Publications

We present a geospatial decision support tool for predicting water yield (precipitation minus evapotranspiration) impacts of forest management strategies across North Florida. Our tool assembles geographic data layers on forest structure, hydrogeology, and hydroclimate, the inputs for an established empirical model to predict stand-level water yield. Users input areas of interest into the ArcGIS interface, and the tool collates information on satellite image-derived leaf area index (LAI), water table depth, and climate aridity to make predictions of baseline (current) water yield, as well as changes in response to user-specified LAI scenarios. Across the study domain, forest LAI was 2.72 ± 0.86 (mean ± standard deviation), while water yield was 33.4 ± 13.4 cm yr−1. The tool successfully predicts baseline water yield observations at the 30 original field site locations (r = +0.860). It is simple to use, enabling participatory exploration of the role of forest management in regional hydrologic processes. © 2025 The Authors

Strategic long-term planning of mountain forests in the European Alps requires a balancing act between sustaining forest biodiversity and ecosystem services (BES) and mitigating disturbance risks, particularly under climate change. Multi-criteria decision support systems (DSSs) address this challenge by integrating climate-sensitive forest modelling into frameworks for the evaluation of BES provision under simulated climate and management trajectories. Recent developments incorporate assessments of disturbance predisposition into DSSs, accounting for risks from bark beetle infestations and windthrow. These DSS frameworks have proven flexible applicability across various forest models, spatial scales, forest types, and environmental conditions. However, climate-change-induced transitions of disturbance regimes require adaptations of existing DSS frameworks by accounting for emerging disturbance agents, such as forest fires. Here, we introduce the integration of a fire predisposition assessment system (PAS) into a DSS, incorporating factors such as topography, climatic conditions, wildland–urban interface, and stand structural characteristics. Particularly in the context of long-term planning in mountain forests, the expanded DSS could enable the identification of conflicting forest management objectives related to BES provision and disturbance mitigation efforts under climate change, leading to more informed management decisions. • A predisposition assessment system for assessing multiple predisposing factors of forest fires is presented. • The fire PAS enables an integrated evaluation with disturbance predisposition to bark beetle infestations and windthrow, as well as BES provision. • The modular structure of the fire PAS enables adaptation and application to various spatial scales, as well as integration with different forest models. © 2025 The Author(s)

In today's complex business landscape, organizations grapple with data overload and dynamic decision environments. This necessitates the implementation of decision support systems (DSS) for effective decision-making. DSSs have become an attractive option for organizations for making decisions in the current digital age of management systems. DSS are used for a variety of reasons across different industries and contexts which also provide individuals, teams and organizations with the tools, information, and insights they need to make informed and effective decisions. The purpose of this study is to review the characteristics of DSS and how it is being used in various areas. The uses of DSS can be divided in multiple sectors such of medical, forest management, education, and business development. This paper will hopefully be a valuable contribution to the ingoing discussion about decision support systems and their impact on sustainable development in organisations. © 2025, Semarak Ilmu Publishing. All rights reserved.

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.