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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 35535

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Dr. Yueh-Hsin Lo

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Dep. Ciencias, Universidad Publica de Navarra Campus de Arrosadia, Pamplona, 31006 Navarra, Spain
Interests: ecological modelling; dendroclimatology; forest management; climate change; forest ecology
Special Issues, Collections and Topics in MDPI journals

Dr. Ester González-de-Andrés

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Instituto Pirenaico de Ecología (IPE-CSIC), 50059 Zaragoza, Spain
Interests: forest ecology; global change; drought resilience; forest decline; dendroecology; ecological stoichiometry; intraspecific variability
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Special Issue Information

Dear Colleagues,

Forest ecosystems are, by definition, complex systems in which biological, climate and geo-topographic factors interact to produce different patterns of species distributions and species colonization, growth and mortality rates. In addition, such natural complexity is combined with socio-economic factors defining different management systems around the world. Therefore, the study and management of forest ecosystems are always challenging tasks. Hence, any tool that can simplify the study of these factors and their interactions and, at the same time, help to predict the effects of altering such factors would be of great help for scientists, technicians and decision makers. Models are such type of tool. Models can be used to know more about how the different parts that form a forest ecosystem work, but also to better understand the interactions among such parts. Nevertheless, even more importantly, models can help to predict possible future ecosystem states, depending on changes in different parts of the ecosystem and environmental factors. Models can be used at a wide range of ecological, spatial and temporal scales, from simulating rapid ecophysiological processes at the leaf level such as photosynthesis to slow environmental changes at the continental level such as regional colonization or distribution patterns. In addition, the challenge of global change brings an even greater need to use predictive tools that can estimate future ecological processes in forests under possible environmental conditions that have not been seen before.

This Special Issue will therefore be devoted to collecting results in the theory and application of forest models, with the aim of improving the understanding of forest ecosystems and their possible futures. The subject of the Issue will be focused on, but not limited to, the following:

New numerical models to understand ecophysiological processes at the plant and tree levels.
New numerical models to explore the relationships between different ecosystem components at the ecosystem level.
New numerical models exploring the implications for future forest distributions and compositions of global change.
New applications of current models exploring novel environmental issues.
New applications of current models exploring possible forest futures for forest types and tree species not modeled before.

Dr. Yueh-Hsin Lo
Dr. Ester González-de-Andrés
Guest Editors

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Keywords

physiological models

process-based model

hybrid models

nutrient cycling models

operational models

disturbance models

individual tree models

stand-level models

landscape-level models

species distribution models

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Published Papers (11 papers)

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Research

20 pages, 3083 KiB

Open AccessArticle

Modelling the Development of Above-Ground Biomass Energy Reserves of Four Economically Important Coniferous Woody Species

by Rudolf Petráš, Julian Mecko, Ján Kukla, Margita Kuklová, František Hnilička, Helena Hniličková and Ivica Pivková

Forests 2023, 14(2), 388; https://doi.org/10.3390/f14020388 - 14 Feb 2023

Cited by 1 | Viewed by 1621

Abstract

The goal of renewable energy is to replace energy production from fossil fuels. In that sense, forest biomass is essential renewables. This article presents the results of the development of energy reserves in fractions, increments and the total above-ground biomass of coniferous stands (spruce, fir, pine, larch) during their economic cycle. The experimental material comes from 22 forest stands located mainly in Central Slovakia, to a lesser extent also in Western and Eastern Slovakia. Energy reserves of coniferous stands were calculated based on the volume production of above-ground biomass fractions taken from mathematical models of yield tables and average values of their basic density and calorific value were determined. The research showed that as the age of the stands increased, the share of energy in the wood fraction increased, while it decreased in the bark fraction, and especially the branch fraction. The curves constructed in relation to the age of the stand and site index have a very similar shape to the curves of the total current annual energy increment of coniferous stands. The energy reserves of stands grew faster at the age of 40 to 80 years than at the age of 80 to 140 years. Spruce had the highest total mean energy increment, followed by fir, larch and pine. As the age of the stands increases, the energy reserves of the increments slightly decrease and the efficiency of solar energy significantly decreases. It peaks practically at the age of reaching the maximum annual energy increment. Full article

(This article belongs to the Special Issue Modelling Forest Ecosystems)

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18 pages, 1289 KiB

Open AccessArticle

Mathematical Model of Basal Sprout Production in Vector-Borne Tree Disease

by Kelly Ruth Buch and Nina H. Fefferman

Forests 2023, 14(2), 349; https://doi.org/10.3390/f14020349 - 9 Feb 2023

Cited by 2 | Viewed by 2048

Abstract

Some tree species respond to disease by producing basal sprouts from the base and root system of a dying tree, which can alter disease dynamics by altering demography. In the case of many lethal, airborne tree diseases, the production of basal sprouts can be a key contributor to population resurgence post-epidemic, but the effect in lethal, vector-borne tree diseases has not yet been studied. To determine the role of basal sprout production and secondary infection via the root system of infected parent trees in lethal, vector-borne tree diseases, we develop a stage-structured SI-X mathematical model and use laurel wilt, a vector-borne tree disease in which infected trees provide suitable material for vector reproduction, as our model system. The mathematical model shows that the production and secondary infection of basal sprouts do not affect the short-term dynamics of laurel wilt but profoundly alter the long-term dynamics of the laurel wilt epidemic. In particular, in the absence of basal sprout infection, basal sprout production yields a larger host population after disease establishment, but as secondary infection increases, the utility of basal sprouts to maintain the host population decreases. Results suggest management strategies for lethal, vector-borne diseases should depend on the ratio of the basal sprout production rate to the secondary infection rate. Full article

(This article belongs to the Special Issue Modelling Forest Ecosystems)

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16 pages, 3611 KiB

Open AccessArticle

Evaluation of the Community Land Model-Simulated Specific Leaf Area with Observations over China: Impacts on Modeled Gross Primary Productivity

by Yuanhao Zheng, Li Zhang, Pan Li, Xiaoli Ren, Honglin He, Yan Lv and Yuping Ma

Forests 2023, 14(1), 164; https://doi.org/10.3390/f14010164 - 16 Jan 2023

Cited by 1 | Viewed by 3061

Abstract

Specific leaf area (SLA) is a key leaf functional trait associated with the ability to acquire light. Substantial variations in SLA have not been well described in the community land model (CLM) and similar terrestrial biosphere models. How these SLA variations influence the simulation of gross primary productivity (GPP) remains unclear. Here, we evaluated the mismatch in SLA between the CLM4.5 and observed data collected from China and quantified the impacts of SLA variation calculated from both observations and the default values across seven terrestrial biosphere models on modeled GPP using CLM4.5. The results showed that CLM4.5 tended to overestimate SLA values at the top and gradient of the canopy. The higher default SLA values could cause an underestimation of the modeled GPP by 5–161 g C m⁻² yr⁻¹ (1%–7%) for temperate needleleaf evergreen tree (NET), temperate broadleaf deciduous tree (BDT), and C3 grass and an overestimation by 50 g C m⁻² yr⁻¹ (2%) for temperate broadleaf evergreen tree (BET). Moreover, the observed SLA variation among species ranged from 21% to 59% for 14 plant functional types (PFTs), which was similar to the variation in default SLA values across models (9%–60%). These SLA variations would lead to greater changes in modeled GPP by 7%–19% for temperate NET and temperate BET than temperate BDT and C3 grass (2%–9%). Our study suggested that the interspecific variation in SLA and its responses to environmental factors should be involved in terrestrial biosphere models; otherwise, it would cause substantial bias in the prediction of ecosystem productivity. Full article

(This article belongs to the Special Issue Modelling Forest Ecosystems)

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23 pages, 4713 KiB

Open AccessArticle

Modeling Number of Trees per Hectare Dynamics for Uneven-Aged, Mixed-Species Stands Using the Copula Approach

by Petras Rupšys and Edmundas Petrauskas

Forests 2023, 14(1), 12; https://doi.org/10.3390/f14010012 - 21 Dec 2022

Cited by 3 | Viewed by 2346

Abstract

For the monitoring and management of forest resources, the main index is the stand volume, which is determined on the basis of the tree diameter, height, and number of trees per hectare of three-dimensional distribution. The development of trees in the forest stand is dynamic and is driven by random phenomena. In this study, the tree diameter, the potentially available area, and the height are described by the mixed-effect parameters of the Gompertz-type diffusion process. A normal copula function is used to connect a three-dimensional distribution to its one-dimensional margins. The newly developed model was illustrated using empirical data from 53 permanent experimental plots (measured for seven cycles), which were characterized as follows: pine forests (Pinus sylvestris), 63.8%; spruce (Picea abies), 30.2%; silver birch (Betula pendula Roth and Betula pubescens Ehrh.), 5.8%; and others, 0.2%. An analysis of the tree diameter and height of growth, including current and mean increments and inflection points, is presented. The models for the change in the number of trees per hectare with age are presented on the basis of the probabilistic density functions of the solutions of stochastic differential equations and the copula function. The dynamics of the number of trees per hectare are visualized graphically, and the goodness of fit of the newly developed models is evaluated using standard statistical measures. Full article

(This article belongs to the Special Issue Modelling Forest Ecosystems)

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19 pages, 2892 KiB

Open AccessArticle

Feasibility of Agarwood Cultivation in Indonesia: Dynamic System Modeling Approach

by Lutfy Abdulah, Ruliyana Susanti, Joeni Setijo Rahajoe, Tika Dewi Atikah, Subarudi Subarudi, Rosita Dewi, Ika Heriansyah, Muhammad Abdul Qirom, Kusuma Rahmawati, Asep Hidayat, Rachman Effendi, Tien Wahyuni, Donny Wicaksono, Darwo Darwo, Yunita Lisnawati, Mawazin Mawazin, Nina Mindawati, Asmanah Widarti, Bayu Arief Pratama, Julianus Kinho, Satria Astana, Rinaldi Imanuddin and Maman Turjaman Show full author list Hide full author list

Forests 2022, 13(11), 1869; https://doi.org/10.3390/f13111869 - 8 Nov 2022

Cited by 3 | Viewed by 5492

Abstract

Most of the Indonesian agarwood in the international market is harvested from the wild, which raises concerns regarding its sustainability. The Government of Indonesia encourages agarwood cultivation produced from the cultivated Agarwood-Producing Tree (APT) to overcome this concern and replace natural agarwood. APT cultivation in Indonesia is not a new development, but it has faced various obstacles, ranging from production quantity and quality to funding and marketing. Therefore, an appropriate policy is needed to support the success of APT cultivation. This study aims to develop a dynamic system model in order to identify the gaps and determine appropriate policy strategies to improve APT cultivation in Indonesia. The model was established by compiling three conceptual stages: planting to harvest, cost–benefit analysis, and feasibility analysis. Agarwood from Aquilaria malaccensis Lam. cultivated by the community and private sector, which produces kemedangan (an agarwood grade in the Indonesian market) and oil, was chosen for the model. The model developed shows that APT cultivation development in the private sector and the community is unfeasible with the business as usual. There are three options to simulate the feasibility of agarwood produces from APT cultivation. The best scenarios are chosen based on the feasibility indicator, when benefit is higher than cost. The development of APT by the private sector that produces kemedangan and oil products is feasible, with the invention of more effective inoculant and processing technology (scenario 1), as well as applying high thinning, which can increase the yield. Oil production requires more investment, so the revenue obtained is lower than the production cost, resulting in the unfeasibility of the production. The development of APT by the community will be feasible with scenario 2, if there is government funding for the establishment of APT cultivation, inoculants application, and harvesting. Based on the model scenario, APT cultivation will be ecologically sustainable, economically feasible, and socially acceptable if carried out by the private sector or the community by applying inoculation techniques and selecting inoculants to increase production effectiveness, and will be supported by lower production costs and market stability. The Indonesian government needs to take several policies to encourage APT development, including financial assistance for APT development, setting inoculant standards at affordable prices, simplifying trade administration, stabilizing agarwood product prices at the local level, and law enforcement. Full article

(This article belongs to the Special Issue Modelling Forest Ecosystems)

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13 pages, 2239 KiB

Open AccessArticle

Do AI Models Improve Taper Estimation? A Comparative Approach for Teak

by Víctor Hugo Fernández-Carrillo, Víctor Hugo Quej-Chi, Hector Manuel De los Santos-Posadas and Eugenio Carrillo-Ávila

Forests 2022, 13(9), 1465; https://doi.org/10.3390/f13091465 - 11 Sep 2022

Cited by 2 | Viewed by 2706

Abstract

Correctly estimating stem diameter at any height is an essential task in determining the profitability of a commercial forest plantation, since the integration of the cross-sectional area along the stem of the trees allows estimating the timber volume. In this study the ability of four artificial intelligence (AI) models to estimate the stem diameter of Tectona grandis was assessed. Genetic Programming (PG), Gaussian Regression Process (PGR), Category Boosting (CatBoost) and Artificial Neural Networks (ANN) models’ ability was evaluated and compared with those of Fang 2000 and Kozak 2004 conventional models. Coefficient of determination (R²), Root Mean Square of Error (RMSE), Mean Error of Bias (MBE) and Mean Absolute Error (MAE) statistical indices were used to evaluate the models’ performance. Goodness of fit criterion of all the models suggests that Kozak’s model shows the best results, closely followed by the ANN model. However, PG, PGR and CatBoost outperformed the Fang model. Artificial intelligence methods can be an effective alternative to describe the shape of the stem in Tectona grandis trees with an excellent accuracy, particularly the ANN and CatBoost models. Full article

(This article belongs to the Special Issue Modelling Forest Ecosystems)

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23 pages, 3050 KiB

Open AccessArticle

Modeling Optimal Forest Rotation Age for Carbon Sequestration in the Great Khingan Mountains of Northeast China

by Yuzhe Li, Tao Luo, Shuzhen Li and Bin Liu

Forests 2022, 13(6), 838; https://doi.org/10.3390/f13060838 - 27 May 2022

Cited by 6 | Viewed by 2570

Abstract

The growing concern about climate change has led to the rise of carbon cycle research. Forest cutting planning affects the carbon cycle due to the carbon sequestration function of forests. In this work, we propose a planning model for determining the regeneration cutting age of forests to optimize carbon sequestration and improving the associated economic and ecological benefits. We first built a model based on the carbon sequestration consumption of forest products and forest carbon sequestration to predict the change in forest carbon sequestration over time. The accuracy of the model was verified with forest data from the Great Khingan mountains. Furthermore, we added in economic and ecological factors to build an improved model, which was also applied to the Great Khingan forest. The improved regeneration cutting ages were calculated as 65, 134, 123, 111 and 73 years for white birch, larch, Scots pine, oak, and poplar trees for natural forests, whereas the ages were 34, 65, 64, 77 and 37 years for planted forests, respectively. It can be predicted that the total carbon sequestration in the Great Khingan forests will accumulate to 974.80 million tons after 100 years. The results of this study can provide useful guidance for local governments to develop a sustainable timeline for forest harvesting to optimize carbon sequestration and improve the associated economic and ecological benefits. Full article

(This article belongs to the Special Issue Modelling Forest Ecosystems)

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21 pages, 4458 KiB

Open AccessArticle

Highlighting Complex Long-Term Succession Pathways in Mixed Forests of the Pacific Northwest: A Markov Chain Modelling Approach

by Juan A. Blanco, Yueh-Hsin Lo, James P. Kimmins and Adrian Weber

Forests 2021, 12(12), 1770; https://doi.org/10.3390/f12121770 - 14 Dec 2021

Cited by 2 | Viewed by 3163

Abstract

Forest succession is an ecological phenomenon that can span centuries. Although the concept of succession was originally formulated as a deterministic sequence of different plant communities by F. Clements more than a century ago, nowadays it is recognized that stochastic events and disturbances play a pivotal role in forest succession. In spite of that, forest maps and management plans around the world are developed and focused on a unique “climax” community, likely due to the difficulty of quantifying alternative succession pathways. In this research, we explored the possibility of developing a Markov Chain model to study multiple pathway succession scenarios in mixed forests of western red cedar, hemlock and Pacific silver fir on northern Vancouver Island (western Canada). We created a transition matrix using the probabilities of change between alternative ecological stages as well as red cedar regeneration. Each ecological state was defined by the dominant tree species and ages. Our results indicate that, compared to the traditional Clementsian, deterministic one-pathway succession model, which is unable to replicate current stand distribution of these forests in the region, a three-pathway stochastic succession model, calibrated by a panel of experts, can mimic the observed landscape distribution among different stand types before commercial logging started in the region. We conclude that, while knowing the difficulty of parameterizing this type of models, their use is needed to recognize that for a given site, there may be multiple “climax” communities and hence forest management should account for them. Full article

(This article belongs to the Special Issue Modelling Forest Ecosystems)

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15 pages, 4321 KiB

Open AccessArticle

Estimating the Potential Impacts of Climate Change on the Spatial Distribution of Garuga forrestii, an Endemic Species in China

by Bashir B. Tiamiyu, Boniface K. Ngarega, Xu Zhang, Huajie Zhang, Tianhui Kuang, Gui-Yun Huang, Tao Deng and Hengchang Wang

Forests 2021, 12(12), 1708; https://doi.org/10.3390/f12121708 - 6 Dec 2021

Cited by 8 | Viewed by 3285

Abstract

Understanding how species have adapted and responded to past climate provides insights into the present geographical distribution and may improve predictions of how biotic communities will respond to future climate change. Therefore, estimating the distribution and potentially suitable habitats is essential for conserving sensitive species such as Garuga forrestii W.W.Sm., a tree species endemic to China. The potential climatic zones of G. forrestii were modelled in MaxEnt software using 24 geographic points and nine environmental variables for the current and future (2050 and 2070) conditions under two climate representative concentration pathways (RCP4.5 and RCP8.5) scenarios. The resulting ecological niche models (ENMs) demonstrated adequate internal assessment metrics, with all AUC and TSS values being >0.79 and a pROC of >1.534. Our results also showed that the distribution of G. forrestii was primarily influenced by temperature seasonality (% contribution = 12%), elevation (% contribution = 27.5%), and precipitation of the wettest month (% contribution = 35.6%). Our findings also indicated that G. forrestii might occupy an area of 309,516.2 km² in southwestern China. We note that the species has a potential distribution in three provinces, including Yunnan, Sichuan, and Guangxi. A significant decline in species range is observed under the future worst case of high-emissions scenario (RCP8.5), with about 19.5% and 20% in 2050 and 2070, respectively. Similarly, higher elevations shift northward to southern parts of Sichuan province in 2050 and 2070. Thus, this study helps highlight the vulnerability of the species, response to future climate and provides an insight to assess habitat suitability for conservation management. Full article

(This article belongs to the Special Issue Modelling Forest Ecosystems)

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14 pages, 1421 KiB

Open AccessArticle

Regularized Regression: A New Tool for Investigating and Predicting Tree Growth

by Stuart I. Graham, Ariel Rokem, Claire Fortunel, Nathan J. B. Kraft and Janneke Hille Ris Lambers

Forests 2021, 12(9), 1283; https://doi.org/10.3390/f12091283 - 18 Sep 2021

Cited by 3 | Viewed by 2847

Abstract

Neighborhood models have allowed us to test many hypotheses regarding the drivers of variation in tree growth, but require considerable computation due to the many empirically supported non-linear relationships they include. Regularized regression represents a far more efficient neighborhood modeling method, but it is unclear whether such an ecologically unrealistic model can provide accurate insights on tree growth. Rapid computation is becoming increasingly important as ecological datasets grow in size, and may be essential when using neighborhood models to predict tree growth beyond sample plots or into the future. We built a novel regularized regression model of tree growth and investigated whether it reached the same conclusions as a commonly used neighborhood model, regarding hypotheses of how tree growth is influenced by the species identity of neighboring trees. We also evaluated the ability of both models to interpolate the growth of trees not included in the model fitting dataset. Our regularized regression model replicated most of the classical model’s inferences in a fraction of the time without using high-performance computing resources. We found that both methods could interpolate out-of-sample tree growth, but the method making the most accurate predictions varied among focal species. Regularized regression is particularly efficient for comparing hypotheses because it automates the process of model selection and can handle correlated explanatory variables. This feature means that regularized regression could also be used to select among potential explanatory variables (e.g., climate variables) and thereby streamline the development of a classical neighborhood model. Both regularized regression and classical methods can interpolate out-of-sample tree growth, but future research must determine whether predictions can be extrapolated to trees experiencing novel conditions. Overall, we conclude that regularized regression methods can complement classical methods in the investigation of tree growth drivers and represent a valuable tool for advancing this field toward prediction. Full article

(This article belongs to the Special Issue Modelling Forest Ecosystems)

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22 pages, 4390 KiB

Open AccessArticle

Modeling in Forestry Using Mixture Models Fitted to Grouped and Ungrouped Data

by Eric K. Zenner and Mahdi Teimouri

Forests 2021, 12(9), 1196; https://doi.org/10.3390/f12091196 - 3 Sep 2021

Cited by 5 | Viewed by 3737

Abstract

The creation and maintenance of complex forest structures has become an important forestry objective. Complex forest structures, often expressed in multimodal shapes of tree size/diameter (DBH) distributions, are challenging to model. Mixture probability density functions of two- or three-component gamma, log-normal, and Weibull mixture models offer a solution and can additionally provide insights into forest dynamics. Model parameters can be efficiently estimated with the maximum likelihood (ML) approach using iterative methods such as the Newton-Raphson (NR) algorithm. However, the NR algorithm is sensitive to the choice of initial values and does not always converge. As an alternative, we explored the use of the iterative expectation-maximization (EM) algorithm for estimating parameters of the aforementioned mixture models because it always converges to ML estimators. Since forestry data frequently occur both in grouped (classified) and ungrouped (raw) forms, the EM algorithm was applied to explore the goodness-of-fit of the gamma, log-normal, and Weibull mixture distributions in three sample plots that exhibited irregular, multimodal, highly skewed, and heavy-tailed DBH distributions where some size classes were empty. The EM-based goodness-of-fit was further compared against a nonparametric kernel-based density estimation (NK) model and the recently popularized gamma-shaped mixture (GSM) models using the ungrouped data. In this example application, the EM algorithm provided well-fitting two- or three-component mixture models for all three model families. The number of components of the best-fitting models differed among the three sample plots (but not among model families) and the mixture models of the log-normal and gamma families provided a better fit than the Weibull distribution for grouped and ungrouped data. For ungrouped data, both log-normal and gamma mixture distributions outperformed the GSM model and, with the exception of the multimodal diameter distribution, also the NK model. The EM algorithm appears to be a promising tool for modeling complex forest structures. Full article

(This article belongs to the Special Issue Modelling Forest Ecosystems)

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