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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.

Context: Forest management practices that conserve biodiversity and maximize carbon sequestration under climate change are needed. Although post-windthrow salvage logging and scarification can reduce carbon dioxide (CO2) emissions within ecosystems by removing downed logs, they can greatly affect species composition. Additionally, salvage logging may increase CO2 emissions based on a cradle-to-grave analysis of salvaged wood. Objectives: We aimed to assess the effects of changes in climate, windthrow regimes and post-windthrow management on aboveground biomass, species composition, and carbon balance in the forest sector by combining forest landscape simulations and life cycle assessment (LCA). Methods: The study landscape is a 12,169 ha hemiboreal forest located in northern Japan. We simulated 115 years (2015–2130) of forest dynamics in 36 scenarios based on features of the climate, windthrow regime, and management using the LANDIS-II forest landscape model. CO2 emissions related to management and salvaged wood were estimated by LCA. Results: Increases in the windthrow area, which was more vulnerable to climate warming, caused a shift to temperate broadleaved forests and a decrease in aboveground biomass. These were accelerated by the removal of advanced seedlings and dead wood, which greatly reduced the recruitment of Picea species. The 115-year cumulative net CO2 absorption of the forest sector, including carbon balance within ecosystems and CO2 emissions estimated by LCA, greatly decreased due to salvage logging (maximum 81%) and scarification (maximum 114%). Conclusions: Leaving downed logs and advanced seedlings is recommended to conserve boreal conifers and carbon sinks and maximize net CO2 absorption under climate change. [ABSTRACT FROM AUTHOR]

Alpine forests in the eastern Tibetan Plateau are important ecological barriers in the upper reaches of the Yangtze River. However, due to continuous high-intensity harvesting, a large number of plantings, and the complete harvesting ban measures in recent decades, the forest tree species and age cohorts have become relatively homogenous, and the biodiversity and ecological functions have been reduced. To design effective forest management options to optimize forest structure and increase carbon sequestration capacity, Mao County in Sichuan Province was selected as the study site and six forest management options (harvesting, planting) of different intensities were tested using the LANDIS-II model to simulate and compare the differences in forest aboveground carbon sequestration rate (ACSR) between these options and the current management option over the next 100 years. Our results showed that (i) the different harvesting and planting intensities significantly changed the ACSR compared with the current management options; (ii) different communities responded differently to the management options, with the ACSR differing significantly in cold temperate conifers and temperate conifers but not in broad-leaved trees (P < 0.05); and (iii) a comprehensive consideration of forest management options at the species, community and landscape levels was necessary. Our results suggest that implementing a longer harvesting and planting interval (20 years) at the study site can maximize forest ACSR. This study provides an important reference for evaluating the ability of forest management options to restore forest ecological functions and increase carbon sequestration capacity and for selecting effective forest management programs in the eastern Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Adequate seed provenance is an important guarantee for the restoration of the mixed Korean pine (Pinus koraiensis) and broadleaf forest (MKPBF). However, the commercial harvest exclusion in natural forests has led to a sharp decline in economic income from timber. Given the economic value of Korean pine seeds, predatory seed harvesting (PSH) has become increasingly serious in the past 20 years and has significantly reduced the seed and seedling bank, which might seriously threaten the restoration and sustainable management of the MKPBF. How the historical PSH has affected the future of the MKPBF is unclear at the landscape scale. In this study, we quantified the effects of seed harvesting at the landscape scale by a forest landscape model LANDIS PRO, and then assessed the legacy effects of the historical PSH on the composition and structure of the MKPBF in the Xiaoxing'an Mountains, Northeast China. Our results showed that the historical PSH decreased the Korean pine basal area of all age cohorts, with an average decrease of 0.06 to 0.19 m2 ha−1 but insignificantly altered the age structure diversity of Korean pine throughout the simulation. Our results indicated that the historical PSH remarkably decreased the dominance of Korean pine by 11.1%, but significantly increased the dominance of spruce (Picea koraiensis and Picea jezoensis) and fir (Abies nephrolepis) by 3.8% and 4.5%, respectively, and had an insignificant effect on the other tree species over the simulation. We found that the historical PSH evidently changed the succession trajectories of the disturbed stands, which would result in the transition from the succession pattern dominated by Korean pine to that dominated by spruce and fir. The historical PSH decreased the importance value of Korean pine by 12.2% on average but increased it by 5.1% and 6.0% for spruce and fir, respectively, and resulted in an average 33.2% increase in the dissimilarity index compared with the initial state during the whole simulation period. Future forest management should strictly limit the intensity and rotation of seed harvesting to protect the seed provenance of Korean pine and consider how to ensure the recovery and sustainable management of the MKPBF through direct seeding or seedling planting. [ABSTRACT FROM AUTHOR]

1. To assess the impacts of climate change on vegetation from stand to global scales, models of forest dynamics that include tree demography are needed. Such models are now available for 50 years, but the currently existing diversity of model formulations and its evolution over time are poorly documented. This hampers systematic assessments of structural uncertainties in model-based studies. 2. We conducted a meta-analysis of 28 models, focusing on models that were used in the past five years for climate change studies. We defined 52 model attributes in five groups (basic assumptions, growth, regeneration, mortality and soil moisture) and characterized each model according to these attributes. Analyses of model complexity and diversity included hierarchical cluster analysis and redundancy analysis. 3. Model complexity evolved considerably over the past 50 years. Increases in complexity were largest for growth processes, while complexity of modelled establishment processes increased only moderately. Model diversity was lowest at the global scale, and highest at the landscape scale. We identified five distinct clusters of models, ranging from very simple models to models where specific attribute groups are rendered in a complex manner and models that feature high complexity across all attributes. 4. Most models in use today are not balanced in the level of complexity with which they represent different processes. This is the result of different model purposes, but also reflects legacies in model code, modelers' preferences, and the ‘prevailing spirit of the epoch’. The lack of firm theories, laws and ‘first principles’ in ecology provides high degrees of freedom in model development, but also results in high responsibilities for model developers and the need for rigorous model evaluation. 5. Synthesis. The currently available model diversity is beneficial: convergence in simulations of structurally different models indicates robust projections, while convergence of similar models may convey a false sense of certainty. The existing model diversity—with the exception of global models—can be exploited for improved projections based on multiple models. We strongly recommend balanced further developments of forest models that should particularly focus on establishment and mortality processes, in order to provide robust information for decisions in ecosystem management and policymaking. [ABSTRACT FROM AUTHOR]

The use of spatially interactive forest landscape models has increased in recent years. These models are valuable tools to assess our knowledge about the functioning and provisioning of ecosystems as well as essential allies when predicting future changes. However, developing the necessary inputs and preparing them for research studies require substantial initial investments in terms of time. Although model initialization and calibration often take the largest amount of modelers’ efforts, such processes are rarely reported thoroughly in application studies. Our study documents the process of calibrating and setting up an ecophysiologically based forest landscape model (LANDIS-II with PnET-Succession) in a biogeographical region where such a model has never been applied to date (southwestern Mediterranean mountains in Europe). We describe the methodological process necessary to produce the required spatial inputs expressing initial vegetation and site conditions. We test model behaviour on single-cell simulations and calibrate species parameters using local biomass estimations and literature information. Finally, we test how different initialization data—with and without shrub communities—influence the simulation of forest dynamics by applying the calibrated model at landscape level. Combination of plot-level data with vegetation maps allowed us to generate a detailed map of initial tree and shrub communities. Single-cell simulations revealed that the model was able to reproduce realistic biomass estimates and competitive effects for different forest types included in the landscape, as well as plausible monthly growth patterns of species growing in Mediterranean mountains. Our results highlight the importance of considering shrub communities in forest landscape models, as they influence the temporal dynamics of tree species. Besides, our results show that, in the absence of natural disturbances, harvesting or climate change, landscape-level simulations projected a general increase of biomass of several species over the next decades but with distinct spatio-temporal patterns due to competitive effects and landscape heterogeneity. Providing a step-by-step workflow to initialize and calibrate a forest landscape model, our study encourages new users to use such tools in forestry and climate change applications. Thus, we advocate for documenting initialization processes in a transparent and reproducible manner in forest landscape modelling.

Abstract Climate change has been observed to significantly influence forest growth, community composition, and species distribution ranges. These influences in turn will impose continuous impacts on forest production and carbon (C) storage potential. Forests in the subtropical China that are experiencing rapid regeneration and recovery may suffer multiple threats in the face of future climate change. Understanding how climate change may affect forest C sequestration and species dynamics over time will help formulate better management strategies for maintaining forest productivity and biodiversity. Here, we used a forest landscape model (LANDIS‐II) to evaluate the long‐term effects of current business‐as‐usual (BAU) management and climate projections (current, RCP4.5, and RCP8.5 climate scenarios; IPCC representative concentration pathways [RCPs] scenarios) on above‐ and belowground forest C storage and tree species dynamics in the Sangzhi County in the subtropical China. Our simulations showed a fast‐growing period of forest total C in the first 70 yr, regardless of climate regime. Moderate climate change (RCP4.5 climate scenario) increased soil organic carbon (SOC) (12%) and detrital C (16%) but reduced live C (5%), contributing to a slight augment of 3% in forest C storage compared to the control climate, while severe climate change (RCP8.5 climate scenario) decreased SOC (16%), detrital C (27%), and live C (12%), resulting in a dramatic reduction of 14% in forest C storage, primarily because severe warming‐induced water stress restrained species establishment and regeneration in temperature‐sensitive areas like the lower elevations. Meanwhile, nature reserves in the higher elevations could act as “safe islands” by providing suitable conditions for most tree species, but the logging ban caused higher canopy closure, which in turn inhibit the growth and establishment of shade‐intolerant species. The results also highlighted the positive responses of native “warm species” to climate warming and suggest that using them to replace some conventional coniferous plantation tree species would better mitigate the future climate change. Poor performance of the current BAU management in maintaining forest productivity and diversity suggests that new climate‐adapted management strategies should be designed accordingly.

[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]

Forest landscape simulation models (FLSMs) – often used to understand and project forest dynamics over space and time in response to environmental disturbance – have rarely included realistic epidemiological processes of plant disease transmission and impacts. Landscape epidemiological models, by contrast, frequently treat forest ecosystems as static or make simple assumptions regarding ecosystem change following disease. Here we present the Base Epidemiological Disturbance Agent (EDA) extension that allows users of the LANDIS‐II FLSM to simulate forest pathogen spread and host mortality within a spatially explicit forest simulation. EDA enables users to investigate forest pathogen spread and impacts over large landscapes (> 105 ha) and long time periods. We evaluate the model extension using Phytophthora ramorum as a case study of an invasive plant pathogen causing emerging infectious disease and considerable tree mortality in California. EDA will advance the utility of LANDIS‐II and forest disease modeling in general. [ABSTRACT FROM AUTHOR]

Abstract: Forest ecosystems contain several climate‐sensitive drivers that respond differentially to changes in climate and climate variability. For example, growth and regeneration processes are “stand‐scale” drivers, while natural disturbances operate at “landscape scale”. The relative contributions of these different scale drivers of change in ecosystems create great uncertainty when simulating potential responses of a forest to changes in climate. Here, we assess those contributions, along with harvesting effects, on biomass (both total and of individual species) in the southern boreal forest of Canada under three climate scenarios (RCP 2.6, RCP 4.5 and RCP 8.5). Projections were performed for three future 30‐year time periods, in four study regions located on an east–west transect, using a forest landscape model (LANDIS‐II), parameterized using a forest patch model (PICUS). Projected future impacts were assessed for each driver of change, and found to vary greatly among regions, species, future period and forcing scenarios. Fire, and stand‐scale climate‐induced impacts, had the strongest effects on forest vegetation, as well as on total and species’ biomass under most RCP scenarios, but the largest impacts occurred mostly after 2050, particularly with the RCP 8.5 scenario. The relative importance and trends in species‐specific impacts varied, both spatially and according to the different RCP scenarios. Western regions were generally more sensitive to stand‐scale climate‐induced changes, whereas eastern regions were more sensitive to changes in fire regime. Our study also highlights the importance of considering the prevalence of species‐level functional traits when assessing the sensitivity of forest landscapes to a given driver of change in the context of increasing anthropogenic climate forcing. Synthesis. Increases in fire activity, and direct impacts of climate change on forest growth and regeneration, will be the most important drivers of future changes in southern boreal forest landscapes. [ABSTRACT FROM AUTHOR]

Abstract: Arrested succession, that is, an ecosystem permanently halted in an early successional, typically non‐forested state, has been suggested to result from intense competition by understorey vegetation, high browsing pressure and additional disturbances, but the relative importance of these factors is unclear. In addition, distinguishing between permanently arrested succession or merely delayed succession may be highly challenging, if not impossible, due to the large time‐scales involved. We used the dynamic vegetation model LandClim to systematically explore the effect of multiple factors on delayed and arrested succession over a time span of 1,000 years, starting from an unforested state. We included abiotic and biotic factors as well as large‐scale overstorey disturbance, in four Central European landscapes. Arrested succession occurred in 1%–14% of the simulations. Among the non‐arrested simulations, 95% reached a forested state (defined as ≥10% canopy cover) within 100 years. Large herbaceous biomass was the most important predictor for arrested succession, followed by browsing and large‐scale disturbances. Combinations of factors were important at particular locations in the landscape, where understorey competition and browsing jointly induced a strong establishment filter. Abiotic conditions consistently influenced the probability of arrested succession, with a low probability under mesic conditions and increased likelihood in more xeric parts of the landscapes. Synthesis. We demonstrated that permanently arrested succession has the potential to occur in temperate forests, particularly under a combination of high amounts of herbaceous biomass and ungulate browsing in drought‐constrained landscape positions. We thus conclude that considering environmental heterogeneity at the landscape scale is key for understanding the conditions that lead to delayed and arrested succession. [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]

Context: Forests in the northeastern United States are currently in early- and mid-successional stages recovering from historical land use. Climate change will affect forest distribution and structure and have important implications for biodiversity, carbon dynamics, and human well-being. Objective: We addressed how aboveground biomass (AGB) and tree species distribution changed under multiple climate change scenarios (PCM B1, CGCM A2, and GFDL A1FI) in northeastern forests. Methods: We used the LANDIS PRO forest landscape model to simulate forest succession and tree harvest under current climate and three climate change scenarios from 2000 to 2300. We analyzed the effects of climate change on AGB and tree species distribution. Results: AGB increased from 2000 to 2120 irrespective of climate scenario, followed by slight decline, but then increased again to 2300. AGB averaged 10 % greater in the CGCM A2 and GFDL A1FI scenarios than the PCM B1 and current climate scenarios. Climate change effects on tree species distribution were not evident from 2000 to 2100 but by 2300 some northern hardwood and conifer species decreased in occurrence and some central hardwood and southern tree species increased in occurrence. Conclusions: Climate change had positive effects on forest biomass under the two climate scenarios with greatest warming but the patterns in AGB over time were similar among climate scenarios because succession was the primary driver of AGB dynamics. Our approach, which simulated stand dynamics and dispersal, demonstrated that a northward shift in tree species distributions may take 300 or more years. [ABSTRACT FROM AUTHOR]

Climate change and forest management practices influence forest productivity and carbon budgets, and understanding their interactions is necessary to develop accurate predictions of carbon dynamics as many countries in the world strive towards carbon neutrality. Here, we developed a model-coupling framework to simulate the carbon dynamics of boreal forests in China. The expected dynamics of forest recovery and change following intense timber harvesting in the recent past and projected carbon dynamics into the future under different climate change scenarios and forest management practices (e.g., restoration, afforestation, tending, and fuel management). We predict that under current management strategies, climate change would lead to increased fire frequency and intensity, eventually shifting these forests from carbon sinks towards being carbon sources. This study suggests that future boreal forest management should be altered to reduce the probability of fire occurrence and carbon losses caused by catastrophic fires through planting deciduous species, mechanical removal, and prescribed fire. [Display omitted] • The effects of forest management on carbon cycle of boreal forests were quantified. • Climate change would lead to increased fire frequency and intensity. • Future fire activity shifting boreal forests from carbon sinks to carbon sources. • Forest management should reduce carbon losses caused by catastrophic fires. [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]

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]

Spatially explicit dynamic forest landscape models have been important tools to study large-scale forest landscape response under global climatic change. However, the quantification of relative importance of different transition pathways among different forest types to forest landscape dynamics stands as a significant challenge. In this study, we propose a novel approach of elasticity and loop analyses to identify important transition pathways contributing to forest landscape dynamics. The elasticity analysis calculates the elasticity to measure the importance of one-directional transitions (transition from one forest type directly to another forest type); while the loop analysis is employed to measure the importance of different circular transition pathways (transition from one forest type through other forest types back to itself). We apply the proposed approach to a spatially explicit dynamic model, LANDIS-II, in a study of forest landscape response to climatic change in the Boundary Waters Canoe Area (BWCA) incorporating the uncertainties in climatic change predictions. Our results not only corroborate the findings of the previous studies on the most likely future forest compositions under simulated climatic variability, but also, through the novel application of the elasticity and loop analyses concepts, provide a quantitative assessment of the specific mechanisms leading to particular forest compositions, some of which might remain undetected with conventional model evaluation methods. By quantifying the importance of specific processes (transitions among forest types) to forest composition dynamics, the proposed approach can be a valuable tool for a more quantitative understanding of the relationship between processes and landscape composition/patterns. [ABSTRACT FROM AUTHOR]

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]