Publications

A Multi-Criteria Analysis (MCA) approach was employed for evaluating and selecting the best management strategy for Mount Kenya Forest Reserve and National Park (MKFRNP). The MCA approach used a set of objectives and criteria (O&C) to address the complexity of the decision problem in a transparent and understandable way, which also facilitated the active participation by diverse professionals, experts, and interest groups. The management strategies were developed to fulfill the key components of MKFRNP management and the current situation in the study area. The seven management strategies focused on climate change mitigation, protection of water catchments, education and research, stakeholder involvement, biodiversity conservation, timber production, and community interests. Forest stations with differing fire danger levels (very high, high, moderate, and low) were selected to compare the performance of the management strategies. The strategies were assessed qualitatively on their potential to improve the current situation according to the entire set of O&C. The Analytic Hierarchy Process (AHP) was employed to identify the best management strategy according to the overall preferences of all stakeholder groups. The AHP indicated that a strategy focusing on community interests provided the best option to address the current management challenges in all the seven forest stations independently of their fire danger levels. Biodiversity conservation should also be considered by resource managers in order to reduce fire danger and increase the benefits obtained by different stakeholders in MKFRNP.

The present study aimed to optimize the location of wood storage yards in forest management for the production of wood in the Brazilian Amazon. The area of forest management studied was 638.17 ha, with 1478 trees selected for harvest with a diameter at breast height of at least 50 cm in accordance with Brazilian legislation. Taking the topography into account—permanent preservation areas, restricted areas, and remaining trees—and using GIS tools, 7896 sites were identified that could be used as wood storage yards. By using mathematical programming techniques, more specifically binary integer linear programming, and based on the classical p-median model, optimal locations for the opening of yards were defined. Four scenarios were proposed combining distance and volume constraints. The scenarios evaluated promoted reductions in infrastructure investment compared with traditional planning. The results showed reductions in the number of forest roads (–6.33%) and trails to extract logs (–15.49%) when compared to traditional planning. The best performing scenario was that with the maximum volume restriction. It was concluded that the application of mathematical programming was able to promote significant gains in the harvest planning of native forests of the Amazon with the potential to reduce environmental damage.

Growing concern about issues such as environmental quality or the sustainability of natural resources has led to the use of the Decision Support System (DSS), which originated in the business field, and is now part of environmental decision-making processes. The presence of environmental, social, or economic dimensions has helped decision support systems to evolve to be able to tackle investigations that can contemplate all these variables, such as in the case of multicriteria decision analyses. In addition, new lifestyles, in which society recognizes more and more the contribution of forests to its welfare, have led to the need to involve stakeholders in decision-making processes. This article presents a review of different Multicriteria Decision Analysis (MCDA) and participatory decision support systems applied to forest environments. This last point is presented from the perspective of stakeholder participation in the processes and from the point of view of procedures or tools used. To do this, some of the research performed in forest environments within this current century is reviewed.

Natural hazard risk is largely projected to increase in the future, placing growing responsibility on decision makers to proactively reduce risk. Consequently, decision support systems (DSSs) for natural hazard risk reduction (NHRR) are becoming increasingly important. In order to provide directions for future research in this growing area, a comprehensive classification system for the review of NHRR-DSSs is introduced, including scoping, problem formulation, the analysis framework, user and organisational interaction with the system, user engagement, monitoring and evaluation. A review of 101 papers based on this classification system indicates that most effort has been placed on identifying areas of risk and assessing economic consequences resulting from direct losses. However, less effort has been placed on testing risk-reduction options and considering future changes to risk. Furthermore, there was limited evidence within the reviewed papers on the success of DSSs in practice and whether stakeholders participated in DSS development and use.

Various kinds of decision support approaches (DSAs) are used in adaptive management of forests. Existing DSAs are aimed at coping with uncertainties in ecosystems but not controllability of outcomes, which is important for regional management. We designed a DSA for forest zoning to simulate the changes in indicators of forest functions while reducing uncertainties in both controllability and ecosystems. The DSA uses a Bayesian network model based on iterative learning of observed behavior (decision-making) by foresters, which simulates when and where zoned forestry activities are implemented. The DSA was applied to a study area to evaluate wood production, protection against soil erosion, preservation of biodiversity, and carbon retention under three zoning alternatives: current zoning, zoning to enhance biodiversity, and zoning to enhance wood production. The DSA predicted that alternative zoning could enhance wood production by 3–11% and increase preservation of biodiversity by 0.4%, but decrease carbon stock by 1.2%. This DSA would enable to draw up regional forest plans while considering trade-offs and build consensus more efficiently.

Climate change has altered and further will change the environmental conditions for many sectors. Whereas annual cropping systems can be adapted yearly, decisions in forest management usually have long-lasting effects. Depending on the region and tree species rotation periods range between 80 and 180 years in Central Europe. Therefore, todays tree species selection should also take into account the impacts of climate change in the future. Although many attempts have been made to understand single aspects of climate change impacts on forests, the available knowledge has to be conflated into an integrated assessment to support decision making. A comprehensive system comprising different impact models and an economic assessment suitable for Central European forests and driven by high-resolution temporal- and spatial data is currently missing. We present a conceptional design and a reference implementation of a Decision Support System (DSS) tailored to assess climate change impacts on German forests. To provide high ease-of-use, the system was implemented as a web application and offers information for single stands on-demand as well as interactive maps and preprocessed assessments on a coarser level for entire Germany. To create such a complex, integrated system from legacy models written in different programming languages the interfaces had to be developed carefully. The key of this DSS is its building blocks: established models describing different climate change impacts. Since the DSS is a very modular system, it is easy to replace submodels and to adapt it to other study areas, forest systems or research questions if suitable parameterized models and input data are available. The presented technical solution is adaptable to other systems integrating existing models and the source code is available.

Climate change may strongly impact on forests and affect the provisioning of forest ecosystem services. The identification, design, selection and implementation of adaptive measures in forest management requires a sound knowledge base as well as tools to support the forest manager in decision making. Decision support systems (DSS) are considered as particularly useful to assist in dealing with ill structured decision making problems. Forest management planning and decision making deals with highly complex socio-ecological systems with multiple interacting spatial and temporal dimensions. Finding ways and means to communicate findings about such complex relationships in forest ecosystems and their management via information technology is a challenge in itself. This is amplified if decision problems include land use and climate change issues as inherent uncertainty in planning outcomes increases. The literature reports numerous attempts to develop DSS for forest management. However, recently several review papers conclude that there has been only limited uptake of DSS into practice in case that the user demands and the characteristic of decision problems are not considered properly.
In this contribution we propose five design principles for forest management DSS: (i) modularity, (ii) accessibility via the internet, (iii) inclusion of different types of knowledge and information, (iv) possibility to use different data sources, and (v) support of specific problem types. Based on these principles we promote a ToolBox approach attempting to meet context specificity and flexibility addressing different user and problem types simultaneously. The AFM (Adaptive Forest Management) ToolBox is introduced and the conceptual design and technical implementation of the ToolBox is presented. The combination of different kind of decision support techniques (e.g. vulnerability assessment, multi-criteria analysis, optimization) allows to support all phases of the decision making process and provides the user with the flexibility to interpret the information in various forms. The results of a self-assessment of the ToolBox against eight evaluation criteria for DSS are combined with a feedback from a panel of expert users who had tested the usability and had evaluated the conceptual approach of the ToolBox. The feedback allows stimulating the further development and increasing the level of acceptance of potential users. It is concluded, that the ToolBox approach focusing on modularity while avoiding to over-emphasis technical integration provides the right frame to secure the flexibility to add new tools and improve the support of decision making processes which is mandatory if a DSS should be taken up by practice.

Current Mexican forest management is the product of a history that dates back to 1926. Earlier approaches were directly or indirectly aimed at attaining the normal forest model. Around 1980, multi-resource and environmental impact considerations were mandated for all private timber operations. Timber-oriented silviculture was deemed insufficient to take proper care of non-timber values in the forest. Concerns about water quality, biodiversity, and natural conservation were the motives for promoting voluntary best management practices, in 2012 and afterwards. In this research, two traditional Mexican forest management schemes, Sicodesi and Plan Costa, enhanced with best management practices, are compared to Mapa, a management method specifically designed to manage landscape attributes. Results from two successive forest inventories 10 and 13 years apart show that Sicodesi and Plan Costa, even when modified to comply with best management practices, failed to maintain proper stewardship of non-timber values. Mapa, however, employed multiple means to drive forest dynamics to fulfill multi-resource objectives, constrained by self-financing and competitive profitability. These capabilities in Mapa enabled some degree of control over non-timber values, but many more important processes occur beyond the property boundary, and beyond the planning scope considered in Mapa and all other forest planning methods.

Integrated forest management is faced with the challenge that the contribution of forests to economic and ecological planning targets must be assessed in a socio-ecological system context. This paper introduces a way to model spatio-temporal dynamics of biomass production at a regional scale in order to derive land use strategies that enhance biomass provision and avoid trade-offs for other ecosystem services. The software platform GISCAME was employed to bridge the gap between local land management decisions and regional planning by linking growth and yield models with an integrative mesoscale modeling and assessment approach. The model region is located in Saxony, Germany. Five scenarios were simulated, which aimed at testing different alternatives for adapted land use in the context of climate change and increasing biomass demand. The results showed, for example, that forest conversion towards climate-change-adapted forest types had positive effects on ecological integrity and landscape aesthetics. In contrast, negative impacts on landscape aesthetics must be expected if agricultural sites were converted into short rotation coppices. Uncertainties with stem from assumptions regarding growth and yield models were discussed. Future developmental steps which consider, for example, accessibility of the resources were identified.

Programme-based Planning of Natural Resources (PBPNR) is an evolving planning frame for solving complex land use, environmental and forest management problems within hierarchically administrated funding and decision-making schemes. PBPNR acknowledges that an effective planning process requires the combined consideration of environmental, technological, economic and socio-political factors. To reach acceptability, commitment and operability, PBPNR processes need to foster collaboration and learning. For this study, an analysis of 43 collaborative planning methods was conducted to examine their potential to be applied within PBPNR. We present the approach of screening the applicability of methods for specific needs that may occur in PBPNR. The approach is based on a list of key criteria for the phases of a collaborative planning process: problem identification, problem modelling and problem solving. The features of each method were qualitatively assessed and peer-reviewed by a team of experts. Most of the methods are able to deal with qualitative data, support processes to increase transparency in planning and capture the preferences of the participating stakeholders. They also produce understandable results for the three phases. Contrarily, many methods do not offer features to handle uncertainty, nor do they satisfactorily stimulate creativity and innovation in the planning process. The results show that the overall applicability of the reviewed methods for the three planning phases varies according to a cluster analysis basing on the capabilities of the methods. Methods such as “Planning for Real”, “Open Space” and “A'WOT” seem to be particularly promising for a broad range of planning situations.