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Growing interest in ecosystem restoration has recently turned the focus on tree planting, one of the most widely used restoration tools globally. Here, we study the restoration potential of tree planting in a cool‐temperate forest in Shiretoko National Park, northern Japan. We used simulation modeling to investigate the long‐term success of tree planting in restoring biodiversity and the climate change mitigation function relative to intact natural forests. Specifically, we investigated 31 different restoration scenarios, consisting of five planting densities (1,000–10,000 trees/ha) × six levels of planted tree species richness (one to six species) + one no‐planting scenario. We examined these scenarios at different distances from natural forests serving as a seed source (0–300 m) to quantify the potential for natural regeneration. In restoration areas in close proximity to a natural forest, species‐rich high‐density planting scenario performed best, reaching >50% of the reference values from intact natural forests within 33 years for both restoration goals. However, variation in restoration outcomes was small when >2,500 trees/ha of more than four species were planted, regardless of distance to seed source. In contrast, biodiversity restoration was considerably delayed in scenarios where planted species richness was low as well as in restoration areas that were far from a seed source yet relied solely on natural regeneration. We here demonstrate how forest landscape simulation can be used to identify viable restoration options for managers across multiple restoration goals as an important step to bridge the research–implementation gap in forest restoration. [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]

Oak-pine forests in the U.S. Central Hardwood Forests are recovering from exploitative harvesting and clearing in the early twentieth century and are undergoing rapid succession changes. Unprecedented red oak borer (ROB, Enaphalodes rufulus) outbreaks in 1999–2003 are associated with the largest oak mortality event reported in the Central Hardwood Region since the arrival of Europeans. Predicting and evaluating the effects of ROB disturbance on forest composition has practical value for forest management plans that aims to minimize ecological and economic loss from ROB disturbances. However, such prediction at a regional scale is rare due to the limited approaches that could explicitly couple insect outbreak mechanisms with forest dynamics under changing climate. We used a newly developed climate-sensitive Biotic Disturbance Agent module in the LANDIS PRO framework to simulate species composition changes due to succession, climate change, and ROB disturbances in 13.5 million ha forests in the U.S. Central Hardwood Region from 2000 to 2300. Our simulation suggested that succession is more important than climate effects and ROB disturbance in predicting regional species composition changes. ROB disturbance interacting with climate change accelerated the decline of primary host species (e.g., Quercus rubra) and then substantially changed forest succession trajectories under warming climates. Our modeling approach improved the simulation realism of ROB disturbance and more realistically projected how tree species will respond to ROB disturbance under changing climate, informing decision-making in silvicultural prescriptions and long-term management plans. [ABSTRACT FROM AUTHOR]

1. Climate warming directly affects insect disturbance regimes by altering temperature-dependent population development. Indirect effects of climate change on insect disturbance may mediate or accelerate direct effects via vegetation feedback (e.g. host tree demographic dynamics). However, such indirect effects have rarely been incorporated in predicting insect disturbance regimes. 2. We included both direct and indirect effects in a forest landscape modelling framework to simulate red oak borer [ROB, Enaphalodes rufulus (Coleoptera: Cerambycidae)] disturbance regimes under a warming climate from 2000 to 2150 in the Central Hardwood Forests, U.S. We quantified effect sizes and relative importance of direct effects and indirect effects of warming climate on the ROB disturbance using a factorial experimental design and two-way ANOVA. 3. Both direct and indirect effects acted positively in the short and medium term (e.g. 0-100 years) while the effect size of indirect effects changed over time from positive (+30.9 kha) to negative (-17.3 kha) and mediated the positive direct effects in the long term (e.g. 100-150 years). 4. Direct effects had greater influence than indirect effects on ROB disturbed areas in the short term (e.g. <50 years), whereas indirect effects were more important (ω² = 0.38 vs. 0.26) in the long term. This was because the host tree abundance significantly declined over time under warming climates, which decreased the forest susceptibility to ROB and thus overrode the outbreak-promoting effects of warming climates in the long term. 5. Synthesis and applications. This study reveals indirect effects of warming climates mediate direct effects on insect disturbance regimes by altering primary host tree demographic dynamics. We highlight that indirect effects are important in understanding insect disturbance regimes under warming climates as they may mediate or even reverse the expectation of increased insect disturbance. Long-term predictions of insect disturbance without considering indirect effects may overestimate its impacts under warming climates. Our findings also indicate that different management interventions are required at different time-scales to maintain oak forests' health and sustainability in the U.S. central temperate broadleaf forests. [ABSTRACT FROM AUTHOR]

Direct effects of climate change (i.e. temperature rise, changes in seasonal precipitation, wind patterns and atmospheric stability) affect fire regimes of boreal forests by altering fire behaviour, fire seasons and fuel moisture. Climate change also alters species composition and fuel characteristics, which subsequently alter fire regimes. However, indirect effects of climate change are often simplified or neglected in the direct climate–fire relationship models and dynamic global vegetation models. This may result in high uncertainties associated with existing projections of fire regimes for climate change scenarios. Moreover, few studies have examined fire regime predictions beyond the 21st century, and consequently, how the fire regimes of boreal forests would respond to climate change at the long term (>100 years) are not clear.We develop a coupled modelling framework integrating direct and indirect effects of climate change to predict fire occurrence probability and burned area for boreal forests in northeastern China. We applied repeated measures ANOVA to quantify direct and indirect effects of climate change on fire regimes in the short (0–50 years), medium (60–100 years) and long term (150–200 years).Results showed that for the 21st century, direct effects of climate change are likely to exert a stronger influence on fire regimes than indirect effects. However, increases in fire occurrence probability and burned area will accelerate the transition of boreal forests to temperate forests in the period 2100–2200, and thereby reduce fire occurrence probability and burned area. This suggests that vegetation change will mediate direct effects of climate change on fire regimes of boreal forests at the long term.Synthesis and applications. Vegetation change will mediate direct effects of climate change on fire regimes of boreal forests at the long term. This finding suggested that policymakers may consider adaptive management by planting deciduous species to reduce fire occurrence probability and resistant management by reducing competition to promote boreal species under changing climate conditions. [ABSTRACT FROM AUTHOR]

[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]

Climate change will drive significant changes in vegetation cover and also impact efforts to restore ecosystems that have been disturbed by human activities. Bitumen mining in the Alberta oil sands region of western Canada requires reclamation to "equivalent land capability," implying establishment of vegetation similar to undisturbed boreal ecosystems. However, there is consensus that this region will be exposed to relatively severe climate warming, causing increased occurrence of drought and wildfire, which threaten the persistence of both natural and reclaimed ecosystems. We used a landscape model, LANDIS‐II, to simulate plant responses to climate change and disturbances, forecasting changes to boreal forests within the oil sands region. Under the most severe climate forcing scenarios (representative concentration pathway [RCP] 8.5) the model projected substantial decreases in forest biomass, with the future forest being dominated by drought‐ and fire‐tolerant species characteristic of parkland or prairie ecosystems. In contrast, less extreme climate forcing scenarios (RCPs 2.6 and 4.5) had relatively minor effects on forest composition and biomass with boreal conifers continuing to dominate the landscape. If the climate continues to change along a trajectory similar to those simulated by climate models for the RCP 8.5 forcing scenario, current reclamation goals to reestablish spruce‐dominated boreal forest will likely be difficult to achieve. Results from scenario modeling studies such as ours, and continued monitoring of change in the boreal forest, will help inform reclamation practices, which could include establishment of species better adapted to warmer and drier conditions. [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]

Aim: Population dynamics and disturbances have often been simplified or ignored when predicting regional‐scale tree species distributions in response to climate change in current climate‐distribution models (e.g., niche and biophysical process models). We determined the relative importance of population dynamics, tree harvest, climate change, and their interaction in affecting tree species distribution changes. Location: Central Hardwood Forest Region of the United States. Major taxa studied: Tree species. Methods: We used a forest dynamic model, LANDIS PRO that accounted for population dynamics, tree harvest, and climate change to predict tree species' distributions at 270 m resolution from 2000 to 2300. We quantified the relative importance of these factors using a repeated measures analysis of variance. We further investigated the effects of each factor on changes in species distributions by summarizing extinction and colonization rates. Results: On average, population dynamics was the most important factor affecting tree species distribution changes. Tree harvest was more important than climate change by 2100 whereas climate change was more important than harvest by 2300. By end of the 21st century, most tree species expanded their distributions irrespective of any climate or harvest scenario. By 2300, most northern, some southern, and most widely distributed species contracted their distributions while most southern species, some widely distributed species, and few northern species expanded their distributions under warmer climates with tree harvest. Harvest accelerated or ameliorated the contractions and expansions for species that were negatively or positively affected by climate change. Main conclusions: Our results suggest that population dynamics and tree harvest can be more important than climate change and thus should be explicitly included when predicting future tree species' distributions. Understanding the underlying mechanisms that drive tree species distributions will enable better predictions of tree species distributions under climate change. [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]

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]

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]

We used a spatially explicit model of forest dynamics, supported by empirical field data and socioeconomic data, to examine the impacts of human disturbances on a protected forest landscape in Kyrgyzstan. Local use of 27 fruit and nut species was recorded and modeled. Results indicated that in the presence of fuelwood cutting with or without grazing, species of high socioeconomic importance such asJuglans regia,Malusspp., andArmeniaca vulgariswere largely eliminated from the landscape after 50–150 yr. In the absence of disturbance or in the presence of grazing only, decline of these species occurred at a much lower rate, owing to competitive interactions between tree species. This suggests that the current intensity of fuelwood harvesting is not sustainable. Conversely, current grazing intensities were found to have relatively little impact on forest structure and composition, and could potentially play a positive role in supporting regeneration of tree species. These results indicate that both positive and negative impacts on biodiversity can arise from human populations living within a protected area. Potentially, these could be reconciled through the development of participatory approaches to conservation management within this reserve, to ensure the maintenance of its high conservation value while meeting human needs. [ABSTRACT FROM AUTHOR]

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]

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: 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]

• Contemporary rates of forest disturbance are high in the northern Acadian Forest. • Aboveground biomass dynamics and timber harvesting were simulated using LANDIS-II. • Projected rates of species turnover were high, 2010–2060, under recent harvest rates. • Co-occurrence patterns likely to shift, destabilizing regional forest types. • Local logging effects on species abundance key to modeling working forest resilience. Preventing declines of native and historically-abundant tree species is an important aspect of sustainable forest management, but predicting future forest composition is challenging when succession does not tend to follow a well-defined path. We evaluated the implications of site-level interactions between timber harvesting and forest succession on the regional landscape dynamics of the complex and species-rich northern Acadian Forest Region. Our expectation was that forest composition would trend away from long-lived and shade-tolerant species, because rates of landscape disturbance from timber harvesting are high relative to historic rates of natural disturbance. We used a novel modeling approach that combined Landsat-derived time series of forest disturbance to inform realistic simulations of timber harvesting across many individual commercial forest landowners using LANDIS-II, and evaluated changes in tree species' distributions and abundance with and without harvesting. Detailed descriptions of initial forest conditions were derived from maps of relative tree species abundance, developed using Landsat satellite imagery, regional inventory data, and an innovative machine learning algorithm. If recent harvest rates persist, simulations suggest timber harvesting will generally be sustainable in our study area; however, projected rates of site-level species turnover were high, predominantly favoring species that were less abundant under the region's natural disturbance regime. As a result, broad-scale patterns of projected species co-occurrence shifted, destabilizing important regional forest types. Our results highlight both the region's capacity for forest growth and the importance of accurately capturing the local effects of land management when projecting forested regions dominated by commercial ownership. [ABSTRACT FROM AUTHOR]

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]