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Special Issue "Simulation Modeling of Forest Ecosystems"

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Quantitative Methods and Remote Sensing".

Deadline for manuscript submissions: closed (15 August 2018)

Special Issue Editors

Guest Editor
Prof. Herman H. Shugart

Department of Environmental Sciences, The University of Virginia, Charlottesville, Virginia, USA
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Interests: forest Ecology; global ecology and climate change; ecological modeling
Guest Editor
Dr. Amanda H. Armstrong

University of Virginia, and Universities Space Research Association, GESTAR, NASA Goddard Space Flight Center, USA
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Interests: forest modeling; forest ecology; forest structure; remote sensing; global change ecology
Guest Editor
Dr. Adrianna C. Foster

NASA Postdoctoral Program Fellow, NASA Goddard Space Flight Center, USA
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Interests: remote sensing and modeling of forest response to climate; disturbances; and other stressors

Special Issue Information

Dear Colleague,

This issue of Forests will treat the development, applications and testing of a set of forest models called “Individual-Based Models” (IBMs). IBMs simulate the birth, growth and death of the individual trees that in summation comprise a forested region. IBMs are in use from local-scale to continental-scale and even global-scale applications to assess the potential consequences of climate and environmental change on forests. This Special Issue of Forests focuses on studies involving the development, testing and application of IBMs projecting change in forest ecosystems. Research articles may focus on any of these areas. Comparisons between models are encouraged, as are papers that focus on new representations of environmental change, incorporation of disturbance effects on forests, and the treatment of spatial effects among trees.

Keywords

  • forest modeling
  • individual-based models
  • disturbance modeling
  • global change and forests
  • forest dynamics

Published Papers (6 papers)

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Research

Open AccessArticle Consequences of a Reduced Number of Plant Functional Types for the Simulation of Forest Productivity
Forests 2018, 9(8), 460; https://doi.org/10.3390/f9080460
Received: 25 June 2018 / Revised: 20 July 2018 / Accepted: 25 July 2018 / Published: 28 July 2018
Cited by 1 | PDF Full-text (15239 KB) | HTML Full-text | XML Full-text
Abstract
Tropical forests represent an important pool in the global carbon cycle. Their biomass stocks and carbon fluxes are variable in space and time, which is a challenge for accurate measurements. Forest models are therefore used to investigate these complex forest dynamics. The challenge [...] Read more.
Tropical forests represent an important pool in the global carbon cycle. Their biomass stocks and carbon fluxes are variable in space and time, which is a challenge for accurate measurements. Forest models are therefore used to investigate these complex forest dynamics. The challenge of considering the high species diversity of tropical forests is often addressed by grouping species into plant functional types (PFTs). We investigated how reduced numbers of PFTs affect the prediction of productivity (GPP, NPP) and other carbon fluxes derived from forest simulations. We therefore parameterized a forest gap model for a specific study site with just one PFT (comparable to global vegetation models) on the one hand, and two versions with a higher amount of PFTs, on the other hand. For an old-growth forest, aboveground biomass and basal area can be reproduced very well with all parameterizations. However, the absence of pioneer tree species in the parameterizations with just one PFT leads to a reduction in estimated gross primary production by 60% and an increase of estimated net ecosystem exchange by 50%. These findings may have consequences for productivity estimates of forests at regional and continental scales. Models with a reduced number of PFTs are limited in simulating forest succession, in particular regarding the forest growth after disturbances or transient dynamics. We conclude that a higher amount of species groups increases the accuracy of forest succession simulations. We suggest using at a minimum three PFTs with at least one species group representing pioneer tree species. Full article
(This article belongs to the Special Issue Simulation Modeling of Forest Ecosystems)
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Open AccessArticle Modeling Production Processes in Forest Stands: An Adaptation of the Solow Growth Model
Forests 2018, 9(7), 391; https://doi.org/10.3390/f9070391
Received: 28 May 2018 / Revised: 27 June 2018 / Accepted: 28 June 2018 / Published: 2 July 2018
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Abstract
The model of forest stand growth proposed in this study is based on R. Solow’s model of economic growth. The variables introduced into the model are the “capital” (the phytomass of the non-synthesizing tree components in the stand—the stem, roots, and branches) and [...] Read more.
The model of forest stand growth proposed in this study is based on R. Solow’s model of economic growth. The variables introduced into the model are the “capital” (the phytomass of the non-synthesizing tree components in the stand—the stem, roots, and branches) and the “labor” (the phytomass of the photosynthesizing tree components in the stand—leaves or needles). Root phytomass is calculated with a special independent model. The process of energy production by the trees is described with the Cobb-Douglas equation. The proposed approach is used to describe growth processes in the forest stands comprising various species in Siberia and the age dynamics of net primary production. The model can explain a number of effects (such as death of the forest stand after the needles have been consumed by defoliating insects) that cannot be explained by standard logistic models. Full article
(This article belongs to the Special Issue Simulation Modeling of Forest Ecosystems)
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Open AccessArticle Climate Sensitive Tree Growth Functions and the Role of Transformations
Forests 2018, 9(7), 382; https://doi.org/10.3390/f9070382
Received: 24 April 2018 / Revised: 11 June 2018 / Accepted: 21 June 2018 / Published: 26 June 2018
Cited by 1 | PDF Full-text (2003 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The aim of this study is to develop climate-sensitive single-tree growth models, to be used in stand based prediction systems of managed forest in Switzerland. Long-term observations from experimental forest management trials were used, together with retrospective climate information from 1904 up to [...] Read more.
The aim of this study is to develop climate-sensitive single-tree growth models, to be used in stand based prediction systems of managed forest in Switzerland. Long-term observations from experimental forest management trials were used, together with retrospective climate information from 1904 up to 2012. A special focus is given to the role of transformation of modelling basal area increment, helping to normalize the random error distribution. A nonlinear model formulation was used to describe the basic relation between basal area increment and diameter at breast height. This formulation was widely expanded by groups of explanatory variables, describing competition, stand development, site, stand density, thinning, mixture, and climate. The models are species-specific and contain different explanatory variables per group, being able to explain a high amount of variance (on the original scale, up to 80% in the case of Quercus spec.). Different transformations of the nonlinear relation where tested and based on the mean squared error, the square root transformation performed best. Although the residuals were homoscedastic, they were still long-tailed and not normal distributed, making robust statistics the preferred method for statistical inference. Climate is included as a nonlinear and interacting effect of temperature, precipitation and moisture, with a biological meaningful interpretation per tree species, e.g., showing better growth for Abies alba in warm and wet climates and good growing conditions for Picea abies in colder and dryer climates, being less sensitive on temperature. Furthermore, a linear increase in growth was found to be present since the 1940s. Potentially this is an effect of the increased atmospheric CO2 concentration or changed management in terms of reduced nutrient subtractions from forest ground, since industrialization lowered the demand of residue and slash uptake. Full article
(This article belongs to the Special Issue Simulation Modeling of Forest Ecosystems)
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Open AccessArticle Simulating Forest Dynamics of Lowland Rainforests in Eastern Madagascar
Forests 2018, 9(4), 214; https://doi.org/10.3390/f9040214
Received: 22 February 2018 / Revised: 9 April 2018 / Accepted: 11 April 2018 / Published: 18 April 2018
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Abstract
Ecological modeling and forecasting are essential tools for the understanding of complex vegetation dynamics. The parametric demands of some of these models are often lacking or scant for threatened ecosystems, particularly in diverse tropical ecosystems. One such ecosystem and also one of the [...] Read more.
Ecological modeling and forecasting are essential tools for the understanding of complex vegetation dynamics. The parametric demands of some of these models are often lacking or scant for threatened ecosystems, particularly in diverse tropical ecosystems. One such ecosystem and also one of the world’s biodiversity hotspots, Madagascar’s lowland rainforests, have disappeared at an alarming rate. The processes that drive tree species growth and distribution remain as poorly understood as the species themselves. We investigated the application of the process-based individual-based FORMIND model to successfully simulate a Madagascar lowland rainforest using previously collected multi-year forest inventory plot data. We inspected the model’s ability to characterize growth and species abundance distributions over the study site, and then validated the model with an independently collected forest-inventory dataset from another lowland rainforest in eastern Madagascar. Following a comparative analysis using inventory data from the two study sites, we found that FORMIND accurately captures the structure and biomass of the study forest, with r2 values of 0.976, 0.895, and 0.995 for 1:1 lines comparing observed and simulated values across all plant functional types for aboveground biomass (tonnes/ha), stem numbers, and basal area (m2/ha), respectively. Further, in validation with a second study forest site, FORMIND also compared well, only slightly over-estimating shade-intermediate species as compared to the study site, and slightly under-representing shade-tolerant species in percentage of total aboveground biomass. As an important application of the FORMIND model, we measured the net ecosystem exchange (NEE, in tons of carbon per hectare per year) for 50 ha of simulated forest over a 1000-year run from bare ground. We found that NEE values ranged between 1 and −1 t Cha−1 year−1, consequently the study forest can be considered as a net neutral or a very slight carbon sink ecosystem, after the initial 130 years of growth. Our study found that FORMIND represents a valuable tool toward simulating forest dynamics in the immensely diverse Madagascar rainforests. Full article
(This article belongs to the Special Issue Simulation Modeling of Forest Ecosystems)
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Open AccessArticle An Inventory-Based Regeneration Biomass Model to Initialize Landscape Scale Simulation Scenarios
Forests 2018, 9(4), 212; https://doi.org/10.3390/f9040212
Received: 18 March 2018 / Revised: 5 April 2018 / Accepted: 12 April 2018 / Published: 17 April 2018
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Abstract
Dynamic landscape simulation of the forest requires an initial regeneration stock specific to the characteristics of each simulated stand. Forest inventories, however, are sparse with regard to regeneration. Moreover, statistical regeneration models are rare. We introduce an inventory-based statistical model type that (1) [...] Read more.
Dynamic landscape simulation of the forest requires an initial regeneration stock specific to the characteristics of each simulated stand. Forest inventories, however, are sparse with regard to regeneration. Moreover, statistical regeneration models are rare. We introduce an inventory-based statistical model type that (1) quantifies regeneration biomass as a fundamental regeneration attribute and (2) uses the overstory’s quadratic mean diameter (Dq) together with several other structure attributes and the Site Index as predictors. We form two such models from plots dominated by European beech (Fagus sylvatica L.), one from national forest inventory data and the other from spatially denser federal state forest inventory data. We evaluate the first one for capturing the predictors specific to the larger scale level and the latter one to infer the degree of landscape discretization above which the model bias becomes critical due to yet unquantified determinants of regeneration. The most relevant predictors were Dq, stand density, and maximum height (significance level p < 0.0001). If plot data sets for evaluation differed by the forest management unit in addition to the average diameter, the bias range among them increased from 0.1-fold of predicted biomass to 0.3-fold. Full article
(This article belongs to the Special Issue Simulation Modeling of Forest Ecosystems)
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Open AccessArticle Development of an Integrated DBH Estimation Model Based on Stand and Climatic Conditions
Forests 2018, 9(3), 155; https://doi.org/10.3390/f9030155
Received: 6 February 2018 / Revised: 9 March 2018 / Accepted: 19 March 2018 / Published: 20 March 2018
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Abstract
Using Korean National Forest Inventory (NFI) data, our study developed a model to estimate stand mean diameter at breast height (DBH) reflecting the influence of site and climate factors on forest growth for the major tree species in South Korea. A DBH estimation [...] Read more.
Using Korean National Forest Inventory (NFI) data, our study developed a model to estimate stand mean diameter at breast height (DBH) reflecting the influence of site and climate factors on forest growth for the major tree species in South Korea. A DBH estimation model was developed using stand-level variables (stand age, site index and number of trees per hectare) as independent factors. The spatial autocorrelation of residuals of the model was identified using semi-variogram analysis for each tree species. Further, a residual model, in which residuals were estimated by climatic factors (mean temperature, sum temperature in the growing season and precipitation), was developed assuming that the spatial autocorrelation of residuals reflects the differences in regional climatic conditions. Linear regression analysis showed that residuals of all tree species were significantly correlated with temperature and precipitation. The DBH and residual models were integrated to estimate the current DBH under different climatic factors (temperature and precipitation) and stand-level variables. This model had high reliability (R2 = 0.74–0.79), and no obvious dependencies or patterns in residuals were noted. Our results indicated that temperature increases caused by climate change would negatively affect the DBH estimate of coniferous trees, but not of oak species. Full article
(This article belongs to the Special Issue Simulation Modeling of Forest Ecosystems)
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