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J. People Plants Environ > Volume 28(5); 2025 > Article
Kim, Bong, Park, Lee, Lim, Pi, Kwon, and Kim: Carbon Fraction of 51 Native Shrubs in Urban Green Spaces in Korea: A Comparative Study of Evergreen, Deciduous, and Vines

ABSTRACT

Background and objective: Sequestering carbon from the atmosphere is becoming increasingly important in the fight against climate change. Urban green spaces are increasingly recognized as effective carbon sinks, and many cities are striving to achieve carbon neutrality. carbon statistics for trees and shrubs are essential to attaining this goal and enhancing the carbon uptake capacity of urban green spaces.
Methods: Historically, carbon studies have primarily focused on forest trees; however, as urban green spaces have expanded in recent years, research on shrubs utilized in these areas is gradually increasing. This study aims to measure the carbon fraction of representative native shrubs in urban green spaces in Korea.
Results: The carbon fraction was compared by component, including evergreen (n = 325) and deciduous (n = 911) shrubs, as well as standard shrubs (n = 1279) and vines (n = 45). The results indicated that evergreen shrubs contained a higher carbon fraction than deciduous shrubs (p < .05), and standard shrubs exhibited a greater carbon fraction than vines (p < .05). Furthermore, biomass allocation shifted from leaves and roots to stems as shrubs aged, with deciduous shrubs allocating more biomass to roots than evergreens.
Conclusion: These results can be integrated with dry weight measurements for each shrub species to estimate carbon storage, providing crucial data to inform spatial planning and species selection for maximizing carbon sequestration.

Introduction

Climate change is leading to extreme events such as droughts, wildfires, floods, and heat waves. In response, countries are implementing strategies to reduce greenhouse gas emissions while expanding carbon sinks to tackle these challenges (Gratani, 2020).
Korea aims to achieve carbon neutrality by 2030 and is expanding green spaces as a means of increasing carbon sinks. However, with approximately 63% of the country’s land area already covered by forests, there is limited space available for further expansion. Consequently, the country is promoting a strategy to expand carbon sinks through urban green spaces, spearheaded by the Korea Forest Service (KFRI, 2012; KFS, 2021).
Urban green spaces not only reduce energy consumption but also absorb carbon dioxide from the atmosphere, as trees help lower city temperatures by absorbing heat. The trees and shrubs that constitute green spaces capture carbon dioxide through photosynthesis in their leaves, branches, stems, and roots, storing it in their biomass. This process is crucial for mitigating greenhouse gas emissions in urban environments (Nowak et al., 2006).
Many cities are developing urban green spaces with the aim of achieving carbon neutrality. Shrubs play an important role in carbon sequestration within these areas due to their rapid growth (Gotmark et al., 2016) and high adaptability to various environments (McKell, 1989). Urban green spaces typically consist of numerous shrubs and a limited number of trees, in contrast to forests, which are characterized by a predominance of trees and a smaller presence of shrubs (Jo et al., 2020; Kim et al., 2024). Consequently, to maximize carbon storage in urban green spaces, it is essential to measure and analyze the carbon storage capacity of both trees and shrubs.
Carbon fraction refers to the amount of carbon in biomass, which varies among different species (Hunt, 2009; Thomas and Martin, 2012; Ruiz-Peinado et al., 2013; Kim et al., 2023). Additionally, species characteristics can differ by location, making it crucial to measure carbon fraction for each species (Weber and Abasse, 2017). This approach is necessary to build accurate carbon statistics and to quantify the carbon uptake effects of urban green spaces.
It is commonly assumed that dry biomass consists of 50% carbon (IPCC, 2006). However, previous studies have measured the carbon fraction of various tree species and found that it ranges from 43.4% to 55.6% for temperate and boreal trees, 41.9% to 51.6% for tropical trees, and 45.7% to 60.6% for subtropical and mediterranean trees, which differs from the IPCC (Intergovernmental Panel in Climate Change) standard (Thomas and Martin, 2012). Therefore, understanding the carbon fraction of different shrubs is essential for building accurate carbon statistics for urban green spaces (Hunt, 2009; Ruiz-Peinado et al., 2013; Tommila et al., 2024; Yu et al., 2024).
This study aims to characterize the carbon fraction of 51 native shrubs planted in urban green spaces in Korea, addressing the urgent need for species-specific data to combat climate change. Although our findings may differ from general IPCC default values (50%), this research is a crucial step towards establishing accurate and reliable carbon statistics, especially given the unique characteristics of urban green spaces. Consequently, this study addresses four key research questions: 1) What percentage of CO2 absorbed from the atmosphere is stored as carbon in shrubs? 2) How does the carbon fraction differ between deciduous and evergreen shrub species? 3) How does the carbon fraction differ between standard shrubs and vines? 4) How does the biomass allocation ratio among different components change with shrub age and life form?

Research Methods

Study site and materials

The shrubs examined were cultivated in nurseries located in Sejong (36°40′42″N, 127°12′12″E) and Jinju (35°06′21″N, 128°05′25″E), Korea. Additionally, 5 species (B. sinica, E. alatus, E. japonicus, R. yedoense, S. prunifolia) were collected from urban green spaces in Daejeon (36°22′08″N, 127°23′06″E), Korea, and their ages ranged from 3 to 25 years.
Sejong and Daejeon experience temperate climates, whereas Jinju falls within a temperate to subtropical climate zone (MOLIT, 2020). The carbon fraction was measured in a total of 1,325 shrubs representing 51 species.
Data collection occurred from 2022 to 2024. As plant biomass and carbon content can exhibit seasonal variability, to minimize these effects, all sample collection and pre-treatment were concentrated during the summer to fall period, specifically from July to September, corresponding to the period of maximum biomass accumulation for each species. The species measured, along with their quantities, types, and life history classifications, are detailed in the Appendix. While nursery samples were grown under standardized conditions, urban green space samples from Daejeon reflected the influence of actual urban environments. This study included both types of samples to comprehensively understand the general carbon content characteristics of shrubs planted in Korean urban green spaces.

Analysis methods

The collected plant materials were dried separately by component (leaf and twig, branch, stem, root) to ensure accurate determination of the carbon fraction. Samples were placed in a dry oven (DH, WOF-L800, DAIHAN, Korea) at 85°C until they reached a stable weight. Drying typically took between one and seven days, with harder or bulkier samples requiring up to 10 days.
To ensure that the subsamples were representative, the entire dried material for each component was first ground together to create a single homogenous powder (less than 0.2 mm) using a grinding mill (HMF-3000S, Hanilelec, Korea). For tougher leaves and stubble roots, the samples were processed using liquid nitrogen in a membrane bowl.
The ground samples were combusted in an elemental analyzer (PRIMACSTM Series, Skalar, Netherlands) based on the dry combustion method (Thomas and Martin). From each homogenized powder, subsamples of 50 ± 5 mg were precisely weighed using a precision balance (ME Balance, METTLER TOLEDO, USA) and analyzed in triplicate. In cases where the carbon content was measured extremely low due to foreign substances, the corresponding value was excluded and re-analyzed. Other detailed procedures were performed according to ASTM D3176-09 standard protocols (Ahmad et al., 2022; Adhikari et al., 2024).

Statistical analysis

The statistical analysis was performed using JASP version 0.18.3. T-tests were employed to compare the mean carbon fraction between evergreen and deciduous shrubs, and between standard shrubs and vines. During the analysis, we assessed the normality of the data and applied Student’s t-test for equal variances, as well as Welch’s t-test for heteroscedasticity. If the data did not meet the normality assumption, we utilized the Mann-Whitney test to compare the mean carbon fraction.
To analyze the differences in biomass allocation ratios according to shrub age and life form, the shrubs were categorized into three age groups (Urza et al., 2019; Barton 2023): juvenile (3–4 years), subadult (5–10 years), and adult (11 years or older). An analysis of variance (ANOVA) was conducted on these groups. After checking the data for normality and homogeneity of variances, post-hoc tests were performed to identify specific differences between groups. The Games-Howell test was used when the assumption of equal variances was not met, and the Dunn’s Test was applied for non-parametric data.

Results and Discussion

The study measured the carbon fraction of 51 shrub species commonly planted in urban green spaces and compared the average carbon fraction across different types and forms. As shown in Table 1, the carbon fraction was assessed in 4,904 samples, yielding an average carbon fraction of 45.6%. The results (Fig. 1, Table 1), in order of plant components (p < .001), were as follows: root (44.7%), leaf & twig (45.6%), stem (46.3%), and branch (46.6%). Additionally, our analysis of dry weight allocation by shrub components revealed that roots constituted the largest proportion of total shrub dry weight (24.8–30.4%), followed by stems (23.0–28.6%), branches (21.1–25.8%), and leaves & twigs (17.6–25.5%). This comprehensive view of biomass distribution, alongside carbon fraction, is crucial for understanding the overall carbon uptake capacity of shrubs (Fig. 2).
The mean carbon fraction (45.5%) in this study is generally lower than the IPCC guidelines (50%) and some previous studies (Alriksson and Eriksson, 1998; Northup et al., 2005; Tolunay, 2009), but it is comparable to the results of Ma et al. (2018). Despite these differences from IPCC default values, our study provides essential data for building precise and reliable carbon statistics tailored to specific regional and species characteristics, which is critical for accurate urban carbon accounting.
This finding aligns with previous studies that estimate the carbon fraction of roots to be lower than that of other plant components. To compare the differences in life form, we analyzed the average carbon fraction of standard shrubs and vine shrubs (Fig. 3). The results indicated that the average carbon fraction of standard shrubs was higher than that of vine shrubs, with statistically significant differences observed in the carbon fraction of branches, stems, and roots (p < .05). However, no statistical difference was found in the average carbon fraction of leaves & twigs (p > .05). These findings align with previous studies showing that vines generally have a lower carbon fraction than shrubs (Ma et al., 2018). This is likely because vines have low tissue density and rapid growth, which result in underdeveloped woody structures and, consequently, lower carbon concentration (Isnard et al., 2009; Gallagher et al., 2011). This tendency appears to stem from their relatively low lignin content (Rowe and Speck, 2005).
To compare the differences between shrub types, we analyzed the mean carbon fraction of evergreen and deciduous (Fig. 4). The results indicated that evergreen shrubs exhibited a higher average carbon fraction than deciduous (p < .05). Variations in carbon fraction were observed in leaves and twigs, stems, and roots (p < .05), but not in branches (p > .05). These findings align with previous studies conducted on trees (Buchmann et al., 1997; Hoch et al., 2003; Martin et al., 2018).
According to previous studies, evergreen and deciduous broad-leaved trees exhibit different ratios of lignin to cellulose depending on the species, and they show differences in shade tolerance (Martin et al., 2018). These differences in shade tolerance are related to the anatomical and physiological characteristics of plants. In general, species with higher shade tolerance tend to have higher tissue density, which is associated with greater carbon content (Lusk et al., 2008). Therefore, evergreen broad-leaved trees are known to have higher shade tolerance than deciduous trees, and they are characterized by higher C/N ratios, greater lignin content, and longer leaf lifespan. As a result, they tend to have higher carbon fraction per unit mass, which is consistent with the findings of previous studies (Buchmann et al., 1997; Hoch et al., 2003).
Furthermore, this study analyzed the change in biomass allocation ratios according to the age of the shrubs. It was found that as shrubs mature, the biomass allocation to stems increases, while the proportion allocated to leaves and roots decreases (Fig. 5 and Fig. 6). The shrubs were categorized into three age groups - juvenile (3–4 years), subadult (5–10 years), and adult (11 years or older) - revealing a clear ontogenetic shift in biomass allocation from leaves and roots toward stems as the shrubs matured (Huang et al., 2024). This trend of increasing stem biomass with age is partially consistent with findings from studies on trees (Tomlinson et al., 2013). However, some studies, such as Yang et al. (2019) and Huang et al. (2024), reported that the proportion of branch and leaf biomass in total tree biomass increases with age, which differs from our findings for shrubs. Notably, in our study, the branch biomass allocation for deciduous shrubs did not show a significant difference with age, a finding that contrasts with previous research on trees.
The comparison between life forms revealed that deciduous shrubs had a lower biomass allocation to leaves & twigs and a higher allocation to roots compared to evergreen shrubs. This is likely related to the strategy of deciduous species, which tend to store more carbohydrates in their roots to support seasonal leaf regeneration, thus exhibiting a different biomass distribution pattern from evergreen species (Tomlinson et al., 2013). This observation supports the idea that different life forms adopt distinct survival and growth strategies, which is reflected in their biomass allocation.
The relatively high value of the IPCC standard has also been noted in the literature, highlighting the necessity for studies that analyze the carbon fraction of various tree and shrub species, categorized by country, to establish accurate carbon statistics for urban green spaces (IPCC, 2006). These results likely indicate the influence of ecological factors such as specific climatic conditions, soil fertility, tree growth stage, or unique urban environmental stress factors in the area (Nowak, 2002; Roman et al., 2014). In particular, urban shrubs can have limited biomass due to restricted growing space and unfavorable soil conditions (Craul, 1999; Trowbridge and Bassuk, 2004; Day et al., 2010; Mullaney et al., 2015; Layman et al., 2016; Ferrini and Fini, 2017; Egerer et al., 2024), which directly impacts their carbon sequestration capacity. These findings are similar to those in existing literature, suggesting they reflect the specific characteristics of the urban nursery environment we studied (Strohbach and Haase, 2012; Rahman et al., 2019).
The practical implications of these findings are significant for urban planning and green space management. Firstly, the fact that the measured carbon fraction (average 45.5%) differs from the IPCC’s default value of 50% highlights the importance of developing country-specific coefficients. The IPCC guidelines recommend the use of this default value primarily for developing nations that may lack the resources to develop their own. However, for developed countries like Korea, establishing national-level, specific coefficients is strongly encouraged to improve the accuracy of national greenhouse gas inventories. This study directly addresses that need by providing foundational, species-specific carbon fraction data for native Korean shrubs, which is an essential step toward developing a more precise national carbon accounting system. Threrfore, the carbon fraction of native shrubs planted in Korean urban green spaces showed a difference from the IPCC’s default values.
Secondly, the clear difference between evergreen (higher carbon fraction) and deciduous shrubs offers direct guidance for landscape design. To maximize the carbon sequestration efficiency of a new park or green spaces, designers could prioritize the selection of evergreen species, as they store more carbon per unit of biomass. This result, combined with biomass allocation patterns, suggests that a planting strategy featuring a mix of fast-growing deciduous shrubs (Nowak et al., 2013; McPherson et al., 2016) for initial biomass accumulation and dense, high-carbon evergreen shrubs for long-term storage could be most effective (Jo and Ahn, 2012; Jo et al., 2020). Furthermore, our findings suggest that urban green space management policies should shift towards identifying and cultivating shrub species that are both resilient to urban environmental stresses and highly efficient in carbon sequestration (Konijnendijk et al., 2005; Escobedo et al., 2011, Nilsson et al., 2011).

Conclusion

Our investigation into the carbon fraction and biomass allocation of 51 urban shrub species revealed an average carbon fraction of 45.5%, with significant variations based on life form and growth type. Evergreen shrubs (46.5%) demonstrated a higher carbon fraction than deciduous shrubs (45.2%), and standard shrubs (45.6%) were higher than vines (44.6%). As shrubs age, biomass allocation shifts from leaves & twigs and roots towards the stems. The higher biomass allocation to roots in deciduous shrubs compared to their evergreen counterparts appears to be a strategy to support seasonal leaf growth.
These results provide a more holistic understanding of carbon dynamics in urban shrubs, moving beyond simple assumptions to offer empirical evidence for strategic species selection. There’s a growing demand to enhance the carbon sink function of urban green spaces by strategically incorporating plants with high carbon fractions into landscape designs. This societal imperative highlights the critical need for detailed studies on shrub carbon fractions, as the choice of species can significantly impact carbon uptake in urban environments. The findings from this research can therefore contribute to developing more effective planting models or guiding species improvement efforts, ultimately boosting carbon uptake in urban green spaces.
Shrubs are vital for carbon sequestration in urban areas. The detailed carbon fraction data for each shrub species provided in this study will enable more precise assessments of carbon storage and improve the accuracy of models, leading to better predictions of urban green spaces’ carbon sequestration potential. This information can empower urban green space designers and landscapers to make informed decisions regarding spatial planning and species selection, thereby maximizing the carbon storage functions of these green spaces.
However, this study marks only an initial step in shrub carbon research within Korea. Further extensive investigations are essential to develop comprehensive models that account for the average biomass increment of various shrub species, in addition to their carbon fractions. This will be crucial for establishing accurate and reliable statistical data for urban carbon accounting and maximizing the carbon sequestration potential of urban green spaces.

Fig. 1
Carbon fraction analysis of shrubs: By plant components. The number of samples is listed below the carbon fraction values, and the differences are indicated above the standard deviations (SD) from each groups based on the t-test.
ksppe-2025-28-5-719f1.jpg
Fig. 2
Average of dry weight ratio by shrub components in classification.
ksppe-2025-28-5-719f2.jpg
Fig. 3
Carbon fraction comparison of standard shrubs and vine shrubs by plant component in urban green spaces. The number of samples is listed below the carbon fraction values, and the differences are indicated above the SD from each groups based on the t-test.
ksppe-2025-28-5-719f3.jpg
Fig. 4
Carbon fraction comparison between deciduous and evergreen shrubs by plant component in urban green spaces. The number of samples is listed below the carbon fraction values, and the differences are indicated above the SD from each groups based on the t-test.
ksppe-2025-28-5-719f4.jpg
Fig. 5
Changes in the allocation ratio of dry weight by component for deciduous shrubs with increasing age groups. The data for the 3-year-old group includes values from Kim et al. (2023). Asterisks (*) indicate significant differences among age groups based on post-hoc analysis (p < .05).
ksppe-2025-28-5-719f5.jpg
Fig. 6
Changes in the allocation ratio of dry weight by component for evergreen shrubs with increasing age groups. The data for the 3-year-old group includes values from Kim et al. (2023). The asterisks (*) indicate significant differences among age groups based on post-hoc analysis (p < .05).
ksppe-2025-28-5-719f6.jpg
Table 1
Mean and standard deviation of carbon fraction (%) by species and component (leaf&twig, branch, stem, root)
Species Leaf (%) Mean Branch (%) Mean Stem (%) Mean Root (%) Mean Mean (%)
A. istichum 46.9 (± 0.7) 47.4 (± 0.5) 48.1 (± 0.4) 35.5 (± 3.1) 44.5 (± 0.8)
A. arguta 47.1 (± 2.1) 46.0 (± 0.8) 43.7 (± 1.3) 42.2 (± 2.1) 44.7 (± 0.6)
B. sinica 49.8 (± 3.4) 49.5 (± 3.9) 49.6 (± 3.8) 48.2 (± 3.6) 49.3 (± 3.5)
C. dichotoma 45.3 (± 1.2) 45.1 (± 2.3) 45.8 (± 1.0) 44.7 (± 0.8) 44.7 (± 1.8)
C. japonica 48.2 (± 1.2) 47.0 (± 2.0) 43.6 (± 5.3) 45.5 (± 2.0) 46.1 (± 1.2)
C. grandiflora 46.0 (± 0.7) 46.1 (± 0.6) 44.6 (± 0.7) 43.4 (± 1.4) 45.0 (± 0.5)
C. chinensis 47.4 (± 2.3) 46.1 (± 0.6) 45.3 (± 0.9) 44.2 (± 1.1) 45.7 (± 0.9)
C. speciosa 45.4 (± 0.6) 45.1 (± 0.7) 45.7 (± 0.5) 43.7 (± 0.4) 45.0 (± 0.4)
C. alba 44.9 (± 1.5) 46.0 (± 1.5) 45.6 (± 1.5) 43.9 (± 2.5) 45.1 (± 1.4)
C. coreana 46.6 (± 1.7) 46.7 (± 1.6) 45.9 (± 1.6) 42.7 (± 1.7) 45.5 (± 1.2)
D. parviflora 39.5 (± 3.2) 46.0 (± 0.7) 42.7 (± 1.9) 40.9 (± 1.7) 40.7 (± 2.2)
E. umbellata 47.4 (± 1.5) 46.7 (± 0.9) 46.2 (± 1.2) 44.0 (± 0.6) 46.1 (± 0.7)
E. alatus 44.1 (± 5.4) 46.5 (± 3.8) 46.7 (± 3.6) 45.2 (± 5.8) 45.5 (± 4.4)
E. fortunei 44.4 (± 1.3) 43.2 (± 1.4) 42.4 (± 0.2) 43.1 (± 0.4) 43.3 (± 0.3)
E. japonicus 44.6 (± 3.2) 44.7 (± 4.8) 45.9 (± 5.2) 44.1 (± 7.6) 44.8 (± 5.0)
F. japonica 44.5 (± 0.9) -* 45.7 (± 1.4) 41.5 (± 1.2) 43.9 (± 0.7)
F. koreana 46.5 (± 0.9) 46.4 (± 1.3) 46.0 (± 1.2) 43.0 (± 1.3) 45.5 (± 0.8)
G. jasminoides 45.6 (± 1.6) 46.7 (± 2.6) 43.8 (± 1.7) 44.4 (± 1.3) 45.1 (± 1.6)
H. rhombea 44.1 (± 0.3) 43.9 (± 0.3) 44.2 (± 0.8) 41.9 (± 1.3) 43.5 (± 0.5)
H. syriacus 48.1 (± 0.4) 46.6 (± 0.9) 42.9 (± 0.9) 45.2 (± 3.7) 45.7 (± 1.2)
H. macrophylla 46.7 (± 0.8) 45.3 (± 0.6) 41.8 (± 0.8) 43.5 (± 1.1) 44.3 (± 0.4)
H. serrata 45.6 (± 1.0) 43.9 (± 0.9) 39.8 (± 0.9) 42.6 (± 1.3) 43.0 (± 0.3)
I. cornuta 47.3 (± 0.6) 46.5 (± 1.1) 42.9 (± 1.5) 44.2 (± 0.7) 45.2 (± 0.5)
I. serrata 46.7 (± 0.8) 46.9 (± 1.0) 48.2 (± 1.4) 42.1 (± 1.5) 46.0 (± 0.7)
L. bicolor 40.5 (± 0.0) 40.5 (± 0.0) 40.9 (± 0.5) 39.7 (± 1.0) 40.0 (± 0.9)
L. obtusifolium 43.5 (± 1.7) 45.6 (± 1.1) 44.9 (± 1.6) 44.3 (± 1.7) 44.4 (± 1.3)
L. obtusiloba 46.4 (± 1.3) 47.0 (± 0.4) 47.7 (± 1.1) 44.8 (± 0.8) 46.5 (± 0.5)
L. japonica 44.3 (± 0.5) 43.6 (± 0.9) 43.1 (± 0.5) 43.3 (± 0.9) 43.6 (± 0.4)
N. domestica 46.7 (± 1.3) 45.9 (± 1.2) 45.9 (± 1.3) 44.6 (± 2.0) 45.8 (± 1.0)
P. tricuspidata 43.2 (± 1.2) 44.1 (± 0.5) 44.9 (± 2.0) 42.5 (± 0.8) 43.7 (± 0.6)
P. schrenkii 46.4 (± 0.7) 46.7 (± 0.7) 45.6 (± 1.4) 45.2 (± 1.8) 46.0 (± 0.7)
P. tobira 44.6 (± 0.4) 43.7 (± 1.3) 44.8 (± 0.7) 42.1 (± 0.8) 43.8 (± 0.3)
P. tomentosa 46.8 (± 0.9) 47.1 (± 0.3) 48.1 (± 0.7) 44.8 (± 1.5) 46.7 (± 0.4)
R. indica 44.1 (± 0.3) -* 44.9 (± 1.1) 45.5 (± 0.8) 44.9 (± 0.4)
R. mucronulatum 49.4 (± 1.9) 47.0 (± 2.2) 46.8 (± 1.9) 44.8 (± 2.7) 46.1 (± 1.7)
R. schlippenbachii 47.1 (± 1.8) 47.0 (± 0.9) 47.2 (± 5.5) 37.9 (± 3.7) 44.7 (± 1.3)
R. yedoense 44.9 (± 4.0) 47.9 (± 5.3) 48.2 (± 5.0) 46.2 (± 6.2) 46.0 (± 4.7)
R. scandens 44.1 (± 1.3) 50.5 (± 4.4) 47.0 (± 2.5) 43.8 (± 0.8) 46.4 (± 1.9)
R. multiflora 46.4 (± 0.5) 46.1 (± 1.1) 44.1 (± 0.8) 44.0 (± 1.1) 45.1 (± 0.5)
S. sorbifolia 48.7 (± 2.6) 47.8 (± 1.6) 44.7 (± 1.5) 43.7 (± 1.3) 46.2 (± 1.0)
S. prunifolia 47.1 (± 4.8) 47.5 (± 4.3) 46.9 (± 4.6) 45.4 (± 5.7) 46.7 (± 4.4)
S. salicifolia 46.2 (± 1.4) 46.9 (± 0.8) 45.0 (± 1.3) 44.0 (± 0.5) 45.6 (± 0.6)
S. incisa 47.4 (± 0.3) 47.6 (± 0.1) 47.2 (± 0.6) 47.3 (± 0.4) 47.4 (± 0.3)
S. oblata 44.4 (± 1.6) 45.9 (± 1.1) 45.3 (± 0.7) 44.5 (± 1.7) 45.0 (± 0.9)
T. asiaticum 47.0 (± 1.7) 46.7 (± 1.4) 46.5 (± 0.6) 49.7 (± 0.5) 47.5 (± 0.4)
V. dilatatum 46.1 (± 2.1) 45.3 (± 1.5) 46.2 (± 0.5) 39.6 (± 1.4) 44.3 (± 0.9)
V. erosum 44.6 (± 3.0) 46.1 (± 3.1) 43.1 (± 2.7) 43.7 (± 2.5) 44.5 (± 2.2)
V. opulus 46.7 (± 0.8) 45.7 (± 0.9) 46.4 (± 0.4) 40.3 (± 1.8) 44.8 (± 0.6)
W. subsessilis 45.6 (± 1.6) 46.7 (± 1.1) 46.3 (± 1.3) 45.1 (± 1.3) 45.9 (± 1.1)
W. floribunda 45.1 (± 1.8) 45.6 (± 1.6) 45.0 (± 0.8) 42.5 (± 0.4) 44.6 (± 0.7)
Z. tyaihyonii 48.2 (± 1.4) 47.9 (± 1.9) 45.2 (± 2.8) 45.0 (± 1.2) 46.6 (± 1.6)

Mean (%) 45.6 (± 3.7) 46.6 (± 3.5) 46.2 (± 3.7) 44.6 (± 4.5) 45.5 (± 3.5)

* underdeveloped branching structure.

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Appendices

Appendix
Number of shrubs classified by species type and form
Species Number of sampling Classification

Type a) Form b)
1 Abeliophyllum distichum 5 D S
2 Actinidia arguta 5 D V
3 Buxus sinica 110 E S
4 Callicarpa dichotoma 65 D S
5 Callicarpa japonica 5 D S
6 Campsis grandiflora 5 D V
7 Cercis chinensis 5 D S
8 Chaenomeles speciosa 5 D S
9 Cornus alba 56 D S
10 Corylopsis coreana 5 D S
11 Deutzia parviflora 30 D S
12 Elaeagnus umbellata 5 D S
13 Euonymus alatus 110 D S
14 Euonymus fortunei 5 E V
15 Euonymus japonicus 110 E S
16 Fatsia japonica 5 E S
17 Forsythia koreana 56 D S
18 Gardenia jasminoides 5 E S
19 Hedera rhombea 5 E V
20 Hibiscus syriacus 5 D S
21 Hydrangea macrophylla 5 D S
22 Hydrangea serrata 5 D S
23 Ilex cornuta 5 E S
24 Ilex serrata 5 D S
25 Lespedeza bicolor 22 D S
26 Ligustrum obtusifolium 79 D S
27 Lindera obtusiloba 5 D S
28 Lonicera japonica 5 D V
29 Nandina domestica 55 E S
30 Componenthenocissus tricuspidata 5 D V
31 Philadelphus schrenkii 5 E S
32 Pittosporum tobira 5 E S
33 Prunus tomentosa 5 D S
34 Rhaphiolepis indica 5 E S
35 Rhododendron mucronulatum 27 D S
36 Rhododendron schlippenbachii 5 D S
37 Rhododendron yedoense 140 D S
38 Rhodotypos scandens 5 E S
39 Rosa multiflora 6 D V
40 Sorbaria sorbifolia 5 D S
41 Spiraea prunifolia 110 D S
42 Spiraea salicifolia 5 D S
43 Stephanandra incisa 5 D S
44 Syringa oblata 57 D S
45 Trachelospermum asiaticum 5 E V
46 Viburnum dilatatum 5 D S
47 Viburnum erosum 62 D S
48 Viburnum opulus 5 D S
49 Weigela subsessilis 55 D S
50 Wisteria floribunda 5 D V
51 Zabelia tyaihyonii 10 D S

Total 1,325

a) D : Deciduous shrub, E : Evergreen shrub,

b) S : Standard shrub, V : Vine

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