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Forests
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Modeling Forest Response to Climate Change
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Modeling Forest Response to Climate Change

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 May 2024) | Viewed by 25892

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Daniela Dalmonech

E-Mail Website
Guest Editor
Forest Modelling Laboratory, Institute for Agriculture and Forestry Systems in the Mediterranean, National Research Council of Italy (CNR-ISAFOM), 06128 Perugia, Italy
Interests: climate change impacts; biogeochemical cycles; forest ecosystem modelling
Special Issues, Collections and Topics in MDPI journals
Gina Marano

E-Mail Website
Guest Editor Assistant
ETH Zürich, Chair of Forest Ecology, Environmental Systems Science, Universitätstrasse, 16 8092 Zürich, Switzerland
Interests: dynamic vegetation modeling; drought impacts; soil water dynamics
Dr. Alessio Collalti

E-Mail Website
Guest Editor
Forest Modelling Lab., Institute for Agriculture and Forestry Systems in the Mediterranean, National Research Council of Italy (CNR-ISAFOM), Via Madonna Alta 128, 06128 Perugia, Italy
Interests: forest modeling; climate change; climate change impacts; forest management scenario; carbon cycle; nitrogen cycle; climate change adaptation; climate change mitigation; forest ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The impacts of climate uncertainty pose important questions regarding the capability of forest ecosystems to buffer current and future climate-induced global changes while still delivering ecosystem services as society demands and future policy requirements advocate (e.g., the European Green Deal). Medium- to long-term forest dynamics (growth, competition, mortality), forest structure, and biodiversity may be profoundly altered by climatic-induced extremes in the future.

Disturbances (e.g., wildfires, droughts, windthrows, bark-beetle outbreaks) risk increasing the susceptibility of forests, thus enhancing tree mortality despite afforestation/reforestation efforts to offset CO2 emissions. In addition, the role of forest management practices (i.e., adaptive forest management) may buffer and/or dampen forest response to extreme events; however, multiple and diversified choices should be tested. In such uncertain scenarios, the role of simulation models and decision support systems is much advocated by the scientific community and policymakers to be able to assess and potentially quantify the behavior and responses of forest ecosystems under varying environmental conditions.

In this Special Issue, we encourage and welcome studies introducing new methods, novel applications, and innovative designs to i) model the impacts of climate change on medium- to long-term forest dynamics; ii) assess the impacts of climate change on the delivery of crucial ecosystem services in all forest ecosystems; and iii) analyze, assess, and quantify the impact of climate-induced disturbances on forest carbon cycle, water dynamics, and on the overall forest productivity, in both data-driven and dynamic vegetation models.

Dr. Daniela Dalmonech
Guest Editor

Gina Marano
Guest Editor Assistant

Dr. Alessio Collalti
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • climate change scenarios
  • forest functions and ecosystem services
  • adaptive forest management
  • long-term forest dynamics
  • decision support systems
  • disturbances

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Published Papers (14 papers)

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Editorial

Jump to: Research

3 pages, 623 KiB  
Editorial
Modeling Forest Response to Climate Change
by Gina Marano, Daniela Dalmonech and Alessio Collalti
Forests 2024, 15(7), 1194; https://doi.org/10.3390/f15071194 - 10 Jul 2024
Viewed by 720
Abstract
In an era marked by unprecedented climate shifts, understanding the intricate responses of forest ecosystems to these changes is of paramount importance [...] Full article
(This article belongs to the Special Issue Modeling Forest Response to Climate Change)

Research

Jump to: Editorial

23 pages, 7931 KiB  
Article
Analysis of Long-Term Vegetation Trends and Their Climatic Driving Factors in Equatorial Africa
by Isaac Kwesi Nooni, Faustin Katchele Ogou, Nana Agyemang Prempeh, Abdoul Aziz Saidou Chaibou, Daniel Fiifi Tawiah Hagan, Zhongfang Jin and Jiao Lu
Forests 2024, 15(7), 1129; https://doi.org/10.3390/f15071129 - 28 Jun 2024
Cited by 1 | Viewed by 979
Abstract
Understanding vegetation seasonality and its driving mechanisms improves decision-making in the management of ecological systems in a warming global climate. Using multiple statistical methods (i.e., trend analysis, abrupt changes, and partial correlation analysis), this study analyzed the spatiotemporal variations in the Normalized Difference [...] Read more.
Understanding vegetation seasonality and its driving mechanisms improves decision-making in the management of ecological systems in a warming global climate. Using multiple statistical methods (i.e., trend analysis, abrupt changes, and partial correlation analysis), this study analyzed the spatiotemporal variations in the Normalized Difference Vegetation Index (NDVI) in the Equatorial Africa (EQA) region and their responses to climate factors from 1982 to 2021. The NDVI values declined at a rate of 0.00023 year−1, while the precipitation (P) and mean temperature (TMEAN) values increased at rates of 0.22 mm year−1 and 0.22 °C year−1, respectively. The mean minimum temperature (TMIN) had a higher rate of 0.2 °C year−1 than the mean maximum temperature (TMAX) at 0.02 °C year−1. An abrupt change analysis showed that the TMAX, P, and NDVI breakpoints occurred in 2000, 2002, and 2009, respectively; TMEAN and TMIN breakpoints occurred in 2001. The NDVI trends declined in forest and cropland areas but increased in shrubland and grassland areas. The summer NDVI trends declined for all vegetation types and were reversed in the winter season. The NDVI positively correlated with the P (r = 0.50) and TMEAN (r = 0.60). All seasonal analyses varied across four seasons. A temporal analysis was conducted using partial correlation analysis (PCR), and the results revealed that TMIN had a greater impact on the NDVI (PCR = −0.45), followed by the TMAX (PCR = 0.31) and then the P (PCR = −0.19). The annual trend showed that areas with significant greening were consistent with stronger wetter and weaker warming trends. Both precipitation and temperature showed a positive relationship with vegetation in semi-arid and arid regions but a negative relationship with humid regions. Our findings improve our insight into scientific knowledge on ecological conservation. Full article
(This article belongs to the Special Issue Modeling Forest Response to Climate Change)
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28 pages, 9303 KiB  
Article
Predicted Future Changes in the Mean Seasonal Carbon Cycle Due to Climate Change
by Mauro Morichetti, Elia Vangi and Alessio Collalti
Forests 2024, 15(7), 1124; https://doi.org/10.3390/f15071124 - 28 Jun 2024
Cited by 2 | Viewed by 1285
Abstract
Through photosynthesis, forests absorb annually large amounts of atmospheric CO2. However, they also release CO2 back through respiration. These two, opposite in sign, large fluxes determine how much of the carbon is stored or released back into the atmosphere. The [...] Read more.
Through photosynthesis, forests absorb annually large amounts of atmospheric CO2. However, they also release CO2 back through respiration. These two, opposite in sign, large fluxes determine how much of the carbon is stored or released back into the atmosphere. The mean seasonal cycle (MSC) is an interesting metric that associates phenology and carbon (C) partitioning/allocation analysis within forest stands. Here, we applied the 3D-CMCC-FEM model and analyzed its capability to represent the main C-fluxes, by validating the model against observed data, questioning if the sink/source mean seasonality is influenced under two scenarios of climate change, in five contrasting European forest sites. We found the model has, under current climate conditions, robust predictive abilities in estimating NEE. Model results also predict a consistent reduction in the forest’s capabilities to act as a C-sink under climate change and stand-aging at all sites. Such a reduction is predicted despite the number of annual days as a C-sink in evergreen forests increasing over the years, indicating a consistent downward trend. Similarly, deciduous forests, despite maintaining a relatively stable number of C-sink days throughout the year and over the century, show a reduction in their overall annual C-sink capacity. Overall, both types of forests at all sites show a consistent reduction in their future mitigating potential. Full article
(This article belongs to the Special Issue Modeling Forest Response to Climate Change)
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16 pages, 21992 KiB  
Article
Stand Age and Climate Change Effects on Carbon Increments and Stock Dynamics
by Elia Vangi, Daniela Dalmonech, Mauro Morichetti, Elisa Grieco, Francesca Giannetti, Giovanni D’Amico, Mahdi (Andre) Nakhavali, Gherardo Chirici and Alessio Collalti
Forests 2024, 15(7), 1120; https://doi.org/10.3390/f15071120 - 27 Jun 2024
Cited by 1 | Viewed by 1543
Abstract
Carbon assimilation and wood production are influenced by environmental conditions and endogenous factors, such as species auto-ecology, age, and hierarchical position within the forest structure. Disentangling the intricate relationships between those factors is more pressing than ever due to climate change’s pressure. We [...] Read more.
Carbon assimilation and wood production are influenced by environmental conditions and endogenous factors, such as species auto-ecology, age, and hierarchical position within the forest structure. Disentangling the intricate relationships between those factors is more pressing than ever due to climate change’s pressure. We employed the 3D-CMCC-FEM model to simulate undisturbed forests of different ages under four climate change (plus one no climate change) Representative Concentration Pathways (RCP) scenarios from five Earth system models. In this context, carbon stocks and increment were simulated via total carbon woody stocks and mean annual increment, which depends mainly on climate trends. We find greater differences among different age cohorts under the same scenario than among different climate scenarios under the same age class. Increasing temperature and changes in precipitation patterns led to a decline in above-ground biomass in spruce stands, especially in the older age classes. On the contrary, the results show that beech forests will maintain and even increase C-storage rates under most RCP scenarios. Scots pine forests show an intermediate behavior with a stable stock capacity over time and in different scenarios but with decreasing mean volume annual increment. These results confirm current observations worldwide that indicate a stronger climate-related decline in conifers forests than in broadleaves. Full article
(This article belongs to the Special Issue Modeling Forest Response to Climate Change)
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17 pages, 2541 KiB  
Article
Environmental Response of Tree Species Distribution in Northeast China with the Joint Species Distribution Model
by Juan Yong, Guangshuang Duan, Shaozhi Chen and Xiangdong Lei
Forests 2024, 15(6), 1026; https://doi.org/10.3390/f15061026 - 13 Jun 2024
Cited by 1 | Viewed by 862
Abstract
The composition, distribution, and growth of native natural forests are important references for the restoration, structural adjustment, and close-to-nature transformation of artificial forests. The joint species distribution model is a powerful tool for analyzing community structure and interspecific relationships. It has been widely [...] Read more.
The composition, distribution, and growth of native natural forests are important references for the restoration, structural adjustment, and close-to-nature transformation of artificial forests. The joint species distribution model is a powerful tool for analyzing community structure and interspecific relationships. It has been widely used in biogeography, community ecology, and animal ecology, but it has not been extended to natural forest conservation and restoration in China. Therefore, based on the 9th National Forest Inventory data in Jilin Province, combined with environmental factors and functional traits of tree species, this study adopted the joint species distribution model—including a model with all variables (model FULL), a model with environmental factors (model ENV), and a model with spatial factors (model SPACE)—to examine the distribution of multiple tree species. The results show that, in models FULL and ENV, the environmental factors explaining the model variation were ranked as follows, climate > site > soil. The explanatory power was as follows: model FULL (AUC = 0.8325, Tjur R2 = 0.2326) > model ENV (AUC = 0.7664, Tjur R2 = 0.1454) > model SPACE (AUC = 0.7297, Tjur R2 = 0.1346). Tree species niches in model ENV were similar to those in model FULL. Compared to predictive power, we found that the information transmitted by environmental and spatial predictors overlaps, so the choice between model FULL and ENV should be based on the purpose of the model, rather than the difference in predictive ability. Both models can be used to study the adaptive distribution of multiple tree species in northeast China. Full article
(This article belongs to the Special Issue Modeling Forest Response to Climate Change)
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20 pages, 5328 KiB  
Article
Effects of Environment Change Scenarios on the Potential Geographical Distribution of Cunninghamia lanceolata (Lamb.) Hook. in China
by Jiajie Feng, Yiwei Cao, Teja Manda, Delight Hwarari, Jinhui Chen and Liming Yang
Forests 2024, 15(5), 830; https://doi.org/10.3390/f15050830 - 9 May 2024
Cited by 3 | Viewed by 1096
Abstract
Changes in climate and environmental conditions have aggravated the severity and unpredictability of plant survival and growth. Cunninghamia lanceolata (Lamb.) Hook. is an economically important timber tree. Exploring its potential distribution and dynamic changes and identifying the leading environmental variables affecting it will [...] Read more.
Changes in climate and environmental conditions have aggravated the severity and unpredictability of plant survival and growth. Cunninghamia lanceolata (Lamb.) Hook. is an economically important timber tree. Exploring its potential distribution and dynamic changes and identifying the leading environmental variables affecting it will help to adjust the planting range reasonably according to the habits and climate change, thus contributing to its survival and growth. Based on the MaxEnt model and ArcGIS tool, climate, soil, terrain, human activities, variable environment layers, and 395 C. lanceolata distribution points were used to simulate and analyze the geographical distribution characteristics of C. lanceolata in the current and future periods (the 2050s and 2070s) under RCP2.6, RCP4.5, RCP6.0, and RCP8.5. The results showed that C. lanceolata was suitable to grow in a subtropical monsoon climate with warm, humid, abundant rainfall and a relatively gentle topography. Additionally, using percent contribution, permutation importance, and the knife-cutting test, we noted that the annual precipitation (Bio12), human activities (Hfp), minimum temperature of the coldest month (Bio6), mean temperature of the coldest quarter (Bio11), precipitation of coldest quarter (Bio19), annual temperature range (Bio7), and elevation were the leading environmental factors affecting the geographical distribution of C. lanceolata. Among them, it should be noted that the impact of human activities was negatively correlated with suitable habitat areas of C. lanceolata and led to the degeneration of suitable habitats and fragmentized distribution. In addition, predictions have shown that the areas of habitats under other scenarios will be characterized by an increasing and then decreasing trend by the 2050s and 2070s, except for the RCP2.6 scenario, under which the suitable habitats area of C. lanceolata will increase continuously. The core distributional shifts showed that the suitable habitats of C. lanceolata will gradually shift and migrate to high-latitude areas due to global warming. This study focused on the characteristics of suitable habitats of C. lanceolata under different climatic scenarios using more environmental factors and scenarios than before, aiming to provide a theoretical basis and guidance for the management and utilization of forest resources, the planning of suitable planting areas, and germplasm protection. Full article
(This article belongs to the Special Issue Modeling Forest Response to Climate Change)
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22 pages, 13332 KiB  
Article
Assessing Forest-Change-Induced Carbon Storage Dynamics by Integrating GF-1 Image and Localized Allometric Growth Equations in Jiangning District, Nanjing, Eastern China (2017–2020)
by Jiawei Liu, Boxiang Yang, Mingshi Li and Da Xu
Forests 2024, 15(3), 506; https://doi.org/10.3390/f15030506 - 8 Mar 2024
Cited by 3 | Viewed by 1197
Abstract
Forest and its dynamics are of great significance for accurately estimating regional carbon sequestration, emissions and carbon sink capacity. In this work, an efficient framework that integrates remote sensing, deep learning and statistical modeling was proposed to extract forest change information and then [...] Read more.
Forest and its dynamics are of great significance for accurately estimating regional carbon sequestration, emissions and carbon sink capacity. In this work, an efficient framework that integrates remote sensing, deep learning and statistical modeling was proposed to extract forest change information and then derive forest carbon storage dynamics during the period 2017 to 2020 in Jiangning District, Nanjing, Eastern China. Firstly, the panchromatic band and multi-spectral bands of GF-1 images were fused by using four different methods; Secondly, an improved Mask-RCNN integrated with Swin Transformer was devised to extract forest distribution information in 2020. Finally, by using the substitution strategy of space for time in the 2017 Forest Management and Planning Inventory (FMPI) data, local carbon density allometric growth equations were fitted by coniferous forest and broad-leaved forest types and compared, and the optimal fitting was accordingly determined, followed by the measurements of forest-change-induced carbon storage dynamics. The results indicated that the improved Mask-RCNN synergizing with the Swin Transformer gained an overall accuracy of 93.9% when mapping the local forest types. The carbon storage of forest standing woods was calculated at 1,449,400 tons in 2020, increased by 14.59% relative to that of 2017. This analysis provides a technical reference for monitoring forest change and lays a data foundation for local agencies to formulate forest management policies in the process of achieving dual-carbon goals. Full article
(This article belongs to the Special Issue Modeling Forest Response to Climate Change)
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22 pages, 10514 KiB  
Article
Habitat Distribution Pattern of Rare and Endangered Plant Magnolia wufengensis in China under Climate Change
by Xiaodeng Shi, Qun Yin, Ziyang Sang, Zhonglong Zhu, Zhongkui Jia and Luyi Ma
Forests 2023, 14(9), 1767; https://doi.org/10.3390/f14091767 - 31 Aug 2023
Cited by 5 | Viewed by 1574
Abstract
Magnolia wufengensis is a newly discovered rare and endangered species endemic to China. The primary objective of this study is to find the most suitable species distribution models (SDMs) by comparing the different SDMs to predict their habitat distribution for protection and introduction [...] Read more.
Magnolia wufengensis is a newly discovered rare and endangered species endemic to China. The primary objective of this study is to find the most suitable species distribution models (SDMs) by comparing the different SDMs to predict their habitat distribution for protection and introduction in China under climate change. SDMs are important tools for studying species distribution patterns under climate change, and different SDMs have different simulation effects. Thus, to identify the potential habitat for M. wufengensis currently and in the 2050s (2041–2060) and 2070s (2061–2080) under different climate change scenarios (representative concentration pathways RCP2.6, RCP4.5, RCP6.0, and RCP8.5) in China, four SDMs, Maxent, GARP, Bioclim, and Domain, were first used to compare the predicted habitat and explore the dominant environmental factors. The four SDMs predicted that the potential habitats were mainly south of 40° N and east of 97° E in China, with a high distribution potential under current climate conditions. The area under the receiver operating characteristic (ROC) curve (AUC) (0.9479 ± 0.0080) was the highest, and the Kappa value (0.8113 ± 0.0228) of the consistency test and its performance in predicting the potential suitable habitat were the best in the Maxent model. The minimum temperature of the coldest month (−13.36–9.84 °C), mean temperature of the coldest quarter (−6.06–12.66 °C), annual mean temperature (≥4.49 °C), and elevation (0–2803.93 m), were the dominant factors. In the current climate scenario, areas of 46.60 × 104 km2 (4.85%), 122.82 × 104 km2 (12.79%), and 96.36 × 104 km2 (10.03%), which were mainly in central and southeastern China, were predicted to be potential suitable habitats of high, moderate, and low suitability, respectively. The predicted suitable habitats will significantly change by the 2050s (2040–2060) and 2070s (2060–2080), suggesting that M. wufengensis will increase in high-elevation areas and shift northeast with future climate change. The comparison of current and future suitable habitats revealed declines of approximately 4.53%–29.98% in highly suitable habitats and increases of approximately 6.45%–27.09% and 0.77%–21.86% in moderately and lowly suitable habitats, respectively. In summary, these results provide a theoretical basis for the response to climate change, protection, precise introduction, cultivation, and rational site selection of M. wufengensis in the future. Full article
(This article belongs to the Special Issue Modeling Forest Response to Climate Change)
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22 pages, 5756 KiB  
Article
Soil Temperature, Organic-Carbon Storage, and Water-Holding Ability Should Be Accounted for the Empirical Soil Respiration Model Selection in Two Forest Ecosystems
by Sergey Kivalov, Valentin Lopes de Gerenyu, Dmitry Khoroshaev, Tatiana Myakshina, Dmitry Sapronov, Kristina Ivashchenko and Irina Kurganova
Forests 2023, 14(8), 1568; https://doi.org/10.3390/f14081568 - 31 Jul 2023
Cited by 2 | Viewed by 1579
Abstract
Soil respiration (SR) is a main component of the carbon cycle in terrestrial ecosystems, and being strongly affected by changes in the environment, it is a good indicator of the ecosystem’s ability to cope with climate change. This research aims to find better [...] Read more.
Soil respiration (SR) is a main component of the carbon cycle in terrestrial ecosystems, and being strongly affected by changes in the environment, it is a good indicator of the ecosystem’s ability to cope with climate change. This research aims to find better empirical SR models using 25-year-long SR monitoring in two forest ecosystems formed on sandy Entic Podzol and loamy Haplic Luvisol. The following parameters were considered in the examined models: the mean monthly soil or air temperatures (Tsoil or Tair), the amount of precipitation during the current (P) and the previous (PP) months, and the storage of soil organic carbon (SOC). The weighted non-linear regression was used for model parameter estimations for the normal, wet, and dry years. To improve the model resolutions by magnitude, we controlled the slope and intercept of the linear model comparison between the measured and modeled data through the change in R0—SR at zero soil temperature. The mean bias error (MBE), root-mean-square error (RMSE), and determination coefficient (R2) were used for the estimation of the goodness of model performances. For the sandy Entic Podzol, it is more appropriate to use the models dependent on SOC (TPPC). While for the loamy Haplic Luvisol, the Raich–Hashimoto model (TPPrh) with the quadratic Tsoil or Tair dependency shows the better results. An application of Tsoil for the model parameterization gives better results than Tair: the TPPC model was able to adequately describe the cold-period SR (Tsoil ≤ 2 °C); the TPPrh model was able to avoid overestimations of the warm-period SR (Tsoil > 2 °C). The TPPC model parameterized with Tsoil can be used for the quality control of the cold-period SR measurements. Therefore, we showed the importance of accounting for SOC and the water-holding ability when the optimal SR model is chosen for the analysis. Full article
(This article belongs to the Special Issue Modeling Forest Response to Climate Change)
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20 pages, 3833 KiB  
Article
3PG-MT-LSTM: A Hybrid Model under Biomass Compatibility Constraints for the Prediction of Long-Term Forest Growth to Support Sustainable Management
by Jushuang Qin, Menglu Ma, Yutong Zhu, Baoguo Wu and Xiaohui Su
Forests 2023, 14(7), 1482; https://doi.org/10.3390/f14071482 - 19 Jul 2023
Cited by 2 | Viewed by 1948
Abstract
Climate change is posing new challenges to forestry management practices. Thinning reduces competitive pressure in the forest by repeatedly reducing the tree density of forest stands, thereby increasing the productivity of plantations. Considering the impact of thinning on vegetation and physiological and ecological [...] Read more.
Climate change is posing new challenges to forestry management practices. Thinning reduces competitive pressure in the forest by repeatedly reducing the tree density of forest stands, thereby increasing the productivity of plantations. Considering the impact of thinning on vegetation and physiological and ecological traits, for this study, we used Norway spruce (Picea abies) data from three sites in the PROFOUND dataset to parameterize the 3-PG model in stages. The calibrated 3-PG model was used to simulate the stand diameter at breast height and the stem, root, and leaf biomass data on a monthly scale. The 3PG-MT-LSTM model uses 3-PG simulation data as the input variable. The model uses a long short-term memory neural network (LSTM) as a shared layer and introduces multi-task learning (MTL). Based on the compatibility rules, the interpretability of the model was further improved. The models were trained using single-site and multi-site data, respectively, and multiple indicators were used to evaluate the model accuracy and generalization ability. Our preliminary results show that, compared with the process model and LSTM algorithm without MTL and compatibility rules, the hybrid model has higher biomass simulation accuracy and shows a more realistic biomass response to environmental driving factors. To illustrate the potential applicability of the model, we applied light (10%), moderate (20%), and heavy thinning (30%) at intervals of 10, 15, 20, 25, 30 years. Then, we used three climate scenarios—SSP1-2.6, SSP2-4.5, and SSP5-8.5—to simulate the growth of Norway spruce. The hybrid model can effectively capture the impact of climate change and artificial management on stand growth. In terms of climate, temperature and solar radiation are the most important factors affecting forest growth, and under warm conditions, the positive significance of forest management is more obvious. In terms of forest management practices, less frequent light-to-moderate thinning can contribute more to the increase in forest carbon sink potential; high-intensity thinning can support large-diameter timber production. In summary, moderate thinning should be carried out every 10 years in the young-aged forest stage. It is also advisable to perform light thinning procedures after the forest has progressed into a middle-aged forest stage. This allows for a better trade-off of the growth relationship between stand yield and diameter at breast height (DBH). The physical constraint-based hybrid modeling approach is a practical and effective tool. It can be used to measure long-term dynamic changes in forest production and then guide management activities such as thinning to achieve sustainable forest management. Full article
(This article belongs to the Special Issue Modeling Forest Response to Climate Change)
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14 pages, 34665 KiB  
Article
Paleo Distribution and Habitat Risks under Climate Change of Helleborus thibetanus
by Xiaohua Shi, Lihui Mao, Miao Sun, Guangying Ma and Kaiyuan Zhu
Forests 2023, 14(3), 630; https://doi.org/10.3390/f14030630 - 20 Mar 2023
Cited by 2 | Viewed by 1550
Abstract
As an endemic species and the only Helleborus species in China, Helleborus thibetanus is highly valued in medicinal and ornamental applications, and basic research is needed for its further resource conservation and utilization. Considering the interesting disjunct distribution of the genus Helleborus, [...] Read more.
As an endemic species and the only Helleborus species in China, Helleborus thibetanus is highly valued in medicinal and ornamental applications, and basic research is needed for its further resource conservation and utilization. Considering the interesting disjunct distribution of the genus Helleborus, we focus on the distribution pattern of H. thibetanus in this research. Based on species distribution models using three different algorithms (MaxEnt, RF, and FDA), we constructed a robust ensemble model and predicted potential distributions under different scenarios: current situation, paleo periods since the Last Glacial Maximum, and simulations of climate change in the 2070s. The habitat suitability of H. thibetanus across geography and scenarios was further analyzed by calculating regional areas and centroids. The results showed that H. thibetanus is currently distributed in southern Shaanxi and northern Sichuan, while central and southern Sichuan used to be suitable 14 thousand years ago but gradually became unsuitable, which may reflect the population decrease in Sichuan and the population expansion in Shaanxi over the last 14 thousand years. Our results showed that current populations are under limited extinction pressure in the soft climate change scenario (ssp126), but most populations in Shaanxi are under extinction pressure in the hardy situation scenario (ssp585). Fortunately, northern Sichuan is predicted to be relatively stable under climate change (both ssp126 and ssp585), and regions in western Sichuan and eastern Qinghai are predicted to become newly suitable for H. thibetanus. These findings should be helpful for the further conservation and utilization of H. thibetanus and also help us understand the history of the conjunct distribution pattern of the Helleborus genus. Full article
(This article belongs to the Special Issue Modeling Forest Response to Climate Change)
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16 pages, 2105 KiB  
Article
Predicting Spruce Taiga Distribution in Northeast Asia Using Species Distribution Models: Glacial Refugia, Mid-Holocene Expansion and Future Predictions for Global Warming
by Kirill Korznikov, Tatyana Petrenko, Dmitry Kislov, Pavel Krestov and Jiří Doležal
Forests 2023, 14(2), 219; https://doi.org/10.3390/f14020219 - 24 Jan 2023
Cited by 3 | Viewed by 4843
Abstract
Spruce taiga forests in Northeast Asia are of great economic and conservation importance. Continued climate warming may cause profound changes in their distribution. We use prognostic and retrospective species distribution models based on the Random Forest machine learning method to estimate the potential [...] Read more.
Spruce taiga forests in Northeast Asia are of great economic and conservation importance. Continued climate warming may cause profound changes in their distribution. We use prognostic and retrospective species distribution models based on the Random Forest machine learning method to estimate the potential range change of the dominant taiga conifer Jezo spruce (Picea jezoensis (Siebold & Zucc.) Carrière) for the year 2070 climate warming scenarios and for past climate epochs–the Last Glacial Maximum (LGM) (~21,000 years before present) and the mid-Holocene Climatic Optimum (MHO) (~7000 years before the present) using the MIROC-ESM and CCSM4 climate models. The current suitable climatic conditions for P. jezoensis are estimated to be 500,000 km2. Both climatic models show similar trends in past and future ranges but provide different quantitative areal estimates. During the LGM, the main part of the species range was located much further south than today at 35–45° N. Projected climate warming will cause a greater change in the distributional range of P. jezoensis than has occurred since the MHO. Overlapping climatic ranges at different times show that the Changbai Mountains, the central parts of the Japanese Alps, Hokkaido, and the Sikhote-Alin Mountains will remain suitable refugia for Jezo spruce until 2070. The establishment of artificial forest stands of P. jezoensis and intraspecific taxa in the future climate-acceptable regions may be important for the preservation of genetic diversity. Full article
(This article belongs to the Special Issue Modeling Forest Response to Climate Change)
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17 pages, 2346 KiB  
Article
Optimal Management Strategies to Maximize Carbon Capture in Forest Plantations: A Case Study with Pinus radiata D. Don
by Alex Altamirano-Fernández, Alejandro Rojas-Palma and Sergio Espinoza-Meza
Forests 2023, 14(1), 82; https://doi.org/10.3390/f14010082 - 1 Jan 2023
Cited by 7 | Viewed by 2442
Abstract
Plantations with fast-growing species play a crucial role in reducing global warming and have great carbon capture potential. Therefore, determining optimal management strategies is a challenge in the management of forest plantations to achieve the maximum carbon capture rate. The objective of this [...] Read more.
Plantations with fast-growing species play a crucial role in reducing global warming and have great carbon capture potential. Therefore, determining optimal management strategies is a challenge in the management of forest plantations to achieve the maximum carbon capture rate. The objective of this work is to determine optimal rotation strategies that maximize carbon capture in forest plantations. By evaluating an ecological optimal control problem, this work presents a method that manages forest plantations by planning activities such as reforestation, felling, thinning, and fire prevention. The mathematical model is governed by three ordinary differential equations: live biomass, intrinsic growth, and burned area. The characterization of the optimal control problem using Pontryagin’s maximum principle is analyzed. The model solutions are approximated numerically by the fourth-order Runge–Kutta method. To verify the efficiency of the model, parameters for three scenarios were considered: a realistic one that represents current forestry activities based on previous studies for the exotic species Pinus radiata D. Don, another pessimistic, which considers significant losses in forest productivity; and a more optimistic scenario which assumes the creation of new forest areas that contribute with carbon capture to prevent the increase in global temperature. The model predicts a higher volume of biomass for the optimistic scenario, with the consequent higher carbon capture than in the other two scenarios. The optimal solution for the felling strategy suggests that, to increase carbon capture, the rotation age should be prolonged and the felling rate decreased. The model also confirms that reforestation should be carried out immediately after felling, applying maximum reforestation effort in the optimistic and pessimistic scenarios. On the other hand, the model indicates that the maximum prevention effort should be applied during the life cycle of the plantation, which should be proportional to the biomass volume. Finally, the optimal solution for the thinning strategy indicates that in all three scenarios, the maximum thinning effort should be applied until the time when the fire prevention strategy begins. Full article
(This article belongs to the Special Issue Modeling Forest Response to Climate Change)
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16 pages, 1809 KiB  
Article
Modeling Climate Effects on Site Productivity of Plantation Grown Jack Pine, Black Spruce, Red Pine, and White Spruce Using Annual/Seasonal Climate Values
by Mahadev Sharma
Forests 2022, 13(10), 1600; https://doi.org/10.3390/f13101600 - 30 Sep 2022
Cited by 8 | Viewed by 1790
Abstract
Site index (SI) is a commonly used measure of forest site productivity and is affected by climate change. Therefore, climate effects on site productivity were analyzed and modeled for jack pine (Pinus banksiana Lamb.), black spruce (Picea mariana (Mill.) B.S.P.), red [...] Read more.
Site index (SI) is a commonly used measure of forest site productivity and is affected by climate change. Therefore, climate effects on site productivity were analyzed and modeled for jack pine (Pinus banksiana Lamb.), black spruce (Picea mariana (Mill.) B.S.P.), red pine (Pinus resinosa Ait.), and white spruce (Picea glauca (Moench) Voss) plantations using annual/seasonal values of climate variables. Jack pine and black spruce trees were each sampled from 25 plantations (sites), and red pine and white spruce trees were sampled from 30 and 31 plantations, respectively, from across Ontario, Canada. Stem analysis data collected from 201 jack pine, 211 black spruce, 90 red pine, and 93 white spruce trees were used in this study. To analyze and model climatic effects on site productivity, parameters of the stand height models were expressed in terms of climate variables. A nonlinear mixed-effects modelling approach was applied to fit the stand height models. Climate effects on site productivity was evaluated by predicting stand heights in three areas (the central, eastern/southeastern, and western parts of Ontario) for the period 2021 to 2080 under three emissions trajectories (representative concentration pathways (RCP) 2.6, 4.5, and 8.5 watts m−2). Climate effects on site productivity depended on tree species and location. For jack pine, climate effects were positive and pronounced only in western Ontario under all emissions scenarios. The effects were negative and mild after breast height age (BHA) 50 in central Ontario for black spruce. Similarly, the effects were negative and more pronounced at all areas after BHA 35 for red pine. On the other hand, for white spruce the effects were negative and highly pronounced from a young age under all scenarios, mainly in the southeast. For all species except for jack pine, climate effects were more pronounced under RCP 8.5 than the other two scenarios. Full article
(This article belongs to the Special Issue Modeling Forest Response to Climate Change)
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