EcoViz: a tool for visual analysis and photorealistic rendering of forest landscape model simulations

The ecological resilience of boreal forests is an important element of measuring forest ecosystem capacity recovered from a disturbance, and is sensitive to broad-scale factors (e.g., climate change, fire disturbance and human related impacts). Therefore, quantifying the effects of these factors is increasingly important for forest ecosystem management. In this study, we investigated the impacts of climate change, climate-induced fire regimes, and forest management schemes on forest ecological resilience using a forest landscape model in the boreal forests of the Great Xing’an Mountains, Northeastern China. First, we simulated the effects of the three studied variables on forest aboveground biomass, growing space occupied, age cohort structure, and the proportion of mid and late-seral species indicators by using the LANDIS PRO model. Second, we calculated ecological resilience based on these four selected indicators. We designed five simulated scenarios: Current fire only scenario, increased fire occurrence only scenario, climate change only scenario, climate-induced fire regime scenario, and climate-fire-management scenario. We analyzed ecological resilience over the five scenarios from 2000 to 2300. The results indicated that the initialized stand density and basal area information from the year 2000 adequately represented the real forest landscape of that year, and no significant difference was found between the simulated landscape of year 2010 and the forest inventory data of that year at the landscape scale. The simulated fire disturbance results were consistent with field inventory data in burned areas. Compared to the current fire regime scenario, forests where fire occurrence increased by 30% had an increase in ecological resilience of 12.4–43.2% at the landscape scale, whereas increasing fire occurrence by 200% would decrease the ecological resilience by 2.5–34.3% in all simulated periods. Under the low climate-induced fire regime scenario, the ecological resilience was 12.3–26.7% higher than that in the reference scenario across all simulated periods. Under the high climate-induced fire regime scenario, the ecological resilience decreased significantly by 30.3% and 53.1% in the short- and medium-terms at landscape scale, while increasing slightly by 3.8% in the long-term period compared to the reference scenario. Compared to no forest management scenario, ecological resilience was decreased by 5.8–32.4% under all harvesting and planting strategies for the low climate-induced fire regime scenario, and only the medium and high planting intensity scenarios visibly increased the ecological resilience (1.7–15.8%) under the high climate-induced fire regime scenario at the landscape scale. Results from our research provided insight into the future forest management and have implications for improving boreal forest sustainability.

Fire is a multi-scale process that is an important component in determining ecosystem age structures and successional trajectories across forested landscapes. In order to address questions regarding fire effects over large spatial scales and long temporal scales researchers often employ forest landscape models which can model fire as a spatially explicit disturbance. Within forest landscape models site-level fire effects are often simplified to the species, functional type, or cohort level due to time or computational resource limitations. In this study we used a subset of publicly available U.S. Forest Service forest inventory data (FIA) to estimate short-term fire effects on tree densities across multiple stem diameter classes in two ecological sections in the central and southern United States. We found that FIA plots where low-intensity fires occurred within the preceding five years in the Ozark Highlands ecological section had significantly reduced stem densities in the two smallest diameter classes and in the Gulf Coastal Plains and Flatwoods fire reduced stem densities in the three smallest diameter classes. Using an independent subset of FIA plots we then parameterized and calibrated a forest landscape model to simulate site-level fire effects using a logistic regression based method and compare the results to previous methods of modeling fire effects. When representative landscapes from both study areas were simulated under a low-intensity fire regime using a forest landscape model the logistic regression probability method of modeling fire effects produced a similar reduction in stem densities while the previous age-cohort method overestimated density reductions across diameter classes. A more realistic representation of fire effects, particularly in low intensity fire regimes, increases the utility of forest landscape models as tools for planning and management.

Quantitative models of forest dynamics have followed a progression toward methods with increased detail, complexity, and spatial extent. We highlight milestones in the development of forest dynamics models and identify future research and application opportunities. We reviewed milestones in the evolution of forest dynamics models from the 1930s to the present with emphasis on forest growth and yield models and forest landscape models We combined past trends with emerging issues to identify future needs. Historically, capacity to model forest dynamics at tree, stand, and landscape scales was constrained by available data for model calibration and validation; computing capacity; model applicability to real-world problems; and ability to integrate biological, social, and economic drivers of change. As computing and data resources improved, a new class of spatially explicit forest landscape models emerged. We are at a point of great opportunity in development and application of forest dynamics models. Past limitations in computing capacity and in data suitable for model calibration or evaluation are becoming less restrictive. Forest landscape models, in particular, are ready to transition to a central role supporting forest management, planning, and policy decisions. Transitioning forest landscape models to a central role in applied decision making will require greater attention to evaluating performance; building application support staffs; expanding the included drivers of change, and incorporating metrics for social and economic inputs and outputs.

An innovative computer design for modeling forest landscape change in very large spatial extents with fine resolutions

Forest landscape models: Definitions, characterization, and classification

Forest landscape models (FLMs) have increasingly become important tools for exploring forest landscape changes by predicting forest vegetation dynamics over large spatial scales. However, two challenges confronting FLMs have persisted: how to simulate fine, site-scale processes while making large-scale (landscape and regional) simulation feasible, and how to fully take advantage of extensive U.S. Forest Service Inventory and Analysis (FIA) data to initialize and constraint model parameters. In this dissertation, first, a new FLM, LANDIS PRO was developed. In LANDIS PRO, forest succession and dynamics are simulated by incorporating species-, stand-, and landscape-scale processes by tracking number of trees by species age cohort. Because stand-scale resource competition is achieved by implementing rather than simulating the emergent properties of stand development, LANDIS PRO is computationally efficient, which makes large-scale simulation feasible. Since model parameters and simulation results are comparatively straightforward to forest inventory data, current intensive forest inventory data can be directly applied for model initialization and to constrain model parameters. Validation of FLMs is essential to ensure users’ confidence in model predictions and achieve reliable management decision making. To date, validation of FLMs has been limited due to lack of suitable data. However, recent advances in FLMs, together with increasingly available spatiotemporal data make FLM validation feasible. In this dissertation, second, I proposed a framework for validating forest landscape projections from LANDIS PRO using Forest Inventory Analysis (FIA) data. The proposed framework incorporated data assimilation techniques to constrain model parameters and the initial state of the landscape by verifying the initialized landscape and iteratively calibrating the model parameters. The model predictions were rigorously validated against independent FIA data at multiple scales, and the long-term natural successional pattern was also verified against empirical studies. Results showed model predictions were able to capture much of the variation overtime in species basal area and tree density at stand-, landtype- , and landscape-scales. Subsequent long-term predictions of natural succession patterns were consistent with expected changes in tree species density of oak-dominated forests in the absence of disturbance. Lastly, I used LANDIS PRO, a forest landscape model that includes stand-scale species density and basal area to evaluate the potential landscape-scale effects of alternative harvest methods (thinning, clearcutting and group selection) on oak decline mitigation. Projections indicated that forest harvesting can be effective in mitigating oak decline. Group selection and clearcutting were the most effective methods in the management of oak decline in the short-term (20 years) and mid-term (50 years), respectively. However, in the long-run (100 years), there was no significant difference predicted among the three methods.

The Forest Landscape Model (FLM) is an efficiency tool of quantified expression of forest ecosystem’s structure and function. This paper, on the basis of identifying FLM, according to the stage of development, summarizes the development characteristics of the model, which includes the theoretical foundation of mathematical model, FLM of stand-scale, primary development of spatial landscape model, rapid development of ecosystem process model as the priority, and developing period of structure and process driven by multi-factor. According to the characteristics of different FLMs, this paper classifies the existing FLM in terms of mechanism, property and application, and elaborates the identifications, advantages and disadvantages of different types of models. It summarizes and evaluates the main application fields of existing models from two aspects which are the changes of spatial pattern and ecological process. Eventually, this paper presents FLM’s challenges and directions of development in the future, including: (1) more prominent service on the practical strategy of forest management’s objectives; (2) construction of multi-modules and multi-plugin to satisfy landscape research demand in various conditions; (3) adoption of high resolution’s spatial-temporal data; (4) structural construction of multi-version module; (5) improving the spatial suitability of model application.

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.

Avian cavity nesters (ACN) are viable indicators of forest structure, composition, and diversity. Utilizing these species responses in multi-disciplinary climate-avian-forest modeling can improve climate adaptive management. We propose a framework for integrating and evaluating climate-avian-forest models by linking two ACN niche models with a forest landscape model (FLM), LANDIS-II. The framework facilitates the selection of available ACN models for integration, evaluation of model transferability, and evaluation of successful integration of ACN models with a FLM. We found selecting a model for integration depended on its transferability to the study area (Northern Rockies Ecoregion of Idaho in the United States), which limited the species and model types available for transfer. However, transfer evaluation of the tested ACN models indicated a good fit for the study area. Several niche model variables (canopy cover, snag density, and forest cover type) were not directly informed by the LANDIS-II model, which required secondary modeling (Random Forest) to derive values from the FLM outputs. In instances where the Random Forest models performed with a moderate classification accuracy, the overall effect on niche predictions was negligible. Predictions based on LANDIS-II simulations performed similarly to predictions based on the niche model’s original training input types. This supported the conclusion that the proposed framework is viable for informing avian niche models with FLM simulations. Even models that poorly approximate habitat suitability, due to the inherent constraints of predicting spatial niche use of irruptive species produced informative results by identifying areas of management focus. This is primarily because LANDIS-II estimates spatially explicit variables that were unavailable over large spatial extents from alternative datasets. Thus, without integration, one of the ACN niche models was not applicable to the study area. The framework will be useful for integrating avifauna niche and forest ecosystem models, which can inform management of contemporary and future landscapes under differing management and climate scenarios.

ContextForest canopies shape subcanopy environments, affecting biodiversity and ecosystem processes. Empirical forest microclimate studies are often restricted to local scales and short-term effects, but forest dynamics unfold at landscape scales and over long time periods.ObjectivesWe developed the first explicit and dynamic implementation of microclimate temperature buffering in a forest landscape model and investigated effects on simulated forest dynamics and outcomes.MethodsWe adapted the individual-based forest landscape and disturbance model iLand to use microclimate temperature for three processes [decomposition, bark beetle (Ips typographus L.) development, and tree seedling establishment]. We simulated forest dynamics with or without microclimate temperature buffering in a temperate European mountain landscape under historical climate and disturbance conditions.ResultsTemperature buffering effects propagated from local to landscape scales. After 1,000 simulation years, average total carbon and cumulative net ecosystem productivity were 2% and 21% higher, respectively, and tree species composition differed in simulations including versus excluding microclimate buffering. When microclimate buffering was included, Norway spruce (Picea abies (L.) Karst.) increased by 9% and European beech (Fagus sylvatica L.) decreased by 12% in mean basal area share. Some effects were amplified across scales, such as a mean 16% decrease in local-scale bark beetle development rates resulting in a mean 45% decrease in landscape-scale bark beetle-caused mortality.ConclusionsMicroclimate effects on forests scaled nonlinearly from stand to landscape and days to millennia, underlining the utility of complex simulation models for dynamic upscaling in space and time. Microclimate temperature buffering can alter forest dynamics at landscape scales.

Exploring component-based approaches in forest landscape modeling

Simulation models are important tools to study the impacts of climate change and natural disturbances on forest ecosystems. Being able to track tree demographic processes in a spatially explicit manner, process-based forest landscape models are considered the most suitable to provide robust projections that can aid decision-making in forest management. However, landscape models are challenging to parameterize and setting up new study areas for application studies largely depends on data availability. The aim of this study is to demonstrate the parameterization process, including model testing and evaluation, for setting up a study area in the Italian Alps in a process-based forest landscape model using available data. We processed soil, climate, carbon pools, vegetation, disturbances and forest management data, and ran iterative spin-up simulations to generate a virtual landscape best resembling current conditions. Our results demonstrated the feasibility of initializing forest landscape models with data that are typically available from forest management plans and national forest inventories, as well as openly available mapping products. Evaluation tests proved the ability of the model to capture the environmental constraints driving regeneration dynamics and inter-specific competition in forests of the Italian Alps, as well as to simulate natural disturbances and carbon dynamics. The model can subsequently be applied to investigate forest landscape development under a suite of future scenarios and provide recommendations for adapting forest management decisions.

Review of forest landscape models: Types, methods, development and applications

Evaluating simulated effects of succession, fire, and harvest for LANDIS PRO forest landscape model

Predicting future patterns, processes, and their interactions: Benchmark calibration and validation procedures for forest landscape models