkit process in Labrador, but fully recognize that this process will continue to evolve. Our proposal is not fully ripe, and certainly suffers from imperfections, but we believe the trend holds the best opportunity of meeting the challenges facing society regarding forest management.


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The SFM toolkit approach was funded under the Sustainable Forest Management Network of Canada. As a collaborative effort, there were many other funding sources, including the National Fire Plan of the U.S. Forest Service, the Centre d’Étude de la Forêt at the University of Quebec at Montreal, the Newfoundland and Labrador Department of Natural Resources, and the British Columbia Ministry of Forests. Robert Sutton completed the yield curves and Darren Jennings prepared GIS data for the Labrador case study; Keith Deering provided guidance on Labrador planning priorities. Brian Miranda assembled the fire weather inputs for the LANDIS-II simulations and assembled the LANDIS-II figures. Robert Scheller, Douglas Shinneman, and Brian Miranda helped design and implement the new LANDIS-II fire extension featured in the Labrador case study. We also thank Eric Gustafson, Dean Anderson, and three anonymous reviewers for valuable comments on the manuscript. We dedicate this paper in kind memory of Neal Simon, a great friend and colleague who passed away during the writing of the manuscript.


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Address of Correspondent:
Brian R. Sturtevant
U.S. Forest Service
Northern Research Station
Forestry Sciences Laboratory
5985 Hwy K
Rhinelander, Wisconsin 54501

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