Forested landscapes provide ecosystem services and resources that are vital to the social, economic, and cultural well-being of human communities (Dale et al., 2001). Adapting to climate change while maintaining ecosystem functioning requires the analysis of complex tradeoffs (Bennett et al., 2009), often in the face of considerable uncertainty (Millar et al., 2007). The purpose of this study was to advance methods to evaluate such tradeoffs, mitigate future uncertainty through simulation modeling, and to assess the potential for adaptation actions to improve ecosystem functioning among seven broad categories of social-ecological health.

Ecosystems confer many benefits directly, including trails and access to open space for recreation, fish and wildlife resources, cultural resources for Indigenous tribes, sustainable fiber production and wood supply, firewood, and edible mushrooms. Indirect benefits are often more diffuse, yet they too can be of profound importance to human communities both near and far. These more indirect benefits include carbon sinks and stocks, plant and animal biotic and genetic diversity, water quality and quantity, self-stabilizing ecosystem processes, and maintaining healthy riparian geomorphology (Bisson et al., 2003).

An ecosystem service of particular importance in the Intermountain West is the preservation of winter snowpacks and the regulation of stream flows. Patchy forest mosaics can reduce snow interception while still providing shade, which can reduce snowpack attenuation and increase late season stream flows, providing critical habitat for anadromous and coldwater fish populations (Lundquist et al., 2013). At regional and global scales, forests provide crucial regulation of atmospheric carbon by storing it in long-term carbon sinks and stocks (Magnani et al., 2007). The carbon storage potential of forests is essential to global efforts to curb anthropogenic global warming, and there has been a surge in interest from governments and industries to enhance the carbon storage in live forests to offset carbon emissions (Badgley et al., 2022).

The viability and integrity of these vital ecosystem functions is jeopardized by the rapid and interactive stresses of climate change, long-term drought, large-scale insect outbreaks, and increasing wildfire activity (Barnosky et al., 2012; Seidl et al., 2016). There is mounting concern that positive feedbacks between climate change and wildfire (Loehman et al., 2014; Calkin et al., 2015) will significantly compromise the capacity of forests to offset climate change (Walker et al., 2019; Coop et al., 2020; Clarke et al., 2022; Wu et al., 2023), and this is driving a paradigm shift in land and resource management. Land managers and policy makers are increasingly concerned with the social-ecological values provided by intact forest landscapes, and management of public forest lands is turning toward managing for multiple benefits, maintaining ecosystem functions, and bolstering resilience to climate warming and novel disturbance regimes (Millar et al., 2007; Franklin and Johnson, 2012; Hessburg et al., 2015, 2021).

This has created an expanding need for both quantitative and qualitative techniques that can aide in strategic landscape management planning and tradeoff analyses at fine, meso-, and broad-scales. Managing for these multiple benefits is a wicked problem (Davies et al., 2015). How does one integrate a diverse, dynamic, and often conflicting set of stakeholder values? How are community needs balanced with ecological priorities? How are adaptation plans reconciled with economic constraints? The planning process can be aided by decision support models that can aggregate spatial data layers and knowledge bases that can inform strategic management planning processes across large landscapes (Vogler et al., 2015; Abelson et al., 2021; Povak et al., 2022; Reynolds et al., 2023).

Decision support systems facilitate the quantitative assessment of potential synergies (i.e., treatments may benefit multiple ecosystem services) and tradeoffs (i.e., benefits to one service necessitates reduction in another) between different ecosystem services (Maron and Cockfield, 2008; Bennett et al., 2009; Povak et al., 2022). These tools are used to translate social-ecological values into quantitative evaluations of resource conditions (i.e., departure from target conditions) and their potential for adaptation and maintenance, enabling managers to develop strategic plans with explicit consideration of the ecosystem services that are of the greatest value locally and regionally (Eriksson et al., 2022). By identifying areas where adaptation treatments can produce synergistic benefits, managers are able to allocate resources to improve the impact of each dollar spent (e.g., Ager et al., 2016).

A common example of a treatment synergy involves wildfire risk reduction, economic recovery, and tree density reduction in fire-prone forests (e.g., Vogler et al., 2015; Ager et al., 2016). Adaptation treatments can produce co-benefits among all three topics, and this forms the basis for many restoration prioritization efforts throughout the west (Prichard et al., 2021). Predicting potential treatment synergies and tradeoffs becomes increasingly complicated as we consider additional ecosystem services. For example, adding in surface fuel reduction can negatively impact carbon sequestration and some wildlife habitats in the short term (Underwood et al., 2010), but can have positive long-term effects by mediating future fire severity and probability of stand-replacing fire (Tempel et al., 2015; Krofcheck et al., 2018).

Evidence also suggests that forest thinning can enhance streamflow directly by reducing evapotranspiration and increasing snowpack retention (Dickerson-Lange et al., 2021), but the relationship between forest cover and snow retention is complex – particularly across large environmental gradients (Dickerson-Lange et al., 2021). Some evidence suggests that forest cover tends to decrease streamflow in warmer climates by increasing evapotranspiration and snow sublimation losses while accelerating snow attenuation through downwelling radiation. However, in cold climates, forest cover can also increase snowpack retention by reducing spring melt out rates (Lundquist et al., 2013). Thus, treatment outcomes relevant to the provision of key ecosystem services and resources can vary by virtue of the intrinsic relationships among the biophysical setting and vegetation characteristics.

The potential to find synergistic effects of forest treatments is further complicated by the uneven distribution of ecosystem functions in large landscapes. From a human community perspective, areas that are a high priority for wildfire risk reduction exist primarily in dry and moist mixed coniferous forests adjacent to human communities in the wildland-urban interface (WUI, Ager et al., 2021), while areas that are most important for carbon storage, habitat, and snowpack regulation occur primarily in mid- and upper-montane forests away from human communities. Consequently, while there may be potential alignment between several priority topics, this potential can be unattainable in forest landscapes where high priority ecosystem services reside in different parts of the landscape (Povak et al., 2022).

Here, we built a spatial decision support model to quantitatively examine these complex tradeoffs in space and time. We evaluated how management actions may enhance ecosystem functioning, and where forest adaptation treatments can produce synergistic effects across ecosystem conditions and functions. We applied this decision support tool to a large, fire-prone landscape in north-central Washington State in the rain shadow on the eastern slopes of the Cascade Mountain Range. We simulated 100 years of forest succession and disturbance dynamics in this landscape using a process-based forest landscape model (LANDIS-II, Scheller et al., 2007). We aimed to advance a generalizable understanding of synergies and tradeoffs deriving from potential adaptation treatments in fire-excluded forests (Hessburg et al., 2005, 2019), and to evaluate where management may be most effective for building resilient, climate-adapted social-ecological landscapes. A key advancement made by the current study was the coupling of the LANDIS-II model with a distributed hydrology-soil-vegetation-model (DHSVM; Wigmosta et al., 1994) to better identify treatment effects on snowpack and streamflow within large landscapes.

Maps of current conditions (simulation year 0) revealed intuitive patterns prior to any trend analysis. For example, many ecosystem services, including those subtopics bundled under the Carbon, Water, and Forest Health primary topics (Table 1), were spatially concentrated in the mid- to upper-elevations, and in wildlands unaffected by past logging but significantly affected by a fire exclusion history (Figure 1). This lack of fire over the past century allowed forests to densify and invade former wet and dry meadows and shrublands producing an uncharacteristic (Hagmann et al., 2021) abundance of forest cover, above normal NPP and net ecosystem exchange values, and related carbon accumulation (Supplementary Figures S2–S4). Water conditions were also best in the upper elevations where fire frequency tended to be lower and snow accumulation was greatest leading to greater snow water relative to lower elevations. Higher quality forest health conditions were also greatest at these elevations owing to a relative lack of past timber harvest, greater abundance of old growth forest patches and those with large old trees, reduced drought, better conifer seed rain after disturbances, and broader heterogeneity of forest successional conditions (Table 1).

Future trends relative to current conditions were also intuitive, with most forests increasing in carbon storage, especially in younger patches. These gains were particularly notable in young to mature forests at mid-elevations where biomass was projected to accumulate rapidly in the absence of continued harvest (Supplementary Figure S2). Snowpack and stream flows were projected to decrease throughout the century, with the most significant declines occurring in upper elevation watersheds (Supplementary Figure S3). Forest health was projected to increase over time in both upper and lower elevations, while mid-elevations showed a decline in forest health in the absence of treatment (Supplementary Figure S4).

Maps of treatment efficacy showed the highest potential efficacy in areas with intermediate current and future conditions (Figure 1; Supplementary Figures S2–S4). Areas that were projected to improve significantly over time in the absence of treatment had reduced potential for treatments to be effective, while areas that were projected to decline also had reduced capacity for treatments to help. Potential treatment efficacy among Economics, Sustainable biomass, and Wildfire topics was concentrated in the WUI and in actively managed parts of the landscape where most mechanical treatments could be focused (Figure 1). Conversely, treatment efficacy for Carbon, Water, and Landscape Integrity, was highest in middle and upper elevation wilderness and roadless areas (north and west parts of the landscape), where wildfire as a treatment was only possible. Forest health was best optimized at mid-elevations where the landscape was more successionally heterogeneous, heavily forested, and distance to seed source after disturbances tended to be shortest.

When taken together, Overall benefits were well-distributed throughout the landscape (Figure 1). They were not easily characterized by ownership or biophysical setting (Figures 2, 3; Supplementary Figures S5, S6). Instead, they were determined by a combination of direct treatment effects and indirect neighborhood benefits associated with reductions in severe wildfire behavior (Figures 4, 5). Economic benefits were concentrated in actively managed forests, especially in private industrial forests managed exclusively for timber production. These private industrial timber lands were most abundant in the central and southern portions of the landscape, in areas dominated by mixed-conifer forests (both moist and dry) at mid to low elevations (Supplementary Figure S1). Potential treatment benefits Overall and among Social and Ecological topics were also greatest in mid-elevation mixed conifer forests (Figures 6, 3), where closer examination revealed differences in the land ownerships (hence, treatment types) that produced the greatest benefits for these topics (Figure 6; Supplementary Figure S6). The Social topic category included secondary topics that required tradeoffs such as Carbon storage and Wildfire risk reduction, where high SOE scores associated with aggressive wildfire risk reduction were offset by the reduced capacity for carbon storage. These tradeoffs created a somewhat muddied map where high SOE scores were found across all four management zones (including untreated areas; Figures 1, 6). The Ecological topic area also included Forest health and Landscape integrity categories, whose SOE scores were greatest in patches that experienced non-stand replacing wildfire or were treated with adaptation treatments.

Much of the ecological functioning in Social and Ecological topic areas (e.g., carbon storage, snowpack retention) resided in federal wildlands where mechanical treatments were not applied; wildfire therefore played the primary role in rendering benefits among these topics. As such, treatments not only delivered important proximal effects within treated patches, but also had significant distal impacts on forest, fuels, and wildfire contagion (neighborhood benefits) in untreated areas.

Tradeoffs and synergies were evident among all topics. SOE scores were strongest at the Primary Topic level, with scores averaging out to result in more intermediate values when we examined Economic, Social, Ecological, and Overall priority areas (Figure 1). The reduction in SOE indicates the presence of tradeoffs, which are inevitable with such diverse topic areas. Areas of alignment did exist, however, primarily in mid-elevation areas where treatments were concentrated and most patches were treatable (Figure 1; Supplementary Figure S1). This is where treatment efficacy was highest because forests were most responsive to forest adaptation treatments (Figure 1), and where neighborhood effects were strongest (Figure 4).

Clearcutting in private industrial timber lands produced the strongest benefits for Economic topic areas, but as would be expected, these treatments were less effective at producing improved benefit scores for Social and Ecological topics. All treatments reduced wildfire risk, but we did not observe that this effect was dependent on treatment intensity (clearcutting versus restorative thinning). Private industrial lands delivered similar levels of wildfire risk reduction as did nearby public lands treated with fuel-reduction treatments, but the clearcut treatments applied on private lands were less effective at producing benefits among other topic categories (Figure 6). Industrial plantations have a place on the forested landscape where fiber production and economic returns are the primary management objective, but these results revealed that adaptation-oriented treatments were better at accomplishing wildfire risk reduction in conjunction with diverse ecosystem benefits.

Forest adaptation treatments aimed at wildfire risk reduction, increasing successional heterogeneity, and restoring landscape resilience produced the intended effects, generally reducing wildfire risk and increasing benefits for the surrounding patches. Because of this, the greatest levels of treatment synergy across topic areas were often observed not in treated patches, but in patches that existed within proximity to those that were treated. These untreated patches benefited from the reduction in fire spread and severity offered by adjacent treated areas while retaining higher levels of carbon, resulting in higher Social, Ecological, and Overall benefits scores (Figure 6).

The existence of neighborhood benefits was a key finding, demonstrating the need to take a holistic and multi-scale approach to landscape restoration and adaptation. This provides evidence that the strategic placement of treatments can effectively impede fire flow (e.g., Stevens et al., 2016), but further work will be necessary to determine how to optimize the spatial allocation of treatments to maximize these potential neighborhood benefits among multiple topic areas. Treating individual patches resulted in relatively intuitive treatment effects within patches, but the more widespread effects were the ways that treatments moderated wildfire activity on over half of the landscape that was not treatable by mechanical methods (Figures 4, 5). This point is especially salient in large, wildland-dominated landscapes that are commonplace throughout the intermountain west. Landscape resilience is not simply the sum of treatment effects in treated areas. True ecological resilience will require more holistic thinking about dynamic disturbance regimes, restabilizing patterns at large to small scales, and re-establishing the ecological feedback mechanisms that operate at fine, meso-, and broad-scales (sensu Hessburg et al., 2019). Such information is crucial as efforts to increase the pace and scale of management across the western US continue.

Our results further suggest that monitoring of treatment impacts needs to consider not only proximal patch-level impacts of treatments but also broader neighborhood effects as treated areas begin to mount over space and time. Direct evidence of landscape-level benefits of treatments are difficult to assess in situ, but simulation models are particularly adept at capturing these dynamics and showing how treatments across a fraction of the landscape can influence broader landscape-level patterns and processes (Stevens et al., 2016).

A feature of our modeling approach is that treatment in one patch will influence dynamics in neighboring patches. As such, the benefit scores that we generated do not correlate perfectly with “priority for restoration,” as many of the areas with high benefit scores are themselves untreatable. And in treatable stands, the benefit score cannot be solely attributed to the treatment applied in that stand, as it was not possible to differentiate direct treatment effects and neighborhood benefits in treated stands. Instead, these benefit scores should be considered as meso-scale information about where on the landscape the greatest potential for improving resource benefits exists. More tactical prioritization and field evaluation methods can be used to determine the sequence of treated patches and to inform optimal treatment staging.

Our simulation methods revealed several tradeoffs and synergies among ecosystem functions. Wildfire and Carbon were hyperdispersed in ordination space, reinforcing the idea that managing for wildfire risk reduction and carbon storage requires tradeoffs (Figure 7). Surprisingly, Water and Wildfire were misaligned as well. One may have expected more synergy between Water and Wildfire as both should have been benefited by the density reduction associated with forest adaptation treatments (Lundquist et al., 2013; Vogler et al., 2015; Furniss et al., 2022b), but this misalignment reflects a spatial segregation of these two topics on the landscape. High treatment efficacy for wildfire risk reduction occurred in low and middle elevations, yet these areas had low treatment efficacy for Water because these low elevation areas are where snowpack is projected to experience significant declines (Mote et al., 2018; Supplementary Figure S3).

Carbon storage was aligned with Landscape Integrity and Forest Health, as all three of these topics valued old trees and high patch biomass. Economics and Sustainable Biomass were also well aligned, landing near the Wildfire topic but at slightly higher elevations. This grouping in the NMDS ordinations confirms the known potential for synergy between wildfire risk reduction and economically viable thinning treatments (Ager et al., 2016).

Neither watershed area nor elevation were related to overall treatment efficacy (Figure 2). The most likely explanation for this is that there were spatial tradeoffs within the treated landscape that balanced overall treatment gains related to the untreated landscape (Figure 1). Some areas were enhanced while other areas were hampered, and these divergent SOE scores averaged out when we examined overall benefits as a function of watershed size and elevation. Treated area (both total and proportion) did increase overall treatment efficacy, and our results suggest that treating over 2,000 ha and at least 20% of the area within an individual HUC12-level watershed was necessary to produce even minimal positive treatment benefits (Figure 2). This is consistent with other studies that have found treatment rates of 20–40% are needed to mediate wildfire spread and severity on large landscapes (Finney, 2007; Povak et al., 2023). Area burned was not related to treatment efficacy, likely because area burned included low-, moderate-, and high-severity fire, which would yield divergent impacts conferred by fire.

One source of uncertainty that we were not able to examine in this study is the relative influence of different management and future climate scenarios on future ecosystem function. Comparing several scenarios with a spatial DST yielded an overwhelming amount of information. To present the results of multiple management scenarios, we prepared a companion study (Furniss et al., 2024) that distilled results for each pathway group, enabling us to evaluate differences between management scenarios over time. In another study, Maxwell et al. (2022) explored the relative influence of different climate scenarios on future trajectories using a similar LANDIS-II model application. In this study, we isolated a single climate projection and a single management scenario to make our results digestible, enabling us to focus on the spatial variability in treatment efficacy and neighborhood benefits associated with treatments.

Integrating potential climate change impacts into strategic landscape planning will be key to help managers make climate informed solutions and determine where investments can be made on the landscape with the highest likelihood of sustained success. While many climate-informed management frameworks have been developed (e.g., Schuurman et al., 2022), few offer the ability to analyze tradeoffs associated with treating forest patches versus allowing natural disturbances to drive forest development and wildfire dynamics. Here, we parsed the problem of identifying treatment needs into three facets: where are resources currently deficient (i.e., current conditions), what is the trajectory of a given resource benefit over time under climate change and natural disturbances (i.e., future trajectory), and where is there evidence that treatments can improve conditions compared to a no-treatment future (i.e., treatment efficacy). This information can be used to partition the landscape into landscape priorities. For example, areas that show improvement over time under natural disturbances can be assigned a low priority for treatment. Conversely, areas that show a steady decline in ecosystem service provisioning over time can be assigned higher treatment priority, with the highest priority assigned to areas where management investments are projected to be most effective. Povak et al. (2024) provide a quantitative framework for evaluating current and potential future conditions using a similar simulation modeling platform. Our future work will explore how integrating treatment efficacy into this framework can further assist management in targeting treatment areas that can sustain ecosystem functions, where they are currently operable but declining, and where treatment has the highest potential to improve conditions over time.

Landscape simulation models offer a way to mechanistically represent a multitude of social-ecological processes shaping forest ecosystems over large spaces and long timespans. By coupling such a model with a distributed hydrology model, we developed novel methodology for simulating the effects of vegetation dynamics, management actions, and climate effects on future snowpack and streamflow dynamics. We synthesized the myriad of data layers resulting from these complex models using a spatial decision support tool to assess future trajectories and potential treatment benefits among 40 different metrics comprising seven Primary Topics areas representing concepts of significant social-ecological value and concern.

Wildfire area burned was projected to increase over time, and this trend was only partially offset by forest adaptation treatments. Overall treatment efficacy was highest in dry and moist mixed conifer forests on actively managed federal lands, an unsurprising result that lends credibility to the modeling framework. Treatments had strong neighborhood benefits, where fire spread and severity were reduced not only in treated patches but in adjacent patches as well. Tradeoffs and synergies were evident in maps of treatment efficacy, with the greatest potential treatment efficacy being found in mid-elevation areas where future conditions were projected to be somewhat worse, but where these trends were mild enough that treatments were able still confer significant benefits. Ordinations provided further evidence for potential treatment synergy, and they revealed surprising tradeoffs as well, primarily due to the high degree of spatial heterogeneity in ecosystem functioning within our large study landscape. These results contribute to improved understanding of synergies and tradeoffs linked to adaptation and restoration efforts in fire-prone forests, and may be used in strategic planning to guide where management may be most effective for building resilient, climate-adapted social-ecological landscapes.

Publicly available datasets were analyzed in this study. This data can be found here: all datasets used in this manuscript are publicly available via the citations provided throughout the manuscript.

TF: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing. NP: Conceptualization, Writing – review & editing, Methodology, Supervision. PH: Conceptualization, Funding acquisition, Supervision, Writing – review & editing, Methodology. RS: Conceptualization, Methodology, Writing – review & editing. ZD: Formal analysis, Methodology, Writing – review & editing. MW: Conceptualization, Methodology, Supervision, Writing – review & editing, Funding acquisition.