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[Display omitted] • The effects of near-natural restoration on post-fire forest in the wildland-urban interface were quantified. • Near-natural restoration increased post-fire forest biomass and biodiversity more than natural succession. • Near-natural restoration shortens the evolution time of fire trails to mature forest. • Near-natural restoration can accelerate post-fire forest recovery in the wildland-urban interface. Forests in the wildland-urban interface (WUI) are of high value but vulnerable to wildland fires due to abundant fire ignitions and flammable forest fuels. Restoring the post-fire Wildland-Urban Interface (WUI) forest landscape is of utmost importance in order to maintain ecosystem service provision. The near-natural restoration strategy is widely employed in vegetation restoration as it enables the formation of healthy, stable, and diverse artificial mixed forests that resemble natural forests. To evaluate the long-term effects of near-natural restoration on the WUI forest landscape, which are largely unclear, we used a wildfire in 2019 near Shenyang City in northeast China as an example and investigated the post-fire forest dynamics under two different scenarios (i.e., natural succession and near-natural restoration) based on the forest landscape model. The results demonstrated that near-natural restoration can significantly accelerate the restoration process in terms of forest biomass, species biodiversity, and age structure. Under the near-natural restoration scenario, the biomass of the burned area can be quickly restored within 20 years after the fire. At the species level, the biomass and proportion of pioneer tree species such as Pinus tabuliformis and Robinia pseudoacacia decreased under the near-natural restoration scenario, while other species started to increase. Then post-fire near-natural planting accelerated the restoration of forest biodiversity, by 2070, the Shannon–Wiener index was predicted to be 1.49 under natural succession and remained at 2.02 under near-natural restoration. In terms of age structure, near-natural restoration shortens the recovery time of fire trails to mature forests. In summary, near-natural restoration accelerates forest recovery in post-fire WUI areas. Our results highlighted the impact of near-natural restoration on forest conservation to inform post-fire forest planning and management practices. [ABSTRACT FROM AUTHOR]

• Accelerated restoration with prescribed fire restored old growth forest structure. • Resource objective wildfire with accelerated restoration led to net zero emissions. • Using resource objective wildfire without restoration reduced summer emissions. • Maximizing forest carbon in the short-term reduced carbon in fire-resistant trees. • Short-term tree removal for forest restoration maximized long-term carbon storage. Wildfire futures and aboveground carbon (C) dynamics associated with forest restoration programs that integrate resource objective wildfire as part of a larger treatment strategy are not well understood. Using simulation modeling, we examined alternative forest and fuel management strategies on a 237,218-ha study area within a 778,000-ha landscape that is a high priority target for federal restoration programs. We simulated two wildfire management scenarios combined with three levels of conventional forest restoration treatments over 64 years using a detailed landscape disturbance and succession model developed in prior work. We found accelerated forest restoration used in concert with resource objective wildfire was the most effective at returning old growth forest structure, while stabilizing aboveground C stocks and restoring the fire return interval to its historic range of variation. In scenarios without forest restoration, the continued practice of resource objective wildfires during shoulder fire seasons reduced summer emissions in a negative feedback loop. In the short term, scenarios without forest restoration increased live tree C, but also increased the likelihood of C loss during wildfire activity driven by extreme fire weather. We found scenarios most effective at restoring fire-excluded pine forests to their historical old growth conditions came at a short-term cost of lost C, but with the long-term benefit of substantially increasing fire-resistant live tree C. Our results inform how local decision making can best balance competing goals of sequestering C, and stabilizing C stocks in frequent-fire pine forests using the principles of local fire ecology to restore and maintain old growth forest structure. [ABSTRACT FROM AUTHOR]

Forest landscape models (FLM) are widely used for simulating forest ecosystems. As FLMs have become more mechanistic, more input parameters are required, which increases model parameter uncertainty. To better understand the increased mechanistic detail provided by LANDIS-II/PnET-Succession, we studied the effects of parameter uncertainty on model outputs based on three different approaches. Global sensitivity analyses summarized the influence of each parameter, a local sensitivity analysis determined the magnitude of and degree of nonlinearity of variation in model outputs alongside variation in individual parameters, and a regression tree analysis identified hierarchical relationships among and interaction effects between parameters. Foliar nitrogen, maintenance respiration, and atmospheric carbon dioxide concentration were the most influential parameters in the global analysis. Knowing where parameter influence is concentrated will help model users interpret results from LANDIS-II/PnET-Succession to address ecological questions and should guide priorities for data acquisition. • Mechanistic forest landscape models increasingly require more input parameters. • We quantified LANDIS-II/PnET-Succession global and local parameter uncertainty. • Foliar nitrogen, maintenance respiration, and carbon dioxide was highly influential. [ABSTRACT FROM AUTHOR]

• A model coupling framework was developed to quantify tree species' migration. • Tree species' altitudinal migration cannot keep pace with climate changes. • Divergent responses of tree migration due to individualistic life history. • Life history traits are essential when predicting future tree migration. Climate change is projected to cause rapid elevational migration of mountain plants. However, it is poorly understood the direction and magnitude of elevational range shifts across species because species' life history traits are highly individualistic. Species with limited dispersal ability, reproductive rate, and ecological generalization may hardly expand into new regions under climate change. Therefore, such species' shifts may not keep pace with climate change. We used a new framework for coupling a forest ecosystem model (LINKAGES) and a landscape model (LANDIS PRO), that accounted for climate change, population dynamics, and species' life history traits to predict tree species' migrations. We quantified the velocity of tree migration under different climate scenarios. We further investigated the effects of climate change and life history traits on tree species' elevational migration. We found climate change accelerated the upward shifts at the optimum elevation and the leading edge, and limited the downward migration capacity of tree species at optimum elevation and the rear edge. The velocities of tree species' elevational shifts (usually < 1.5 m/year) lagged behind those of climate change (about 25 m/year) under the climate change scenario. Range shifts at the leading edge, optimum elevation, and rear edge tended to be associated with temperature, precipitation, and thermal tolerance. However, species' drought tolerance, shade tolerance, and seed dispersal ability had little effect on the velocity of simulated range shifts. Our results suggest that wide variation in tree range shift is likely driven by individual species' life history traits in response to interacting environmental factors. This study underscores the importance of understanding the role of species' life history traits when predicting future tree species' distribution. [ABSTRACT FROM AUTHOR]

• Formal methods for calibration/corroboration of forest landscape models (FLM) needed. • Developed a novel site-level framework to tune and evaluate forest dynamics for a FLM. • Equivalence testing compares model predictions to permanent national inventory plots. • Approach shows wider local variability range relative to landscape summary statistics. • Results increased confidence in the new density succession extension for LANDIS-II. Forest landscape simulation models (FLM) have been extensively used for projecting ecosystem dynamics and carbon fluxes. However, more guidance and methods for formal calibration and corroboration of FLMs are needed to ensure higher fidelity results from these models. We developed a novel systematic methodology for calibrating and corroborating a FLM at the grid-cell scale using empirical estimates from the national forest inventory in the United States (US) which uses an equal probability sample design. We illustrate our approach by using the Forest Inventory and Analysis (FIA) data from the US Department of Agriculture Forest Service across the state of Wisconsin to represent initial site conditions and calibrate parameters for the Density Succession extension of the LANDIS-II model, formally coupled with an iterative model corroboration stage focused on the growth and internal competition component of the model at a site-scale using plot remeasurement data that span 20 years. We used a formal equivalence testing approach to compare model predictions to empirical estimates for permanent sample plots. We found that the model performed well, with 21 out of 30 species demonstrating equivalence in the estimator used to characterize basal area following initial parameterization. Upon calibration of the estimator, the nine species not initially equivalent all showed reduced bias, with five of the species ultimately passing the equivalence test. The species that did not achieve equivalence through calibration were generally the least abundant ones. These results have increased our confidence that the Density Succession algorithms were well implemented within the model and provides a useful additional succession option within the LANDIS-II framework. We believe this methodology can capture a wider range of local scale variability compared to previous methods conducted at a landscape level using summary statistics. The iterative nature of the method also gives an opportunity for learning about the model components and the reference plot data. Our approach will help researchers using landscape simulation models to follow a replicable framework for corroborating their results and prioritize forest management activities and land use planning, which can help reduce external pressures on forests and help mitigate climate change effects. [Display omitted] [ABSTRACT FROM AUTHOR]

The recording and simulation data of forest landscapes are massive, high-dimensional, and abstract, requiring intuitive representation. 3-D visualization is an efficient tool to comprehend possible landscape changes generated by real-world or forest landscape models. Based on current advantages of game engines (realism and convenience), we developed a platform for 3-D visualization of forest landscapes (FLV). FLV streamlines multiple software and programming languages to break barrier between geographic data and game engine and transforms outputs of forest landscape models into visualization parameters. Compared with previous 3-D visualizations, FLV has better realism, efficiency, and navigation. We used simulation data of post-volcanic eruption forest landscape recovery in Changbai Mountain as a case study and demonstrated functionalities of FLV. FLV can visualize seamlessly from individual tree to forest stand and landscape scales, and from individual year to decades and centuries temporal scales. It offers potential solutions for the digital representation of complex environmental change. • Development and application of a 3-D forest landscape visualization platform. • The platform has high realism and efficiency, friendly interaction, and versatility. • The platform has been applied to post-volcanic eruption forest landscape visualization. • Provides an intuitive and stereoscopic visualization solution for the dynamics of the forest landscape in time and space. [ABSTRACT FROM AUTHOR]

Forest landscape models (FLMs) are widely used to examine the influence of disturbances on long-term and broad-scale forest ecosystem dynamics. However, FLMs are not well-suited to simulating some types of management or disturbance regimes, including land-use change. Consequently, there are situations in which a researcher may wish to estimate the timing and location of events externally, either from a different model, empirical observations, or some other source, and then incorporate them into an FLM. We present Land Use Plus (LU+), an extension for the LANDIS-II FLM that allows users to integrate externally-developed, spatially and temporally explicit representations of land use or other disturbance into simulations. LU+ allows users to model the proximate effects of these events on forest composition and biomass, as well as subsequent dynamics, including tree establishment and the potential for future management. LU+ will significantly increase the breadth of research questions for which LANDIS-II may be appropriately used. [ABSTRACT FROM AUTHOR]

Global change poses considerable challenges for ecosystems and their managers. To address these challenges it is increasingly clear that a coupled human and natural systems perspective is needed. While this science has advanced greatly in recent years, its mainstreaming into operational ecosystem management has proven to be difficult. One aspect complicating the application of a coupled human and natural systems approach has been the lack of tools that are simultaneously able to accommodate the complexities of ecological and social systems. However, neglecting their full interactions and feedbacks could lead to either an overestimation of the systems’ vulnerability to global change (e.g., where the social adaptive capacity is disregarded in assessments based solely on ecosystem models), or to the pretense of stability (e.g., where the dynamic responses of ecosystem processes to environmental changes are neglected in models of the social system). These issues are of particular importance in forest ecosystems, where human interventions affect ecosystem dynamics for decades to centuries. In order to improve the assessments of future forest trajectories, our objectives here were (i) to operationalize and describe the coupling of human and natural systems in the context of landscape-scale forest ecosystem management, and (ii) to demonstrate simulated interactions between the social and ecological spheres in the context of adaptation to a changing climate. We developed an agent-based model accounting for different spatial (stand and management unit) and temporal (operational and strategic) levels of forest management decision making and coupled it with the forest landscape simulator iLand. We show that the coupled human and natural systems model is autonomously able to reproduce meaningful trajectories of managed mountain forest landscape in Central Europe over the extended period of multiple centuries. Experimenting with different decision heuristics of managing agents suggests that both passive (reactive) and active (prospective) adaptive behavior might be necessary to successfully stabilize system trajectories under rapidly changing environmental conditions. Furthermore, investigating multi-agent landscapes we found that diversity in managerial responses to environmental changes increases the heterogeneity on the landscape, with positive effects on the temporal stability of ecosystem trajectories. We conclude that an integrated consideration of human and natural systems is important to realistically project trajectories of managed forests under global change, and highlight the potential of social–ecological feedbacks and heterogeneity in stabilizing the provisioning of ecosystem services in a changing environment. [ABSTRACT FROM AUTHOR]

Forest landscape models (FLMs) have become a valuable tool for projecting broad-scale forest dynamics, but incomplete knowledge about model behavior can make parameterization challenging and outcomes unreliable. FLMs generally model forest growth as a set of interacting processes, and, consequently, predictions can be influenced by process or parameter uncertainty. A sensitivity analysis can potentially help identify sources of uncertainty, but if it does not use global measures of sensitivity nor consider that sensitivity in a process-based model is likely time-dependent, results could be misleading. Our aim was to evaluate the sensitivity of nine key parameters when predicting live aboveground biomass (AGB) with the widely used FLM, LANDIS-II. To fully explore parameter interactions and nonlinear model behavior, we selected a range of parameter values based on LANDIS-II applications in North America that was considerably wider than in previous local sensitivity analyses. Our results showed commonalities with previous studies, which concluded the maximum allowable biomass and maximum annual net primary productivity specified for a species were most influential when predicting AGB. In contrast to earlier work, we also clearly demonstrated how relative importance was time-dependent for all but the least important parameters. Interactions between parameters and with simulation duration generated substantial variability in AGB and number of cohorts established. Results will improve future calibration efforts and may offer insight into opportunities for possible model refinements. This study also suggests, however, that parameters which cannot be calibrated based on empirical data will continue to be a major source of model uncertainty. [ABSTRACT FROM AUTHOR]

Forest landscape models are effective tools for exploring the effects of long-term and large-scale landscape processes such as seed dispersal, fire, and timber harvest. These models have been widely used for about a decade, and although significant advances in theory and technology have been incorporated into their development, evaluating the veracity of simulated results from forest landscape models remains challenging. In this study, we evaluated simulated forest succession and the effects of simulated fire and harvest by a spatially explicit forest landscape model (LANDIS PRO), initialized using forest inventory data (second and third tier data from years 2000 and 2010). Our results suggested that the initialized forest landscape constructed from the year 2000 forest inventory data adequately represented the forest composition and structure from that year. The simulated density and basal area from year 2010 adequately represented the forest inventory data from that year at landscape scales. Our results indicated that the simulated fire and harvest effects were comparable to the field data (measured density and basal area). Results in this study quantified the near-term reliability and confidence of the model as well as prediction uncertainties. [ABSTRACT FROM AUTHOR]

Future climate conditions will alter the frequency and intensity of typhoons. Thus, post-windthrow management, which can enhance tree recovery in wind-disturbed forests with dense understory species, is essential for sustainable forest management to adapt to climate change. This study explores management options that can recover the above-ground biomass (AGB) and tree species composition after windthrow damage even under climate change. A case study area was established in the Oshima–Hiyama National Forest in southern Hokkaido, northern Japan, which were damaged by typhoons in late August 2016. We incorporated the understory species Sasa kurilensis as understory vegetation into the LANDIS-II Net Ecosystem Carbon and Nitrogen Succession extension v6.3 model to simulate the outcome of tree establishment under climate change. AGB recovery up to the year 2100 at 1,753 damaged grid cells was simulated for the Intergovernmental Panel on Climate Change representative concentration pathway (RCP) 2.6 and 8.5 scenarios. Different post-windthrow management cases were designed by varying the treatment of fallen trees and the types of trees planted. The results demonstrated that salvage logging and planting successfully recovered the AGB by 2050 at the landscape scale regardless of the climate change scenario, whereas leaving fallen trees in the damaged site or salvage logging only did not facilitate the recovery of AGB. Leaving fallen trees in damaged grid cells as ecological legacies recovered the AGB only in damaged grid cells with a sufficient number of advanced seedlings of adequate types of species irrespective of the climate change scenario. The decreasing water equivalent of snowpack in the RCP scenarios caused Sasa kurilensis mortality and promoted the recovery of AGB of trees. The dominant species recovered in natural forests, which experienced either salvage logging or leaving trees in the damaged site, varied among climate change scenarios. The warmer climate condition facilitated the recovery of Fagus crenata by 2100. These results can help designing a robust forest recovery even in uncertain future climate. [ABSTRACT FROM AUTHOR]

Ecological models built on phenomenological relationships and behavior of the past may not be robust under novel conditions of the future because global changes are producing environmental conditions that forests have not experienced historically. We developed a new succession extension for the LANDIS-II forest landscape model, PnET-Succession, to simulate forest growth and succession using physiological first principles. PnET-Succession integrates the tree physiology model PnET-II with the existing LANDIS-II Biomass Succession extension. PnET-Succession simulates the competition of tree species cohorts for water and light as a function of photosynthetic processes driven by foliar nitrogen. Competition for water is simulated on each grid cell through a dynamic soil-water balance that receives precipitation and loses water through runoff, consumption in photosynthesis, and evapotranspiration. Competition for light is modeled by tracking solar radiation through canopy layers according to a standard Beer-Lambert formula. PnET-Succession requires average monthly photosynthetically active radiation, atmospheric CO2 concentration, temperature and precipitation as inputs. The new extension also dynamically calculates species establishment probabilities in each time step as a function of water and radiation stress. We calibrated PnET-Succession to biomass and LAI measurements from the Duke Experimental Forest in North Carolina (USA) and tested the calibrated model against data from the Green Ridge State Forest in Maryland. The new extension shows considerable promise for studying forest response to climate change, including changes in carbon stocks. [ABSTRACT FROM AUTHOR]

Most predictions of tree species' distributions at broad spatial scales are based only on one possible characterization of environmental heterogeneity. Evaluating the effects of multiple heterogeneities on predictions may help in quantifying prediction uncertaintie. In this study we investigated the effects of three levels of environmental heterogeneity on landscape-scale predictions. In addition, we analyzed how seed dispersal and interspecies competition contributes to prediction uncertainty. We used a coupled ecosystem and landscape modeling approach to predict tree species' abundance at the landscape scale. We designed multiple-species and single-species scenarios, each with the three levels of environmental heterogeneity. Our results showed the importance of considering environmental heterogeneity when predictioning tree species' abundance. For early-successional species landscape-scale predictions differed significantly among heterogeneity levels. For late-successional species, prediction uncertainties based on different heterogeneity levels were comparatively low. Seed dispersal may be a source of variation in predictions, whereas interspecies competition may reduce such variation. [ABSTRACT FROM AUTHOR]

Highlights: [•] TreeMig-Aval is a spatially explicit forest model coupled with an avalanche module. [•] The simulated avalanche-forest feedback depends strongly on environmental factors. [•] We evaluated sensitivity to slope steepness, additional disturbances and temperature. [•] Model complexity could not be reduced by omitting the feedback or forest dynamics. [•] Explicit simulation of avalanche-forest feedbacks is crucial for treeline simulation. [Copyright &y& Elsevier]

Climate change could alter species composition, with feedback on fire disturbances by modifying fuel types and loads. However, the existing fire predictions were mainly based on climate-fire linkages that might overestimate the probability and size of fire disturbances due to simplifying or omitting vegetation feedback. We applied a model-coupling framework that combines forest succession, climate-fire linkages, and vegetation feedback to predict burned area, aboveground biomass, and species composition of boreal forests in Northeast China under climate change conditions. Results showed that climate change and fire would favor the recruitment of deciduous species, but these species need a long-time to replace the existing coniferous species. Burned area would increase with climate change. Climate change, historical and future fire disturbances affect aboveground biomass by altering tree mortality and regeneration. Further studies should address strategies for altering species composition through forest management practices to adaptation climate change and reduce carbon losses from fire. [Display omitted] • We applied a model-coupling framework to simulate the response of boreal forest to climate-fire-vegetation interactions. • Climate change and fire would favor the recruitment of drought-tolerant species. • Climate change would significantly increase the burned area of boreal forests. • Climate change, historical and future fire disturbance would affect aboveground biomass of boreal forests. • It is necessary to implement forest management strategies to alter species composition for reducing burned area and carbon losses from catastrophic fires. [ABSTRACT FROM AUTHOR]

• Remnant trees location matters more than abundance in post-disturbance recovery. • Dispersed distribution of remnant trees promotes faster forest recovery. • Abundance of remnant trees has greater roles for late-seral tree species. • Strategic spatial configuration is important in post-disturbance tree planting. Remnant trees have great ecological importance in post-disturbance forest landscape recovery which is strongly affected by their spatial configuration. Location and abundance, the two key attributes of spatial configuration, often act jointly, but their relative roles have rarely been investigated. Here, we spatially reconstructed a 300-year time series (1710–2010) of the post-volcanic-eruption forest landscape in Changbai Mountain. We designed a factorial experiment for location (expected vs random) and abundance (expected vs 50–100 % increase) of remnant trees to quantify their relative effects on forest recovery at landscape level and by species. Results showed that random location and increased abundance had significantly higher total basal area than that with the expected location and expected abundance in most simulation periods (P < 0.01). Location of remnant trees had greater effects on total basal area (more than twofold) and landscape pattern (measured by aggregation index) than abundance. Meanwhile, abundance of remnant trees displayed larger effects (∼or > 50 %) on the importance of coniferous species and large-seeded hardwood species. Abundance also played a greater role on the coverage area of the late-successional species. Our study could provide important management implications that planting individual patches in a dispersed pattern and increasing the abundance of late-successional species can best facilitate the recovery in a disturbed forest landscape. [ABSTRACT FROM AUTHOR]

Although forest landscape models (FLMs) have benefited greatly from ongoing advances of computer technology and software engineering, computing capacity remains a bottleneck in the design and development of FLMs. Computer memory overhead and run time efficiency are primary limiting factors when applying forest landscape models to simulate large landscapes with fine spatial resolutions and great vegetation detail. We introduce LANDIS PRO 6.0, a landscape model that simulates forest succession and disturbances on a wide range of spatial and temporal scales. LANDIS PRO 6.0 improves on existing forest landscape models with two new data structures and algorithms (hash table and run-length compression). The innovative computer design enables LANDIS PRO 6.0 to simulate very large (>108 ha) landscapes with a 30-m spatial resolution, which to our knowledge no other raster forest landscape models can do. We demonstrate model behavior and performance through application to five nested forest landscapes with varying sizes (from 1 million to 100 million 0.09-ha cells) in the southern Missouri Ozarks. The simulation results showed significant and variable effects of changing spatial extent on simulated forest succession patterns. Results highlighted the utility of a model like LANDIS PRO 6.0 that is capable of efficiently simulating large landscapes and scaling up forest landscape processes to a common regional scale of analysis. The programming methodology presented here may significantly advance the development of next generation of forest landscape models. [Copyright &y& Elsevier]

Abstract: Systematic approaches to conservation planning and management require spatially explicit information on the dynamics of multiple disturbance processes, but progress in providing such information has been limited to date. Here we examine the use of a spatially explicit model of forest dynamics (LANDIS-II), incorporating a range of ecological processes, to examine the impacts of different types of disturbance on a dryland forest landscape in Central Chile. The model was parameterized using spatial data and results of a field survey, in which 21 native tree species were recorded, and one invasive exotic (Acacia dealbata). Seven disturbance scenarios were simulated, with different combinations of fire, browsing and tree cutting. Model results indicated relatively little impact of disturbance on forest cover but substantial differences in forest structure, with relatively old-growth forest stands (>120years old) being virtually eliminated from the landscape in scenarios with both browsing and cutting. Tree species richness tended to be lower in those scenarios without disturbance, highlighting potential trade-offs between forest structure and species richness. Spread of A. dealbata was projected only to occur in the presence of fire when combined with browsing and/or cutting. These interactive effects of different forms of disturbance illustrate the value of process-based modeling approaches for exploring the spatial dynamics of multiple disturbance processes, but highlight the difficulty of identifying an optimum disturbance regime that would enable conservation objectives to be achieved. It is suggested that process-based models should form part of an analytical ‘toolkit’ to support the practical implementation of systematic conservation planning approaches. [Copyright &y& Elsevier]

Abstract: Forest landscape disturbance and succession models have become practical tools for large-scale, long-term analyses of the cumulative effects of forest management on real landscapes. They can provide essential information in a spatial context to address management and policy issues related to forest planning, wildlife habitat quality, timber harvesting, fire effects, and land use change. Widespread application of landscape disturbance and succession models is hampered by the difficulty of mapping the initial landscape layers needed for model implementation and by the complexity of calibrating forest landscape models for new geographic regions. Applications are complicated by issues of scale related to the size of the landscape of interest (bigger is better), the resolution at which the landscape is modeled and analyzed (finer is better), and the cost or complexity of applying a landscape model (cheaper and easier is better). These issues spill over to associated analyses that build on model outputs or become integrated as auxiliary model capabilities. Continued development and application of forest landscape disturbance and simulation models can be facilitated by (1) cooperative efforts to initialize more and larger landscapes for model applications, (2) partnerships of practitioners and scientists to address current management issues, (3) developing permanent mechanisms for user support, (4) adding new capabilities to models, either directly or as compatible auxiliary models, (5) increasing efforts to evaluate model performance and compare multiple models running on the same landscape, and (6) developing methods to choose among complex, multi-resource alternatives with outputs that vary over space and time. [Copyright &y& Elsevier]

Abstract: Geostatistical stochastic simulation is always combined with Monte Carlo method to quantify the uncertainty in spatial model simulations. However, due to the relatively long running time of spatially explicit forest models as a result of their complexity, it is always infeasible to generate hundreds or thousands of Monte Carlo simulations. Thus, it is of great importance to generate a relatively small set of conditional realizations capturing most of the spatial variability. In this study, we introduced an effective sampling method (Latin hypercube sampling) into a stochastic simulation algorithm (LU decomposition simulation). Latin hypercube sampling is first compared with a common sampling procedure (simple random sampling) in LU decomposition simulation. Then it is applied to the investigation of uncertainty in the simulation results of a spatially explicit forest model, LANDIS. Results showed that Latin hypercube sampling can capture more variability in the sample space than simple random sampling especially when the number of simulations is small. Application results showed that LANDIS simulation results at the landscape level (species percent area and their spatial pattern measured by an aggregation index) is not sensitive to the uncertainty in species age cohort information at the cell level produced by geostatistical stochastic simulation algorithms. This suggests that LANDIS can be used to predict the forest landscape change at broad spatial and temporal scales even if exhaustive species age cohort information at each cell is not available. [Copyright &y& Elsevier]

LANDIS is a cell-based spatially explicit forest landscape model designed to explore successional dynamics under natural and anthropogenic disturbances. Species age cohort (10-year cohort of a given tree species) information is required for each cell in LANDIS. However, providing such information for a landscape comprising millions of cells is challenging. In this study, a stand-based assignation (SBA) approach was developed to stochastically assign species age cohorts to each cell based on forest inventory data. As a probability-based approach, SBA will introduce errors in LANDIS input. In order to assess the effect of errors produced by SBA on LANDIS results, 20 Monte Carlo simulations were produced. For each species simulated in LANDIS, the recurrence frequency (RF) of the majority species age cohort (MSAC, the most frequently occurring species age cohort) from 20 Monte Carlo simulations were used to quantify the uncertainty in species age cohorts for individual cell. Average recurrence frequency (ARF) of the MSAC was used to quantify the overall uncertainty in species age cohorts at the cell level. For each species, the coefficient of variation (CV) for the percent area and an aggregation index for the 20 Monte Carlo simulations was used to quantify the uncertainty at the landscape level. Results showed that at the cell level, uncertainty was relatively low at the beginning of the simulation (ARF was larger than 10). Seed dispersal, seedling establishment, mortality, and fire disturbance caused uncertainty to increase with simulation year. The uncertainty finally reached an equilibrium state, where input errors in original species age cohorts had little effect on the simulation outcomes. At the landscape level, species percent area and their spatial patterns were not substantially affected by the uncertainties in species age structure at the cell level. Since the typical use of LANDIS is to predict the long-term landscape pattern change, SBA can be used to parameterize species age cohorts for individual cells. [Copyright &y& Elsevier]

• We incorporated regeneration on downed logs into LANDIS-II forest landscape model. • We revealed the long-term effects of post windthrow management on forest recovery. • Birch-dominated forests regenerated and persisted over 100 years at scarified sites. • Salvaging after a first windthrow delayed biomass recovery after a second one. • CWD-dependent species hardly recover after multiple windthrows in case of salvaging. Post-windthrow management delays forest biomass recovery by altering the situation of disturbance legacies and can change the species composition. Although the short-term effects of post-windthrow management have been well studied, we do not have enough knowledge about the long-term effects of post-windthrow management on species composition and biomass recovery. Those effects associated with an increase in the windthrow frequency are also unknown. Although forest landscape models can effectively evaluate these effects, conventional models do not represent the regeneration process on downed logs, which is essential for simulating forest succession. We focused on hemiboreal forests in northern Japan and aimed to (1) incorporate the regeneration process on downed logs into LANDIS-II, which is one of the most used forest landscape models; (2) evaluate the long-term effects of post-windthrow management on tree species composition and aboveground biomass recovery; and (3) evaluate the associated long-term effects of interactions between post-windthrow management and increased windthrow frequency. We incorporated the regeneration process on downed logs into LANDIS-II by regulating the probability of the establishment of species that depend on dead wood, such as spruce, according to the availability of well-decayed dead wood. The incorporation of this process resulted in simulations of trends in species composition and aboveground biomass recovery after post-windthrow management that were more accurate than those produced by the original model. In the modified LANDIS-II simulation, reductions in dead wood and advanced seedlings due to salvage logging had little effect on the tree species composition or aboveground biomass recovery; however, the complete destruction of advanced seedlings by scarification induced a delay in aboveground biomass recovery and a shift to birch-dominated forests that continued for 100 years. In addition, the reduction in dead wood due to salvage logging decreased the number of seedlings, especially of dead wood-dependent species, that established after windthrow. When the windthrow frequency doubled, this decrease in seedlings induced a delay in aboveground biomass recovery, and a substantial decrease in dead wood-dependent species biomass occurred after a subsequent windthrow event. However, after the second windthrow event and following scarification, the forest recovered in the same way as after the first windthrow because the destruction of advanced seedlings and understory plants, namely, dwarf bamboo (Sasa spp.), by scarification reset the site conditions. To conserve the species composition and aboveground biomass of hemiboreal forests under climate change, which is expected to increase windthrow frequency, salvage logging and scarification should be avoided. [ABSTRACT FROM AUTHOR]

Abstract: Over the last 20 years, we have seen a rapid development in the field of forest landscape modeling, fueled by both technological and theoretical advances. Two fundamental challenges have persisted since the inception of FLMs: (1) balancing realistic simulation of ecological processes at broad spatial and temporal scales with computing capacity, and (2) validating modeled results using independent, spatially explicit time series data. The paper discusses the current status and future directions regarding these two challenges. [Copyright &y& Elsevier]

Megafires are large wildfires that occur under extreme weather conditions and produce mixed burn severities across diverse environmental gradients. Assessing megafire effects requires data covering large spatiotemporal extents, which are difficult to collect from field inventories. Remote sensing provides an alternative but is limited in revealing post-fire recovery trajectories and the underlying processes that drive the recovery. We developed a novel framework to spatially reconstruct the post-fire time-series of forest conditions after the 1987 Black Dragon fire of China by integrating a forest landscape model (LANDIS) with remote sensing and inventory data. We derived pre-fire (1985) forest composition and the megafire perimeter and severity using remote sensing and inventory data. We simulated the megafire and the post-megafire forest recovery from 1985 to 2015 using the LANDIS model. We demonstrated that the framework was effective in reconstructing the post-fire stand dynamics and that it is applicable to other types of disturbances. • We presents a novel framework for spatially reconstructing the post-fire time-series of forest conditions. • The framework generates detailed stand attributes that can be validated against field inventory data. • The framework is highly scalable and can be applied to filling the gaps where inventory data are not available and to derive recovery trajectories across landscapes. • The framework is applicable to other types of disturbances such as harvest and windstorm. [ABSTRACT FROM AUTHOR]

• We built a new forest landscape management model that leveraged existing US Forest Service models. • The model was used to simulate wildfires and landscape restoration over 50 yrs. • High levels of management eliminated the restoration backlog in 20 years. • Wildfire activity was highly stochastic among replicate scenarios. • Management was more effective at reducing fire intensity compared to burned area. We integrated a widely used forest growth and management model, the Forest Vegetation Simulator, with the FSim large wildfire simulator to study how management policies affected future wildfire over 50 years on a 1.3 million ha study area comprised of a US national forest and adjacent lands. The model leverages decades of research and development on the respective forest growth and wildfire simulation models, and their integration creates a strategic forest landscape model that has a high degree of transparency in the existing user communities. The study area has been targeted for forest restoration investments in response to wildland fires that are increasingly impacting ecological conditions, conservation areas, amenity values, and surrounding communities. We simulated three alternative spatial investment priorities and three levels of management intensity (area treated) over a 50-year timespan and measured the response in terms of area burned, fire severity, wildland-urban interface exposure and timber production. We found that the backlog of areas in need of restoration on the national forest could be eliminated in 20 years when the treatment rate was elevated to a maximum of 3× the current level. However, higher rates of treatments early in the simulation created a future need to address the rapid buildup of fuels associated with understory shrub and tree regeneration. Restoration treatments over time had a large effect on fire severity, on average reducing potential flame length by up to 26% for the study area within the first 20 years, whereas reductions in area burned were relatively small. Although wildfire contributed to reducing flame length over time, area burned was only 16% of the total disturbed area (managed and burned with prescribed fire) under the 3× management intensity. Interactions among spatial treatment scenarios and treatment intensities were minimal, although inter-annual variability was extreme, with the coefficient of variation in burned area exceeding 200%. We also observed simulated fires that exceeded four times the historically recorded fire size. Fire regime variability has manifold significance since very large fires can homogenize fuels and eliminate clumpy stand structure that historically reduced fire size and maintained landscape resiliency. We discuss specific research needs to better understand future wildfire activity and the relative influence of climate, fuels, fire feedbacks, and management to achieve fire resiliency goals on western US fire frequent forests. [ABSTRACT FROM AUTHOR]