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Simulation der Baumartenmigration im Klimawandel Der Klimawandel verändert die Lebensbedingungen von Bäumen. Verschlechtern sich für bestimmte Baumarten die Umweltbedingungen stark, bedeutet dies nicht unbedingt deren sofortiges lokales Aussterben. Oft können die Baumarten noch eine Weile überdauern, bevor sie wegen fehlender Verjüngung verschwinden. Neue, an die klimatischen Bedingungen angepasste Baumarten sind jedoch nicht immer schon an Ort und Stelle, um sich selbstständig und natürlich zu verjüngen. Das Waldlandschaftsmodell TreeMig beschreibt die Walddynamik im Raum: In Zellen eines regelmässigen Gitters wird die Änderung der Populationsdichte der verschiedenen Baumarten in den einzelnen Höhenklassen mit klimaabhängigen populationsdynamischen Prozessen formuliert. Ergänzt durch Samenproduktion, -transport, Samenbankdynamik und Entwicklung der Jungbäume, simuliert Tree-Mig so die Migration der Baumarten. Simulationen mit Klimaänderungsszenarien deuten darauf hin, dass die räumlich-zeitliche Walddynamik wegen der begrenzten Wandergeschwindigkeit der Bäume der klimawandelbedingten Veränderung der Standortbedingungen hinterherhinkt. Der Rückstand ist in Gebieten mit flachen Temperaturgradienten, zum Beispiel in der borealen Zone, ausgeprägt. In der Schweiz mit ihren steilen Temperaturgradienten ist er geringer. Es zeigt sich zudem, dass die Alpenpässe als Flaschenhals für die Ausbreitung von trockenheitsadaptierten submediterranen Arten wirken können.

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.

Validation of the long-term biomass predictions of forest landscape models (FLMs) has always been a challenging task. Using the space-for-time substitution method, forest biomass curves over stand age were generated from a forest survey dataset (FSD) in the Lesser Khingan Mountains area (LKM), Northeastern China and compared with long-term biomass predictions of LANDIS-II model. The results showed that mean forest age and mean biomass of the LKM in 2000 were 51.6 years and 84.2 Mg ha−1, respectively. Significant linear correlations were found between FSD derived biomass and simulated biomass in the aggradation phase for the entire LKM and most subregions. However, a considerable difference in the mean maximum biomass (53.45 Mg ha−1) existed between from FSD and simulation during the post-aggradation phase. The space-for-time substitution method has potential in validating time series biomass predictions of FLMs in aggradation phase when only limited forest inventory data is available.

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.

Hurricanes in the southeast United States are infrequent disturbances that affect large areas and have a large effect on forest succession. In order to understand and quantify this effect, we added a new module to the LANDIS-II landscape change model. Focusing on the southeast coast of the United States, we simulated stochastic hurricanes for 50 years. For each simulated storm, the new model extension generates the maximum sustained wind speed over the region and uses the resulting parameter surface to compute maximum sustained wind speed for each cohort cell in a raster grid. Mortality is estimated for each species and age cohort in each cell based on the maximum sustained wind speed, altering forest succession. Results indicate that hurricanes reduce average aboveground biomass by > 20% over 50 years on a landscape in Fort Bragg, North Carolina (USA) compared to a scenario without hurricanes and increased uncertainty of projected succession.

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.

Evaluation of forest landscape model (FLM) predictions is indispensable to establish the credibility of predictions. We present a framework that evaluates short- and long-term FLM predictions at site and landscape scales. Site-scale evaluation is conducted through comparing raster cell-level predictions with inventory plot data whereas landscape-scale evaluation is conducted through comparing predictions stratified by extraneous drivers with aggregated values in inventory plots. Long-term predictions are evaluated using empirical data and knowledge. We demonstrate the applicability of the framework using LANDIS PRO FLM. We showed how inventory data were used to initialize the landscape and calibrate model parameters. Evaluation of the short-term LANDIS PRO predictions based on multiple metrics showed good overall performance at site and landscape scales. The predicted long-term stand development patterns were consistent with the established theories of stand dynamics. The predicted long-term forest composition and successional trajectories conformed well to empirical old-growth studies in the region. We present a framework for evaluating the short- and long-term forest landscape model predictions at site and landscape scales.Site-scale evaluation is conducted through comparing cell-level predictions with inventory plot data.Landscape-scale evaluation is conducted through comparing predictions stratified by extraneous drivers with aggregated values in inventory plots.We successfully evaluated the LANDIS PRO forest landscape model predictions using empirical data and knowledge and showed reasonable performances at both scales.

Fire and timber harvest are two major forest disturbances in boreal forests. Predicting the dynamics of boreal forest biomass requires accounting for both of those effects. Related stochasticity and other uncertainties can produce great variation in predicted responses of forests to fire and timber harvest. In this study, we investigated the effects of fire and timber harvest on landscape-level predictions of the tree component of stand biomass in a boreal forest landscape in Northeast China. We used a forest landscape model (LANDIS PRO) to predict the tree biomass over three time intervals (0–50, 50–150, and 150–300 years). We then compared the simulated results of fire and timber harvest and their interactions with observed biomass and its spatial distribution over short-, mid-, and long-term intervals. For additional prediction comparisons, we observed uncut, unburned stands (i.e., the succession-only scenario). Compared to the succession-only scenario, we found that predicted biomass was reduced by 3.8 ± 2.1, 9.1 ± 3.6, and 11.2 ± 5.1 tons/ha in fire-only, harvest-only, and combined fire and harvest scenarios, respectively. Our results indicated that the effect of harvest on biomass exceeded that of fire, and that the interaction of fire and harvest was more effective in reducing biomass than the effects of fire or harvest separately. Biomass predictions that did not consider effects of fire and timber harvest tended to inflate biomass estimates. The spatial distribution of tree biomass moreover changed with simulation period. These results have important implications in designing prescriptions for improving forest sustainability.

Under climate change, the adoption of historical reference as the objective of forest restoration is being questioned. In this study, the spatially explicit forest landscape model LANDIS was utilized to analyze how the forest landscape in the upper Hun River area of Liaoning province in northeast China would be affected under current climate trends and future climate change; and to explore whether the historical reference should be the objective of restoration efforts. The results showed that (1) the area percentage (AP) of Quercusmongolica under climate change is always higher than that under the current climate regime, while the AP of Pinuskoraiensis is lower than that under current climate; and (2) the competitive ability of Q. mongolica and Populus davidiana increases, while that of other species decreases under climate change. As interspecies competition shifts under climate change, the historical reference appears in appropriate to serve as the objective of forest restoration. In addition, although Q. mongolica would likely benefit from a warmer and drier climate, use of this species for forest restoration under climate change still requires further research.

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.

This book accounts the early history of the US Regional Association of the International Association for Landscape Ecology (USIALE) . In this chapter, the coeditors of this volume, we describe the revision in the mission of USIALE that was influenced by officers during the first 30 years since its conception, recipients of society awards, timelines of annual symposia, and hosts for these annual events.

Forests are under pressure from land use change due to anthropogenic activities. Land use change and harvest are the main disturbances of forest landscape changes. Few studies have focused on the relative contributions of different disturbances. In this study, we used the CA-Markov model, a land-use change model, coupled with a forest landscape model, LANDIS-II, to simulate dynamic change in Taihe County, China, from 2010 to 2050. Scenarios analysis was conducted to quantify the relative contributions of land use change and harvest. Our results show that forestland and arable land will remain the primary land-use types in 2050, whereas the built-up land will sprawl drastically. Land use change and harvest may result in the significant loss of forest area and changes in landscape structure. The simulated forest area will increase by 16.2% under the no disturbance scenario. However, under harvest, forest conversion, and integrated scenario, the area will be reduced by 5.2%, 16.5%, and 34.9%, respectively. The effect of harvest is gradually enhanced. The land use change will account for 60% and harvest will account for 40% of forest landscape change in 2050, respectively. Our results may benefit from the integration of regional forest management and land-use policy-making, and help to achieve a trade-off between economy and ecological environment.

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

LANDIS is a forest landscape model that simulates the interaction of large landscape processes and forest successional dynamics at tree species level. We discuss how object-oriented design (OOD) approaches such as modularity, abstraction and encapsulation are integrated into the design of LANDIS. We show that using OOD approaches, model decisions (olden as model assumptions) can be made at three levels parallel to our understanding of ecological processes. These decisions can be updated with relative efficiency because OOD components are less interdependent than those designed with traditional approaches. To further examine object design, we examined how forest species objects, AGELIST (tree age-classes), SPECIE (single species) and SPECIES (species list), are designed, linked and functioned. We also discuss in detail the data structure of AGELIST and show that different data structures can significantly affect model performance and model application scopes. Following the discussion of forest species objects, we apply the model to a real forest landscape in northern Wisconsin. We demonstrate the model's capability of tracking species age cohorts in a spatially explicit manner at each time step. The use of these models at large spatial and temporal scales reveals important information that is essential for the management of forested ecosystem.

Landscape-scale forest succession models are often used to simulate forest dynamics, and the results of these simulations are used to forecast future forest states. Such forecasts are frequently the basis for strategic decisions about forest management policy and planning. However, large gaps in empirical data stemming from insufficient sampling of the landscapes or poorly understood processes often make it difficult to design and apply the models (Kangas and Leskinen 2005). In consequence, expert knowledge is often used to supplement empirical data during the design and application of the models (e.g., Forbis et al. 2006), though its use remains mostly implicit or inadequately explained by researchers.

This chapter reviews the LANDIS and other forest landscape models for integration with wildlife models. LANDIS is a vital-attribute, raster-based forest landscape model. Each raster unit or cell tracks the presence or absence of one to ten year age cohorts of individual species; fuel accumulation level; the time since last disturbance and the time since last management; and species establishment ability in particular land types. For each cell, species birth, growth, death, regeneration, random mortality, and vegetative reproduction are simulated at one to ten year time steps. In LANDIS, vegetation heterogeneity is modeled at multiple hierarchical levels from the landscape to the pixel. Land type captures the highest level (coarse grain) of spatial vegetation-heterogeneity caused by various environmental controls. A somewhat uniform suite of ecological conditions that results in similar species establishment patterns is assumed for each land type. To simulate different types of disturbances and their effects on tree species composition and landscape pattern is central to the design of LANDIS. LANDIS can simulate three different types of natural disturbances (fire, wind, and biological), which can be applied in any combination.

This document contains metadata and description of land-cover classification of approximately 5.1 million acres of land bordering Minnesota, U.S.A. and Ontario, Canada. The classification focused on the separation and identification of specific forest-cover types. Some separation of the nonforest classes also was performed. The classification was derived from multi-temporal Landsat TM/ETM+ imagery (collected from the years 1999-2001) using eCognition segmentation software to identify stands and the kNN algorithm to perform the spectral classification. Reference data sites were obtained for Minnesota from the Forest Inventory and Monitoring program dataset and for Ontario from the Forest Resource Inventory program. This dataset was created as an input layer in the LANDIS-II forest landscape model for simulation of future forest disturbance and compositional change over the Border Lakes landscape.

Models used to investigate impacts of climatic changes on spatio-temporal vegetation dynamics need to balance required accuracy with computational feasibility. To enhance the computational efficiency of these models, upscaling methods are required that maintain key fine-scale processes influencing vegetation dynamics. In this paper, an adjustable method – the dynamic two-layer classification concept (D2C) – for the upscaling of time- and space-discrete models is presented. D2C aims to separate potentially repetitive calculations from those specific to single grid cells. The underlying idea is to extract processes that do not require information about a grid cell's neighbourhood to a reduced-size non-spatial layer, which is dynamically coupled to the original two-dimensional layer. The size of the non-spatial layer is thereby adaptive and depends on dynamic classifications according to pre-specified similarity criteria. I present how D2C can be used in a model implementation on the example of TreeMig-2L, a new, efficient version of the intermediate-complexity forest-landscape model TreeMig. To discuss the trade-off between computational expenses and accuracy, as well as the applicability of D2C, I compare different model stages of TreeMig-2L via simulations of two different application scenarios. This comparison of different model stages demonstrates that applying D2C can strongly reduce computational expenses of processes calculated on the new non-spatial layer. D2C is thus a valuable upscaling method for models and applications in which processes requiring information about the neighbourhood constitute the minor share of the overall computational expenses.