Editorial: Building Decision Support Tools and Functions for Forest Landscape Planning and Restoration

Patricia Nicole Manley^1*Nicholas Adam Povak¹Keith M. Reynolds²Paul Francis Hessburg^2,3

• ¹Pacific Southwest Research Station, Forest Service (USDA), Albany, United States
• ²USDA Forest Service Pacific Northwest Research Station, Portland, United States
• ³University of Washington, Seattle, United States

Decision support tools (DSTs) have been used for decades to help users make critical decisions related to natural resource and ecosystem management (Reynolds et al. 1999). In the most basic sense, DSTs are interactive computer-based programs that are used to acquire, store and present data, conduct analyses, and provide a quantitative foundation for evaluating tradeoffs among alternative decisions (Reynolds et al. 2014). DSTs in natural resource management are simultaneously ecological, social and cultural constructs that, when applied, also serve to document the social contract associated with a decision. DSTs are commonly built through a codevelopment exchange between scientists, managers, stakeholders, and partners, reflecting the interface of current conditions, in some cases potential future conditions, and a broad array of perspectives across multiple social, cultural and economic sectors (e.g., Manley et al., 2023). As such, decision support lives at the intersection of science, management, policy and culture, and effective DSTs help managers and policy makers navigate this intersection.Decision theory suggests that decision making is anything but a straightforward process with predictable outcomes based on available data (Groeneveld et al., 2017;Peterson, 2017). Decisions are inherently laden with uncertainty, differences of opinion and values, risks and the burden of associated consequences, and incomplete information that forces a leap of faith that the selected path forward will be the most successful. Without decision support analytics, many decisions pertaining to complex systems will be unduly influenced by the individuals involved, the decision process, and developments exogenous to the decision but perhaps related to the decision maker, their organization, or influential parties and their activities (De Vente et al., 2016;Zasada et al., 2017). It is well documented that decision-makers want to retain the prerogative to make decisions based on their interpretation of what they think is most important to the outcomes expected to result from a decision (Groeneveld et al., 2017).In fact, the greater the visibility and political impact of a decision, the greater is the individual incentive to strongly hold onto the narrative of what drove a decision and its expected outcomes. This paradox is relevant to decision support tool (DST) development and its application because in essence they must span this complex pushpull dynamic to provide transparency on one hand, and to retain prerogative or "decision space" on the other.Complexity and uncertainty associated with many decisions amplifies the need for and the value of DSTs. Ecosystems have always been complex, representing eons of co-evolution and adaptation, which have resulted in intricate interactions and interdependencies -biological, social, and cultural. Up through the closing of the 20th century, the prevailing perspective in land management and forestry has been one of gradual ecological change, with human activity (e.g., harvesting trees, domestic grazing, cultural burning, wildfire suppression) and population growth (e.g., land conversion from natural to agriculture to urban) being the primary drivers of landscape change (Millenium Ecosystem Assessment, 2005). As climate change accelerates, however, the pace and scale of change for nearly all facets of social and ecological systems are also expected to rapidly shift in response (Malakar et al., 2023). For example, the Earth's climate has fluctuated within a bounded range of variability + 0.5 ∘ C for the millennium prior to the 20th century (Salinger, 2005); however, over the course of the remainder of the 21st century, global mean temperatures are projected to increase by an unprecedented 2 to 4.5 ∘ C. Temperature changes will be accompanied by changes in precipitation and extreme climate and weather events as we are already beginning to experience (Salinger, 2005).These climatic shifts will stress-test regional, continental, and global systems, particularly those associated with agriculture and forestry, as well as water and power supplies and their respective distribution networks. Associated impacts have the potential to exceed the management systems and coping abilities of communities and society, writ large (Malakar et al., 2023). For example, natural resource management, traditionally focused at site, stand or small watershed scales, is now grappling with disturbance dynamics and interactions that are manifesting across very large landscapes over short periods of time and simultaneously affecting a broad suite of ecosystem conditions (Povak et al., 2023;Manley et al., 2025aManley et al., , 2025b)). Concurrently, the ability of ecosystems to provide essential and desired ecosystem services is in peril, if not already declining (Sun and Shi, 2020). Decision support tools are becoming essential mechanisms for grappling with multiple factors, multiple scales, substantial potential impacts, and significant uncertainty.The erosion of ecosystem functions and services is challenging long-standing societal expectations for a sufficient and sustainable supply of goods and services traditionally provided by native terrestrial and aquatic ecosystems (Runting et al., 2017).Scientists and managers are seeking a more in-depth understanding of ecosystem capacity and sustainability under various future management and climate scenarios, and DSTs are playing a vital role in moving scientific information into accessible, practical applications and facilitating all aspects of the evaluation process (e.g., Kangas et al., 2000;Abelson et al., 2022;Furniss et al., 2023). The highly interconnected nature of ecosystems combined with rapid climate and weather changes suggest that longstanding top-down and bottom-up controls on ecosystems are changing rapidly as well, placing an even greater importance on the availability of DSTs that can enable scientists and managers to explore unconstrained "what if" future scenarios (Gustafson, 2013;Maxwell et al., 2022a,b;Manley et al., 2023). Decision support tools and applications to assist with various aspects of forest management began appearing in the 1970s, and were common by the 1980s. However, these early applications were typically developed to address relatively narrow, welldefined problems such as timber-harvest scheduling (Johnson et al., 1986;Barber and Rodman, 1990) or forest pest management (Mowrer et al., 1997;Reynolds ,2005). By the late 1980s, a 'whole ecosystem' management concept was born. It was adopted by national resource agencies in the US and elsewhere (Rauscher 1999), along with the need for the associated practices of sustainable forest management and adaptive ecosystem management (Holling, 1978;Walker, 1986).Since then, the requirements for forest management decision support began to change significantly from addressing narrow, well-defined problems to those that were much larger, more complex, and more abstract, such as improving forest ecosystem sustainability and social-ecological landscape resilience (Reynolds, 2005;Manley et al., 2025a). Meanwhile, human communities engaged as stakeholders in land management in general, and in forest management on public lands in particular, were demanding more of a say in resource agencies' planning and implementation work (e.g., Schultz et al., 2012). In the US, federal oversight agencies such as the General Accountability Office (GAO) were reviewing the business practices of federal resource agencies, and they consistently identified the need for much more rational, transparent, repeatable decision processes (GAO, 2002(GAO, , 2003(GAO, , 2004(GAO, , 2007)). Federal agencies responded by establishing DSTs for some central functions, such as allocating annual budgets for forest fuels management in the US Forest Service and the US Department of the Interior. While fuels management was the specific focus of initial GAO reports (Reynolds et al., 2009), calls from oversight agencies for more transparent and repeatable decision processes quickly spread to many other aspects of environmental resource management in the US and beyond (Reynolds, 2003).All the above factors inspired multiple innovations in DST form and function, and subsequently precipitated a new generation of forest management DSTs, such as the Ecosystem Management Decision Support System (EMDS; Reynolds et al., 2014). Indeed, a hallmark of second-generation forest DSTs (akin to EMDS) is their focus on rational, transparent, and repeatable solutions (Reynolds, 2005). The need for transparency in DSTs became a compelling issue with the US Forest Service, an agency that stewards ~30% (~80 M hectares) of the forested lands in the US, when it mandated the use of FORPLAN for all forest management planning in the 1990s. Plans were required to incorporate ecosystem management principles (Rauscher, 1999), which required very large linear programming (LP) solutions to track multiple resources that operated essentially as a black box to everyone except LP programmers. In the burgeoning era of environmental protection and regulation in the 1980s and 1990s (and up to the present day), it was clear that more was at stake than timber harvest schedule and non-declining timber yields, and the US Forest Service determined that black-box LP solutions for large, complex, and abstract problems were neither politically nor socially viable approaches. As a result, FORPLAN solutions for forest planning were largely abandoned by the late 1990s, although the system is still used by the agency for a few narrow, well-defined problems.Current challenges and future threats to environmental integrity and resilience call for DSTs that can be adapted to address complexities and concerns as they pertain to existing and emerging issues (e.g. see Povak and Manley 2024, Povak et al., 2020, 2022, 2024;Churchill et al., 2022;Pascual et al., 2022;Furniss et al., 2023Furniss et al., , 2024Furniss et al., , 2025;;Reynolds et al., 2023). Continued investment in a breadth and depth of DSTs (e.g., Smallman et al., 2022;Lamas et al., 2023;Reynolds et al., 2023) will be essential to providing a collection of options to draw from as decision support needs arise and change over time. Increasingly, it is clear that decision support functions are being increasingly pushed and tested to reflect social and cultural requirements and limitations, as well as ecological complexities and uncertainties. Natural disasters worldwide cause widespread human and economic losses each year (Wallemacq and House, 2018). In the western United States, wildfires have emerged as one of the most consequential extant disturbances, with burned area and severity increasing markedly in recent decades (Parks et al., 2025). These trends are attributed to over a century of fire suppression, past management practices, expansion of the wildland-urban interface (WUI), population growth, and climate change (Hessburg et al., 2016(Hessburg et al., , 2019;;Hagmann et al. 2021). The dominant fire management response today still emphasizes continued fire exclusion through prompt fire suppression, and fuel-reduction treatments near human communities to reduce hazard and vulnerability. Hazard refers to both the likelihood of fire in a location and its expected intensity (e.g., flame length), whereas vulnerability reflects susceptibility (anticipated damage) and exposure (likelihood of fire spread into a community).Extant risk-reduction strategies are primarily accomplished through mechanical removal of living and dead woody materials (Prichard et al., 2021), designed to prevent wildfire spread into the WUI to increase public safety and reduce impacts to infrastructure. Furthermore, mechanical treatments in forests more distal to infrastructure are more focused on reducing the impacts and enhancing the benefits of fire on forest conditions, habitat quality, and climate resilience (Hessburg et al., 2021) However, wildfire is a keystone disturbance that historically shaped vegetation structure and landscape patterns (see references in Agee, 1996;Hessburg et al., 2019;Hagmann et al., 2021), and in most dry forest ecosystems, it is essential to forest health. When allowed to operate as a functional disturbance process, fire creates and maintains forest conditions that are more likely to be resilient to future disturbances and climate change (Prichard et al., 2021). Resilience-the capacity of ecosystems to maintain essential functions, structure, and processes despite disturbance-thus represents a long-term property distinct from short-term hazard mitigation. This divergence creates a management conflict: reintroducing fire to natural landscapes can advance long-term resilience but may appear to conflict with immediate risk-reduction objectives for built environments.Given the vast extent of western US landscapes with altered fire regimes (Hagmann et al., 2021) and scarce personnel and financial resources, prioritizing where, how, how much, and how often to apply treatments is essential. Policies geared largely toward community protection have dominated discussions in recent decades (Calkin et al., 2024), yet an exclusive focus on WUI defense inadvertently maintains hazardous conditions elsewhere, increasing the probability that high-severity fires will occur and escape into communities, many of which are not fully prepared for wildfire.Moreover, quantifying landscape resilience remains challenging because ecosystems are dynamic, conditions are nonstationary and multi-dimensional, and resilience is context dependent. For example, large, severe fire events may be interpreted as a loss of resilience, but they can also be integral to disturbance-prone systems and can reveal a necessary reset toward a more active and constructive disturbance regime (Povak et al., 2023). These complexities make reconciling priorities for restoration treatment locations and outcomes both difficult and time consuming, also making the measurement of success challenging and potentially controversial. Despite these challenges, shifting toward a resilience-oriented framework offers multiple benefits. Treatments that restore heterogeneous forest structures, re-establish natural disturbance regimes in the context of changing climate, and reduce fuel connectivity can inhibit fire spread and reduce severity, buffer vegetation conditions to a changing climate, and sustain a predictable flow of ecosystem services such as water quality, wildlife habitat, carbon sequestration, and recreation. For example, Alcesena et al. (2022) found that combining treatments within the home ignition zone (a subset of the WUI) and the surrounding landscape led to the highest reductions in home exposure and loss to future fires. Fuel-reduction treatments outside the WUI can also provide benefits in addition to reducing wildfire spread and severity, including improving wildlife habitat, hydrological function, nutrient cycling, and sustainable carbon storage (Stephens et al., 2021;Hessburg et al., 2025) -all of which contribute to supporting long-term resilience goals.Recognizing this, several initiatives now aim to integrate community protection (risk reduction) with landscape-scale restoration (enhanced resilience). California's Wildfire and Forest Resilience Task Force explicitly seeks to "deliver significant progress in protecting people and property while improving the health and resilience of the natural lands we love and rely on." Within this scope, the Tahoe-Central Sierra Initiative (TCSI) launched a 2.4-million-acre restoration effort that included some of the most visited recreation areas in the country, including Lake Tahoe. This work produced the Ten Pillars of Socio-Ecological Resilience (TPOR; Manley et al., 2025a), a hierarchical framework of pillars, elements, and metrics to define, quantify, and track resilience outcomes. Complementary tools such as the PROMOTE model (Povak et al., 2024) integrate climate-change evaluations into planning by identifying monitoring, protection, adaptation, and transformation options based on both current and projected future resource conditions. Wildfire risk reduction fits well within these frameworks. For example, the Fire Adapted Communities Pillar within the TPOR Framework and management treatments directed at wildfire risk reduction within WUI can be balanced with or usurp the importance of the type, location, and frequency of treatments directed at benefiting other pillars and conditions across landscapes. Together, these approaches demonstrate how DSTs can aid in identifying pathways toward understanding trade-offs and ideally balancing outcomes associated with short-term wildfire risk reduction and the longer-term goal of resilient, self-sustaining landscapes. Science-management partnerships are an essential component of DST development and implementation. Partnerships serve to ground problem definitions and solutions in real-world applications and constraints, and they serve to expand perspectives and deepen user understanding of complexities, uncertainties, ambiguities, and best available science. Decision support tools can then reflect this common ground, and make best available science readily applied to the target functions of the DST, be they assessment, planning, or management application (e.g., see Churchill et al, 2022). However, it is well established across different disciplines that more information is not necessarily helpful in making decisions, and better information does not always lead to decisions that reflect the available information (Peterson, 2017). This phenomenon is particularly pervasive with regard to acting on the climate crisis in a manner that is aligned with scientific evidence (e.g., Wright and Nyberg, 2016).Decision makers with land management responsibilities in this environment of rapidly changing climate are faced with an extremely challenging task: reconciling the short-term and long-term values of security and benefits. Current conditions are measurable, relatable, and their benefits are tangible and potentially fungible. In contrast, future outcomes are fraught with uncertainty, and are commonly presented in the form of probabilities, statistical surrogates for likely futures (Allen and Stein, 2013).Scientists and other experts are frequently engaged with managers in land management decision processes to help identify expected or potential social-ecological outcomes associated with different courses of management action (Littell et al., 2012;Manley et al. 2025b). Highly certain short-term gains weighed against a set of probabilities associated with an array of future benefits predictably will lead to the persistence of management approaches based on the status quo (Allen and Stein, 2013;Wright and Nyberg, 2016). Managers face substantial scrutiny and lack firm ground on which to venture away from the known and put near-term benefits at increased risk based on a statistical probability of improving future conditions across landscapes and socio-ecological systems (Hall, 2010).Perhaps even more challenging are improved certainties of negative outcomes regardless of the actions of managers (e.g., Povak and Manley, 2024), or cases in where management actions effective in reducing future impacts will have certain impacts on current conditions and benefits. For example, some would argue that old forests, as they existed historically in the western US, may no longer be viable -they may be ghosts of climates past and attempts to protect them against climate and fire may be futile (Abella et al., 2007;Spies et al., 2016;Hill et al., 2023). Attempts to save them may result in the complete loss of forested conditions, thereby suggesting that management objectives might transition from conserving old forests to simply conserving forests. Another example is carbon storage in temperate forests as a mitigation measure to help reduce the impacts of carbon emissions. Across the western US, dry temperate forests dominate forested landscapes, and fire suppression and other management policies and actions over the past century have resulted in forests that have uncharacteristically high densities of wood, and by extension, carbon (Campbell et al., 2011). Managing these forests in a manner that improves their probability of persistence into the future, as well as managing forests after fire, is likely to involve reducing carbon to reduce the risk of further losses from fire (e.g., Scott et al., 2023;Hessburg et al., 2025;Manley et al., 2025b). In extreme cases, management may not even be able to reduce the risk of loss of forests, putting managers in an uncomfortable position of admitting that their actions may not be effective in achieving desired outcomes in certain locations.The burden of reconciling uncomfortable truths and less desirable futures rests with both scientists and managers. Science has always served society, but the existential threats of a changing climate are steering investments in ecosystem sciences even more toward dual functions of near-term applied and foundational scientific contributions (McNie, 2013). Science-management partnerships are critically important to crafting effective responses to environmental challenges, and DSTs are an outgrowth of these partnerships. Understanding the challenges that science-management partnerships face is likely to translate into growth areas for DSTs of the future. For example, translating probabilities and uncertainties into relatable correlates that managers, stakeholders, and policy makers can use is likely to enhance their ability to communicate and socialize options that involve some degree of short-term sacrifice to improve prospects for future resilience (Manley et al., 2024). Natural resource science spans a broad array of topic areas, including landscape ecology, fire ecology, forest ecology, biodiversity conservation, social-ecological systems, and climate change. The conduct and results of this research no longer simply lives within the pages of journals -it is a partnership between scientists, managers, and policy makers. Research studies are increasingly designed and implemented to facilitate their application through DSTs and other avenues to inform and support changing conditions on the ground in real time. This partnership ensures that the scientists are answering the most critical questions, and results can be readily incorporated into planning and on-the-ground actions. In tandem, managers are striving to have at hand the best available scientific information -translated into concepts, terms, and values that anyone can understand, discussed as needed, and moved expeditiously to effective actions. DSTs have a critical role to play in facilitating many of these functions so that society can keep pace with, adapt to, and cope with impacts and threats affecting forested ecosystems, landscapes, and human communities across the western US and globally.Given the complexities and uncertainties associated with managing natural resources over the long term, DSTs can no longer remain static, off-the shelf resources for planners. The next generation of DSTs must be flexible and adaptive as new data become available, as environmental conditions continue to change and impact ecosystem function and stability, as novel or unexpected events alter the landscape, and as social and political environments change and alter the desired outcomes from management. Much progress has been made towards these goals, but as advancements in data science continue at a breakneck pace, the field of decision science is ripe for innovation to better meet the many complex resource problems on the horizon.