Next Article in Journal
Partial Organic Substitution Fertilization Improves Soil Fertility While Reducing N Mineralization in Rubber Plantations
Next Article in Special Issue
Unfolding the Roles of Particulate- and Mineral-Associated Organic Carbon in Soil Microbial Communities
Previous Article in Journal
The Characteristics of Visitor Behavior and Driving Factors in Urban Mountain Parks: A Case Study of Fuzhou, China
Previous Article in Special Issue
Chemical and Microbial Differences of Root and Rhizosphere Soil among Different Provenances of Fokienia hodginsii
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 15
Issue 9
10.3390/f15091520
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Enhanced Carbon Storage in Mixed Coniferous and Broadleaf Forest Compared to Pure Forest in the North Subtropical–Warm Temperate Transition Zone of China

by
Wenbin Xu
1,2,
Beibei Zhang
1,2,
Qing Xu
1,2,*,
Deqiang Gao
1,2,
Haijun Zuo
1,
Ranran Ren
1,2,
Ke Diao
1 and
Zhicheng Chen
1,2
1
Key Laboratory of Forest Ecology and Environment of National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Beijing 100091, China
2
Baotianman Forest Ecosystem Research Station, Nanyang, Henan 474365, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(9), 1520; https://doi.org/10.3390/f15091520
Submission received: 30 July 2024 / Revised: 19 August 2024 / Accepted: 27 August 2024 / Published: 29 August 2024
(This article belongs to the Special Issue Soil Organic Matter and Soil Multifunctionality in Forest Ecosystems)
Browse Figures
Versions Notes

Abstract

:
Enunciating the carbon storage across various types of forests is a precondition for comprehending forest ecosystem carbon cycling. However, previous studies regarding forest carbon storage were primarily conducted in the general temperature zones, with a limited understanding of carbon storage in different forest types within climate transition zones. In this study, we employed biomass models to explore the carbon storage in three types of natural secondary forests (Pinus armandii forest, Quercus aliena forest, and Q. aliena–P. armandii mixed forest) in the transition zone between the northern subtropical and warm temperate regions of China. Furthermore, we used the variance decomposition analysis and random forest model to determine the key factors influencing carbon storage in three types of natural secondary forests. Our results indicated that the carbon storage of wood and soil layers in the Q. aliena–P. armandii mixed forest was significantly higher than that in the P. armandii and Q. aliena forests. Total carbon storage was ranked as follows: Q. aliena–P. armandii mixed forest (266.09 t/ha) > P. armandii forest (222.89 t/ha) > Q. aliena forest (212.46 t/ha). Our results also revealed that carbon storage of the wood layer was jointly regulated by environmental factors, plant physiological characteristics, and soil properties, while soil carbon storage was mainly affected by soil properties. These results highlight the significant advantages of mixed conifer–broadleaf forests in carbon storage, emphasizing the importance of mixed natural secondary forests in carbon cycling and ecosystem services. This study provides scientific evidence for enhancing forest carbon sink functions and developing forest conservation and management policies to combat climate change.

1. Introduction

Forests are the ecosystems with the richest carbon storage, accounting for approximately 57% of the carbon in terrestrial ecosystems [1]. With climate warming intensifying, assessing forest carbon stocks is critical to understanding the impacts of environmental change on the global carbon cycle [2,3]. Forests convert carbon dioxide into biomass and soil organic carbon through photosynthesis, significantly reducing carbon dioxide concentration in the atmosphere and making forests a vital carbon sink [4,5,6]. However, given the differences in vegetation characteristics, species composition, soil types, and soil nutrients, carbon storage varies significantly among different types of forests [7,8,9,10]. For example, the carbon storage of various forest types ranged from 79.0 Mg C ha−1 to 373.4 Mg C ha−1 in the eastern Himalayas of India [11]. The wide range of carbon storage across different types of forests increases the uncertainty in understanding the carbon cycle of forest ecosystems. Therefore, exploring the variability of carbon storage in different forest types enhances our understanding of forest carbon cycling processes and provides a scientific basis for formulating effective forest conservation and management policies to mitigate climate change [12].
Despite its importance, large uncertainties remain in carbon storage in different types of forests, as described in the following three aspects. First, previous studies mainly focused on cold-temperate, warm-temperate, tropical, or subtropical regions [11,12,13,14], with a limited understanding of forest carbon storage in climate transition zones [15,16,17]. As a buffer zone between different climate regions, the climate transition zone has a higher sensitivity of forest carbon storage to climate warming. For example, it was reported that climate change altered the forest carbon storage in Yunnan Province, a tropical–subtropical transition zone [15], which would inevitably affect regional carbon dynamics and ecosystem stability [10]. Thus, studying forest carbon storage in climate transition zones can provide insights for mitigating climate change and maintaining ecological balance. Unfortunately, direct experimental evidence in this field is still lacking [16,17].
Secondly, research findings have persistent discord regarding the carbon storage of distinct forest types [18,19]. Previous studies have illustrated that coniferous forests store more carbon than broadleaf forests [20]. For instance, studies on the natural secondary forests in Qinling Mountains showed that coniferous forests (Pinus armandii and Picea asperata) had higher carbon storage than mixed conifer–broadleaf forests (Pinus armandii and Quercus L.) and broadleaf mixed forests (Betula albosinensis, Quercus L., and Acer davidii) [20]. A similar observation was also reported by Hao et al. [21], suggesting that the carbon storage of Pinus armandii forest was significantly higher than that of Quercus liaotungensis forest. In contrast, a study conducted in northern Massachusetts revealed that the carbon absorption rate of Quercus rubra forests was considerably higher than that of Tsuga canadensis forests [22]. Similarly, according to He et al. [23], broadleaf forests have a 35.1% higher carbon sequestration capacity than coniferous forests. These inconsistent observations might be ascribed to the differences in regions and species. Therefore, exploring carbon storage across various forest types is essential.
Thirdly, the factors influencing carbon storage in different types of forests remain uncertain. Generally, environmental, plant, and soil factors were supposed drivers affecting the forest carbon storage [24,25,26,27]. For instance, elevated atmospheric CO2 concentration (Ca) could enhance photosynthesis, boosting biomass and carbohydrate accumulation [28,29,30], ultimately increasing forest carbon storage. Relative humidity (RH) could affect forest carbon storage by influencing organic matter decomposition rates [31,32]. In addition to the above-mentioned environmental factors, plant physiological traits significantly influence carbon storage. Michelot et al. [33] observed that the difference in carbon sequestration potential among species was mainly attributed to the distinct biomass accumulations induced by various photosynthetic intensities. In addition, soil physical and chemical properties were important in carbon storage [34]. Simard et al. [35] demonstrated that lower soil temperature and higher soil moisture reduced the carbon mineralization rate and organic matter decomposition, thus improving soil carbon storage. Moreover, soil with higher porosity could retain more moisture and limited oxygen infiltration, further reducing organic matter decomposition and enhancing soil carbon storage. In addition, increased soil nutrient content could enhance forest carbon storage by boosting soil fertility and promoting plant growth [36]. However, despite the existing publications on primary regulators of carbon storage, it is still necessary to clarify the dominant factors influencing carbon storage in different types of forests within this specific study context.
To address the issues mentioned above, we selected three types of natural secondary forests (Pinus armandii forest, Quercus aliena forest, and Q. aliena–P. armandii mixed forest) in the Baotianman National Nature Reserve, located in the transition zone between the northern subtropical and warm temperate regions of Henan, China. Then, we conducted tree-by-tree measurements to determine carbon content in the organs of dominant trees and soil layers, ultimately calculating the carbon storage for each type of forest. Additionally, we assessed environmental, plant, and soil factors in each plot to identify the main factors influencing carbon storage. Our study aimed to answer the following questions: (1) Which type of forest demonstrated the highest carbon storage? (2) What are the dominant factors affecting carbon storage in different types of forests?

2. Materials and Methods

2.1. Study Site and Experimental Design

The study area is located at the Baotianman Forest Ecosystem National Field Scientific Observation and Research Station in Henan, China (111°47′–112°04′ E, 30°20′–33°36′ N). This region lies on the Funiu Mountains’ southern slope, at the Qinling Mountains’ eastern end. It is a transition zone between the northern subtropical and warm temperate zones and between the second and third steps of China’s topography. The area experiences a continental monsoon climate, with an average annual rainfall of 991.6 mm and an average annual relative humidity of 68%. Rain and heat co-occur, with heavy and frequent summer rainfall accounting for 55%–62% of the total annual precipitation. In contrast, winter sees less and weaker rainfall, comprising 4%–6% of the yearly total. Spring and autumn have moderate rain, contributing 19%–25% of the annual precipitation.
The region’s vegetation belongs to the northern subtropical evergreen–deciduous broadleaf mixed forest zone. The core area’s natural forest retains the primitive state of the mountain ecosystem in the transition zone. Vertically, the forest vegetation includes evergreen broadleaf trees, deciduous broadleaf forests, mixed coniferous and deciduous broadleaf forests, and subalpine shrub meadows. Dominant tree species include Quercus aliena, Pinus armandii, Quercus variabilis, and Pinus tabuliformis. Sub-canopy tree species include Cornus controversa, Cornus kousa, and Lindera obtusiloba. The shrub layer is dominated by Viburnum betulifolium, Prunus tomentosa, and Rubus corchorifolius. The herbaceous layer primarily consists of Carex siderosticta and Phlomoides umbrosa. The main rock types are granite, limestone, and sandstone, with mountain brown soil being the predominant soil type (Chinese Soil System Classification, CSSC).
Natural secondary forests result from natural regeneration and succession following human activities or natural disturbances [37,38]. These forests are typically characterized by rich species diversity and complex structures, and they play a significant role in carbon storage and providing essential ecosystem services [39,40]. Within the Baotianman Nature Reserve, we focused on different representative types of natural secondary forests: Quercus aliena forests, Q. aliena–P. armandii mixed forests, and Pinus armandii forests. All are natural secondary forests. Three random 20 m × 20 m plots were delineated in each study area. We measured site conditions and soil physical and chemical properties in different forest ecosystems [41,42] (Table 1 and Table S1). Additionally, we surveyed the dominant trees and their root systems in the plots.

2.2. Sample Collection and Processing

We measured each dominant tree species in the standard plot, recording the tree’s diameter at breast height, tree height, and density. In each plot, we randomly selected 3 to 5 trees, collected branches from the middle and upper parts of the crown with high-branch shears, separated the branches and leaves, and mixed them into one sample. We also used a tree growth cone to drill 3 to 5 tree cores (containing trunk and bark) as trunk samples and mixed them into one sample. Then, we randomly dug three 1 m soil profiles in each plot. Soil and root samples were taken from five layers: 0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm, and 80–100 cm.
Plant samples were collected and transported to the laboratory. They were rinsed with water to remove dust, dried at 105 °C for 30 min, and then at 65 °C for 48 h to reach a constant weight. The dried samples were ground with a grinder. Soil samples were also collected, sieved through a 2 mm mesh to remove roots and gravel, and air-dried. The air-dried soil was ground using a grinder. Both the plant and soil samples were sieved through a 100-mesh sieve and stored for carbon, nitrogen, and phosphorus content analysis.

2.3. Calculation of Biomass and Carbon Storage

Using biomass models for Quercus aliena and Pinus armandii organs, we computed the organ biomass for different forest types in the tree layer. Subsequently, we estimated the carbon storage of pure and mixed forests based on basal area (BA), carbon content, and biomass.
Quercus aliena biomass model [43].
BiTrunks = 0.31079(D2H)0.67428
BiBranches = 0.02933(D2H)0.75662
BiLeaves = 0.09220(D2H)0.39445
BiRoots = 0.16723(D2H)0.64106
Pinus armandii biomass model [44].
BiTrunks = 0.01308(D2H)1.0038
BiBranches = 0.00550(D2H)1.0439
BiLeaves = 0.00110(D2H)1.12566
BiRoots = 0.00330(D2H)1.0148
Vegetation carbon storage formula.
CStock = CCon × Bi × dTree
Soil carbon storage formula.
SCStock = A × SCCon × BD × HSoil
Bi represents the biomass of various plant organs. D stands for diameter at breast height (DBH). H refers to the height of the tree. CStock represents the carbon storage in each plant organ (t/ha). CCon indicates the proportion of C in the organ biomass (%). dTree refers to tree density (tree/ha). SCStock denotes soil carbon storage (t/ha), A is the forest area (m2), and SCCon represents soil carbon content (%). BD is the soil bulk density (g/m3), and HSoil is the soil thickness (m).

2.4. Measurement of Impact Factors

To assess the photosynthetic physiological parameters of dominant mature tree species in each plot, we used the LI-6400XT portable photosynthesis system (Li-cor, Inc., Lincoln, NE, USA). The parameters measured included net photosynthetic rate (Pn, μmol·m⁻2·s⁻1), transpiration rate (Tr, mmol·m⁻2·s⁻1), stomatal conductance (Gs, mmol·m⁻2·s⁻1), intercellular CO2; concentration (Ci, μmol·mol⁻1), leaf vapor pressure deficit (Vpd), atmospheric CO2; concentration (Ca, μmol·mol⁻1), and relative humidity (RH, %). Measurements were taken monthly, once or twice, and instantaneous water use efficiency (WUE) was used using the formula WUE = Pn/Tr. The physical properties of the soil, including soil moisture content (SMC), bulk density (BD), soil total porosity (STP), soil capillary porosity (SCP), and soil air-filled porosity (SAP), were determined using the ring knife method. Soil carbon (TC) and nitrogen (TN) contents were analyzed using a CN elemental analyzer (vario MICRO cube, Elementar, Langenselbold, Germany), and soil phosphorus content (TP) was measured using the molybdenum-antimony colorimetric method.

2.5. Data Analysis

We applied one-way ANOVA and the least significant difference (LSD) test to compare the carbon storage differences among various forest ecosystem types. Duncan’s post hoc test was used to perform the multiple comparisons. To identify the main drivers of carbon storage in the tree and soil layers of different forest types, we first introduced variation partitioning analysis to evaluate the individual and combined influences of environmental factors (Ca, RH, etc.), physiological characteristics (Vpd, Pn, Tr, Gs, WUE, Ci, etc.), and soil physical and chemical properties (TN, TP, SMC, STP, SCP, BD, etc.) on carbon storage (Tables S3 and S4). This analysis was performed with the “vegan” package in R (Package Version 1.15–1) [45]. To verify the accuracy of the variation partitioning results, we used a random forest model to select the dominant factors influencing carbon storage in the tree and soil layers of different forest types. The number of decision trees in the random forest was 1000, with other parameters kept at their default settings. This analysis used R’s randomForest package (Package Version 4.6–1.4) [46]. Before statistical analysis, tests for variance homogeneity and normal distribution were performed. The significance level was set at p < 0.05 or 0.01. All analyses were performed using SPSS 27.0 and R 4.3.2. Figures were generated using Origin 2022.

3. Results

3.1. Carbon Storage and Its Allocation Patterns in the Tree Layer of Different Forest Types

The carbon storage in the tree layer of the Q. aliena–P. armandii mixed forest (142.70 t/ha) was significantly higher than that in the Pinus armandii forest (133.02 t/ha), with the lowest value observed in the Quercus aliena forest (116.84 t/ha) (Figure 1a). Carbon storage was primarily allocated to trunks and branches in the Q. aliena–P. armandii mixed forest and the Pinus armandii forest. In contrast, the Quercus aliena forest was allocated principally to trunks and roots (Figure 1b). The order of carbon storage in leaves and branches among the three forest types was: Pinus armandii forest > Q. aliena–P. armandii mixed forest > Quercus aliena forest (p < 0.05). For trunk carbon storage, the Q. aliena–P. armandii mixed forest had the highest value of 81.08 t/ha, followed by the Quercus aliena forest, with the Pinus armandii forest having the lowest. In terms of root carbon storage, the tree layer of the Quercus aliena forest showed an increase of 2.34 t/ha and 10.73 t/ha compared to the Q. aliena–P. armandii mixed forest and the Pinus armandii forest, respectively (Table S2).

3.2. Soil Carbon Storage and Allocation Patterns in Different Forest Ecosystems

Soil carbon storage in the mixed forest of Quercus aliena and Pinus armandii (123.39 t/ha) is significantly higher than that in the pure stands of Quercus aliena (95.62 t/ha) and Pinus armandii (89.87 t/ha) (Figure 2a, p < 0.01). However, there was no significant difference in soil carbon storage between the pure stands of Quercus Aliena and Pinus armandii (p = 0.217). Soil carbon storage decreased with increasing soil depth across all forest types, with 73.15% to 77.49% of the carbon stored within the 0–40 cm soil layer (Figure 2b). Q. aliena–P. armandii mixed forest exhibited significantly higher carbon storage in the 0–20 cm, 20–40 cm, 60–80 cm, and 80–100 cm soil layers than pure forests. In the 40–60 cm soil layer, the ranking of soil carbon storage was Quercus aliena forest > Q. aliena–P. armandii mixed forest > Pinus armandii forest (p < 0.01).

3.3. Total Carbon Storage and Its Allocation Patterns in Different Forest Ecosystems

As shown in Figure 3a, the carbon storage in the Q. aliena–P. armandii mixed forest was 266.09 t/ha, which was significantly higher than that in the Pinus armandii forest (222.89 t/ha), and the Quercus aliena forest (212.46 t/ha). There was no significant difference in carbon storage between the Pinus armandii forest and the Quercus aliena forest (p = 0.151). Among the three forest types, the allocation of carbon storage was significantly greater in the tree layer compared to the soil layer (Figure 3b). Specifically, the tree layer carbon storage in the Quercus aliena forest, Q. aliena–P. armandii mixed forest, and Pinus armandii forest represented 55.03%, 53.65%, and 59.68% of their total carbon storage. In comparison, the soil layer accounted for 44.97%, 46.35%, and 40.32%, respectively. Furthermore, the Pinus armandii forest exhibited a significantly higher proportion of carbon storage in the tree layer than the Q. aliena–P. armandii mixed forest and the Quercus aliena forest. Conversely, the proportion of carbon storage in the soil layer of the Quercus aliena forest and the Q. aliena–P. armandii mixed forest was significantly higher than that in the Pinus armandii forest (Figure 3b).

3.4. Dominant Factors of Carbon Storage in the Tree Layer of Different Forest Types

The results from variance decomposition analysis indicate that carbon storage in the tree layer was jointly influenced by environmental factors, plant physiological characteristics, and soil properties, with an explanatory rate of 94.3% and significant interactions among the three components (62.3%) (Figure 4a). The random forest model further confirmed that these factors regulated carbon storage in the tree layer, showing a total explanatory rate of 88.35%. Specifically, Vpd, Tr, SMC, STP, RH, Gs, SCP, Ca, WUE, and TN were found to jointly influence tree layer carbon storage (Figure 4b). In summary, environmental factors, plant physiological characteristics, and soil properties regulated carbon storage in the tree layer.

3.5. Dominant Factors of Soil Carbon Storage in Different Forest Types

Similarly, this study applied variance decomposition analysis and the random forest model to determine the primary factors affecting soil carbon storage in different forest types. The variance decomposition analysis findings suggested that soil carbon storage was primarily impacted by soil physicochemical properties, which accounted for 40.3% of the variation (Figure 5a). The random forest model corroborated these findings, demonstrating that TP, Vpd, Ca, and TN were significant determinants of soil carbon storage, with a cumulative model explanatory rate of 71.01% (Figure 5b). The study identified soil physicochemical properties as the primary determinants of soil carbon storage through variance decomposition and random forest modeling. This approach significantly explicated the observed variance.

4. Discussion

4.1. Carbon Storage and Distribution Patterns in Three Types of Forests

This study found that the carbon storage of the Q. aliena–P. armandii mixed forest was 266.09 t/ha, which exceeded the average value of forest ecosystems in China (258.8 t/ha) [47], indicating a solid carbon sequestration capacity for this forest type in the Baotianman region. Additionally, the ranking of carbon storage among three types of forests in this area was as follows: Q. aliena–P. armandii mixed forest > P. armandii forest > Q. aliena forest (i.e., mixed conifer–broadleaf forest > coniferous forest > broadleaf forest), which did not support the findings of Wei and Blanco [48] (broadleaf forest > mixed conifer–broadleaf forest > coniferous forest). The inconsistency of results may be attributed to two aspects. Firstly, distinct carbon content among different tree species leads to variations in forest carbon storage. In this study, coniferous species’ higher average carbon content (Pinus armandii) resulted in higher total carbon storage in mixed conifer–broadleaf and coniferous forests (Table S2). Conversely, Wei and Blanco [48] found that broadleaf species’ carbon content was higher than coniferous species, leading to more excellent total carbon storage in broadleaf forests. Secondly, regional heterogeneity in forest stand structure diversity results in varying carbon storage capacities. Generally, given that diverse ecosystems can efficiently utilize resources and space, thereby increasing overall productivity and carbon storage, forests with higher stand structure diversity tend to have more excellent carbon storage [20]. In this study, the Q. aliena–P. armandii mixed forest in the transition zone exhibited higher species diversity, leading to higher carbon storage. In contrast, Wei et al.’s study [48] was conducted in the subtropical zone, where broadleaf forests demonstrated higher species diversity and resource use efficiency than mixed conifer–broadleaf forests, resulting in higher carbon storage in broadleaf forests. Additionally, this study found that carbon storage in the tree layer was significantly higher among the three types of forests than in the soil layer. This phenomenon may be due to the rapid growth rate of the tree during the growing season, which increases biomass accumulation and carbon fixation [49,50].
In terms of carbon storage in the tree layer, our study revealed that the Q. aliena–P. armandii mixed forest had the highest tree layer carbon storage (142.70 t/ha), followed by the Pinus armandii forest (133.02 t/ha), with the Quercus aliena forest being the lowest (116.84 t/ha). This finding suggested a significant advantage of mixed forests in carbon storage. This outcome may be attributed to three factors. First, the complementary effects of different species in mixed forests enhance overall carbon sequestration efficiency. Studies have indicated that mixed forests experiencing interspecific facilitation to improve resource use efficiency have 20% to 30% higher productivity than monocultures [51]. Second, the Q. aliena–P. armandii mixed forest boasts a higher stand density, leading to the highest biomass and tree layer carbon storage. High-density stands promote uniform leaf distribution, enhancing photosynthesis and resource use efficiency in tree species [33,52], thereby increasing biomass and carbon sequestration. Third, mixed forests’ species and structural diversity improve the ecosystem’s resilience and adaptability to environmental changes, impacting long-term carbon storage [53].
Regarding the carbon storage distribution in the tree layer, our study found that in the mixed forest of Quercus aliena and Pinus armandii, as well as in the Pinus armandii forest, the majority of the carbon storage was concentrated in the trunks and branches. This finding supports the “optimal biomass allocation theory”, which posits that trees prioritize the allocation of resources to slow-growing but long-lived organs (such as trunks and branches) to enhance long-term survival and carbon accumulation [54,55]. In contrast, in the Quercus aliena forest, a significant proportion of metabolic products was allocated to the roots and the trunk. This pattern aligns with the “allometric growth allocation theory”, suggesting that the growth of the trunk and roots as the primary support and resource acquisition structures enhance structural stability and resource acquisition efficiency, thereby improving survival and competitiveness [56,57]. The results also indicated that coniferous species, such as Pinus armandii, allocated significantly more carbon to branches and leaves than broadleaf species. This disparity may be attributed to the species’ physiological characteristics and ecological strategies, including leaf area index, branch structure, and photosynthesis strategies [32,33,50]. These characteristics collectively influence the carbon allocated to branches and leaves in coniferous species. Our findings underscore the varying carbon allocation strategies in different forest types, influenced by species-specific growth patterns and ecological adaptations.
In the context of soil carbon storage and distribution patterns, the results of our study indicated that the soil carbon storage in the Q. aliena–P. armandii mixed forest (123.39 t/ha) was significantly higher than that in the Quercus aliena forest (95.62 t/ha) and Pinus armandii forest (89.87 t/ha). This could be attributed to the diversity in the mixed forest structure, as the root morphology, depth, and distribution of different tree species varied, leading to better utilization of soil resources and enhanced formation and accumulation of organic matter [58]. Additionally, the study found that soil carbon storage was primarily concentrated in the 0–40 cm surface soil, accounting for 73.15% to 77.49% of the total storage, indicating a phenomenon of surface aggregation in the study area [59,60]. Numerous studies have observed this phenomenon [13]; for instance, Brunn et al. [61] found that forest soil carbon storage decreases with increasing soil depth, with the surface soil being the main carbon storage area. This may be closely related to soil organic matter accumulation and root activity. Specifically, surface soil contains more plant residues, leaf litter, and root exudates, the primary sources of soil organic carbon. Since the surface soil primarily contains these organic materials, it naturally becomes the main carbon storage area [62,63]. Moreover, the Q. aliena–P. armandii mixed forest shows higher soil carbon storage at multiple soil depths, suggesting that mixed forests have the potential to enhance soil carbon storage, possibly due to their higher biodiversity and complex root structures.

4.2. Dominant Factors of Forest Carbon Storage

Employing VPA analysis and random forest models, we revealed the relative importance of environmental factors (RH, Ca), plant physiological characteristics (Vpd, Gs, WUE, Tr), and soil physicochemical properties (SMC, STP, SCP, TN) on carbon storage in the tree layer. RH emerged as a crucial factor affecting tree layer carbon storage among environmental factors. Higher air humidity increased stomatal conductance, enhancing photosynthetic carbon assimilation [64] and boosting carbon storage. Additionally, higher air humidity elevated soil moisture, aiding root water and nutrient uptake and supporting aboveground growth and carbon accumulation [65,66]. In addition to RH, Ca also significantly impacted carbon storage due to its fertilization effect, enhancing plant photosynthetic rates, biomass, and carbon storage [67]. In this study, Ca levels were notably higher in mixed forests than in Pinus armandii and Quercus aliena forests (Table S3), likely due to elevated CO2 concentrations acting as a fertilization effect, increasing carbon storage in mixed forests [68]. In addition to environmental factors, plant physiological characteristics such as Vpd, Gs, WUE, and Tr also regulated carbon storage. Vpd affected carbon storage through two mechanisms: high Vpd caused stomatal closure to reduce water loss, limiting CO2 uptake and photosynthetic carbon assimilation, thus reducing biomass accumulation and carbon storage; and high Vpd increased respiration, consuming more carbohydrates and reducing carbon storage [65,66,69]. Increased Gs enhanced CO2 uptake, promoting carbon fixation and biomass accumulation [70]. High Tr-induced water stress causes stomatal closure, reduces photosynthesis, and lowers biomass and carbon storage [69,70]. Higher WUE means plants use less water for the same amount of carbon fixation, increasing the carbon storage growth rate [71]. In this study, Vpd, Gs, and Tr were significantly lower in mixed forests compared to Quercus aliena forests. At the same time, WUE was considerably higher in mixed forests (Table S4), favoring more efficient water and carbon use, thus leading to higher carbon storage in mixed forests [70]. Furthermore, soil physicochemical properties such as SMC, STP, SCP, and TN significantly influence carbon storage. SMC was essential for photosynthetic carbon fixation; adequate soil moisture ensured sufficient water for photosynthesis and other physiological activities, enhancing dry matter accumulation and carbon fixation capacity [72]. STP and SCP were crucial for root development, gas exchange, nutrient availability, and soil hydraulic properties. Optimal STP and SCP provided sufficient oxygen for plant roots and soil microbes, promoting root respiration and microbial activity and enhancing plant growth and carbon fixation [36,73,74,75]. TN promoted leaf nitrogen and chlorophyll content, increasing photosynthetic pigments and total protein, thus boosting photosynthetic rates and carbon fixation [76]. In this study, SMC and TN were significantly higher in most mixed forest soil layers than in Quercus aliena forests (Table S1), contributing to higher carbon storage in mixed forests. In summary, environmental factors, plant physiological characteristics, and soil physicochemical properties jointly regulate carbon storage in the tree layers of Quercus alien forests, Pinus armandii forests, and mixed forests.
Our results showed that soil properties, such as total phosphorus (TP) and total nitrogen (TN), primarily regulate soil carbon storage. Vapor pressure deficit (Vpd) and atmospheric CO2 concentration (Ca) also influenced soil carbon storage. The contribution of soil properties was mainly attributed to their direct impact on the decomposition and accumulation of soil organic matter. Specifically, TP is a crucial element in synthesizing adenosine triphosphate (ATP), essential for photosynthesis. Adequate phosphorus supply can enhance photosynthetic efficiency, thereby increasing carbon sequestration. Additionally, TP promoted root development, improving the effective utilization of nutrients and water, which was essential for enhancing plant growth and carbon storage [77]. Furthermore, TP in the soil can stimulate soil microorganisms to release more nutrients during the decomposition of organic matter, thereby promoting plant growth and indirectly increasing soil carbon storage [78]. TN influenced soil carbon storage in two main ways. First, nitrogen is a crucial component of proteins, chlorophyll, and nucleic acids; sufficient nitrogen supply promotes plant growth and photosynthetic efficiency, enhancing carbon fixation capacity. Second, nitrogen facilitated the reproduction and activity of soil microorganisms, accelerating the decomposition of organic matter and nutrient mineralization, which promoted plant growth and carbon sequestration [68,79,80]. Environmental and physiological factors such as Vpd and Ca indirectly affected soil carbon storage by influencing plant water status and photosynthetic assimilation rate. A lower Vpd indicated a more humid environment, reducing the decomposition rate of soil organic matter and indirectly increasing soil carbon storage [31]. Moreover, an increased Ca concentration accelerated plant growth, leading to more carbon sequestered in biomass and ultimately growing soil organic matter input [68]. In this study, TP, TN, and Ca were highest in mixed forests, while Vpd was lower (Tables S1, S3, and S4), resulting in higher carbon storage. In conclusion, soil properties primarily regulate soil carbon storage.

5. Conclusions

In this study, we analyzed the carbon storage of different types of natural secondary forests in Baotianman, Henan. The results indicated that the carbon storage in the Q. aliena–P. armandii mixed forest was 266.09 t/ha, significantly higher than that in pure Pinus armandii and Quercus aliena forests. This finding demonstrates the significant advantage of coniferous–broadleaf mixed forests in carbon storage. Further analysis based on variance decomposition and random forest models revealed that environmental factors, plant physiological characteristics, and soil physicochemical properties jointly influence the carbon storage of the forest canopy in this region. In contrast, soil physicochemical properties mainly influence soil carbon storage. These results suggest that coniferous–broadleaf mixed forests exhibit a clear advantage in carbon storage due to their higher diversity and resource use efficiency. This finding underscores the importance of natural secondary coniferous–broadleaf mixed forests in carbon cycling and ecosystem services. Enhancing the protection of mixed forests in climate transition zones could improve forest carbon sequestration, providing a scientific basis for more effectively addressing climate change.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/f15091520/s1, Table S1: Soil physical and chemical properties in three types of forests; Table S2: Carbon content of plant organs in three types of forests; Table S3: Environmental factors in three types of forests; Table S4: Plant physiological characteristics in three types of forests.

Author Contributions

Conceptualization, W.X., B.Z. and Q.X.; methodology, W.X.; software, W.X. and B.Z.; validation, D.G., K.D. and H.Z.; formal analysis, W.X.; investigation, W.X., R.R., Z.C., D.G. and K.D.; resources, B.Z.; data curation, W.X.; writing—original draft preparation, W.X.; writing—review and editing, B.Z. and Q.X.; visualization, R.R.; supervision, B.Z.; project administration, Q.X.; funding acquisition, Q.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key R&D Program of China (2021YFD2200401) and the National Nonprofit Institute Research Grant of CAF (CAFYBB2021ZE002; CAFYBB2022SY022).

Data Availability Statement

The datasets used and/or analyzed during this study are available from the corresponding author upon reasonable request.

Acknowledgments

We are very grateful to the Baotianman Forest Ecosystem Research Station for their support and assistance in the fieldwork.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. FAO; UNEP. The State of the World’s Forests 2020. In Forests, Biodiversity and People; Sprep Library: Rome, Italy, 2020. [Google Scholar] [CrossRef]
  2. Kumar, R.; Bhardwaj, D.R.; Yadav, R.P.; Negi, V. Budgeting of biomass and carbon stock as ecosystem service from Himalayan dry temperate and alpine forest ecosystem, India. Appl. Ecol. Environ. Res. 2023, 21, 2881–2896. [Google Scholar] [CrossRef]
  3. Li, Z.R.; Lai, Q.; Bao, Y.H.; Sude, B.; Bao, Z.Y.; Liu, X.Y. Carbon Allocation to Leaves and Its Controlling Factors and Impacts on Gross Primary Productivity in Forest Ecosystems of Northeast China. Forests 2024, 15, 129. [Google Scholar] [CrossRef]
  4. Bowman, D.; Murphy, B.; Boer, M.; Bradstock, R.; Cary, G.; Cochrane, M.; Fensham, R.; Krawchuk, M.; Price, O.; Williams, R. Forest fire management, climate change, and the risk of catastrophic carbon losses. Front. Ecol. Environ. 2013, 11, 66–68. [Google Scholar] [CrossRef]
  5. Ruiz-Peinado, R.; Bravo-Oviedo, A.; López-Senespleda, E.; Montero, G.; Río, M. Do thinnings influence biomass and soil carbon stocks in Mediterranean maritime pinewoods? Eur. J. Forest Res. 2013, 132, 253–262. [Google Scholar] [CrossRef]
  6. Pan, Y.D.; Birdsey, R.A.; Fang, J.Y.; Houghton, R.; Kauppi, P.E.; Kurz, W.A.; Phillips, O.L.; Shvidenko, A.; Lewis, S.L.; Canadell, J.G.; et al. A large and persistent carbon sink in the world’s forests. Science 2011, 333, 988–993. [Google Scholar] [CrossRef]
  7. Hu, P.L.; Liu, S.J.; Ye, Y.Y.; Zhang, W.; Wang, K.L.; Su, Y.R. Effects of environmental factors on soil organic carbon under natural or managed vegetation restoration. Land Degrad. Dev. 2018, 29, 387–397. [Google Scholar] [CrossRef]
  8. Wang, J.S.; Sun, J.; Xia, J.Y.; He, N.P.; Li, M.L.; Niu, S.L. Soil and vegetation carbon turnover times from tropical to boreal forests. Funct. Ecol. 2018, 32, 71–82. [Google Scholar] [CrossRef]
  9. Ma, Z.L.; Chen, H.Y.H.; Bork, E.W.; Carlyle, C.N.; Chang, S.X. Carbon accumulation in agroforestry systems is affected by tree species diversity, age and regional climate: A global meta-analysis. Glob. Ecol. Biogeogr. 2020, 29, 1817–1828. [Google Scholar] [CrossRef]
  10. Raj, A.; Jhariya, M.K. Carbon storage, flux and mitigation potential of tropical Sal mixed deciduous forest ecosystem in Chhattisgarh, India. J. Environ. Manag. 2021, 293, 112829. [Google Scholar] [CrossRef]
  11. Anudip, G.; Jitendra, A.; Uttam, K.S. Evaluation of ecosystem carbon storage in major forest types of Eastern Himalaya: Implications for carbon sink management. J. Environ. Manag. 2022, 302, 113972. [Google Scholar] [CrossRef]
  12. Zhang, Y.Q.; Ai, J.J.; Sun, Q.W.; Li, Z.C.; Hou, L.Y.; Song, L.G.; Tang, G.Y.; Li, L.; Shao, G.D. Soil organic carbon and total nitrogen stocks as affected by vegetation types and altitude across the mountainous regions in the Yunnan Province, south-western China. Catena 2021, 196, 104872. [Google Scholar] [CrossRef]
  13. Xiao, R.H.; Man, X.L.; Duan, B.X. Carbon and Nitrogen Stocks in Three Types of Larix gmelinii Forests in Daxing’an Mountains, Northeast China. Forests 2020, 11, 305. [Google Scholar] [CrossRef]
  14. Yuan, Z.X.; Jin, X.M.; Xiao, W.Y.; Wang, L.; Sun, Y.; Guan, Q.W.; Meshack, A.O. Comparing soil organic carbon stock and fractions under natural secondary forest and Pinus massoniana plantation in subtropical China. Catena 2022, 212, 106092. [Google Scholar] [CrossRef]
  15. Duan, X.W.; Rong, L.; Hu, J.M.; Zhang, G. Soil organic carbon stocks in the Yunnan Plateau, southwest China: Spatial variations and environmental controls. J. Soil. Sediment. 2014, 14, 1643–1658. [Google Scholar] [CrossRef]
  16. Wu, X.G.; Wang, W.P.; Li, B.; Liang, Y.L.; Liu, Y.Z. Altitudinal Gradient of Soil Organic Carbon in Forest Soils in the Mid-Subtropical Zone of China. Acta Pedol. Sin. 2020, 57, 1539–1547. (In Chinese) [Google Scholar] [CrossRef]
  17. Zhu, W.T.; Xie, F.L.; Li, T.; He, N.J.; Zhang, K.R.; Zhang, Q.F.; Dang, H.S. Species-habitat association of a deciduous broadleaved forest in the subtropical and temperate transition zone. Chin. J. Appl. Ecol. 2021, 32, 2755–2762. (In Chinese) [Google Scholar] [CrossRef]
  18. Yao, Y.T.; Piao, S.L.; Wang, T. Future biomass carbon sequestration capacity of Chinese forests. Sci. Bull. 2018, 63, 1108–1117. [Google Scholar] [CrossRef]
  19. Favero, A.; Daigneault, A.; Sohngen, B. Forests: Carbon sequestration, biomass energy, or both? Sci. Adv. 2020, 6, eaay6792. [Google Scholar] [CrossRef]
  20. Pang, Y.; Tian, J.; Zhao, X.; Zhao, Z.; Wang, Y.C.; Zhang, X.P.; Wang, D.X. The linkages of plant, litter and soil C: N: P stoichiometry and nutrient stock in different secondary mixed forest types in the Qinling Mountains, China. PeerJ 2020, 8, e9274. [Google Scholar] [CrossRef]
  21. Hao, Z.; Quan, Z.; Han, Y.; Lv, C.; Zhao, X.; Jing, W.J.; Zhu, L.H.; Ma, J.Y. Soil mineralized carbon drives more carbon stock in coniferous-broadleaf mixed plantations compared to pure plantations. PeerJ 2022, 10, e13542. [Google Scholar] [CrossRef]
  22. Hadley, J.L.; Kuzeja, P.S.; Daley, M.J.; Phillips, N.G.; Mulcahy, T.; Singh, S. Water use and carbon exchange of red oak-and eastern hemlock-dominated forests in the northeastern USA: Implications for ecosystem-level effects of hemlock woolly adelgid. Tree Physiol. 2008, 28, 615–627. [Google Scholar] [CrossRef]
  23. He, N.P.; Wen, D.; Zhu, J.X.; Tang, X.L.; Xu, L.; Zhang, L.; Hu, H.F.; Huang, M.; Yu, G.R. Vegetation carbon sequestration in Chinese forests from 2010 to 2050. Global Change Biol. 2017, 23, 1575–1584. [Google Scholar] [CrossRef]
  24. Baddeley, J.A.; Edwards, A.C.; Watson, C.A. Changes in soil C and N stocks and C:N stoichiometry 21 years after land use change on an arable mineral topsoil. Geoderma 2017, 303, 19–26. [Google Scholar] [CrossRef]
  25. Lutter, R.; Kõlli, R.; Tullus, A.; Tullus, H. Ecosystem carbon stocks of estonian premature and mature managed forests: Effects of site conditions and overstorey tree species. Eur. J. For. Res. 2018, 138, 125–142. [Google Scholar] [CrossRef]
  26. Wu, X.D.; Zhao, L.; Hu, G.J.; Liu, G.M.; Li, W.P.; Ding, Y.J. Permafrost and land cover as controlling factors for light fraction organic matter on the southern Qinghai-Tibetan plateau. Sci. Total Environ. 2018, 613, 1165–1174. [Google Scholar] [CrossRef]
  27. Yue, C.; Ciais, P.; Luyssaert, S.; Li, W.; McGrath, M.J.; Chang, J.F.; Peng, S.S. Representing anthropogenic gross land use change, wood harvest, and forest age dynamics in a global vegetation model ORCHIDEE-MICT v8. 4.2. Geosci. Model Dev. 2018, 11, 409–428. [Google Scholar] [CrossRef]
  28. Yu, Z.; You, W.B.; Agathokleous, E.; Zhou, G.Y.; Liu, S.R. Forest management required for consistent carbon sink in China’s forest plantations. For. Ecosyst. 2021, 8, 54. [Google Scholar] [CrossRef]
  29. Walker, A.P.; De Kauwe, M.G.; Bastos, A.; Belmecheri, S.; Georgiou, K.; Keeling, R.F.; McMahon, S.M.; Medlyn, B.E.; Moore, D.J.P.; Norby, R.J.; et al. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2. New Phytol. 2021, 229, 2413–2445. [Google Scholar] [CrossRef] [PubMed]
  30. Haverd, V.; Smith, B.; Canadell, J.G.; Cuntz, M.; Mikaloff-Fletcher, S.; Farquhar, G.; Woodgate, W.; Briggs, P.R.; Trudinger, C.M. Higher than expected CO2 fertilization inferred from leaf to global observations. Glob. Chang. Biol. 2020, 26, 2390–2402. [Google Scholar] [CrossRef]
  31. Zhang, X.B.; Sun, Z.G.; Liu, J.; Ouyang, Z.; Wu, L.H. Simulating greenhouse gas emissions and stocks of carbon and nitrogen in soil from a long-term no-till system in the North China Plain. Soil Tillage Res. 2018, 178, 32–40. [Google Scholar] [CrossRef]
  32. Li, Y.Y.; Dong, S.K.; Wen, L.; Wang, X.X. Soil carbon and nitrogen pools and their relationship to plant and soil dynamics of degraded and artificially restored grasslands of the Qinghai-Tibetan Plateau. Geoderma 2014, 213, 178–184. [Google Scholar] [CrossRef]
  33. Michelot, A.; Simard, S.; Rathgeber, C.; Dufrêne, E.; Damesin, C. Comparing the intra-annual wood formation of three European species (Fagus sylvatica, Quercus petraea and Pinus sylvestris) as related to leaf phenology and non-structural carbohydrate dynamics. Tree Physiol. 2012, 32, 1033–1045. [Google Scholar] [CrossRef] [PubMed]
  34. Yuan, Y.; Zhao, Z.; Li, X.; Wang, Y.Y.; Bai, Z.K. Characteristics of labile organic carbon fractions in reclaimed mine soils: Evidence from three reclaimed forests in the Pingshuo opencast coal mine, China. Sci. Total Environ. 2018, 613, 1196–1206. [Google Scholar] [CrossRef]
  35. Simard, M.; Lecomte, N.; Bergeron, Y.; Bernier, P.Y.; Paré, D. Forest productivity decline caused by successional paludification of boreal soils. Ecol. Appl. 2007, 17, 1619–1637. [Google Scholar] [CrossRef] [PubMed]
  36. Augusto, L.; De Schrijver, A.; Vesterdal, L.; Smolander, A.; Prescott, C.; Ranger, J. Influences of evergreen gymnosperm and deciduous angiosperm tree species on the functioning of temperate and boreal forests. Biol. Rev. 2015, 90, 444–466. [Google Scholar] [CrossRef] [PubMed]
  37. Zhang, H.N.; Chen, S.F.; Zheng, X.; Ge, X.M.; Li, Y.; Fang, Y.M.; Cui, P.; Ding, H. Neighborhood diversity structure and neighborhood species richness effects differ across life stages in a subtropical natural secondary forest. For. Ecosyst. 2022, 9, 100075. [Google Scholar] [CrossRef]
  38. Seidl, R.; Turner, M.G. Post-disturbance reorganization of forest ecosystems in a changing world. Proc. Natl. Acad. Sci. USA 2022, 119, e2202190119. [Google Scholar] [CrossRef]
  39. Rozendaal, D.M.A.; Bongers, F.; Aide, T.M.; Alvarez-Dávila, E.; Ascarrunz, N.; Balvanera, P.; Becknell, J.M.; Bentos, T.V.; Brancalion, P.H.S.; Cabral, G.A.L.; et al. Biodiversity recovery of Neotropical secondary forests. Sci. Adv. 2019, 5, eaau3114. [Google Scholar] [CrossRef]
  40. Hua, F.; Bruijnzeel, L.A.; Meli, P.; Martin, P.A.; Zhang, J.; Nakagawa, S.; Miao, X.; Wang, W.; McEvoy, C.; Pena-Arancibia, J.L.; et al. The biodiversity and ecosystem service contributions and trade-offs of forest restoration approaches. Science 2022, 376, 839–844. [Google Scholar] [CrossRef]
  41. Özcan, M.; Gökbulak, F.; Hizal, A. Exclosure effects on recovery of selected soil properties in a mixed broadleaf forest recreation site. Land Degrad. Dev. 2013, 24, 266–276. [Google Scholar] [CrossRef]
  42. Sun, L.; Yang, L.; Chen, L.; Li, S.; Zhao, F.; Sun, S. Tracing the soil water response to autumn rainfall in different land uses at multi-day timescale in a subtropical zone. Catena 2019, 180, 355–364. [Google Scholar] [CrossRef]
  43. Chen, C.G.; Gong, L.Q.; Peng, H.; Liu, X.Z. Biomass and Productivity of the Sharptooth Oak Forests in the Qinling Moutains. J. Northwest For. Univ. 1996, 11 (Suppl. S1), 103–114. (In Chinese) [Google Scholar]
  44. Chen, C.G. Study on the Production of Pinus armandii Forests in the Qinling Mountains Biomass and Production of the Arbor-Layers in Pinus armandii Forests (1). J. Northwest For. Univ. 1984, 1, 1–18. (In Chinese) [Google Scholar]
  45. Oksanen, J.; Kindt, R.; Legendre, P.; O’Hara, B.; Simpson, G.L.; Solymos, P.; Steven, M.H.H.; Wagner, H.; Vegan: Community Ecology Package. R Package Version 1.15–1. 2008. Available online: http://www.R-project.org (accessed on 19 December 2008).
  46. Leo, B.; Adele, C.; Andy, L.; Matthew, W.; Package “randomForest” title Breiman and cutler’s random forests for classification and regression. R Package Version 4.6–14. 2018. Available online: https://cran.r-project.org/src/contrib/Archive/randomForest (accessed on 25 March 2018).
  47. Zhou, Y.R.; Yu, Z.L.; Zhao, S.D. Carbon storage and budget of major Chinese forest types. Chin. J. Plant Ecol. 2000, 24, 518–522. (In Chinese) [Google Scholar]
  48. Wei, X.H.; Blanco, J.A.; Hui, D.F. Significant Increase in Ecosystem C Can Be Achieved with Sustainable Forest Management in Subtropical Plantation Forests. PLoS ONE 2014, 9, e89688. [Google Scholar] [CrossRef]
  49. Köhl, M.; Neupane, P.R.; Lotfiomran, N. The impact of tree age on biomass growth and carbon accumulation capacity: A ret-rospective analysis using tree ring data of three tropical tree species grown in natural forests of Suriname. PLoS ONE 2017, 12, e0181187. [Google Scholar] [CrossRef] [PubMed]
  50. Zhang, Y.D.; Gu, F.X.; Liu, S.R.; Liu, Y.C.; Li, C. Variations of carbon stock with forest types in subalpine region of southwestern China. Forest Ecol. Manag. 2013, 300, 88–95. [Google Scholar] [CrossRef]
  51. Forrester, D.I. The spatial and temporal dynamics of species interactions in mixed-species forests: From pattern to process. Forest Ecol. Manag. 2014, 312, 282–292. [Google Scholar] [CrossRef]
  52. Falster, D.S.; Duursma, R.A.; FitzJohn, R.G. How functional traits influence plant growth and shade tolerance across the life cycle. Proc. Natl. Acad. Sci. USA 2018, 115, E6789–E6798. [Google Scholar] [CrossRef]
  53. Kaushal, S.; Baishya, R. Stand structure and species diversity regulate biomass carbon stock under major Central Himalayan forest types of India. Ecol. Process 2021, 10, 14. [Google Scholar] [CrossRef]
  54. Weiner, J. Allocation, Plasticity and Allometry in Plants. Perspect. Plant Ecol. 2004, 6, 207–215. [Google Scholar] [CrossRef]
  55. McConnaughay, K.D.M.; Coleman, J.S. Biomass Allocation in Plants: Ontogeny or Optimality? A Test along Three Resource Gradients. Ecology 1999, 80, 2581–2593. [Google Scholar] [CrossRef]
  56. Niklas, K.J.; Enquist, B.J. Canonical rules for plant organ biomass partitioning and annual allocation. Am. J. Bot. 2002, 89, 812–819. [Google Scholar] [CrossRef] [PubMed]
  57. Poorter, H.; Niklas, K.J.; Reich, P.B.; Oleksyn, J.; Poot, P.; Mommer, L. Biomass allocation to leaves, stems and roots: Meta-analyses of interspecific variation and environmental control. New Phytol. 2012, 193, 30–50. [Google Scholar] [CrossRef] [PubMed]
  58. Forrester, D.I. Ecological and physiological processes in mixed versus monospecific stands. In Mixed-Species Forests; Pretzsch, H., Forrester, D., Bauhus, J., Eds.; Springer: Berlin/Heidelberg, Germany, 2017. [Google Scholar] [CrossRef]
  59. Yang, Y.H.; Li, P.; Ding, J.Z.; Zhao, X.; Ma, W.H.; Ji, C.J.; Fang, J.Y. Increased topsoil carbon stock across China’s forests. Global Change Biol. 2014, 20, 2687–2696. [Google Scholar] [CrossRef]
  60. Zhou, L.; Sun, Y.J.; Saeed, S.; Zhang, B.; Luo, M. The difference of soil properties between pure and mixed Chinese fir (Cunninghamia lanceolata) plantations depends on tree species. Glob. Ecol. Conserv. 2020, 22, e01009. [Google Scholar] [CrossRef]
  61. Brunn, M.; Hafner, B.D.; Zwetsloot, M.J.; Weikl, F.; Pritsch, K.; Hikino, K.; Ruehr, N.K.; Sayer, E.J.; Bauerle, T.L. Carbon allocation to root exudates is maintained in mature temperate tree species under drought. New Phytol. 2022, 235, 965–977. [Google Scholar] [CrossRef]
  62. Díaz-Pinés, E.; Rubio, A.; Van Miegroet, H.; Montes, F.; Benito, M. Does tree species composition control soil organic carbon pools in Mediterranean mountain forests? Forest Ecol. Manag. 2011, 262, 1895–1904. [Google Scholar] [CrossRef]
  63. Cremer, M.; Kern, N.V.; Prietzel, J. Soil organic carbon and nitrogen stocks under pure and mixed stands of European beech, Douglas fir and Norway spruce. Forest Ecol. Manag. 2016, 367, 30–40. [Google Scholar] [CrossRef]
  64. McElligott, K.M.; Seiler, J.R.; Strahm, B.D. The Impact of Water Content on Sources of Heterotrophic Soil Respiration. Forests 2017, 8, 299. [Google Scholar] [CrossRef]
  65. Novick, K.A.; Miniat, C.F.; Vose, J.M. Drought limitations to leaf-level gas exchange: Results from a model linking stomatal optimization and cohesion tension theory. Plant Cell Environ. 2016, 39, 583–596. [Google Scholar] [CrossRef]
  66. McAdam, S.A.M.; Sussmilch, F.C.; Brodribb, T.J. Stomatal responses to vapour pressure deficit are regulated by high speed gene expression in angiosperms. Plant Cell Environ. 2016, 39, 485–491. [Google Scholar] [CrossRef] [PubMed]
  67. Mund, M.; Kummetz, E.; Hein, M.; Bauer, G.A.; Schulze, E.D. Growth and carbon stocks of a spruce forest chronosequence in central Europe. Forest Ecol. Manag. 2002, 171, 275–296. [Google Scholar] [CrossRef]
  68. Hyvönen, R.; Ågren, G.I.; Linder, S.; Persson, T.; Cotrufo, M.F.; Ekblad, A.; Freeman, M.; Grelle, A.; Janssens, I.A.; Jarvis, P.G.; et al. The likely impact of elevated CO2, nitrogen deposition, increased temperature and management on carbon sequestration in temperate and boreal forest ecosystems: A literature review. New Phytol. 2007, 173, 463–480. [Google Scholar] [CrossRef]
  69. Ruehr, N.K.; Law, B.E.; Quandt, D.; Williams, M. Effects of heat and drought on carbon and water dynamics in a regenerating semi-arid pine forest: A combined experimental and modeling approach. Biogeosciences 2014, 11, 4139–4156. [Google Scholar] [CrossRef]
  70. Buckley, T.N. The control of stomata by water balance. New Phytol. 2005, 168, 275–292. [Google Scholar] [CrossRef] [PubMed]
  71. Wang, M.; Ding, Z.; Wu, C.Y.; Song, L.S.; Ma, M.G.; Yu, P.J.; Lu, B.Q.; Tang, X.G. Divergent responses of ecosystem water-use efficiency to extreme seasonal droughts in Southwest China. Sci. Total Environ. 2021, 760, 143427. [Google Scholar] [CrossRef] [PubMed]
  72. Flexas, J.; Hipólito, M.; Bota, J. Is photosynthesis limited by decreased Rubisco activity and RuBP content under progressive water stress? New Phytol. 2004, 162, 671–681. [Google Scholar] [CrossRef]
  73. Laclau, P.B.; Laclau, J.P. Growth of the whole root system for a plant crop of sugarcane under rainfed and irrigated environments in Brazil. Field Crops Res. 2009, 114, 351–360. [Google Scholar] [CrossRef]
  74. Luo, Z.K.; Feng, W.T.; Luo, Y.Q.; Baldock, J.; Wang, E.L. Soil organic carbon dynamics jointly controlled by climate, carbon inputs, soil properties and soil carbon fractions. Glob. Chang. Biol. 2017, 23, 4430–4439. [Google Scholar] [CrossRef]
  75. Wang, T.; Kang, F.; Cheng, X.; Han, H.R.; Ji, W.J. Soil organic carbon and total nitrogen stocks under different land uses in a hilly ecological restoration area of North China. Soil Tillage Res. 2016, 163, 176–184. [Google Scholar] [CrossRef]
  76. de Groot, C.C.; van den Boogaard, R.; Marcelis, L.F.M.; Harbinson, J.; Lambers, H. Contrasting effects of N and P deprivation on the regulation of photosynthesis in tomato plants in relation to feedback limitation. J. Exp. Bot. 2003, 54, 1957–1967. [Google Scholar] [CrossRef] [PubMed]
  77. Wassen, M.J.; Venterink, H.O.; Lapshina, E.D.; Tanneberger, F. Endangered plants persist under phosphorus limitation. Nature 2005, 437, 547–550. [Google Scholar] [CrossRef] [PubMed]
  78. van Groenigen, K.J.; Six, J.; Hungate, B.A.; de Graaff, M.A.; van Breemen, N.; van Kessel, C. Element interactions limit soil carbon storage. Proc. Natl. Acad. Sci. USA 2006, 103, 6571–6574. [Google Scholar] [CrossRef]
  79. Tang, X.Y.; Bernard, L.; Brauman, A.; Daufresne, T.; Deleporte, P.; Desclaux, D.; Souche, G.; Placella, S.A.; Hinsinger, P. Increase in microbial biomass and phosphorus availability in the rhizosphere of intercropped cereal and legumes under field conditions. Soil Biol. Biochem. 2014, 75, 86–93. [Google Scholar] [CrossRef]
  80. Van der Sande, M.T.; Arets, E.J.; Peña-Claros, M.; Hoosbeek, M.R.; Cáceres-Siani, Y.; van der Hout, P.; Poorter, L. Soil fertility and species traits, but not diversity, drive productivity and biomass stocks in a Guyanese tropical rainforest. Funct. Ecol. 2018, 32, 461–474. [Google Scholar] [CrossRef]
Forests 15 01520 g001
Figure 1. Carbon storage (a) and allocation in different plant organs of tree layers (b) in various forest types. Different letters refer to significant differences at p < 0.05.
Figure 1. Carbon storage (a) and allocation in different plant organs of tree layers (b) in various forest types. Different letters refer to significant differences at p < 0.05.
Forests 15 01520 g001
Forests 15 01520 g002
Figure 2. Soil carbon storage in different forest types (a) and vertical variation of soil carbon storage at different soil depths (b). Different letters refer to significant differences at p < 0.05.
Figure 2. Soil carbon storage in different forest types (a) and vertical variation of soil carbon storage at different soil depths (b). Different letters refer to significant differences at p < 0.05.
Forests 15 01520 g002
Forests 15 01520 g003
Figure 3. Forest carbon storage (a) and allocation (b) in different forest types. Different letters refer to significant differences at p < 0.05.
Figure 3. Forest carbon storage (a) and allocation (b) in different forest types. Different letters refer to significant differences at p < 0.05.
Forests 15 01520 g003
Forests 15 01520 g004
Figure 4. The VPA analysis (a) and random forest model (b) of the effects of environmental factors, plant physiological characteristics, and soil physical and chemical properties on tree layer carbon storage. Vpd: vapor pressure deficit; Tr: transpiration; SMC: soil moisture content; STP: soil total porosity; BD: bulk density; RH: relative humidity; Gs: stomatal conductance; SCP: soil capillary porosity; Ca: CO2 concentration of atmosphere; WUE: water use efficiency; TN: total soil nitrogen; TP: total soil phosphorus; Ci: intercellular CO2 concentration; Pn: Net photosynthetic rate. *: significance; ns: insignificance.
Figure 4. The VPA analysis (a) and random forest model (b) of the effects of environmental factors, plant physiological characteristics, and soil physical and chemical properties on tree layer carbon storage. Vpd: vapor pressure deficit; Tr: transpiration; SMC: soil moisture content; STP: soil total porosity; BD: bulk density; RH: relative humidity; Gs: stomatal conductance; SCP: soil capillary porosity; Ca: CO2 concentration of atmosphere; WUE: water use efficiency; TN: total soil nitrogen; TP: total soil phosphorus; Ci: intercellular CO2 concentration; Pn: Net photosynthetic rate. *: significance; ns: insignificance.
Forests 15 01520 g004
Forests 15 01520 g005
Figure 5. The VPA analysis (a) and random forest model (b) of the effects of environmental factors, plant physiological characteristics, and soil physical and chemical properties on soil carbon storage. Vpd: vapor pressure deficit; Tr: transpiration; SMC: soil moisture content; STP: soil total porosity; BD: bulk density; RH: relative humidity; Gs: stomatal conductance; Ca: CO2 concentration of atmosphere; WUE: water use efficiency; TN: total soil nitrogen; TP: total soil phosphorus; Ci: intercellular CO2 concentration; Pn: Net photosynthetic rate. *: significance; ns: insignificance.
Figure 5. The VPA analysis (a) and random forest model (b) of the effects of environmental factors, plant physiological characteristics, and soil physical and chemical properties on soil carbon storage. Vpd: vapor pressure deficit; Tr: transpiration; SMC: soil moisture content; STP: soil total porosity; BD: bulk density; RH: relative humidity; Gs: stomatal conductance; Ca: CO2 concentration of atmosphere; WUE: water use efficiency; TN: total soil nitrogen; TP: total soil phosphorus; Ci: intercellular CO2 concentration; Pn: Net photosynthetic rate. *: significance; ns: insignificance.
Forests 15 01520 g005
Table 1. Study area profile.
Table 1. Study area profile.
PlotMain SpeciesGeographic
Location		Altitude
(m)		Density
(Tree/ha)		Average Height
(m)		Average DBH
(cm)
Quercus aliena forestQuercus aliena111°55′59″ E
33°29′51″ N		1320–132369221.19 ± 0.3928.06 ± 0.68
Q. aliena–P. armandii mixed forestQuercus aliena,
Pinus armandii		111°55′51″ E
33°30′49″ N		1296–3120501
592		20.03 ± 0.45
16.38 ± 0.48		24.38 ± 0.97
21.61 ± 0.86
Pinus armandii forestPinus armandii111°55′44″ E
33°30′55″ N		1274–128173818.62 ± 0.6825.62 ± 0.86
Note: DBH represents diameter at breast height.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Xu, W.; Zhang, B.; Xu, Q.; Gao, D.; Zuo, H.; Ren, R.; Diao, K.; Chen, Z. Enhanced Carbon Storage in Mixed Coniferous and Broadleaf Forest Compared to Pure Forest in the North Subtropical–Warm Temperate Transition Zone of China. Forests 2024, 15, 1520. https://doi.org/10.3390/f15091520

AMA Style

Xu W, Zhang B, Xu Q, Gao D, Zuo H, Ren R, Diao K, Chen Z. Enhanced Carbon Storage in Mixed Coniferous and Broadleaf Forest Compared to Pure Forest in the North Subtropical–Warm Temperate Transition Zone of China. Forests. 2024; 15(9):1520. https://doi.org/10.3390/f15091520

Chicago/Turabian Style

Xu, Wenbin, Beibei Zhang, Qing Xu, Deqiang Gao, Haijun Zuo, Ranran Ren, Ke Diao, and Zhicheng Chen. 2024. "Enhanced Carbon Storage in Mixed Coniferous and Broadleaf Forest Compared to Pure Forest in the North Subtropical–Warm Temperate Transition Zone of China" Forests 15, no. 9: 1520. https://doi.org/10.3390/f15091520

APA Style

Xu, W., Zhang, B., Xu, Q., Gao, D., Zuo, H., Ren, R., Diao, K., & Chen, Z. (2024). Enhanced Carbon Storage in Mixed Coniferous and Broadleaf Forest Compared to Pure Forest in the North Subtropical–Warm Temperate Transition Zone of China. Forests, 15(9), 1520. https://doi.org/10.3390/f15091520

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop