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Rapid environmental changes expected in the 21st century challenge the resilience of wildlands around the world. The western portion of the Lake Tahoe basin (LTW) in California is an important ecological and cultural hotspot that is at risk of degradation from current and future environmental pressures. Historical uses, fire suppression, and a changing climate have created forest landscape conditions at risk of drought stress, destructive fire, and loss of habitat diversity. We prospectively modeled forest landscape conditions for a period of 100 years to evaluate the efficacy of five unique management scenarios in achieving desired landscape conditions across the 23,600 hectares of LTW. Management scenarios ranged from no management other than fire suppression to applying treatments consistent with historical fire frequencies and extent (i.e., regular and broadscale biomass reduction). We developed a decision support tool to evaluate environmental and social outcomes within a single framework to provide a transparent set of costs and benefits; results illuminated underlying mechanisms of forest resilience and provided actionable guidance to decision makers. Sixteen attributes were assessed in the model after assigning weights to each, derived through a survey of stakeholder priorities, so that the contribution of each attribute to evaluations of scenario performance was influenced by the combined priorities of stakeholders. We found that removing forest biomass across the landscape, particularly when accomplished using extensive fire-based removal techniques, led to highly favorable conditions for environmental quality and promoting overall landscape resilience. Environmental conditions resulting from extensive fire-based biomass removal also had nominal variation over time, in contrast with strategies that had less extensive and/or used physical removal techniques, namely thinning. Our analysis provided a transparent approach to data assessment, considering the priorities of stakeholders, to provide insights into the complexities of maintaining optimal conditions and managing landscapes to promote ecosystem resilience in a changing world.

Forward thinking conservation-planning can benefit from modeling future landscapes that result from multiple alternative management scenarios. However, long-term landscape modeling and downstream analyses of modeling results can lead to massive amounts of data that are difficult to assemble, analyze, and to report findings in a way that is easily accessible to decision makers. In this study, we developed a decision support process to evaluate modeled forest conditions resulting from five management scenarios, modeled across 100 years in California’s Lake Tahoe basin; to this end we drew upon a large and complex hierarchical dataset intended to evaluate landscape resilience. Trajectories of landscape characteristics used to inform an analysis of landscape resilience in the Lake Tahoe basin were modeled with the spatially explicit LANDIS-II vegetation simulator. Downstream modeling outputs of additional landscape characteristics were derived from the LANDIS-II outputs (e.g., wildlife conditions, water quality, effects of fire). The later modeling processes resulted in the generation of massive data sets with high dimensionality of landscape characteristics at both high spatial and temporal resolution. Ultimately, our analysis distilled hundreds of data inputs into trajectories of the performance of the five management scenarios over the 100-year time horizon of the modeling. We then evaluated each management scenario based on inter-year variability, and absolute and relative performance. We found that the management scenario that relied on prescribed fire, outperformed the other four management approaches. Both these results, and the process that led to them, provided decision makers with easy-to-understand results based on a rational, transparent, and repeatable decision support process.One sentence descriptionWe present a novel approach to employ decision support tools for conservation over long time horizons.

Forest management planning is a challenge due to the diverse criteria that need to be considered in the underlying decision-making process. This challenge becomes more complex in joint collaborative management areas (ZIF) because the decision now may involve numerous actors with diverse interests, preferences, and goals. In this research, we present an approach to identifying and quantifying the most relevant criteria that actors consider in a forest management planning process in a ZIF context, including quantifying the performance of seven alternative stand-level forest management models (FMM). Specifically, we developed a combined multicriteria decision analysis and group decision-making process by (a) building a cognitive map with the actors to identify the criteria and sub-criteria, (b) structuring the decision tree, (c) structuring a questionnaire to elicit the importance of criteria and sub-criteria in a pairwise comparison process, and to evaluate the FMM alternatives, and (d) applying a Delphi survey to gather actors’ preferences. We report results from an application to a case study area, ZIF of Vale do Sousa, in North-Western Portugal. Actors assigned the highest importance to the criteria income (56.8% of all actors) and risks (21.6% of all actors) and the lowest to cultural services (27.0% of all actors). Actors agreed on their preferences for the sub-criteria of income (diversification of income sources), risks (wildfires) and cultural services (leisure and recreation activities). However, there was a poor agreement among actors on the sub-criteria of the wood demand and biodiversity criteria. For 27.0% of all actors the FMM with the highest performance was the pedunculate oak and for 43.2% of all actors the eucalypt FMM was the least preferable alternative. The findings indicate that this approach can support ZIF managers in enhancing forest management planning by improving its utility for actors and facilitating its implementation.

This paper synthesizes vulnerability, risk, resilience, and sustainability (VRRS) in a way that can be used for decision evaluations about sustainable systems, whether such systems are called coupled natural–human systems, social–ecological systems, coupled human–environment systems, and/or hazards influencing global environmental change, all considered geospatial open systems. Evaluations of V-R-R-S as separate concepts for complex decision problems are important, but more insightful when synthesized for improving integrated decision priorities based on trade-offs of V-R-R-S objectives. A synthesis concept, called VRRSability, provides an overarching perspective that elucidates Tier 2 of a previously developed four-tier framework for organizing measurement-informed ontology and epistemology for sustainability information representation (MOESIR). The new synthesis deepens the MOESIR framework to address VRRSability information representation and clarifies the Tier 2 layer of abstraction. This VRRSability synthesis, composed of 13 components (several with sub-components), offers a controlled vocabulary as the basis of a conceptual framework for organizing workflow assessment and intervention strategies as part of geoinformation decision support software. Researchers, practitioners, and machine learning algorithms can use the vocabulary results for characterizing functional performance relationships between elements of geospatial open systems and the computing technology systems used for evaluating them within a context of complex sustainable systems.

The Special Issue “Decision Support to Address Multiple Ecosystem Services in Forest Management Planning” includes nine research papers, two review papers, and a white paper presenting highlights of focused research initiatives. The papers provide a comprehensive framework for the analysis and review of advanced Decision Support Systems (DSS), which are multi-criteria decision approaches. Their emphasis is on how these methods and tools may contribute to address the multi-functionality of forests, to support scenario and trade-off analysis of ecosystem services, and to represent interests and behavior of various stakeholders. In the context of forest ecosystem management, a need has arisen to consider various dimensions in the design of the planning process. This calls for the development of appropriate mixes of decision making tools and methods and for its testing with the support of case studies. In this Special Issue, comments on, and implications of, the improvement of innovative decision methods and systems to address the provision of a wide range of ecosystem services and support scenario analysis with the active involvement of stakeholders are presented.

The paper compares two multi-criteria methods, the analytic hierarchy process (AHP) and the best–worst method (BWM), in assessing criteria related to the quality of urban parks. The criteria assessed were accessibility, location, biodiversity preservation, park equipment, water elements, terrain configuration, cultural and historical value, and the presence of small architectural objects. Five decision-makers participated in the research, having expertise in urban greenery, urban forestry, environmental protection, landscape design, and cultural and historical heritage. The results of decision-makers’ evaluations were compared at individual and group levels after the application of three aggregation procedures: CRITIC, ENTROPY, and WGGM (weighted geometric mean method). Similarities in results, i.e., priorities of analyzed criteria after applying the two different decision support methods, indicated high consistency between experts during the cognitive evaluation processes. All applied aggregation schemes performed well and may be considered trustworthy in identifying the group solution. One of the conclusions is that either the AHP or the BWM can be efficiently used in evaluations of criteria for assessing the quality of urban parks if the members of a group are consistent, regardless of whether the consensus process is properly carried out before the decision-making process.

Non-native species invasions, growing human populations, and climate change are central ecological concerns in tropical island communities. The combination of these threats have led to losses of native biota, altered hydrological and ecosystem processes, and reduced ecosystem services. These threats pose complex problems to often underfunded management entities. We developed a watershed decision support tool (WDST) for the windward coast of Hawai‘i Island aimed at prioritizing catchments for invasive species removal and native forest protection from non-native species invasions. Using the Ecosystem Management Decision Support (EMDS) system, we integrated spatial data from four sources: (i) native and invasive species coverage; (ii) modeled water yield; (iii) treatment cost and efficacy; and (iv) native species conservation value. We used a distributed hydrology model (DHSVM) to estimate catchment-level (∼90 ha) water yield under six climate and non-native species invasion scenarios to identify where (1) invasive species removal and (2) protection from invasion would have the greatest benefit to increasing or maintaining native biodiversity and hydrologic functioning. The hydrology model predicted a 30% decline (386 Gl yr−1) in total water yield under a drier future climate (20% reduction in rainfall), with an additional 2% reduction when catchments were fully invaded by non-native species. Increased temperatures had a small compensatory effect on water yield. The WDST identified 6.3% of the study area as high priority for invasive species removal, based on characteristics of large hydrological response to the removal treatment (concentrated in high rainfall areas), high quality road or trail access, and high conservation value. High protection priority from invasive species (5.9% by area) occurred in higher elevation catchments, near the upper range of strawberry guava (the main invasive species), where water yield was most sensitive to invasion. Climate change scenarios had little influence on the spatial distribution of priority scores despite large changes in overall water yield. In contrast, priority scores were sensitive to very high variation in treatment costs, which were influenced largely by travel times to catchments via road and trail networks. This last finding suggests that future management feasibility will hinge on improvements to road and trail networks, or development of alternative management strategies that reduce travel costs and time.

A common motivation of ecological research is to identify the main environmental drivers of ecological processes using empirical modeling. By developing statistical relationships between patterns and processes, empirical models serve two main functions: (1) making accurate predictions over space and time, and (2) making inferences regarding how ecological patterns and processes change across environmental gradients, and determining the relative importance of key variables in driving these patterns.

In this chapter, we explain how to create and work with the main types of data in R: vectors, matrices, and data frames. In addition, we briefly explain how to create lists and merge them with data frames. At the end of the chapter, we discuss how to deal with missing data and outliers in data sets.

Two main groups of statistical tests are parametric and nonparametric tests (Good, 2005). Parametric tests assume that the underlying data are normally distributed and nonparametric tests start off with the assumption that the underlying data do not have a normal distribution (Adler, 2012). Assuming that data are normally distributed is usually correct (Adler, 2012), and therefore parametric tests are more commonly applied than nonparametric ones. Nonparametric tests are also referred to as distribution-free tests, and they can be particularly useful when the data are measured on a categorical scale, i.e., when we deal with an ordered data set (Petrie and Sabin, 2005). It should be noted that nonparametric tests are less powerful in comparison to equivalent parametric tests (Vukovic, 1997).

Nearshore ecosystems (e.g., mangrove forests, sea grass beds, coral reefs) are some of the most biologically diverse and ecologically productive on Earth, while providing essential goods and services to human communities. Because these ecosystems are subject to threats from both land and sea, their conservation and management requires a ridge to reef approach. Here, we developed a watershed decision support tool (DST) for Babeldaob Island, Republic of Palau, aimed at prioritizing catchments for reforestation of fire degraded savanna or protection of native forest against conversion to savanna. We use a distributed hydrology model to estimate catchment-level sedimentation and water yield for three vegetation scenarios: (1) current vegetation; (2) a hypothetical fully-forested Babeldaob Island; and (3) a hypothetical Babeldaob Island that has been fully converted to savanna. Using the DST, we integrated model results with geospatial information on treatment cost, efficacy, and conservation value to identify where reforestation and forest protection investments would provide the greatest benefits to coral reef health. Modeled sediment yields were lowest for catchments with > 80% tropical forest cover and highest for those with

The study explores use of the Ecosystem Management Decision Support (EMDS) System to standardize the process of allocating Management Areas for Fire Suppression Support (MASSs) in Catalonia, Spain. MASSs are defined as those areas in the landscape that change fire behavior, reducing the magnitude of the wildfire, and improve significantly fire suppression effectiveness/capacity. Considerations for allocating MASSs include high likelihood of large fires in the vicinity, potential for spread, proximity of the location to valuable resources at risk, proximity to adequate water supply, accessibility by mechanized means, and fuel management opportunities. The combination of accessibility, water supply and fuel management opportunities, when allocating MAASs, provide the minimum requirements to allow fire suppression actions, while improving effectiveness and safety levels. For these purposes, we combine the newest data available, outputs from fire simulators and expert knowledge to define a problem that could be solved using EMDS within a participatory planning framework. To support the fire suppression mission of the firefighting service in Catalonia, this study uses a combination of strategic and tactical solutions, in which the strategic solution identifies high priority locations within the landscape for fire suppression activities, and tactical solutions identify high priority management activities within specific locations.

The Ecosystem Management Decision Support (EMDS) system has been further enhanced with an analytical engine--BayesFusion's SMILE (Structural Modeling, Inference, and Learning Engine) that comes with the GeNIe (Graphical Network Interface) software--for creating Bayesian network (BN) models. BNs are graphical networks of variables linked by probabilities, that have proven useful in decision-aiding for risk analysis and risk management.

This study examines the potential of combining decision support approaches to identify optimal bundles of ecosystem services in a framework characterized by multiple decision-makers. A forested landscape, Zona de Interven&ccedil, &atilde, o Florestal of Paiva and Entre-Douro and Sousa (ZIF_VS) in Portugal, is used to test and demonstrate this potential. The landscape extends over 14,388 ha, representing 1976 stands. The property is fragmented into 376 holdings. The overall analysis was performed in three steps. First, we selected six alternative solutions (A to F) in a Pareto frontier generated by a multiple-criteria method within a web-based decision support system (SADfLOR) for subsequent analysis. Next, an aspatial strategic multicriteria decision analysis (MCDA) was performed with the Criterium DecisionPlus (CDP) component of the Ecosystem Management Decision Support (EMDS) system to assess the aggregate performance of solutions A to F for the entire forested landscape with respect to their utility for delivery of ecosystem services. For the CDP analysis, SADfLOR data inputs were grouped into two sets of primary criteria: Wood Harvested and Other Ecosystem Services. Finally, a spatial logic-based assessment of solutions A to F for individual stands of the study area was performed with the NetWeaver component of EMDS. The NetWeaver model was structurally and computationally equivalent to the CDP model, but the key NetWeaver metric is a measure of the strength of evidence that solutions for specific stands were optimal for the unit. We conclude with a discussion of how the combination of decision support approaches encapsulated in the two systems could be further automated in order to rank several efficient solutions in a Pareto frontier and generate a consensual solution.

The Ecosystem Management Decision Support (EMDS) system is an application framework for designing and implementing spatially enabled knowledge-based decision support systems for environmental analysis and planning at any geographic scale(s). The system integrates state-of-the-art geographic information system, as well as knowledge-based reasoning and decision modeling, technologies to provide decision support for the adaptive management process of ecosystem management. It integrates a logic engine to perform landscape evaluations, and a decision engine for developing management priorities. The logic component: (1) reasons about large, abstract, multi-faceted ecosystem management problems, (2) performs useful evaluations with incomplete information, (3) evaluates the influence of missing information, and (4) determines priorities for missing information. The planning component determines priorities for management activities, taking into account not only ecosystem condition, but also criteria that account for logistical concerns of potential management actions. Both components include intuitive diagnostic features that facilitate communicating modeling results to a broad audience. Features of the system design that have figured in its success over the past 20 years are highlighted, together with design features planned for the next several versions needed to provide spatial decision support for adaptive management under climate change.

Atmospherically deposited sulfur (S) causes stream water acidification throughout the eastern U.S. Southern Appalachian Mountain (SAM) region. Acidification has been linked with reduced fitness and richness of aquatic species and changes to benthic communities. Maintaining acid-base chemistry that supports native biota depends largely on balancing acidic deposition with the natural resupply of base cations. Stream water acid neutralizing capacity (ANC) is maintained by base cations that mostly originate from weathering of surrounding lithologies. When ambient atmospheric S deposition exceeds the critical load (CL) an ecosystem can tolerate, stream water chemistry may become lethal to biota. This work links statistical predictions of ANC and base cation weathering for streams and watersheds of the SAM region with a steady-state model to estimate CLs and exceedances. Results showed that 20.1% of the total length of study region streams displayed ANC100 μeq∙L(-1), a level at which effects to biota may be anticipated; most were 4th or lower order streams. Nearly one-third of the stream length within the study region exhibited CLs of S deposition50 meq∙m(-2)∙yr(-1), which is less than the regional average S deposition of 60 meq∙m(-2)∙yr(-1). Owing to their geologic substrates, relatively high elevation, and cool and moist forested conditions, the percentage of stream length in exceedance was highest for mountain wilderness areas and in national parks, and lowest for privately owned valley bottom land. Exceedance results were summarized by 12-digit hydrologic unit code (subwatershed) for use in developing management goals and policy objectives, and for long-term monitoring.

National law and policy direct the management of the National Forests, with restoring resilient forest conditions being an overarching theme. Climate is a major driver of disturbances that affect ecosystems, especially those with vegetation that show large departures from historical conditions. Drought, fire, insects, and diseases are common forest stressors whose impacts are being exacerbated by climate change. These stressors are threatening the ecosystem services that people value in the forests of the northern Rocky Mountains. The forests of most concern are the dry ponderosa pine and Douglas-fir, moist western white pine mixed with western larch, and cool lodgepole pine mixed with quaking aspen and whitebark pine. Potential reductions in some ecosystem services pose a challenge in terms of not only direct biophysical consequences but also social and economic values that flow from these forests. Values at risk include forest integrity, wildlife habitat, watershed condition, fish habitat, recreation opportunities and investments, community infrastructure, and public safety. This article describes a decision support tool developed by the USDA Forest Service Northern Region in 2010 to support integrated restoration planning. The Northern Region's Integrated Restoration and Protection Strategy (IRPS) is premised on identifying historical conditions through application of the natural range of variability, comparing them with current conditions, and considering a broader range of future issues, including climate change. The IRPS assists managers in the complex task of evaluating factors that influence national forest planning efforts. The assessment and IRPS, by integrating ecological, social, and economic considerations, will help managers of national forests prioritize restoration opportunities using increasingly scarce financial resources. The IRPS will be most beneficial when associated with planning silvicultural practices and fire management directed at restoring the forests of the Northern Region to a more resilient condition.

Accurate estimates of soil mineral weathering are required for regional critical load (CL) modeling to identify ecosystems at risk of the deleterious effects from acidification. Within a correlative modeling framework, we used modeled catchment-level base cation weathering (BCw) as the response variable to identify key environmental correlates and predict a continuous map of BCw within the southern Appalachian Mountain region. More than 50 initial candidate predictor variables were submitted to a variety of conventional and machine learning regression models. Predictors included aspects of the underlying geology, soils, geomorphology, climate, topographic context, and acidic deposition rates. Low BCw rates were predicted in catchments with low precipitation, siliceous lithology, low soil clay, nitrogen and organic matter contents, and relatively high levels of canopy cover in mixed deciduous and coniferous forest types. Machine learning approaches, particularly random forest modeling, significantly improved model prediction of catchment-level BCw rates over traditional linear regression, with higher model accuracy and lower error rates. Our results confirmed findings from other studies, but also identified several influential climatic predictor variables, interactions, and nonlinearities among the predictors. Results reported here will be used to support regional sulfur critical loads modeling to identify areas impacted by industrially derived atmospheric S inputs. These methods are readily adapted to other regions where accurate CL estimates are required over broad spatial extents to inform policy and management decisions.

Aim of study: Knowledge Management (KM) tools facilitate the implementation of knowledge processes by identifying, creating, structuring, and sharing knowledge through use of information technology in order to improve decision-making. In this contribution, we review the way in which KM tools and techniques are used in forest management, and categorize a selected set of them according to their contribution to support decision makers in the phases of problem identification, problem modelling, and problem solving.Material and Methods: Existing examples of cognitive mapping tools, web portals, workflow systems, best practices, and expert systems as well as intelligent agents are screened for their applicability and use in the context of decision support for sustainable forest management. Evidence from scientific literature and case studies is utilized to evaluate the contribution of the different KM tools to support problem identification, problem modelling, and problem solving.Main results: Intelligent agents, expert systems and cognitive maps support all phases of the forest planning process strongly. Web based tools have good potential to support participatory forest planning. Based on the needs of forest management decision support and the thus-far underutilized capabilities of KM tools it becomes evident that future decision analysis will have to consider the use of KM more intensively. Research highlights: As the problem-solving process is the vehicle for connecting both knowledge and decision making performance, the next generation of DSS will need to better encapsulate practices that enhance and promote knowledge management. Web based tools will substitute desktop applications by utilizing various model libraries on the internet.Keywords: best practices; cognitive mapping; expert systems; intelligent agents; web portals; workflow systems; Decision Support Systems.

[1] In many industrialized regions of the world, atmospherically deposited sulfur derived from industrial, nonpoint air pollution sources reduces stream water quality and results in acidic conditions that threaten aquatic resources. Accurate maps of predicted stream water acidity are an essential aid to managers who must identify acid-sensitive streams, potentially affected biota, and create resource protection strategies. In this study, we developed correlative models to predict the acid neutralizing capacity (ANC) of streams across the southern Appalachian Mountain region, USA. Models were developed using stream water chemistry data from 933 sampled locations and continuous maps of pertinent environmental and climatic predictors. Environmental predictors were averaged across the upslope contributing area for each sampled stream location and submitted to both statistical and machine-learning regression models. Predictor variables represented key aspects of the contributing geology, soils, climate, topography, and acidic deposition. To reduce model error rates, we employed hurdle modeling to screen out well-buffered sites and predict continuous ANC for the remainder of the stream network. Models predicted acid-sensitive streams in forested watersheds with small contributing areas, siliceous lithologies, cool and moist environments, low clay content soils, and moderate or higher dry sulfur deposition. Our results confirmed findings from other studies and further identified several influential climatic variables and variable interactions. Model predictions indicated that one quarter of the total stream network was sensitive to additional sulfur inputs (i.e., ANC

Land managers in the western US are beginning to understand that early 20th century forests displayed complex patterns of composition and structure at several different spatial scales, that there was interplay between patterns and processes within and across scales, and that these conditions have been radically altered by management. Further, they know that restoring integrity (see Definition of Terms) of these conditions has broad implications for the future sustainability (see Definition of Terms) of native species, ecosystem services, and ecological processes. Many are looking for methods to restore (see Definition of Terms) more natural landscape patterns of habitats and more naturally functioning disturbance regimes; all in the context of a warming climate. Attention is turning to evaluating whole landscapes at local and regional scales, deciphering recent changes in trajectories, and formulating landscape prescriptions that can restore ecological functionality and improve landscape resilience (see Definition of Terms). The business of landscape evaluation and developing landscape prescriptions is inherently complex, but with the advent of decision support systems, software applications are now available to conduct and document these evaluations. Here, we review several published landscape evaluation and planning applications designed with the Ecosystem Management Decision Support (EMDS) software, and present an evaluation we developed in support of a landscape restoration project. We discuss the goals and design of the project, its methods and utilities, what worked well, what could be improved and related research opportunities. For readability and compactness, fine and broad-scale landscape evaluations that could be a part of multi-scale restoration planning, are not further developed here.

The development and use of decision support systems for forest management (FMDSS) have considerably increased worldwide in the last decades in accordance with the recognition of the multifunctional role of forests. The Vegetation Dynamics Development Tool (VDDT) and Ecosystem Management Decision Support (EMDS) system are used for forest planning purposes in the Pacific Northwest Region of the United States Forest Service (USFS). Lessons learned have been explored using case study methods. Important working knowledge derived from success or failure according to the users’ perspective concern data availability for modeling, expertise needed to run the tool, the models’ level of resolution, and communication between the modelers and the team involved in forest planning. Lessons learned coming from developers highlight the success of the DSS evaluating the models’ characteristics, the tool's innovation and the integration of the best available science into the models.

This paper presents modeling methods for mapping fire hazard and fire risk using a research model called FIREHARM (FIRE Hazard and Risk Model) that computes common measures of fire behavior, fire danger, and fire effects to spatially portray fire hazard over space. FIREHARM can compute a measure of risk associated with the distribution of these measures over time using 18 years of gridded DAYMET daily weather data used to simulate fuel moistures to compute fire variables. We detail the background, structure, and application of FIREHARM and then present validation results of six of the FIREHARM output variables that revealed accuracy rates ranging from 20 to 80% correct depending on the quality of input data and the behavior of the fire behavior simulation framework. Overall accuracies appeared acceptable for prioritization analysis and large scale assessments because precision was high. We discuss advantages and disadvantages of the fire hazard and risk approaches and a possible agenda for future development of comprehensive fire hazard and risk mapping is presented.

The Ecosystem Management Decision Support (EMDS) system has been used by the US Department of Agriculture, Forest Service and Bureaus of the Department of the Interior since 2006 to evaluate wildfire potential across all administrative units in the continental US, and to establish priorities for allocating fuel-treatment budgets. This article discusses an EMDS fuels-treatment decision-support application, agency experiences with the application, and the extent to which it addressed concerns in Congress, and those of the General Accountability Office. EMDS aids the budget allocation process by providing a rational, transparent, and reproducible process that can be clearly communicated to Congressional staff and oversight personnel. However, practical application of this decision-support process was not without challenges, which included missing or suboptimal data, clearly articulated fuels management objectives, and improved understanding (via re-assessing decision logic from prior years) of trade-offs in decision-making.

We evaluated changes (hereafter, departures) in spatial patterns of various patch types of forested landscapes in two subwatersheds (“east” and “west”) in eastern Washington, USA, from the patterns of two sets of reference conditions; one representing the broad variability of pre-management era (∼1900) conditions, and another representing the broad variability associated with one possible warming and drying climate-change scenario. We used a diagnostic set of class and landscape spatial pattern metrics to compare current spatial patterns of test subwatersheds against the two sets of reference conditions. In a companion decision support model built with the EMDS modeling system, we considered the degree of departure in the subwatersheds, relative to the two sets of reference conditions along with two additional criteria (vulnerability to severe wildfire and timber harvest opportunity), to determine the relative priority of landscape restoration treatments, and the potential for timber harvest to underwrite the treatments. In the decision support model, the current spatial pattern conditions of physiognomic types, cover types, forest structural classes, and those of late-successional and old forest patches of the two subwatersheds were compared against the two sets of reference conditions. The degree of departure in spatial patterns of physiognomic conditions was moderate in both subwatersheds in the pre-management era and climate-change comparisons. The situation was similar for the cover-type departure analysis, but spatial patterns of cover types increased in similarity to the reference conditions in the western subwatershed under the climate-change scenario. Spatial patterns of structural conditions showed a high degree of departure in both subwatersheds when compared to either set of reference conditions, but similarity improved in the eastern subwatershed under the climate-change scenario. Spatial patterns of late-successional + old forest structure were strongly similar to the broad envelope of conditions represented by the pre-management era reference in the western and moderately similar in the eastern subwatershed, but declined in both subwatersheds when compared with the climate-change reference conditions. When the degree of departure in spatial patterns of all patch types was considered along with vulnerability to severe wildfire and timber harvest opportunity, the eastern subwatershed rated higher priority for landscape improvement using either set of reference conditions. We conclude by considering uncertainties inherent in the analysis approach, types of sensitivity analysis needed to investigate model performance, and broad implications for forest managers.

The process of selecting candidate areas for inclusion in a regional conservation network should include not only delineating appropriate land units for selection and defining targets for representing features of interest, but also determining the suitability of land units for conservation purposes. We developed an explicit rating of conservation suitability by applying fuzzy-logic functions in a knowledge base to ecological condition and socio-economic attributes of land units in the interior Columbia River basin, USA. Suitability was converted to unsuitability to comprise a cost criterion in selecting regional conservation networks. When unsuitability was the sole cost criterion or was combined with land area as cost, only about one-third of the area selected was rated suitable, due to inclusion of unsuitable land to achieve representation of conservation targets (vegetation cover-type area). Selecting only from land units rated suitable produced networks that were 100% suitable, reasonably efficient, and most likely to be viable and defensible, as represented in our knowledge-based system. However, several conservation targets were not represented in these networks. The tradeoff between suitability and effectiveness in representing targets suggests that a multi-stage process should be implemented to address both attributes of candidate conservation networks. The suitability of existing conservation areas was greater than that of most alternative candidate networks, but 59% of land units containing conservation areas received a rating of unsuitable, due in part to the presence of units only partially occupied by conservation areas, in which unsuitability derived from conditions in non-conserved areas.

Decision support systems have played a prominent role in the implementation of forest management since the early 1980s. However, whereas early systems were typically designed to address relatively simple management questions, more modern systems are increasingly being called upon to address the challenges posed by issues surrounding forest ecosystem management, sustainable forest management, and adaptive management. This paper considers some of the key requirements as they apply to forestry in the United States, and reviews recent decision support system designs in the United States, considering the extent to which they are satisfying the requirements, and opportunities for their continued evolution. The three systems discussed, NED, LMS, and EMDS, are typical of recent approaches to system design insofar as each has taken an evolutionary approach to system implementation in order to develop effective, integrated decision support for forest management in this new, complex problem domain. On considering the current state of system development for the three systems, it is concluded that significant progress has, in fact, been made in the last few years in providing support for evaluation and planning, although it is equally true that substantial opportunities remain for continued development to support plan implementation and forest ecosystem monitoring.

The historical patterns of Inland Northwest United States forests have been dramatically altered by a little more than two centuries of human settlement and land use. Spatial patterns of forest structural conditions, tree species composition, snags and down wood, and temporal variation in these patterns, have been altered to such an extent that the natural ebb and flow of terrestrial habitats and their linkages has been disrupted. Closely coupled with these changes, fire and other disturbance processes in most dry and many mesic forest types have also shifted, with a bias for increased severity and extent. Here, in the context of planning restoration of some semblance of historical vegetation pattern–disturbance process interactions, we briefly revisit why it is theoretically sound to estimate the range and variation in historical forest spatial patterns. We call these estimates of range and variation, reference conditions or reference variation (RV), and discuss how forest managers might use them when evaluating current landscape patterns to identify changes that may have important ecological implications. We term such evaluations, departure analyses, and we describe how departure analysis is implemented in a decision support system (DSS) for integrated landscape evaluation and restoration planning. The initial phase of the DSS uses logic-based modeling to evaluate existing patterns of forest vegetation in subwatersheds of one ecoregion against a corresponding envelope of historical reference conditions for the same region, thereby highlighting key departures. The secondary planning phase uses results from the analysis phase in a decision model to prioritize watersheds for possible management actions related to landscape restoration and maintenance. We conclude from our example that there are at least two advantages to a decision-support approach that treats evaluation and planning as distinct but integrated phases: (1) the overall decision process is rendered conceptually simpler and (2) practical considerations of efficacy and feasibility of management actions can be easily accommodated.

Numerous efforts around the world are underway to apply the Montreal criteria and indicators to assess the sustainability of temperate and boreal forests. In this paper, we describe a logic-based system for evaluating the sustainability of forests at regional and national levels. We believe that such a system can make evaluation of sustainability more consistent and transparent. This effort also makes two points abundantly clear: (1) a systematic way to organize expert judgment about ecological, economic, social and institutional relationships (here, using ‘fuzzy logic’) is crucial to building such a system and (2) that the structure of this logic-based system reflects a policy framework and a series of decisions about values and what is meant by ‘sustainability’.

The terrestrial condition assessment (TCA) evaluates effects of uncharacteristic stressors and disturbance agents on land-type associations (LTAs) to identify restoration opportunities on national forest system (NFS) lands in the United States. A team of agency scientists and managers, representing a broad array of natural resource disciplines, developed a logic structure for the TCA to identify appropriate data sources to support analyses. Primary national data sources included observed insect- and pathogen-induced mortality, key critical loads for soil and the atmosphere, long term seasonal departures in temperature and precipitation, road densities, uncharacteristic wildfires, historical fire regime departure, wildfire potential, insect and pathogen risk, and vegetation departure from natural range of variability. The TCA was implemented with the ecosystem management decision support (EMDS) system, a spatial decision support system for landscape analysis and planning. EMDS uses logic models to interpret data, synthesizes information over successive layers of logic topics, and draws inferences about the ecological integrity of LTAs as an initial step to identifying high priority LTAs for landscape restoration on NFS lands. Results from the analysis showed that about 74 percent of NFS lands had moderate or better overall ecological integrity. Major impacts to ecological integrity included risk of mortality due to insects and disease, extent of current mortality, extent of areas with high and very high wildfire hazard potential, uncharacteristically severe wildfire, and elevated temperatures. In the discussion, we consider implications for agency performance reporting on restoration activities, and subsequent possible steps, including strategic and tactical planning for restoration. The objective of the paper is to describe the TCA framework with results from a national scale application on NFS lands.