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Modeling Forest Response to Climate Change

Forests

July 2024
15(7):1194

DOI:10.3390/f15071194

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CC BY 4.0

Authors:

Gina Marano

ETH Zurich

Alessio Collalti

Italian National Research Council

In an era marked by unprecedented climate shifts, understanding the intricate responses of forest ecosystems to these changes is of paramount importance. The research presented in this Special Issue delves deeply into various dimensions of forest dynamics under the influence of climate change, offering critical insights that can guide effective conservation and management strategies. Vegetation seasonality, a crucial component of ecological systems, is under significant stress due to global warming.

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Citation: Marano, G.; Dalmonech, D.;

Collalti, A. Modeling Forest Response

to Climate Change. Forests 2024,15,

1194. https://doi.org/10.3390/

f15071194

Received: 2 July 2024

Accepted: 5 July 2024

Published: 10 July 2024

Licensee MDPI, Basel, Switzerland.

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4.0/).

Editorial

Modeling Forest Response to Climate Change

Gina Marano 1, 2, *,† , Daniela Dalmonech 1, † and Alessio Collalti 1,†

1Forest Modelling Laboratory, Institute for Agriculture and Forestry Systems in the Mediterranean,

National Research Council of Italy (CNR–ISAFOM), Via Madonna Alta 128, 06128 Perugia, Italy;

daniela.dalmonech@cnr.it (D.D.); alessio.collalti@cnr.it (A.C.)

2Forest Ecology, Institute of Terrestrial Ecosystems, Department Environmental Systems Science,

ETH Zurich, 8092 Zurich, Switzerland

*Correspondence: gina.marano@usys.ethz.ch

†These authors contributed equally to this work.

In an era marked by unprecedented climate shifts, understanding the intricate re-

sponses of forest ecosystems to these changes is of paramount importance. The research

presented in this Special Issue delves deeply into various dimensions of forest dynamics

under the inﬂuence of climate change, offering critical insights that can guide effective

conservation and management strategies.

Vegetation seasonality, a crucial component of ecological systems, is under signiﬁcant

stress due to global warming. Nooni et al.’s study [

] highlights how Normalized Difference

Vegetation Index (NDVI) trends in Equatorial Africa (EQA) have been inﬂuenced by

changes in precipitation and temperature over the past four decades. The research reveals

that while forest and cropland areas have experienced declining NDVI trends, shrubland

and grassland areas have tended to increase, suggesting that there is a complex interplay

between climate factors and vegetation types. This nuanced understanding is essential for

ecological conservation and resource management in the face of ongoing climate change.

Similarly, the capacity of forests to act as carbon sinks is under threat. In their study,

Morichetti et al. [

] examine carbon ﬂuxes within forest ecosystems using the 3D-CMCC-

FEM model. Their analysis of ﬁve contrasting European forest sites under current and

future climate scenarios demonstrates the model’s robust ability to estimate net ecosystem

exchange (NEE). The study predicts a consistent reduction in the carbon sink capabilities

of forests due to climate change and forest aging. Despite an increase in the number of

days that evergreen forests act as carbon sinks, their overall annual capacity is projected to

decrease. Similarly, deciduous forests maintain stable carbon sink days but also show a

reduction in their annual capacity. This highlights the need for the implantation of adaptive

forest management practices that mitigate the anticipated decline in carbon sequestration.

The same model was employed by Vangi et al. [

] by simulating carbon stocks and

wood production across different forest ages and climate scenarios. Their ﬁndings indi-

cate a pronounced decline in biomass for older coniferous stands, such as spruce, under

warming conditions; meanwhile, beech forests may sustain or even enhance their carbon

storage capacity. Scots pine forests display intermediate behavior, with a stable stock

capacity but decreasing annual increment. These insights highlight the variable resilience

of different forest types to climate change, necessitating tailored management approaches

and, most importantly, underscoring the differential impacts of climate change on conif-

erous and broadleaf forests; in addition, they highlight the necessity of species-speciﬁc

management practices.

An important component of the carbon cycle and its dynamics is soil respiration;

therefore, its inﬂuence on the carbon cycle was explored by Kivalov et al. [

]. The au-

thors developed empirical models to better understand soil respiration in different forest

ecosystems. Their research highlights the importance of soil’s organic carbon and water-

holding capacity in predicting soil respiration, providing a foundation for the enhanced

Forests 2024,15, 1194. https://doi.org/10.3390/f15071194 https://www.mdpi.com/journal/forests

Forests 2024,15, 1194 2 of 3

modeling of the carbon cycle in terrestrial ecosystems. Moving to a management-oriented

perspective, several studies investigated how management practices, namely restoration,

plantation and thinning techniques, can alleviate forest ecosystems from the future pressure

of environmental stressors.

The restoration and conservation of native forests also emerge as critical themes in

Yong et al.’s study [

]. The authors employ a joint species distribution model to analyze

the distribution of tree species in China’s Jilin Province. The study identiﬁes climate,

site, and soil as the key environmental factors inﬂuencing tree species niches, with the

model demonstrating strong explanatory power. Their work emphasizes the importance

of environmental factors—climate, site, and soil—in shaping tree species niches, thus

providing a robust framework for forest restoration and proactive forest management.

The impact of climate change on economically signiﬁcant timber trees is a crucial

aspect of timber-based bioeconomies. In their work, Feng et al. [6] focus on Cunninghamia

lanceolata by using the MaxEnt model to project its distribution under future climate

scenarios. Their research identiﬁes the key environmental variables affecting its growth

and suggests that suitable habitats will shift to higher latitudes as the climate warms.

This predictive modeling is crucial for the planning of future planting strategies and

conservation efforts to ensure the survival of this valuable species.

Innovative methodologies also play a pivotal role in forest management. In their study,

Liu et al. [

] integrate remote sensing, deep learning, and statistical modeling to monitor

forest changes and carbon storage dynamics in China. Their approach demonstrates high

accuracy in mapping forest types and quantifying carbon storage, offering a valuable tool

in local forest management and the achievement of carbon neutrality.

On the same level, predictive models of species distribution under various climate sce-

narios offer critical insights into conservation planning. For instance, rare and endangered

species such as Magnolia wufengensis ‘Jiaolian’ are projected to experience signiﬁcant habitat

shifts due to climate change, as reported by Shi et al. [

]. According to their study, the suit-

able habitats for such species will move to higher elevations and latitudes, highlighting the

need for dynamic conservation strategies that can adapt to these changes. Understanding

these shifts is crucial for the protection and sustainable management of biodiversity.

Thinning practices, which are an essential technique in sylviculture and the opti-

mization of its management, were examined by Qin et al. [

] through a hybrid modeling

approach; this combined the 3-PG process model and a long short-term memory neural

network. Their study offers practical guidelines for thinning practices that enhance forest

growth and carbon sequestration, demonstrating the signiﬁcance of adaptive management

in response to climate and anthropogenic pressures.

The conservation of endemic ornamental species was explored by Shi et al. [

]

who reported that, under more severe scenarios of climate change, the populations of

Helleborus tibetanus Franchet, are at high risk of destruction. These insights are critical for

the conservation and sustainable utilization of this species in China.

Similarly, Korznikov et al. [

] employed Random Forest models to explore changes in

the distribution of Jezo spruce (Picea jezoensis (Siebold and Zucc.) Carrière) in Northeast Asia

under climate change scenarios. For this species, however, the key refugia are predicted

to remain suitable; hence, the establishment of artiﬁcial stands in these future climate-

acceptable regions may be vital for preserving genetic diversity.

The potential ability of forest plantations to mitigate climate change was also explored

by Altamirano-Fernandez et al. [

], who developed a mathematical model to optimize

carbon capture in forest plantations. Their work underscores the importance of strategic

planning in reforestation, thinning, and ﬁre prevention to maximize carbon sequestration

and combat global warming.

Climate change impacts the productivity of sites differently across tree species and

regions. For example, in Ontario, Canada, the effects of climate on site productivity vary

among jack pine, black spruce, red pine, and white spruce plantations [

]. Sharma reports

that while jack pine shows positive climate effects in western Ontario, black spruce, red

Forests 2024,15, 1194 3 of 3

pine, and white spruce exhibit negative impacts, especially under high-emission scenarios.

These ﬁndings highlight the need for localized management strategies that account for

species-speciﬁc and regional climate responses in order to sustain forest productivity.

In conclusion, the collective research presented in this Special Issue underscores the

multifaceted responses of forest ecosystems to climate change by means of both statistical

and process-based models. Through modeling techniques and comprehensive analyses,

these studies provide critical insights and practical solutions regarding the management

and conservation of forests in a warming world. The knowledge gained from these in-

vestigations is vital for informing policy and guiding actions that will help sustain forest

ecosystems and their invaluable services for future generations.

Conﬂicts of Interest: The authors declare no conﬂicts of interest.

References

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Trends and Their Climatic Driving Factors in Equatorial Africa. Forests 2024,15, 1129. [CrossRef]

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Kivalov, S.; Lopes de Gerenyu, V.; Khoroshaev, D.; Myakshina, T.; Sapronov, D.; Ivashchenko, K.; Kurganova, I. Soil Temperature,

Organic-Carbon Storage, and Water-Holding Ability Should Be Accounted for the Empirical Soil Respiration Model Selection in

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Distribution Model. Forests 2024,15, 1026. [CrossRef]

Feng, J.; Cao, Y.; Manda, T.; Hwarari, D.; Chen, J.; Yang, L. Effects of Environment Change Scenarios on the Potential Geographical

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Liu, J.; Yang, B.; Li, M.; Xu, D. 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). Forests 2024,15, 506. [CrossRef]

Shi, X.; Yin, Q.; Sang, Z.; Zhu, Z.; Jia, Z.; Ma, L. Habitat Distribution Pattern of Rare and Endangered Plant Magnolia wufengensis

in China under Climate Change. Forests 2023,14, 1767. [CrossRef]

Qin, J.; Ma, M.; Zhu, Y.; Wu, B.; Su, X. 3PG-MT-LSTM: A Hybrid Model under Biomass Compatibility Constraints for the

Prediction of Long-Term Forest Growth to Support Sustainable Management. Forests 2023,14, 1482. [CrossRef]

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Monitoring and Predicting Forest Growth and Dynamics

Book

Full-text available

Dec 2024

The book ‘Monitoring and Predicting Forest Growth and Dynamics’ describes the theoretical background underlying the ‘Three Dimensional — Coupled Model Carbon Cycle — Forest Ecosystem Module’ (3D-CMCC-FEM), developed by Alessio Collalti and his team at the Forest Modelling Laboratory of the National Research Council of Italy. The model is, in our view, a remarkable piece of work. It incorporates virtually all current, and relevant historical knowledge, about tree physiology and the interactions between physiological processes and environmental conditions. Their book describes the history, development, and refinement of 3D-CMCC-FEM which, it could be said, is representative of the general evolution of ecosystem models. These have developed from those that deal with forest canopies as single leaves (‘big leaf’ models) to those with multiple foliage layers, with climatic inputs ranging from monthly data to daily time-steps, and spatial resolutions reduced from a square kilometre down to 100 square meters. 3D-CMCC-FEM produces detailed predictions of almost every aspect of tree and forest growth, incorporating detailed calculations of radiation interception at various levels with sub-routines for (among other processes) carbon dynamics, nutrient uptake and cycling and water relations. It uses daily weather data as inputs and can provide multi-variable outputs for daily or monthly time steps. As such things must, 3D-CMCC-FEM builds on the base provided by earlier work of this kind: in this respect, our much simpler 3-PG model, written 25 years ago, which remains a widely used operational and research tool, was an important part of the foundation, but there were of course, many others. They are reviewed and acknowledged in the section on ‘Model History’, which provides a good and comprehensive introduction to the ideas behind the model, and review of the works that influenced it. It is interesting to note that the evolution of 3D-CMCC-FEM from 3-PG, which uses about 100 lines of code, has involved writing 30,000 lines of code, which might give pause for thought with regard to bugs and errors. The code for 3D-CMCC-FEM is free (it can be downloaded from GitHub, a repository for codes), anyone can download, use, and implement it, provided that they cite the authors and provided it is not used for commercial purposes. Making the code freeware, albeit under some understandable constraints, is important: without that 3D-CMCC-FEM would not become widely used, however good it is. The authors have made the code available to thousands of collaborators and the model has been, and is being, widely and thoroughly tested. This approach provides some insurance in relation to the matter of errors and de-bugging: with hundreds possibly thousands of scientists using and testing the model, most problems will be identified and solved. An obvious drawback to this kind of model may be the high demand for initialisation data, and the detailed data needed to test and parameterize the various sub-models and sub-routines. However, the widespread collaborative testing that is being done will ensure that many of the data required will be obtained. Furthermore, the global network of eddy-flux towers, where carbon and water vapor exchange are monitored continuously, provides detailed data that can be used as inputs to 3D-CMCC-FEM and to test its outputs. In recent years the frequency of global coverage by satellites has increased from a few days per month to almost daily, while spatial resolution has improved from square kilometres down to 100 square meters, or less. Furthermore, the spectro-resolution of satellite-borne sensors has increased to the extent that changes in biodiversity can often be discerned. It has therefore become increasingly feasible to examine the consequences of planned and unplanned disturbances at a range of spatial resolutions. Combining satellite coverage with airborne LiDAR measurements allows identification, analysis and validation of detailed structural changes in forests at spatial scales across large regions where, in the past, only major disturbances could be recognised. 3D-CMCC-FEM therefore provides very powerful tools for assessing the consequences of planned and unplanned forest disturbances, and the impacts of adverse conditions, at a range of spatial resolutions. The assessments can include impacts on wood production as well as evaluations of the roles of forests as ecosystems in the global carbon balance and in hydrology. It follows that, in this era of climate change, 3D-CMCC-FEM provides an excellent tool for assessing the effects of predicted changes in climate, whether they be in temperature regimes, atmospheric CO2 concentrations or water regimes. Used in association with powerful modern remote sensing and LiDAR measurements, the prospects are good for accurate, tested analytical predictions of climatic impacts as well as evaluations of the effects of management of forests for wood production and assessments of their roles in global carbon balance, hydrology and as ecosystems. 3D-CMCC-FEM is now freely available to download. This means that the model can be widely tested and further improved by scientists and practitioners around the world. All will have the opportunity to test the model in many ways, including the extent that monthly time-steps, with much-reduced data requirements, provide suitable information for foresters and environmentalists to make many practical decisions. We congratulate the authors on this contribution and look forward to their model’s widespread usage. Richard Waring & Joe Landsberg (Preface of the book)

Analysis of Long-Term Vegetation Trends and Their Climatic Driving Factors in Equatorial Africa

Article

Full-text available

Jun 2024

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.

Predicted Future Changes in the Mean Seasonal Carbon Cycle Due to Climate Change

Article

Full-text available

Jun 2024

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.

Stand Age and Climate Change Effects on Carbon Increments and Stock Dynamics

Article

Full-text available

Jun 2024

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.

Environmental Response of Tree Species Distribution in Northeast China with the Joint Species Distribution Model

Article

Full-text available

Jun 2024

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.

Stand Age and Climate Change Effects on Carbon Increments and Stock Dynamics

Preprint

Full-text available

Jun 2024

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 before due to the pressure of climate change. Yet, our understanding of how future climate will interact with forests of different ages is particularly limited, and only a few studies have explored this relationship under changing climate conditions. We employed a validated process-based forest model for simulating undisturbed forests of different ages under four climate change scenarios (plus one no climate change) coming from five Earth System Models. In this context, carbon stocks and increment were simulated via total carbon woody stocks (MgC ha—1) and the mean annual increment (m3 ha—1year—1), which depend mainly on age and long-term processes, such as climate trends. We find greater differences among different age cohorts under the same scenario than in different climate scenarios under the same age class. We found different C-accumulation patterns under climate change between coniferous stands and broadleaves. 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 at DK-Sor 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. We, therefore, advocate for a better understanding of the interaction between forests and climate to better inform forest management strategies, ultimately dampening the impacts of climate change on forest ecosystems.

3PG-MT-LSTM: A Hybrid Model under Biomass Compatibility Constraints for the Prediction of Long-Term Forest Growth to Support Sustainable Management

Article

Full-text available

Jul 2023

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.

Predicted Future Changes in the Mean Seasonal Carbon Cycle Due to Climate Change

Preprint

Full-text available

May 2024

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, much of the carbon that is stored or released back to the atmosphere. The mean seasonal cycle (MSC) is an interesting metric that associate phenology and carbon (C) partition-ing-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 of the forest's capabilities to act as a C-sink under climate change and stand-ageing at all sites. Such a reduction is predicted despite the number of annual days of 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.

Effects of Environment Change Scenarios on the Potential Geographical Distribution of Cunninghamia lanceolata (Lamb.) Hook. in China

Article

Full-text available

May 2024

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.

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)

Article

Full-text available

Mar 2024

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.

Habitat Distribution Pattern of Rare and Endangered Plant Magnolia wufengensis in China under Climate Change

Article

Full-text available

Aug 2023

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.