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Article

Wood Density and Carbon Concentration Jointly Drive Wood Carbon Density of Five Rosaceae Tree Species

by
Pingping Guo
1,
Xiping Zhao
1,*,
Xingchang Wang
2,3,
Qi Feng
1,
Xinjing Li
1 and
Yangyang Tan
2
1
College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang 471023, China
2
Center for Ecological Research, Northeast Forestry University, Harbin 150040, China
3
Key Laboratory of Sustainable Forest Ecosystem Management, Northeast Forestry University, Ministry of Education, Harbin 150040, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(7), 1102; https://doi.org/10.3390/f15071102
Submission received: 29 April 2024 / Revised: 19 June 2024 / Accepted: 24 June 2024 / Published: 26 June 2024
(This article belongs to the Special Issue Phenological Patterns of Wood Formation and Allocation of Coniferous and Broadleaved Species)
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Abstract

:
Wood can store carbon and help mitigate global climate change. Carbon density (CD), the basis for measuring and analyzing C storage, is the product of wood density (WD) and C concentration, which are dependent on wood structure, cellulose concentration (CC), hemicellulose concentration (HC), and lignin concentration (LC). However, little attention has been paid to the C concentration of cellulose, hemicellulose, and lignin, which are fundamental factors in C storage and affect the credibility of accurate CD estimates. In order to disentangle the CD drives, WD, C concentration, CC, HC, and LC of the branch, stem, and root were quantified for five Rosaceae species from temperate forests in Northeastern China. The species were Sorbus alnifolia (Sieb.et Zucc.) K. Koch, Pyrus ussuriensis Maxim., Malus baccata (L.) Borkh., Crataegus pinnatifida var. major N. E. Brown, and Padus racemosa (Linn.) Gilib. The WD, CC, HC, and LC differed among species and tree organs, with the highest variability for the HC. The structural carbon concentration (SCC) was lower than the organic carbon concentration (OCC) and even the Intergovernmental Panel on Climate Change (IPCC) default value of 45%, with a maximum deviation of 2.6%. CD differed dramatically among species and tree organs. Based on SCC calculations, the highest CD was found in Sorbus alnifolia root (0.27 × 106 g/m3), while the lowest was found in Padus racemosa branch (0.22 × 106 g/m3). The results suggest that when estimating CD accurately at species level, it is important to consider not only WD but also structural carbohydrates and lignin concentration, providing important information on C fluxes and long-term C sequestration for forests. The study findings provide valuable insights into CD variations among tree species and organs and are valuable for forest management and policy development to improve carbon sequestration.

1. Introduction

The 2023 IPCC (Intergovernmental Panel on Climate Change) report on global climate change shows that the global average temperature in 2023 was 1.45 °C higher than the pre-industrial average temperature [1]. The global climate is continuously warming, and the CO2 emissions from human activities are considered the main cause [2]. As the main body of terrestrial ecosystems, forests have the function of storing carbon (C) and play an important role in mitigating climate warming. It is estimated that forest vegetation can absorb about 2.4 Gt of C per year [3], reducing human annual C emissions by 21% [4]. In forests, trees dominate and have important ecological significance, serving as important C sinks. However, as trees grow, mature, and age, the amount of C released will be equal to or even greater than the amount of C stored. At this point, trees will become C sources, which are C emissions outside of fossil fuels [5]. Harvested wood products originating from forests have delayed the return of their fixed C to the atmosphere due to utilization [6]. In addition, some of the C contained in wood products at the end of their lifespan can be permanently stored in landfill facilities [7]. Therefore, wood has the potential to alleviate climate change in the long term.
Five Rosaceae species, namely, Sorbus alnifolia (Sieb.et Zucc.) K. Koch, Pyrus ussuriensis Maxim., Malus baccata (L.) Borkh., Crataegus pinnatifida var. major N. E. Brown, and Padus racemosa (Linn.) Gilib., are important economic fruit and timber species in China [8,9] and have underlying ecological adaptations to effectively address varying environmental conditions and thus are important greening tree species for addressing climate change [10,11]. However, the C sequestration potential of these five Rosaceae species is unknown. In order to understand the importance of these five Rosaceae wood species in mitigating climate change, it is necessary to accurately quantify the C content of wood. Understanding wood C density (CD) is the basis for measuring and analyzing C storage in a regional forest ecosystem. Wood CD is usually obtained by an assumed C concentration (0.45 or 0.50 g/g) multiplied by a constant wood density (WD) [12,13]. However, the estimated CD has a deviation of about 10% relative to the actual observed values at tree level [14,15,16]. C storage in a regional forest ecosystem is seriously overestimated or underestimated [17]. Thus, the disentangling drives of C density of these five Rosaceae species will be an extremely interesting and significant topic.
Many studies highlight the importance of measuring WD to estimate C storage [17,18], as it is not only a C investment trait but is also related to various wood properties needed in wood use [19]. WD is variable among tree species [20]. The main reason for the interspecific difference in WD is the high heritability. At more refined scales, people may pay more attention to intraspecific variation in WD than to interspecific variation because intraspecific variation is more likely to reflect phenotypic plasticity associated with environmental conditions. For example, Sousa et al. [21] found that the WD of Quercus faginea varied from 0.914 g/cm3 to 1.037 g/cm3 in two naturally regenerated stands with large environmental differences in Portugal. Even in the same region, WD varies between and within trees due to different site environments [22]. Silvicultural management leads to rapid growth, producing trees with high yields and relatively low-density wood [23]. Within a tree, WD usually increases from juvenile to mature wood in the radial direction and decreases from root to branch in the axial direction [24,25], but there are exceptions. Petrea et al. [26] found that branches showed a higher WD than stems for European beech. A serious consequence of ignoring the high variability of WD is reduced accuracy in tree CD estimation.
Variations in WD are an indirect representation of changes in the structure and composition of wood materials [18]. The main cell wall compounds from wood are cellulose, hemicellulose, and lignin. Lignin is a complex and heterogeneous mixture of polymers, mainly composed of p-hydroxyphenyl, guaiacyl, and syringyl units [27]. In hardwood, lignin comprises both guaiacyl (C10H14O2) and syringyl (C11H16O3) in a roughly equal proportion [28], containing a C concentration of 63.22%–72.29%. In the same dry weight of wood, the higher the proportion of lignin, the higher its C sequestration [29,30]. Cellulose is the base polymer in wood cell walls, consisting of ring units of D-glucopyranose (C6H12O6) and containing a fixed C concentration of 44.44%. Despite cellulose being a strong, essentially irreversible C pool, the study of [29] showed that there appears to be no significant correlation between the cellulose concentration (CC) and C concentration in plants. Material consisting of hemicellulose and cellulose is often referred to as ‘holocellulose’, meaning whole cellulosic material. However, the properties of hemicellulose and cellulose are very different. Hemicelluloses are low molecular-weight polysaccharides. In hardwood, hemicellulose mainly consists of D-xylose (C5H10O5) and D-mannose (C6H12O6) units and contains a C concentration of 40%–44.44%. Hemicelluloses could serve as a C pool and could also serve as potential mobile C reservoirs in addition to their structural function as starch and soluble sugar [31,32]. They might be better expressed as the uncertainty of the C concentration, thus influencing the uncertainty of CD. Evidence, however, showing correlations between the abundance of wood chemical compounds and CD in C storage in a regional forest ecosystem is limited.
In the present study, we aimed to disentangle CD drives of these five Rosaceae tree species. We examined the (1) WD; (2) CC, hemicellulose concentration (HC), and lignin concentration (LC); and (3) organ carbon concentration in parts of tree wood, including rootwood, stemwood, and branchwood. We calculated wood CD for all five species. We hypothesized that: (1) the CD among species and parts of tree is statistically different, and (2) the abundance of wood chemical compounds affects CD. This study is the first attempt to understand the relationship between CD and chemical components. To the best of our knowledge, no such effort has been made to date.

2. Materials and Methods

2.1. Study Region

The present study was carried out in the Maoershan Forest Ecosystem Research Station in Heilongjiang Province, Northeastern China (127°30′–34′ E, 45°20′–25′ N, elevation 400 m). The region is characterized by a continental monsoon climate, according to the Koppen Climate Classification (Dwa), with a warm, rainy summer and a long, cold, dry winter [33]. The annual average temperature is 3.8 °C, and the annual precipitation is 683 mm. The soil is mainly dark brown. The dominant vegetation types are temperate, species-rich, deciduous broadleaf forest.

2.2. Plant Materials

It should be emphasized that China is committed to implementing natural forest conservation projects. Even for scientific research, there is strict control over the logging area and the number of trees harvested. In addition, efforts to ensure the sustainable management of the Maoershan Forest Ecosystem Research Station are critical. On the other hand, the destructive method has high accuracy in WD assessment and C concentration estimation but involves complex logistics with high costs and is also time consuming. Therefore, this study selected only one sampling plot within the station, and only three sample trees were harvested for each tree species. The sampling plot was located in the mature secondary forest (Figure 1) and 100 m away from the forest edges to minimize the influence of the margins. Three healthy and dominant trees of each species were selected to minimize potential intraspecies effects. The basic characteristics of the sample trees are summarized in Table 1. From each tree, old branches (diameter > 5 mm) were collected from the lower canopy. Proximal coarse roots (diameter > 5 mm) were excavated from root collars near the soil surface. To minimize tree damage, only two radial cores of each tree were collected using an increment borer at breast height (1.3 m). One (core A) was used for C concentration measurement, and the other (core B) was used for wood properties measurement. All samples were placed in paper bags and taken to the laboratory.

2.3. Organic Carbon Concentration (OCC) Measurement

Samples of core A, branch, and root wood were oven dried and then thoroughly ground. For each ground sample, 50 mg was taken for OCC determination with a Multi N/C 2100 (Analytik Jena AG, Jena, Germany) [34]. The OCC was calculated and expressed as g carbon mass per gram dry mass (%).

2.4. WD Measurement

Core B and five small wood blocks with a certain volume (2 × 2 × 2 cm) obtained from each branch and root were used to measure WD in accordance with the Chinese Standard GB/T 1927.5-2021 [35]. The green volumes of the blocks or core segments were measured using the water displacement method. Dry mass was measured after oven drying at 103 ± 2 °C until the block or core reached constant mass. WD was calculated as the ratio of oven dry mass (g) to green volume (cm3).

2.5. Chemical Components Measurement

Core B and small wood blocks used to measure WD were thoroughly ground to measure chemical components using wet chemical analysis. Following the Chinese standard GB/T 35818-2018 [36], The LC, holocellulose concentration, and CC were measured by the Klason method, glacial acetic acid-sodium chlorite method, and nitric acid–ethanol method, respectively [37]. The HC was calculated as the difference between the holocellulose concentration and CC. Chemical component concentrations were calculated as g chemical component mass per g dry powder mass (%).

2.6. Calculating Concentration and Density of Carbon

As already stated (see Section 1, Introduction), the carbon concentration in cellulose, hemicellulose, and lignin is 44.44%, 42.22%, and 67.75%, respectively. Thus, the structural carbon concentration (SCC) was calculated as
SCC = CC × 44.44% + HC × 42.22% + LC × 67.75%
The carbon density estimated with the SCC (CDS) was calculated as
CDS = SCC × WD
And the carbon density estimated with the OCC (CDO) was calculated as
CDO = OCC × WD
The SCC was compared with the OCC and Intergovernmental Panel on Climate Change (IPCC) default carbon concentrations of 45% [38] and 50% [39].

2.7. Statistical Analysis

Descriptive statistics were used for the analysis and presentation of the results, such as the minimum, maximum, and arithmetic mean values; standard deviation; and coefficient of variation. Variance analysis (ANOVA) was used to examine the effects of species, tree parts, and their interaction on wood properties. ANOVA was also used to estimate the effects of wood properties on CD. Duncan multiple comparisons were used to compare variables between species and tree parts. Data are reported as mean ± standard deviation. Regression analyses were used to show the relationship between wood properties and the CD, OCC, and SCC. All statistical analyses were performed using IBM SPSS software (Version 22.0, International Business Machines Corporation, Armonk, NY, USA). Statistical significance (a p-value equal to 0.05 or 0.01) was determined using the F-test.

3. Results

3.1. Variations in Wood Properties

Wood properties varied 1- to 2-fold across the five species, with the highest variability for the HC (Table 2). The coefficients of HC and LC variation were approximately 10 and 8 times that of the SCC, respectively. The coefficient of variations in the SCC was close to that of the OCC.
Species and tree parts significantly affected wood properties (Table 3). The interaction of species and tree parts also significantly affected wood properties except the SCC and OCC.
Across organs, WD generally tended to decline from root to branch (Figure 2). The lowest WD occurred in Padus racemosa branch (0.51 g/cm3), while the highest occurred in Crataegus pinnatifida root (0.62 g/cm3). For the whole tree, the highest WD value occurred in Crataegus pinnatifida (0.59 g/cm3), while the lowest value occurred in Padus racemosa (0.54 g/cm3).
For the whole tree, the highest CC values occurred in Padus racemosa (47.37%), while the lowest values occurred in Malus baccata (40.10%). Overall, the highest CC values occurred in the stem, while the lowest values occurred in the branch (Figure 2b).
Multiple comparisons showed that Crataegus pinnatifida had a significantly lower HC than the other four species, only 16.22% (Figure 2c). In tree parts, the highest HC occurred in Padus racemosa branch (24.63%), while the lowest values occurred in Crataegus pinnatifida root (14.85%).
The change in the LC contrasted with the CC. The highest values of the CC (50.03%) and the lowest values of the LC (18.70%) all occurred in Padus racemosa stem (Figure 2d). The lowest values of the CC occurred in Sorbus alnifolia branch (39.17%), while the highest values of the LC occurred not in Sorbus alnifolia but in Malus baccata branch (29.73%).

3.2. Variations in OCC and SCC

Figure 3a showed that the SCC was much lower than the IPCC default value of 50% and even the default value of 45%, with a maximum deviation of 2.6%. The OCC was also much lower than the default value of 50% but was close to or slightly above the value of 45% (Figure 3b). For the whole tree, significant differences in the SCC and OCC were found among species (p < 0.05). The highest values of the SCC and OCC occurred in Sorbus alnifolia (44.46%) and Pyrus ussuriensis (46.65%), respectively, while the lowest values occurred in Padus racemosa (43.32%) and Crataegus pinnatifida (44.67%). Tree parts had a significant impact on the OCC but not the SCC (Table 3). The highest and lowest values of the SCC occurred in Sorbus alnifolia (44.87%) and Padus racemosa branch (43.00%), respectively. The highest and lowest OCC values occurred in Pyrus ussuriensis root (46.81%) and Crataegus pinnatifida stem (44.35%), respectively.
According to the observed trends, as the value of SCC increases, the value of OCC also increases (Figure 4). The adjusted determination coefficient (R2) was calculated for the relationship between the SCC and OCC using linear regression, which was 0.426. The slope (14.506) significantly deviated from 1.0 (p < 0.01), and the y-axis intercept significantly deviated from 0 (p < 0.01).

3.3. Variations in CD

Differences in the CDS and CDO among species and tree parts were shown in Figure 5. For the whole tree, the highest CDS values in Crataegus pinnatifida (0.26 × 106 g/m3) were 8.97% higher than the lowest values in Padus racemosa (Figure 5a). The highest CDO values occurred in Malus baccata (0.26 × 106 g/m3) but were not significantly higher than in other species (Figure 5b). Among organs, both the CDS and CDO tended to decline from root to branch. The highest values of the CDS (0.27 × 106 g/m3) and CDO (0.28 × 106 g/m3) occurred in Sorbus alnifolia root, while the lowest values occurred in Padus racemosa branch.

3.4. Factors Affecting CD

The linear relationship between CD and WD is apparent (Figure 6). As indicated by the slope of the major axis regression, CD values were all higher using the OCC and IPCC default values of 45% than using the SCC.
Variance analysis showed that species affected the CDS, while tree parts and their interaction with species did not significantly affect the CDS (Table 4). The WD, CC, HC, and LC have a significant impact on the CDS, but the interaction of the CC, HC, and LC has no significant impact on the CDS.
Negative correlations of the CDS (or WD) with the CC and HC were found, as well as positive relationships with the LC (Figure 7). When the effect on the CDS was compared among the CC, HC, and LC, R2 was the largest for the LC, which revealed that the LC accounted for most of the variation in the CDS among the three variables. Compared to WD’s dependence on the CC, HC, and LC, R2 was the largest for the HC despite low values in wood.
The dependence of the CDS and WD on the CC, HC, and LC varied across the five species (Table 5). There was a significant negative correlation between the HC and CDS (or WD) only in Padus tree species. The dependence of the CDS and WD on the CC, HC, and LC varied across different tree parts (Table 6). In stems, the CDS and WD were significantly negatively correlated with the CC and HC and significantly positively correlated with the LC. In branches, the CDS and wood density (WD) were significantly negatively correlated with the CC and positively correlated with the LC. In roots, the correlation between the CDS (or WD) and chemical properties is not significant.

4. Discussion

We found that accurate estimation of CD is dependent on wood properties, not only WD but also the CC, HC, and LC.

4.1. Interspecific and Intra-Tree Differences in Wood Properties

Although all the species studied belonged to Rosaceae and grew in the same temperate forest, wood properties differed across species and tree parts. WD has received the most attention in plant ecology studies, mainly due to its close relationship with biomass and C sequestration [26]. WD ranged from 0.489 g/cm3 to 0.647 g/cm3 (Table 2). The species factor was shown to be the most important source of variation in WD (Table 3). The Global Wood Density database provides WD for 8412 species worldwide [20], but could not provide WD for the species we studied for comparison. Accurately understanding WD of tree species is necessary, as large interspecific differences in WD have been confirmed in many studies and influence methods and approaches for estimating forest biomass and C sequestration [26]. WD tended to decline from root to branch, except Malus baccata and Sorbus alnifolia. The lowest and highest values were given by Padus racemosa branchwood and Crataegus pinnatifida rootwood, respectively (Figure 2a). Our study and others [25] demonstrate the difference in WD among tree parts that vary with species. Reasons for WD variation are complex. It may be, for example, due to differences in anatomical, physical, and chemical properties of wood produced by trees with different growth habits [40].
The concentration variations in chemical components was greater than the WD. In particular, for the HC, the maximum value is twice the minimum value (Table 2). In general, hemicellulose has the lowest concentration compared to cellulose and lignin, but there are exceptions. For example, the highest value (24.633%) of the HC occurred in Padus racemosa branch, which had a low LC, only 21.560% (Figure 2c). Although hemicellulose is low in concentration, it plays a key role in cell walls, regulates cell wall extension, forms a network with cellulose, and links lignin to certain types of connections [41]. Large variations in the HC can be associated with its ‘mobility’ [31]. When the C required by trees is limited, hemicellulose can degrade in an orderly manner, move, be recycled as stored non-structural carbohydrates, and thus used for other tree functions such as growth or respiration [32]. However, little is known about the C source of hemicelluloses in living trees. Cellulose in wood is the real structural carbohydrate with recalcitrant C. Species and tree part factors were all important sources of CC variation (Table 3). The highest CC occurred in stemwood for the species studied, with the exception of Crataegus pinnatifida (Figure 2b). Cellulose is well known for its service as the main structural component of the cell wall and the main source of tensile strength for the wall. Sufficient cellulose provides essential mechanical support for the stem.
LC variation is also large. Like cellulose and hemicellulose, lignin is composed of C, hydrogen, and oxygen. However, lignin is not a carbohydrate with at least one aldehyde- or keto group but a hydrophobic and three-dimensional polymer with a mixture of aromatic and aliphatic moieties, making the C more recalcitrant [27]. Lignin is functionally associated with mechanical support, sap conduction, and defense mechanisms in living trees [42]. The pattern of variation in the LC is basically opposite to that in the CC, presumably because the concentration of structural carbohydrates and lignin in the cell wall can be modified in response to developmental and environmental cues. For example, when cellulose synthesis decreases, plants will strengthen the cell wall by increasing the LC and create barriers to pathogen ingress [43].

4.2. Effect of CC and WD on CD

The coefficient of variation for the SCC was small (Table 2), most likely due to the opposite pattern of variation between the CC and HC (Figure 2b,c). The OCC yielded a similar result. The SCC and OCC were much lower than the IPCC default value of 50% [39] and close to the value of 45% [38] (Figure 3), indicating that long-term C sequestration for our studied species will be overestimated based on the default value of 50% C concentration. Using the default value of 45% to roughly estimate long-term C sequestration for the five Rosaceae species may be acceptable if the accuracy requirement is not high. The OCC was correlated with the OCC (Figure 4), but the average OCC was higher than the SCC mainly because organic C comes from both structural and non-structural parts (i.e., extracts and non-structural carbohydrates). As described in the introduction, C in non-structural parts is volatile and can quickly become a C source in a changing environment, indicating that using the OCC may be inaccurate in estimating long-term C sequestration of trees [44]. Interestingly, we found that the greater the WD, the greater the deviation in using the OCC or default value (45%) to estimate long-term C sequestration CD (Figure 6). Based on the current study, it is unclear whether WD is closely related to the concentration of nonstructural parts for the five species. Some exploratory research has been conducted on this topic. Lloret et al. [45] found that species characterized by low WD have lower concentrations of nonstructural carbohydrates. Kiaei [46] found positive correlations between ironwood WD and one type of extractive compound, 1,2-benzendicarboxylic. Still, there is much to be learned about WD’s relationship with non-structural parts.
Regardless of which C concentration value is quoted, WD is the most basic parameter for estimating CD. Thus, WD’s positive impact on CD is beyond question. The higher the WD, the more matter the same volume of wood has. Our studies showed WD to be positively related to the LC but negatively related to the CC and HC (Figure 7, Table 4). Ray et al. [47] also confirmed the correlation between WD and chemical properties. These studies indicated that wood chemical properties are important drivers of CD in trees.

4.3. Interspecific and Intra-Tree Differences in CD

Interspecific differences in CD were significant (Figure 5). It should be noted that the highest values of the CDS and CDO occurred in Crataegus pinnatifida (0.255 × 106 g/m3) and Malus baccata (0.262 × 106 g/m3), respectively. This finding implies that organic C sources include extractives and nonstructural carbohydrates, which leads to different estimation of C sequestration [44,48]. Accurate C sequestration estimates require careful consideration of CD drivers, particularly structural carbohydrates and lignin [47].
From root to branch, the CD tended to decrease, indicating that root wood has a higher C sequestration capacity than other parts of trees, except Malus baccata trees. The study of [25] found that the fixed carbon content of the root was lower than of the stem wood of the genus Eucalyptus spp. Without a doubt, this result improved our confidence in using four other Rosaceae species except Malus baccata to fix C in soil. Increasing the soil C concentration is an important process for reducing greenhouse gases [49]. Meanwhile, dead roots play an important role in replenishing soil nutrients. However, high uncertainty exists as to the C concentration below a depth of 100 cm in soil [50]. It is unclear whether these roots will eventually be converted into organic C to stabilize the soil [51,52]. For C long-term sequestration, roots in the topsoil should be removed after logging, rather than abandoned at their original location and allowed to decompose to release CO2 into the atmosphere. Most of the removed roots are coarse and can achieve long-term C sequestration as stems through scientific wood processing and utilization [53].

5. Conclusions

Forest C is an important climate indicator, with wood dominating the forest as a significant sink and source of C. To improve the prediction model of how future climate will affect global C cycling, it is necessary to reduce cognitive uncertainty in existing measurements of wood C storage and identify key factors related to tree growth and wood formation.
Variations in the wood properties, C concentration, and CD among five Rosaceae tree species and their tree parts were investigated in this study. Variations in WD and chemical properties affected the C concentration. CD values based on the SCC were lower than those based on OCC and IPCC default values. Such non-negligible deviations from C in the extract and non-structural carbohydrates are included. Based on SCC calculations, the highest CD was found in Sorbus alnifolia root (0.27 × 106 g/m3), while the lowest was found in Padus racemosa branch (0.22 × 106 g/m3). Considering structural carbohydrates and lignin can help with the accurate estimation of CD.
This is the first study to use structural carbohydrates and the LC to estimate the CD of tree species in temperate deciduous broadleaved forests in Northeastern China, providing important information on C flows and long-term C sequestration for this region. Due to the limitations of the selected region and tree numbers in this study, further research is needed to determine that the estimated patterns revealed are universal. In the future, there is a need for a better understanding of the natural growth process of forests in the region, the process of litter decomposition, and the use of wood products made from logging. Based on changes in C sequestration, reasonable logging strategies should be developed to achieve greater economic and ecological benefits.

Author Contributions

Conceptualization, P.G., X.Z. and X.W.; methodology, X.Z.; software, X.L. and Y.T.; validation, P.G., X.Z. and Q.F.; formal analysis, P.G. and X.Z.; investigation, X.W., Q.F., X.L. and Y.T.; resources, X.W.; data curation, Q.F., X.L. and Y.T.; writing—original draft preparation, X.Z.; writing—review and editing, X.W.; visualization, P.G.; supervision, X.Z.; project administration, P.G. and X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Science Foundation of China, grant number 32171701.

Data Availability Statement

Data is contained within the article.

Acknowledgments

The author acknowledges the assistance of the Maoershan Forest Ecosystem Research Station for granting access to the study sites and granting permission to collect tree samples, and the students of Henan University of Science and Technology for processing the tree samples.

Conflicts of Interest

The authors declare no conflicts of interest.

Nomenclature

WDWood density (g/cm3)
CCCellulose concentration (g/g)
HCHemicellulose concentration (g/g)
LCLignin concentration (g/g)
SCCStructural carbon concentration (g/g)
OCCOrganic carbon concentration (g/g)
CDCarbon density (×106 g/m3)
CDSCarbon density estimated with the SCC (×106 g/m3)
CDOCarbon density estimated with the OCC (×106 g/m3)

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Figure 1. Location of the sampling plot.
Figure 1. Location of the sampling plot.
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Figure 2. Comparison of WD (a), CC (b), HC (c), and LC (d) among species and tree parts. Different uppercase letters above the bars indicate a significant difference among species based on the Duncan test (p < 0.05). Different lowercase letters below the bars indicate a significant difference among tree parts based on the Duncan test (p < 0.05). WD—wood density, CC—cellulose concentration, HC—hemicellulose concentration, LC—lignin concentration.
Figure 2. Comparison of WD (a), CC (b), HC (c), and LC (d) among species and tree parts. Different uppercase letters above the bars indicate a significant difference among species based on the Duncan test (p < 0.05). Different lowercase letters below the bars indicate a significant difference among tree parts based on the Duncan test (p < 0.05). WD—wood density, CC—cellulose concentration, HC—hemicellulose concentration, LC—lignin concentration.
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Figure 3. Comparison of SCC (a) and OCC (b) among species and tree parts. Different uppercase letters above the bars indicate a significant difference among species based on the Duncan test (p < 0.05). Different lowercase letters below the bars indicate a significant difference among tree parts based on the Duncan test (p < 0.05). The dotted line and dashed line indicate the Intergovernmental Panel on Climate Change default carbon concentrations of 45% [38] and 50% [39], respectively. SCC—structural carbon concentration; OCC—organic carbon concentration.
Figure 3. Comparison of SCC (a) and OCC (b) among species and tree parts. Different uppercase letters above the bars indicate a significant difference among species based on the Duncan test (p < 0.05). Different lowercase letters below the bars indicate a significant difference among tree parts based on the Duncan test (p < 0.05). The dotted line and dashed line indicate the Intergovernmental Panel on Climate Change default carbon concentrations of 45% [38] and 50% [39], respectively. SCC—structural carbon concentration; OCC—organic carbon concentration.
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Figure 4. Relationship between organic carbon concentration (OCC) and structural carbon concentration (SCC) calculated using linear regression (p < 0.01). The circles denote the observed values.
Figure 4. Relationship between organic carbon concentration (OCC) and structural carbon concentration (SCC) calculated using linear regression (p < 0.01). The circles denote the observed values.
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Figure 5. Comparison of CDS (a) and CDO (b) among species and tree parts. Different uppercase letters above the bars indicate a significant difference among species based on the Duncan test (p < 0.05). Different lowercase letters below the bars indicate a significant difference among tree parts based on the Duncan test (p < 0.05). CDS—estimated carbon density with structural carbon concentration, CDO—estimated carbon density with organic carbon concentration.
Figure 5. Comparison of CDS (a) and CDO (b) among species and tree parts. Different uppercase letters above the bars indicate a significant difference among species based on the Duncan test (p < 0.05). Different lowercase letters below the bars indicate a significant difference among tree parts based on the Duncan test (p < 0.05). CDS—estimated carbon density with structural carbon concentration, CDO—estimated carbon density with organic carbon concentration.
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Figure 6. Relationship between carbon density (CD) and wood density (WD). The theoretical CD fitted the major axis regression without an intercept (p < 0.05). WD—wood density, CD—carbon density. The CD45, CDO, and CDS indicate the CD value calculated based on the Intergovernmental Panel on Climate Change default carbon concentration of 45%, organic carbon concentration, and structural carbon concentration, respectively.
Figure 6. Relationship between carbon density (CD) and wood density (WD). The theoretical CD fitted the major axis regression without an intercept (p < 0.05). WD—wood density, CD—carbon density. The CD45, CDO, and CDS indicate the CD value calculated based on the Intergovernmental Panel on Climate Change default carbon concentration of 45%, organic carbon concentration, and structural carbon concentration, respectively.
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Figure 7. Dependences of CDS (a–c) and WD (d–f) on CC, HC, and LC across five Rosaceae species. Linear regression analysis was performed, in which the determination coefficient (R2) with significant levels (P) is shown. CDS—carbon density estimated with structural carbon concentration, WD—wood density, CC—cellulose concentration, HC—hemicellulose concentration, LC—lignin concentration.
Figure 7. Dependences of CDS (a–c) and WD (d–f) on CC, HC, and LC across five Rosaceae species. Linear regression analysis was performed, in which the determination coefficient (R2) with significant levels (P) is shown. CDS—carbon density estimated with structural carbon concentration, WD—wood density, CC—cellulose concentration, HC—hemicellulose concentration, LC—lignin concentration.
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Table 1. Basic characteristics of the trees sampled (mean ± standard deviation, n = 3).
Table 1. Basic characteristics of the trees sampled (mean ± standard deviation, n = 3).
SpeciesHeight (m)Diameter at Breast Height (cm)
Sorbus alnifolia19.2 ± 2.324.3 ± 1.0
Crataegus pinnatifida8.4 ± 0.418.8 ± 0.9
Malus baccata12.0 ± 0.722.1 ± 5.0
Pyrus ussuriensis12.0 ± 2.528.1 ± 5.3
Padus racemosa12.7 ± 2.115.4 ± 0.8
Table 2. Descriptive statistics of wood properties of five Rosaceae species. SD—standard deviation, CV—coefficient of variation. WD—wood density, CC—cellulose concentration, HC—hemicellulose concentration, LC—lignin concentration, SCC—structural carbon concentration, OCC—organic carbon concentration.
Table 2. Descriptive statistics of wood properties of five Rosaceae species. SD—standard deviation, CV—coefficient of variation. WD—wood density, CC—cellulose concentration, HC—hemicellulose concentration, LC—lignin concentration, SCC—structural carbon concentration, OCC—organic carbon concentration.
VariableMinimumMaximumMeanSDCV (%)
WD (g/cm3)0.490.650.560.046.80
CC (%)36.7351.4343.863.748.53
HC (%)12.8425.0119.623.4617.61
LC (%)18.0231.9026.293.7414.22
SCC (%)42.3844.9343.940.751.72
OCC (%)43.8647.2345.650.821.79
Table 3. Analysis of variance for wood properties of five Rosaceae species. WD—wood density, CC—cellulose concentration, HC—hemicellulose concentration, LC—lignin concentration, SCC—structural carbon concentration, OCC—organic carbon concentration.
Table 3. Analysis of variance for wood properties of five Rosaceae species. WD—wood density, CC—cellulose concentration, HC—hemicellulose concentration, LC—lignin concentration, SCC—structural carbon concentration, OCC—organic carbon concentration.
VariableSpeciesTree PartsSpecies × Tree Parts
FPFPFP
WD4.670.00515.660.0001.840.11
CC10.120.0006.960.0032.330.045
HC8.850.0005.710.0083.850.003
LC19.380.00016.730.0005.570.000
SCC7.020.0002.340.111.430.23
OCC31.120.0004.480.021.440.22
Table 4. Variance analysis of CDS for five Rosaceae species. WD—wood density, CDS—carbon density estimated with structural carbon concentration, CC—cellulose concentration, HC—hemicellulose concentration, LC—lignin concentration.
Table 4. Variance analysis of CDS for five Rosaceae species. WD—wood density, CDS—carbon density estimated with structural carbon concentration, CC—cellulose concentration, HC—hemicellulose concentration, LC—lignin concentration.
SourceFp
Species2.710.045
Tree parts0.480.63
Species × Tree parts1.180.35
WD2568.460.000
CC90.460.000
HC80.590.000
LC122.810.000
CC × HC × LC2.700.11
Table 5. Pearson correlation coefficient between WD, CDS, and chemical properties in wood tissues of five different Rosaceae species. WD—wood density, CDS—carbon density estimated with structural carbon concentration, CC—cellulose concentration, HC—hemicellulose concentration, LC—lignin concentration. * p < 0.05, ** p < 0.01.
Table 5. Pearson correlation coefficient between WD, CDS, and chemical properties in wood tissues of five different Rosaceae species. WD—wood density, CDS—carbon density estimated with structural carbon concentration, CC—cellulose concentration, HC—hemicellulose concentration, LC—lignin concentration. * p < 0.05, ** p < 0.01.
Species CCHCLC
Sorbuss alnifoliaWD−0.40−0.100.41
CDS−0.50−0.140.52
Crataegus pinnatifidaWD−0.230.160.13
CDS−0.130.030.26
Malus baccataWD−0.14−0.380.16
CDS−0.03−0.460.35
Pyrus ussuriensisWD−0.10−0.310.16
CDS0.040.340.06
Padus racemosaWD0.12−0.82 **0.53
CDS0.02−0.79 *0.60
Table 6. Pearson correlation coefficient between WD, CDS, and chemical properties in different tree parts. WD—wood density, CDS—carbon density estimated with structural carbon concentration, CC—cellulose concentration, HC—hemicellulose concentration, LC—lignin concentration. * p < 0.05, ** p < 0.01.
Table 6. Pearson correlation coefficient between WD, CDS, and chemical properties in different tree parts. WD—wood density, CDS—carbon density estimated with structural carbon concentration, CC—cellulose concentration, HC—hemicellulose concentration, LC—lignin concentration. * p < 0.05, ** p < 0.01.
Species CCHCLC
RootWD0.001−0.1200.163
CDS0.046−0.0100.203
StemWD−0.615 *−0.719 **0.651 **
CDS−0.722 **−0.662 **0.805 **
BranchWD−0.589 *−0.1920.614 *
CDS−0.646 **−0.1980.680 **
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Guo, P.; Zhao, X.; Wang, X.; Feng, Q.; Li, X.; Tan, Y. Wood Density and Carbon Concentration Jointly Drive Wood Carbon Density of Five Rosaceae Tree Species. Forests 2024, 15, 1102. https://doi.org/10.3390/f15071102

AMA Style

Guo P, Zhao X, Wang X, Feng Q, Li X, Tan Y. Wood Density and Carbon Concentration Jointly Drive Wood Carbon Density of Five Rosaceae Tree Species. Forests. 2024; 15(7):1102. https://doi.org/10.3390/f15071102

Chicago/Turabian Style

Guo, Pingping, Xiping Zhao, Xingchang Wang, Qi Feng, Xinjing Li, and Yangyang Tan. 2024. "Wood Density and Carbon Concentration Jointly Drive Wood Carbon Density of Five Rosaceae Tree Species" Forests 15, no. 7: 1102. https://doi.org/10.3390/f15071102

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