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Abstract: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 OPEN ACCESSForests 2015, 6 28 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.

Spatial decision support systems for forest management have steadily evolved over the past 20+ years in order to better address the complexities of contemporary forest management issues such as the sustainability and resilience of ecosystems on forested landscapes. In this paper, we describe and illustrate new features of the Ecosystem Management Decision Support (EMDS) system that extend the system's traditional support for landscape analysis and strategic planning to include a simple approach to feature-based tactical planning priorities. The study area for this work was the Chewaucan watershed of the Fremont-Winema National Forest, located in south-central Oregon, USA. The analysis of strategic priorities recommended five subwatersheds as being of high priority for restoration activities, based primarily on decision criteria related to the stream accessibility to headwaters and upland condition. Among high priority subwatersheds, the most common tactical action recommended was the removal of artificial barriers to fish passages. Other high priority tactical actions recommended in high priority subwatersheds to improve fish habitats were reducing the road density and restoring riparian vegetation. In the discussion, we conclude by describing how the simple tactical planning methods illustrated in this paper can be extended in EMDS to provide a more sophisticated hybrid approach to strategic and tactical planning that can evaluate alternative portfolios of designed management actions applied across landscapes. The latter planned improvement to decision support capabilities in EMDS encapsulates Carl Steinitz's concept of geodesign.

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.