Graduation date: 2007The complexity of forest management has increased with the scope of resources of concern and the level of scrutiny from stakeholders. The design and use of specialized computer software, often referred to as “decision support systems” (DSS), is one method for helping managers deal with this complexity. DSS have proven helpful in a wide range of fields, including business planning, medical diagnosis, and transportation. In the forestry sector, they have been used intensively for timber supply modeling, but their application to the more diverse and nebulous goals of ecosystem management and sustainable forestry has not been as straightforward. This study investigates the availability and utility of such DSS in relation to questions about forest biodiversity. Part one of this research was based on a written survey of the capabilities of existing decision support systems relevant to forest biodiversity issues (FBDSS). The primary objectives of the survey were to (1) help potential FBDSS users find systems which meet their needs and (2) help FBDSS designers and funders identify unmet needs. Thirty systems met the screening criteria from a pool of over 100 tools generated from previous reviews and other sources. These systems were reviewed against three themes: (1) classes of forest biodiversity indicators used, (2) major forest influences addressed, and (3) abilities to tackle complex political decisions. The results show only one system appears to address the full suite of biodiversity indicator classes based on the Montreal Process Criteria and Indicators. While there are a number of forest modeling tools that evaluate the influences of fire and biological threats on forest ecosystems, these systems do not generally deal with related biodiversity effects, and only one system was found which attempts to integrate the influence of climate change. Very few FBDSS appear to have capabilities explicitly designed to address the often value-based, political nature of forest biodiversity decisions. Part two comprises four in depth case studies on how FBDSS were actually used in different problem solving situations. Participant interviews and available documentation were reviewed using a four-part, qualitative framework. First, participants’ were asked how they judged success of the efforts (success measures) and what factors contributed the most to the outcome (success factors). Contrary to the analytical view of FBDSS, social measures of “stakeholder evaluations” and “contribution to consensus building” were found to be the most popular measures of success. The second part of the framework compared and contrasted the applicability of success factors taken from existing analytical and social theories on these cases. Three analytical factors were drawn from information systems theory (system quality, information quality, and service quality), and four social factors were taken from the environmental assessment literature (participation, communication, translation, and mediation). These factors covered participants’ explanations well and helped reveal additional aspects of the cases not directly expressed. Third, the cases were examined for a “mutual and recursive” pattern of analysis and deliberation. The least successful case also had the most difficulty in realizing this pattern. Fourth, it was hypothesized that participants in less conflicted situations would use fewer social indicators of success, and that as social complexity increased, simpler tools would be more successful. Neither of these expectations were supported by this group of cases. Part three of the study brought together information from the written survey, four in depth case studies, ten more cursory cases, and the literature to construct a framework help practicioners think about the “why, when, what, how, and who” of adoption and use of FBDSS. Important threads through these considerations include the question(s) of interest, the decision context, and the available capacity and time. The social and political uses of FBDSS should be explicitly considered because, as shown in the Part II case studies, these uses can be as important as the more traditionally recognized analytical benefits. A number of authors have suggested guidelines for choosing decision making methods (e.g. computation, expert judgment, stakeholder negotiation, integrated deliberation) best suited to different types of decision contexts. Lack of value agreement on and a dearth of knowledge about biodiversity means that these guidelines will rarely recommend a purely analytical approach. Therefore, I argue that if a DSS is used, it should be explicitly structured to serve the more preferred decision method. Reviewing the cases in this study has also provided some more specific suggestions on DSS use, such as understanding the (not necessarily scientific) information credibility demands of decision makers, the importance of incorporating local information, and how DSS can help structure group work and accumulate results. Finally, further research is suggested in the taxonomy of biodiversity decisions, the ability of DSS to address the more unique aspects of ecosystem management, and ways to gauge compatibility between different analytic and deliberative methods.