Wen J. Wang

A large-scale forest landscape model incorporating multi-scale processes and utilizing forest inventory data

Two challenges confronting forest landscape models (FLMs) are how to simulate fine, stand-scale processes while making large-scale (i.e., >107 ha) simulation possible, and how to take advantage of extensive forest inventory data such as U.S. Forest Inventory and Analysis (FIA) data to initialize and constrain model parameters. We present the LANDIS PRO model that addresses these needs. LANDIS PRO adds density and size mechanisms of resource competition. This is achieved through incorporating number of trees and DBH by species age cohort within each raster cell. Forest change is determined by the interactions of species-, stand-, and landscape-scale processes. Species-scale processes include tree growth, establishment, and mortality. Stand-scale processes include density and size-related resource competition that regulates self-thinning and seedling establishment. Landscape-scale processes include seed dispersal, as well as natural and anthropogenic disturbances. LANDIS PRO is designed to be straightforwardly comparable with forest inventory data, and thus the extensive FIA data can be directly utilized to initialize and constrain model parameters before predicting future forest change. We initialized a large landscape (∼107 ha) from historical FIA data (1978) and the predicted forest structure and composition following 30 years of simulation were statistically calibrated against a prior time-series of sequential FIA data (1978 to 2008). The results showed that the initialized conditions realistically represented the historical forest composition and structure at 1978, and the constrained model parameters predicted reasonable outcomes at both landscape and land type scales. The subsequent evaluation of model predictions showed that the predicted forest composition and structure were comparable with old-growth oak forests; predicted forest successional trajectories were consistent with the expected successional patterns in oak-dominated forests in the study region; and the predicted stand development patterns were in agreement with the established theories of forest stand development. This study demonstrated a framework for forest landscape modeling including model initialization, calibration, and evaluation of predictions.

Central Hardwoods ecosystem vulnerability assessment and synthesis: a report from the Central Hardwoods Climate Change Response Framework project

The forests in the Central Hardwoods Region will be affected directly and indirectly by a changing climate over the next 100 years. This assessment evaluates the vulnerability of terrestrial ecosystems in the Central Hardwoods Region of Illinois, Indiana, and Missouri to a range of future climates. Information on current forest conditions, observed climate trends, projected climate changes, and impacts to forest ecosystems was considered in order to assess vulnerability to climate change. Mesic upland forests were determined to be the most vulnerable to projected changes in climate, whereas many systems adapted to fire and drought, such as open woodlands, savannas, and glades, were perceived as less vulnerable. Projected changes in climate and the associated ecosystem impacts and vulnerabilities will have important implications for economically valuable timber species, forest-dependent wildlife and plants, recreation, and long-range planning.

Importance of succession, harvest, and climate change in determining future composition in U.S. Central Hardwood Forests

Most temperate forests in U.S. are recovering from heavy exploitation and are in intermediate successional stages where partial tree harvest is the primary disturbance. Changes in regional forest composition in response to climate change are often predicted for plant functional types using biophysical process models. These models usually simplify the simulation of succession and harvest and may not consider important species-specific demographic processes driving forests changes. We determined the relative importance of succession, harvest, and climate change to forest composition changes in a 125-million ha area of the Central Hardwood Forest Region of U.S. We used a forest landscape modeling approach to project changes in density and basal area of 23 tree species due to succession, harvest, and four climate scenarios from 2000 to 2300. On average, succession, harvest, and climate change explained 78, 17, and 1% of the variation in species importance values (IV) at 2050, respectively, but their contribution changed to 46, 26, and 20% by 2300. Climate change led to substantial increases in the importance of red maple and southern species (e.g., yellow-poplar) and decreases in northern species (e.g., sugar maple) and most of widely distributed species (e.g., white oak). Harvest interacted with climate change and accelerated changes in some species (e.g., increasing southern red oak and decreasing American beech) while ameliorated the changes for others (e.g., increasing red maple and decreasing white ash). Succession was the primary driver of forest composition change over the next 300 years. The effects of harvest on composition were more important than climate change in the short term but climate change became more important than harvest in the long term. Our results show that it is important to model species-specific responses when predicting changes in forest composition and structure in response to succession, harvest, and climate change.

Multi-model comparison on the effects of climate change on tree species in the eastern U.S.: results from an enhanced niche model and process-based ecosystem and landscape models

ContextSpecies distribution models (SDM) establish statistical relationships between the current distribution of species and key attributes whereas process-based models simulate ecosystem and tree species dynamics based on representations of physical and biological processes. TreeAtlas, which uses DISTRIB SDM, and Linkages and LANDIS PRO, process-based ecosystem and landscape models, respectively, were used concurrently on four regional climate change assessments in the eastern Unites States.ObjectivesWe compared predictions for 30 species from TreeAtlas, Linkages, and LANDIS PRO, using two climate change scenarios on four regions, to derive a more robust assessment of species change in response to climate change.MethodsWe calculated the ratio of future importance or biomass to current for each species, then compared agreement among models by species, region, and climate scenario using change classes, an ordinal agreement score, spearman rank correlations, and model averaged change ratios.ResultsComparisons indicated high agreement for many species, especially northern species modeled to lose habitat. TreeAtlas and Linkages agreed the most but each also agreed with many species outputs from LANDIS PRO, particularly when succession within LANDIS PRO was simulated to 2300. A geographic analysis showed that a simple difference (in latitude degrees) of the weighted mean center of a species distribution versus the geographic center of the region of interest provides an initial estimate for the species’ potential to gain, lose, or remain stable under climate change.ConclusionsThis analysis of multiple models provides a useful approach to compare among disparate models and a more consistent interpretation of the future for use in vulnerability assessments and adaptation planning.

A framework for evaluating forest landscape model predictions using empirical data and knowledge

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.

Modeling the effects of harvest alternatives on mitigating oak decline in a Central Hardwood Forest landscape

Oak decline is a process induced by complex interactions of predisposing factors, inciting factors, and contributing factors operating at tree, stand, and landscape scales. It has greatly altered species composition and stand structure in affected areas. Thinning, clearcutting, and group selection are widely adopted harvest alternatives for reducing forest vulnerability to oak decline by removing susceptible species and declining trees. However, the long-term, landscape-scale effects of these different harvest alternatives are not well studied because of the limited availability of experimental data. In this study, we applied a forest landscape model in combination with field studies to evaluate the effects of the three harvest alternatives on mitigating oak decline in a Central Hardwood Forest landscape. Results showed that the potential oak decline in high risk sites decreased strongly in the next five decades irrespective of harvest alternatives. This is because oak decline is a natural process and forest succession (e.g., high tree mortality resulting from intense competition) would eventually lead to the decrease in oak decline in this area. However, forest harvesting did play a role in mitigating oak decline and the effectiveness varied among the three harvest alternatives. The group selection and clearcutting alternatives were most effective in mitigating oak decline in the short and medium terms, respectively. The long-term effects of the three harvest alternatives on mitigating oak decline became less discernible as the role of succession increased. The thinning alternative had the highest biomass retention over time, followed by the group selection and clearcutting alternatives. The group selection alternative that balanced treatment effects and retaining biomass was the most viable alternative for managing oak decline. Insights from this study may be useful in developing effective and informed forest harvesting plans for managing oak decline.

Revision and application of the LINKAGES model to simulate forest growth in central hardwood landscapes in response to climate change

ContextGlobal climate change impacts forest growth and methods of modeling those impacts at the landscape scale are needed to forecast future forest species composition change and abundance. Changes in forest landscapes will affect ecosystem processes and services such as succession and disturbance, wildlife habitat, and production of forest products at regional, landscape and global scales.ObjectivesLINKAGES 2.2 was revised to create LINKAGES 3.0 and used it to evaluate tree species growth potential and total biomass production under alternative climate scenarios. This information is needed to understand species potential under future climate and to parameterize forest landscape models (FLMs) used to evaluate forest succession under climate change.MethodsWe simulated total tree biomass and responses of individual tree species in each of the 74 ecological subsections across the central hardwood region of the United States under current climate and projected climate at the end of the century from two general circulation models and two representative greenhouse gas concentration pathways.ResultsForest composition and abundance varied by ecological subsection with more dramatic changes occurring with greater changes in temperature and precipitation and on soils with lower water holding capacity. Biomass production across the region followed patterns of soil quality.ConclusionsLinkages 3.0 predicted realistic responses to soil and climate gradients and its application was a useful approach for considering growth potential and maximum growing space under future climates. We suggest Linkages 3.0 can also can used to inform parameter estimates in FLMs such as species establishment and maximum growing space.

The past and future of modeling forest dynamics: from growth and yield curves to forest landscape models

ContextQuantitative models of forest dynamics have followed a progression toward methods with increased detail, complexity, and spatial extent.ObjectivesWe highlight milestones in the development of forest dynamics models and identify future research and application opportunities.MethodsWe 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.ResultsHistorically, 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.ConclusionsWe 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.RecommendationsTransitioning 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.

Future forest aboveground carbon dynamics in the central United States: the importance of forest demographic processes

The Central Hardwood Forest (CHF) in the United States is currently a major carbon sink, there are uncertainties in how long the current carbon sink will persist and if the CHF will eventually become a carbon source. We used a multi-model ensemble to investigate aboveground carbon density of the CHF from 2010 to 2300 under current climate. Simulations were done using one representative model for each of the simple, intermediate, and complex demographic approaches (ED2, LANDIS PRO, and LINKAGES, respectively). All approaches agreed that the current carbon sink would persist at least to 2100. However, carbon dynamics after current carbon sink diminishes to zero differ for different demographic modelling approaches. Both the simple and the complex demographic approaches predicted prolonged periods of relatively stable carbon densities after 2100, with minor declines, until the end of simulations in 2300. In contrast, the intermediate demographic approach predicted the CHF would become a carbon source between 2110 and 2260, followed by another carbon sink period. The disagreement between these patterns can be partly explained by differences in the capacity of models to simulate gross growth (both birth and subsequent growth) and mortality of short-lived, relatively shade-intolerant tree species.

Effects of species biological traits and environmental heterogeneity on simulated tree species distribution shifts under climate change

Demographic processes (fecundity, dispersal, colonization, growth, and mortality) and their interactions with environmental changes are not well represented in current climate-distribution models (e.g., niche and biophysical process models) and constitute a large uncertainty in projections of future tree species distribution shifts. We investigate how species biological traits and environmental heterogeneity affect species distribution shifts. We used a species-specific, spatially explicit forest dynamic model LANDIS PRO, which incorporates site-scale tree species demography and competition, landscape-scale dispersal and disturbances, and regional-scale abiotic controls, to simulate the distribution shifts of four representative tree species with distinct biological traits in the central hardwood forest region of United States. Our results suggested that biological traits (e.g., dispersal capacity, maturation age) were important for determining tree species distribution shifts. Environmental heterogeneity, on average, reduced shift rates by 8% compared to perfect environmental conditions. The average distribution shift rates ranged from 24 to 200myear-1 under climate change scenarios, implying that many tree species may not able to keep up with climate change because of limited dispersal capacity, long generation time, and environmental heterogeneity. We suggest that climate-distribution models should include species demographic processes (e.g., fecundity, dispersal, colonization), biological traits (e.g., dispersal capacity, maturation age), and environmental heterogeneity (e.g., habitat fragmentation) to improve future predictions of species distribution shifts in response to changing climates.