Simulated Patterns of Forest Succession and Productivity as a Consequence of Altered Precipitation

Wullschleger, Stan D.; Gunderson, Carla A.; Tharp, Marie; West, D.C.; Post, W. M.

doi:10.1007/978-1-4613-0021-2_25

Cited by 18 publications

(24 citation statements)

References 24 publications

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“…The LINKAGES model was used to predict tree growth (biomass) for 23 species within the assessment area after 30 years of establishment and growth on a plot from bare ground (Chapter 2) (Wullschleger et al 2003). Section-level estimates were derived from the weighted average of 0.2-acre plots within landforms in 2 subsections.…”

Section: Linkagesmentioning

confidence: 99%

“…The LINKAGES model (version 2.2; Wullschleger et al 2003) is a forest succession and ecosystem dynamics process model modified from an earlier version of LINKAGES (Pastor and Post 1985). The LINKAGES model integrates establishment and growth of individual tree species over 30 years on a plot from bare ground, incorporating ecosystem functions such as soil-water balance, litter decomposition, nitrogen cycling, soil hydrology, and evapotranspiration.…”

Section: Linkagesmentioning

confidence: 99%

“…These models generally fall in one of two main categories: species distribution models (SDMs) and process models. In this assessment, we used one species distribution model, the Climate Change Tree Atlas (Landscape Change Research Group 2014), and two process models, LINKAGES (version 2.2; Wullschleger et al 2003) and LANDIS PRO (Wang et al 2013). These models operate at different spatial scales and provide different kinds of information.…”

Section: Impact Modelsmentioning

confidence: 99%

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

Butler¹,

Iverson²,

Thompson³

et al. 2015

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Forest ecosystems across the Central Appalachians will be affected directly and indirectly by a changing climate over the 21st century. This assessment evaluates the vulnerability of nine forest ecosystems in the Central Appalachian Broadleaf Forest-Coniferous Forest-Meadow and Eastern Broadleaf Forest Provinces of Ohio, West Virginia, and Maryland for a range of future climates. We synthesized and summarized information on the contemporary landscape, provided information on past climate trends, and described a range of projected future climates. This information was used to parameterize and run multiple vegetation impact models, which provided a range of potential tree responses to climate. Finally, we brought these results before a multidisciplinary panel of scientists, land managers, and academics familiar with the forests of this region to assess ecosystem vulnerability through a formal consensus-based expert elicitation process.The summary of the contemporary landscape identifies major forest trends and stressors currently threatening forests in the region. Observed trends in climate over the past century reveal that average minimum temperatures have increased in the area, particularly in summer and fall. Precipitation has also increased in the area, particularly in fall. Projected climate trends for the next 100 years using downscaled global climate model data indicate a potential increase in mean annual temperature of 2 to 8 °F for the assessment area. Projections for precipitation indicate increases in winter and spring precipitation, and summer and fall precipitation projections vary by scenario. We identified potential impacts on forests by incorporating these future climate projections into three forest impact models (DISTRIB, LINKAGES, and LANDIS PRO). Model projections suggest that many mesic species, including American beech, eastern hemlock, eastern white pine, red spruce, and sugar maple may fare worse under future conditions, but other species such as eastern redcedar may benefit from projected changes in climate. Published literature on climate impacts related to wildfire, invasive species, and forest pests and diseases also contributed to the overall determination of climate change vulnerability.We assessed vulnerability for nine forest ecosystems in the assessment area. The assessment was conducted through a formal elicitation process of 19 science and management experts from across the area, who considered vulnerability in terms of the potential impacts on a forest ecosystem and the adaptive capacity of the ecosystem. Appalachian (hemlock)/northern hardwood forests, large stream floodplain and riparian forests, small stream riparian forests, and spruce/fir forests were determined to be the most vulnerable ecosystems. Dry/mesic oak forests and dry oak and oak/pine forests and woodlands were perceived as less vulnerable to projected changes in climate. These projected changes in climate and the associated impacts and vulnerabilities will have important implications for economically valuable ti...

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Section: Linkagesmentioning

confidence: 99%

Section: Linkagesmentioning

confidence: 99%

Section: Impact Modelsmentioning

confidence: 99%

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

Butler¹,

Iverson²,

Thompson³

et al. 2015

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“…SEP is the same parameter used in previous LANDIS models (Mladenoff and He 1999), LANDIS II (Scheller et al 2007), and LANDCLIM (Schumacher et al 2004), which are estimated from a coupled landscape and ecosystem modeling approach to simulate the effects of climate change on seedling establishment. For example, SEPs used in LANDIS PRO are derived from LINKAGES II (Wullschleger et al 2003) and SEPs used in LANDIS II are derived from PnET II. MGSO is determined by environmental conditions.…”

Section: Methodsmentioning

confidence: 99%

“…4). SEPs by species for each land type were derived from existing forest inventory data for the Boston Mountains, Arkansas (Spetich and He 2008) that were estimated from ecosystem model LINKAGES II (Wullschleger et al 2003). Four values of GSO (GSO 1 , GSO 2 , GSO 3 and MGSO) were defined by land type and calibrated against FIA data.…”

Section: Landscape Initializationmentioning

confidence: 99%

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

Wang

Spetich

et al. 2013

Ecosphere

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Abstract. Two challenges confronting forest landscape models (FLMs) are how to simulate fine, standscale processes while making large-scale (i.e., .10 7 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 (;10 7 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.

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Comparison of a species distribution model and a process model from a hierarchical perspective to quantify effects of projected climate change on tree species

Schneiderman

Thompson

et al. 2015

Landscape Ecol

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Context Tree species distribution and abundance are affected by forces operating across a hierarchy of ecological scales. Process and species distribution models have been developed emphasizing forces at different scales. Understanding model agreement across hierarchical scales provides perspective on prediction uncertainty and ultimately enables policy makers and managers to make better decisions. Objective Our objective was to test the hypothesis that agreement between process and species distribution models varies by hierarchical level. Due to the top-down approach of species distribution models and the bottom-up approach of process models, the most agreement will occur at the mid-level of the hierarchical analysis, the ecological subsection level, capturing the effects of soil variables. MethodsWe compared projections of a species distribution model, Climate Change Tree Atlas, and a process model, LINKAGES 2.2. We conducted a correlation analysis between the models at regional, ecological subsection, and species level hierarchical scales. Results Both models had significant positive correlation (q = 0.53, P \ 0.001) on the regional scale. The majority of the ecological subsections had greater model correlation than on the regional level when all climate scenarios were pooled. Correlation was poorest for the analysis of individual species. Models had the greatest correlation at the regional scale for the GFDL-A1fi scenario (the scenario with the most climate change). Species near their range edge generally had stronger correlation (loblolly pine, northern red oak, black oak). Conclusion Our general hypothesis was partly accepted. This suggests that uncertainties are relatively low when interpreting model results at subsection level.

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Simulated Patterns of Forest Succession and Productivity as a Consequence of Altered Precipitation

Cited by 18 publications

References 24 publications

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

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

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

Comparison of a species distribution model and a process model from a hierarchical perspective to quantify effects of projected climate change on tree species

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