Article Effects of Urban Forest Types and Traits on Soil Organic Carbon Stock in

Xinhui Xu 1,2, Zhenkai Sun 1,2,*, Zezhou Hao 1,2, Qi Bian 1,2, Kaiyue Wei 1,2 and Cheng Wang 1,2

1 Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, ; [email protected] (X.X.); [email protected] (Z.H.); [email protected] (Q.B.); [email protected] (K.W.); [email protected] (C.W.) 2 Key Laboratory of Tree Breeding and Cultivation and Urban Forest Research Centre, National Forestry and Grassland Administration, Beijing 100091, China * Correspondence: [email protected]

Abstract: Forests can affect soil organic carbon (SOC) quality and distribution through forest types and traits. However, much less is known about the influence of urban forests on SOC, especially in the effects of different forest types, such as coniferous and broadleaved forests. Our objectives were to assess the effects of urban forest types on the variability of SOC content (SOC concentration (SOCC) and SOC density (SOCD)) and determine the key forest traits influencing SOC. Data from 168 urban forest plots of coniferous or broadleaved forests located in the Beijing urban area were used to predict the effects of forest types and traits on SOC in three different soil layers, 0–10 cm, 10–20 cm, and 20–30 cm. The analysis of variance and multiple comparisons were used to test the differences in SOC between forest types or layers. Partial least squares regression (PLSR) was used to explain the influence of forest traits on SOC and select the significant predictors. Our results showed that

 in urban forests, the SOCC and SOCD values of the coniferous forest group were both significantly  higher than those of the broadleaved group. The SOCC of the surface soil was significantly higher

Citation: Xu, X.; Sun, Z.; Hao, Z.; than those of the following two deep layers. In PLSR models, 42.07% of the SOCC variance and Bian, Q.; Wei, K.; Wang, C. Effects of 35.83% of the SOCD variance were explained by forest traits. Diameter at breast height was selected Urban Forest Types and Traits on Soil as the best predictor variable by comparing variable importance in projection (VIP) scores in the Organic Carbon Stock in Beijing. models. The results suggest that forest types and traits could be used as an optional approach to Forests 2021, 12, 394. https:// assess the organic carbon stock in urban forest soils. This study found substantial effects of urban doi.org/10.3390/f12040394 forest types and traits on soil organic carbon sequestration, which provides important data support for urban forest planning and management. Academic Editor: Cate Macinnis-Ng Keywords: forest types; forest traits; partial least squares regression; soil organic carbon; urban forest Received: 13 February 2021 Accepted: 24 March 2021 Published: 26 March 2021 1. Introduction Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in Urban green spaces, including urban forests, play a pivotal role as substitutes for published maps and institutional affil- the lost natural environment in the city’s original location [1]. Urban forests provide a iations. large number of ecosystem services [2], such as enhancing amenity values [3], maintaining biodiversity [4], and increasing carbon sequestration [5]. The increase and decrease of soil organic carbon (SOC) may affect climate change greatly [6,7]. Moreover, SOC storage impacts the other ecological functions of soil as well, such as biomass production, nutrient and water-holding capacity, infiltration capacity and resistance to erosion, and providing Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. habitats for biological activity [8,9]. Studies of forest groups and species characteristics This article is an open access article affecting soil fertility parameters have been widely carried out in the non-urban envi- distributed under the terms and ronment [10], but less so in an urban context [11]. An in-depth understanding of the conditions of the Creative Commons relationship between urban forest and SOC is key to maintain and enhance the quality of Attribution (CC BY) license (https:// urban ecosystem services. creativecommons.org/licenses/by/ There is growing recognition of the top-down and bottom-up regulation between 4.0/). plants and soil organisms [12]. Studies of the effects of aboveground forest types and

Forests 2021, 12, 394. https://doi.org/10.3390/f12040394 https://www.mdpi.com/journal/forests Forests 2021, 12, 394 2 of 16

traits on SOC distribution are also underway. Jobbagy and Jackson (2000) found that different forest types significantly affected the vertical distribution of SOC [13]. There is generally high variability in urban SOC, and Canedoli et al. (2019) showed that the SOC concentration (SOCC) of urban parks was higher than that of non-parks, defined as green squares, private gardens, tree lines, or street greens [14]. In an urban context, soils are not only under the disturbance of an artificial environment but are also affected by vegetation [15]. Urban trees can affect the soil’s biological, physical, and chemical properties through their root systems and the quantity and quality of fallen leaves [16]. Understanding the impact of urban trees on soil properties is critical [17]. However, the effect of urban forests on SOC and to what extent urban forests can increase SOC reserves remain unclear. It has been long recognized that forest types differ greatly in forest traits [18] and thereby can affect ecosystem properties, including soil organic matter [5]. The previous studies of forest traits in relation to soil carbon stocks have focused on some key factors, such as tree canopy [19], diameter at breast height [20,21], litterfall [22–24], fine root biomass [25], and herbaceous vegetation [26]. These forest traits affect SOC and other soil properties through direct or indirect pathways. The direct pathway of trees interacting with soil includes both leaf litter input and root release. The litterfall from woody plants is the main source of soil organic matter [27–31]. Tree species vary significantly in their litter quality, as well as in their litter decomposition rates [32]. In the urban environment, the litter disposal methods of different forest types may influence soil organic carbon differently. The low crown and needle leaves of coniferous species mean that their fallen leaves are cleaned up less often [33,34], whereas the fallen leaves of deciduous broad-leaved forests are often cleaned up. Experiments have demonstrated that tree roots release substantial quantities of carbon into soil [35,36]. An indirect pathway by which forests interact with SOC—whereby forests or a single tree may affect soil respiration—is providing a microenvironment and microclimate for the soil. Thereby, urban forest types and tree species may impact soil organization and related process through the microenvironment, litterfall, and tree roots, thereby leading to differences in urban forest SOC. So far, Beijing has implemented two rounds of the Million-Mu (667 m2) Plain Afforesta- tion Project. In recent years, Beijing’s urban forest afforestation area is constantly increasing, which provided good conditions for the development of this study [37,38]. Currently, the connections between urban forest and soil organic carbon stock are still under-explored. To help address these knowledge gaps, we studied 107 urban parks in Beijing. Based on SOC stock and urban forest types and traits, we hypothesized that: (1) urban forest soil organic carbon is different between coniferous and broadleaved forests, (2) urban forest traits have a significant influence on soil organic carbon, and (3) soil organic carbon in different layers has a different relationship with urban forest traits. In this study, we aimed to elucidate the relationship between urban forest and soil organic carbon so as to provide data support for urban forest planning and management in the future.

2. Study Area and Methods 2.1. Study Area Beijing, the capital city of China, has a history of over 3000 years. Over the past decades, Beijing has been undergoing rapid development, and a series of ring roads were constructed as the city expanded outward from its historic center. This urbanization occurred in all four directions, started from the central area inside the , then sequentially toward the surrounding areas [39]. The study area was all within the sixth ring road, which covers the central and suburban areas of Beijing (Figure1). Forests 2021, 12, 394 3 of 15 Forests 2021, 12, 394 3 of 16

FigureFigure 1. The 1. The layout layout of soil of soil sampling sampling parks parks (A) ( Aand) and the the ring ring road road location location (B ().B ). Based on maps and reachable park directories on the Baidu Map, we located over Based on maps and reachable park directories on the Baidu Map, we located over 200 200 parks on the map. With the map gridded by 5 km × 5 km, we randomly selected one parks on the map. With the map gridded by 5 km × 5 km, we randomly selected one park park in each grid. We discarded parks that were built on former factories to ensure that they in each grid. We discarded parks that were built on former factories to ensure that they were only affected by the typical urban forest environment. After this, we manually added were only affected by the typical urban forest environment. After this, we manually added some parks more than 50 years old to make the age of parks more even. Then, 107 parks some parks more than 50 years old to make the age of parks more even. Then, 107 parks were selected in total. We tried to sample both coniferous and broadleaved forests in each were selected in total. We tried to sample both coniferous and broadleaved forests in each park to compare the SOC content between forest types. Finally, we sampled 168 plots, park to compare the SOC content between forest types. Finally, we sampled 168 plots, which contained 99 broadleaved and 69 coniferous plots. There were 61 parks with both whichconiferous contained and 99 broadleaved broadleaved forest and types. 69 conifero The informationus plots. There on sampling were 61 sitesparks and with species both is coniferousdisplayed and in AppendixbroadleavedA, Tableforest A1 types.. The information on sampling sites and species is displayedWe followed in Appendix five principlesA, Table A1. to select the sampling plots in parks. Firstly, the plots wereWe set followed more than five 5 principles m away from to select the walking the sampling path and plots 20 m in away parks. from Firstly, the traffic the plots artery. wereSecondly, set more the than plot’s 5 m understoryaway from the vegetation walking waspath stable and 20 and m away was notfrom to the be traffic changed artery. every Secondly,year to avoidthe plot’s large understory soil disturbances. vegetation Thirdly, was stable plots wereand was selected not to that be werechanged not wateredevery yearwithin to avoid three large days soil to reducedisturbances. the short-term Thirdly, impact plots were of human selected management. that were not Fourthly, watered the withinplot’s three size days was requiredto reduce to the be short-term bigger than impact 10 m ×of 10human m. The management. sizes of plots Fourthly, ranged the from plot’s100 size m2 (10 was m required× 10 m) to to be 225 bigger m2 (15 than m ×1015 m m)× 10 in m. this The study. sizes Fifthly,of plots monocultures ranged from 100 were m2sampled (10 m × 10 to m) focus to 225 on them2 (15 effects m × of15 treem) in species this study. on SOC. Fifthly, The monocultures monocultures were were sampled composed to offocus common on the tree effects species of tree in Beijingspecies and on SO wereC. artificialThe monocultures single communities. were composed of com- mon tree species in Beijing and were artificial single communities. 2.2. Methods 2.2.2.2.1. Methods Soil Sampling and Vegetation Investigation 2.2.1. SoilSoil Sampling was sampled and Vegetation in September Investigation and October 2018 at the edge of the canopy projection, soSoil the distancewas sampled to the in nearest September tree trunk and October ranged from 2018 1.2at the m to edge 10 m, of withthe canopy an average projection, distance so ofthe 4 distance m from theto the trunk nearest [40,41 tree]. Attrunk each ranged sampling from point, 1.2 m we to 10 removed m, with the anlitter average layer dis- and tancecollected of 4 m samples from the by trunk using [40,41]. a steel pushAt each corer sampling with a 3.8-cm point, innerwe removed diameter the and litter a length layer of and20 collected cm (Soil Sampler;samples Hongguangby using a steel Instrument push corer Inc., Shaoxing,with a 3.8-cm China) inner to obtaindiameter three and layers a lengthof 0–10 of cm,20 cm 10–20 (Soil cm, Sampler; and 20–30 Hongguang cm samples Instrument [41,42]. Six Inc., samples Shaoxing, distributed China) evenly to obtain in each threeplot layers were sampledof 0–10 cm, for each10–20 vertical cm, and soil 20–30 layer. cm The samples soil samples [41,42]. were Six mixedsamples and distributed transported evenlyin a coolerin each to plot the were laboratory sampled for for further each analyses. vertical soil layer. The soil samples were mixed and transportedWe investigated in a cooler the to vegetation the laboratory density for (VD) further of trees,analyses. tree height (H), under crown height (UC), diameter at breast height (DBH, 1.30 m above the ground level), leaf area We investigated the vegetation density (VD) of trees, tree height (H), under crown index (LAI), canopy density (CD), fine root biomass (FRB), canopy area (CA), herbaceous height (UC), diameter at breast height (DBH, 1.30 m above the ground level), leaf area vegetation cover (HB), and semi-decomposed litter (LIT) as forest traits. LAI was measured index (LAI), canopy density (CD), fine root biomass (FRB), canopy area (CA), herbaceous

Forests 2021, 12, 394 4 of 16

with an LAI-2200C plant canopy analyzer (LI-COR Inc., Lincoln, NE, USA), following the method as described in the study by Thimonier et al. [43]. Plant density of trees was calculated using the number of trees divided by the area of the sample plot. In the herb survey, 5 small subplots of 1 m × 1 m were selected, and the herbaceous vegetation cover and herbaceous species were measured in the small samples. We also collected fine roots of three soil layers in each plot and collected three 10 cm × 10 cm frames of litter samples in each plot to obtain the fine root biomass and semi-decomposed litter quantity. Root samples were collected with the steel corer mentioned above. Soil samples with roots were labeled and put into 1-mm mesh nylon bags and we used a flotation method to separate the roots from the soil [44,45]. The nylon bags were soaked in fresh water for 12 h and washed under regular running water to obtain clean roots [46]. Fine roots were divided as those with a diameter less than 2 mm, based on the cleaned roots. We further differentiated live roots from dead roots based on the shape, color, xylem, and elasticity of the roots and eliminated the roots of shrubs and herbs. Fine root samples were dried at 65 ◦C until the mass was constant and weighed with an electronic balance (±0.0001 g). We obtained the fine root biomass (g/m3) by dividing the fine root weight by the soil sample volume. The semi-decomposed litter samples were dried to a constant weight at 65 ◦C and weighed [47]. We used another set of tools to dig the complete core samples to calculate soil moisture content (SMC) and soil bulk density (BD). This set of tools was specially designed for study areas which could not be dug up for soil profiles. The set contains a fixed sleeve in which the ring knife can be put in and used to take intact soil samples.

2.2.2. Soil Preparation and Analysis We analyzed the soil moisture content (SMC), soil pH, soil bulk density (BD), soil organic matter concentration (SOCC), and density (SOCD). In brief, all laboratory tests followed standard methods. SMC was determined by gravimetric methods. To measure soil pH, water and field-moist soil were mixed in a 1:1 volumetric ratio, allowed to stand for 10 min, and pH was then estimated in the supernatant using a bench-top pH meter, reflecting the soil acidity. To test BD, the intact soil cores were dried at a temperature of 105 ◦C and soil BD was obtained by dividing the dry soil weight by the volume of the intact soil core (100 cm3). The SOCC (g kg−1) was measured using a SOC analyzer (Multi N/C 3100, Analytik, Jena, Germany). SOCD (kg m−2) within each sample was calculated according to Equation [48]: SOCD = BD × SOC × h (1) where BD is the bulk density (g/cm3), SOC is the soil organic carbon content (g/kg), and h is the depth of each soil layer (0.1 m).

2.3. Statistical Analyses In this study, statistical analyses were performed using the statistical software R 4.0.3 (R Core Team, 2016) to reveal the relationships between the urban forest and soil variables. All datasets were checked for normality using the Shapiro–Wilk test (p > 0.05) prior to analysis and then normalized or scaled if necessary. Soil parameter differences between the two forest types (coniferous and broadleaved) in three layers (0–10 cm, 10–20 cm, and 20–30 cm) were tested by two-way ANOVA, whereas no interaction effect was found in this study. Therefore, a one-way ANOVA test was separately performed on forest types and layers. The Levene test was performed to test the homogeneity of variance. The Kruskal–Wallis test was performed on pH and SMC values as these data failed for normality, and other soil variables (SOCC, SOCD, and BD) were analyzed using the parameter approach. The Steel–Dwass test and the Tukey HSD (honest significant difference) post-hoc tests were used to analyze differences among three layers. Forests 2021, 12, 394 5 of 16

Partial least squares regression (PLSR) was used to evaluate the influence of forest traits on SOCC and SOCD. PLSR integrates the advantages of principal component analysis (PCA), canonical correlation analysis (CCA), and multiple linear regression. PLSR has been widely applied to the study of forest ecosystems and has been proven to reveal the relationship between environmental factors and forest structure. In PLSR, forest traits and soil variables were chosen as predictors. Ten forest traits and three soil variables were chosen as predictor variables for PLSR—H, FRB, LAI, HC, LIT, VD, UC, CD, soil pH, soil moisture content, and bulk density. SOCC and SOCD were chosen as response variables. All variables were inspected for outliers before modeling. Leave-one-out cross-validation was used to select the optimum number of components. The root mean squared error of prediction (RMSEP) and the coefficient of determination (R2) were used to evaluate the model’s performance. Variable importance in projection (VIP) scores were used to evaluate the predictors’ contribution to PLSR [49]. Predictors with VIP scores greater than 1 were considered important to the models. The R packages ‘pls’ and ‘plsVarSel’ were used to perform the analyses.

3. Results 3.1. Variance of Soil Properties under Urban Forests The soil organic carbon of urban forests varied significantly. The average value of SOCC and SOCD across all the parks was 8.02 ± 0.12 g kg−1 (mean ± standard error) and 1.21 ± 0.02 kg m−2 (mean ± standard error), respectively (Table1). SOCC showed significant difference between forest types (F-value = 6.556, p < 0.05) and so did SOCD (F-value = 5.264, p < 0.05). Soils under coniferous trees had higher SOCC and SOCD than those under broadleaved trees (Table1). There was a significant difference in SOCC (p < 0.01) among different layers but not in SOCD. According to multiple comparisons, the 0–10 cm layer held significantly higher SOCC than both the 10–20 cm and 20–30 cm layers. Bulk density (1.53 ± 0.19 g cm−3) and pH (7.82 ± 0.55) showed no significant difference between the coniferous and broadleaved plant groups (p > 0.05). SMC (12 ± 5%) was significantly different between these two forest types. The soil under coniferous trees was moister than that under broadleaved trees. There were significant differences among the three layers only in BD.

Table 1. SOCC (g kg−1) and SOCD (kg m−2) in the coniferous and broadleaved groups in three layers. Results are presented as means ± standard error. Abbreviations: SOCC, soil organic carbon concentration; SOCD, soil organic carbon density. Different letters in the same row of SOCC and SOCD indicate significant differences between layers at the 0.05 level.

Total Coniferous Group Broadleaved Group Layers n Mean ± SE Mean ± SE Mean ± SE SOCC 0–10 cm 166 8.81 ± 0.19 b 9.19 ± 0.30 b 8.54 ± 0.25 b 10–20 cm 166 7.62 ± 0.21 a 7.95 ± 0.31 a 7.40 ± 0.28 a 20–30 cm 162 7.65 ± 0.21 a 8.04 ± 0.28 a 7.38 ± 0.29 a Total (each group) 494 8.02 ± 0.12 8.40 ± 0.18 7.77 ± 0.16 SOCD 0–10 cm 166 1.21 ± 0.03 1.24 ± 0.04 1.19 ± 0.03 10–20 cm 166 1.17 ± 0.03 1.24 ± 0.05 1.15 ± 0.04 20–30 cm 162 1.24 ± 0.03 1.29 ± 0.04 1.20 ± 0.04 Total (each group) 494 1.21 ± 0.02 1.26 ± 0.03 1.17 ± 0.03

3.2. Variance of Forest Traits The results showed that two popular families, Salicaceae and Pinaceae, dominated the forests. The Wilcoxon test for paired samples was performed to identify the differences in forest traits between plant groups. There were significant differences in forest traits between coniferous and broadleaved groups (Figure2). The results showed that there were significant differences in most forest traits between the two forest types. Among them, LAI, FRB, VD, HB, and CD values were significantly higher in coniferous forests. In Forests 2021, 12, 394 6 of 16

contrast, H, UC, DBH and CA were significantly higher in broadleaved trees. Meanwhile, the semi-decomposed litter quantity showed no significant difference between forest types, which may be due to clearing and leaf management in parks.

Figure 2. Forest traits compared between coniferous and broadleaved forests. Means and standard errors are shown. ** means p < 0.01, *** means p < 0.001, ns means not significant.

3.3. Effect of Forest Traits on SOC The performance of the PLSR models is shown in Table2. For the total SOCC and SOCD models, higher R2 and lower RMSEP values were observed in the total SOCC model. In the total SOCC model, the components explained 42.07% of the variance in SOCC; in the 10–20 cm model, the components explained 45.50% of the variance in SOCC, which was the highest level of explanation among the three layers. In the total SOCD model, the components explained 35.83% of the variance in SOCD; in the 10–20 cm model, the com- ponents explained 45.83%. These results showed that forest traits had a more substantial explanatory power for total SOCC. Simultaneously, three components were observed in both the best performing SOCC and SOCD models, and the addition of more components did not improve the explanatory power of the model. The following components did not have a strong correlation with the residuals of the predictors. Different models in different layers analysis showed that forest traits had more substantial explanatory power for the SOCC and SOCD models at a depth of 10–20 cm. In comparison, the explanatory power for the 0–10 cm and 20–30 cm models was slightly weaker than that of the 10–20 cm model.

1

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Table 2. Summary of the best supported partial least squares regression (PLSR) models for forest traits and SOC parameters in different soil layers. RV, response variables; SOCC, soil organic carbon concentration; SOCD, soil organic carbon density; RMSEP, root mean squared error of prediction.

RV Layer R2 Component Explained in RV (%) RMSEP Total 0.35 3 42.07 0.79 0–10 cm 0.25 2 39.24 0.86 SOCC 10–20 cm 0.35 2 45.50 0.80 20–30 cm 0.27 2 38.29 0.85 Total 0.29 3 35.83 0.83 0–10 cm 0.18 2 33.70 0.90 SOCD 10–20 cm 0.36 2 45.83 0.79 20–30 cm 0.21 2 33.02 0.88

Concerning the VIP scores of each of the forest traits, DBH had an important contribu- tion in all models (Table3). The traits of LAI, DBH, CA, HC, and CD showed important contributions to the models of both the total SOCC and SOCD, indicating that forest traits associated with forest biomass had a strong relationship with SOC in urban forests. Moreover, comparing the results of different layer models showed that more forest traits contributed to the SOCC and SOCD models at the 10–20 cm layer than the other two layers. FRB’s VIP score was greater than 1 only in the 10–20 cm layer model, which showed a relationship between forest fine roots and SOCC and SOCD in urban forests. The con- tribution of forest traits was the least in the 0–10 cm model. Additionally, based on the regression coefficient of each forest trait, it was found that except for the weak negative correlation coefficient between CA and SOC in the 0–10 cm layer model, the other forest traits positively correlated with SOC. The VIP scores of LIT, VD, and UC in all models were less than 1. The results showed that these three forest traits had no statistically significant relationships with SOCC and SOCD in urban forests. Overall, these results showed that forest traits positively affected SOCC and SOCD in urban forests.

Table 3. The variable importance in projection (VIP) scores of different traits in each model projection. The traits with VIP scores < 1 were culled. Abbreviations: LAI, leaf area index; LIT, semi-decomposed leaf litter; FRB, fine root biomass; H, tree height; UC, under crown height; DBH, diameter at breast height; CA, canopy area; HC, herbaceous vegetation cover; CD, crown density.

Models LAI LIT FRB VD H UC DBH CA HC CD SOCC Total 1.29 1.06 1.61 1.03 1.00 1.23 0–10 cm 1.49 1.62 1.33 10–20 cm 1.11 1.01 1.24 1.53 1.02 1.04 20–30 cm 1.09 1.07 1.49 1.21 1.22 SOCD Total 1.12 1.07 1.67 1.06 1.13 1.13 0–10 cm 1.25 1.74 1.05 1.22 10–20 cm 1.02 1.01 1.20 1.58 1.03 1.09 20–30 cm 1.04 1.46 1.14 1.45 1.08

4. Discussion Our results showed that the coniferous and broadleaved urban forest groups sig- nificantly affected soil organic carbon. Specifically, SOCC and SOCD were higher in the coniferous group than in the broadleaved group. This result is similar to findings in a natural context by Bło´nskaand Gruba [50] and findings in urban parks in Finland by Setälä et al. [41]. Here, even in an urban context, SOC of the coniferous type forest was higher than that of the broadleaved forest, similar to forest communities in natural ecosystems—for instance, the carbon concentration under a spruce forest was found to be higher than Forests 2021, 12, 394 8 of 16

under broadleaves [51,52]. This trend in SOC content of different forest types may be because conifers have a higher leaf area index, even though broadleaved trees have larger crowns. Our result is similar to Bae and Ryu’s findings that complex canopy structure may boost organic carbon in forest soils [53]. The FRB of coniferous trees is also higher than that of broadleaved trees, and fine roots are another factor that directly affects soil organic carbon [54]. Some studies on urban forest roots have revealed that the roots of urban trees can maintain or enhance organic matter in soil [55]. In the three soil layers, SOCC in 0–10 cm was significantly higher than the other two layers, whereas there was no difference in SOCD among layers. Edmondson reported similar results, demonstrating that not only SOCC but also SOCD of urban forest differed among different layers [17]. In our study, the top layer in the coniferous group held the highest SOCC (Table1). Due to the input of nutrients from vegetation and artificial management, the content of organic carbon in the surface layer of soil is usually high, which exhibits a dynamic interaction with biological and anthropogenic activities [56,57]. Therefore, in cities, soils under coniferous trees, especially the upper layer, can provide greater potential for organic carbon storage. The PLSR of SOCC and SOCD models selected significant variables with VIP scores > 1. DBH was the only variable in all models showing VIP > 1 and the highest VIP, which indicated that DBH was the most important predictor for SOC content in our study. Our result is similar to the study of Eni, which found that DBH was the only canonical vari- able revealing soil-vegetation interrelationships [58]. In the two forest types, the DBH of broadleaves was higher than that of conifers, whereas the soil organic carbon under broadleaves was lower than that of conifers. Moreover, the result of PLSR showed that there was a positive correlation between DBH and SOCC. This means that the difference in coniferous and broadleaved forest soil organic carbon is likely to be caused by many factors. Moreover, we found that all variables of crown characteristics—LAI, canopy area, and canopy density—were also important for the prediction of urban forest soil organic carbon, with VIP scores > 1 in at least seven models (Table3). Thus, the results indicate that the canopy structure of urban forest communities has an important impact on soil organic carbon. The difference in forest traits among forest types showed that the LAI and canopy density of conifers were higher than those of broadleaved trees, whereas canopy area dis- played the opposite result. These results, combined with the PLSR results, may emphasize that LAI and canopy density have a stronger effect on soil organic carbon and are more suitable to be predictors. On the one hand, a complete and complex canopy structure in urban forests can increase the input of litter in the area, which becomes an important source for accumulating organic carbon in the soil [59]. According to the significant difference between forest types in the leaf area index and the lack of differences between them in litter, we can infer that human activities strongly influence this mechanism, such as litter removal through management practices [60–62]. On the other hand, changes in canopy structure affect the microclimate of the soil habitat, including environmental factors such as temperature, light, wind speed, and precipitation [63]. The environments of forests can affect the activities of fauna and organisms in the soil [64,65], thus changing the overall soil respiration intensity and affecting the decomposition and transformation of soil organic carbon [66]. Based on the models of total SOCC and SOCD, the forest traits mentioned showed a positive correlation with urban soil organic carbon. This positive correlation revealed a correlation between tree biomass and soil organic carbon content, suggesting that urban forests with more complex canopies and higher leaf areas may have higher organic carbon stock in their soils. In addition, our results also showed that forest type did not account for all variance in SOC, indicating that more factors need to be taken into account, such as urban forest age and position. Many studies have shown that urban forest age has a positive effect on SOC and SOC [11,67,68]. Habitats with a more mature and stable urban forest structure can provide good shelter and necessary food sources for aboveground organisms [68] and play a positive role in the underground ecological environment [21]. Forests 2021, 12, 394 9 of 16

In addition, we used forest traits to predict the overall SOCC and SOCD and studied the relationship between SOC content and forest traits at different soil layers. The results showed that SOC content in 10–20 cm layer was most closely related to forest traits, with the highest model interpretation and stability (Table2). Notably, the VIP score of fine root biomass was greater than 1 in the models both of SOCC and SOCD in the 10–20 cm layer, which indicated the contribution of fine roots to soil organic carbon in this layer. Huyler et al. also found that tree roots played a role in maintaining the carbon level below the soil surface [69]. Fine roots are important plant organs used to absorb water and nutrients [70], and fine roots are analogous to leaves and central organic matter inputs, given that the sloughed roots are added to the soil humus pool [68]. Due to the rapid renewal of fine roots, the annual return of carbon, nutrients, and energy from fine roots to the soil is even higher than that of ground litter [71], which was confirmed by the study of Hui et al. [72]. At 0–10 cm, the models showed that the contribution of forest traits to SOC content was the weakest. The contribution of litter content to SOC was not found in any model, which indicates that the organic carbon in urban forest soil was affected by other factors than litter input in our study. This study’s sampling area was concentrated in urban parks, and the sampling area was disturbed by human factors to varying degrees. At present, most of the litter management in parks in Beijing area is still dominated by regular cleaning, which leads to the litter being cleared away and degrading soil organic carbon. The high-intensity manual management pattern in urban parks destroys surface soil structure and accelerates the decomposition of organic carbon and therefore SOC cannot accumulate effectively [73,74]. In contrast, green space management in Paris may help to increase SOC stock in open soil [75]. However, researchers of urban forest soil organic carbon need to learn more from the research experience of natural forest soil and carry out more systematic and comprehensive research in the world [76].

5. Conclusions A better understanding of the effects of urban forests on SOC content is crucial to advancing urban ecosystem services, even though quantifying and predicting carbon in urban forest soil remains difficult. Thus, our results demonstrated that urban forests affect the concentration and density of soil organic carbon through forest types and forest traits in temperate climates. We found that there was a significant difference in SOC content between coniferous and broadleaved types in urban forests in Beijing. Using PLSR, we selected diameter at breast height, leaf area index, crown area, and canopy density as the significant predictors of soil organic carbon in urban forests. The data show that analyzing forest traits could be an optional approach for cost-effectively predicting urban forest SOC with higher accuracy and practicability.

Author Contributions: Conceptualization, X.X., C.W. and Z.S.; investigation, X.X., Z.H., Q.B. and K.W.; writing—original draft, X.X.; writing—review and editing, Z.S. and Z.H. All authors have read and agreed to the published version of the manuscript. Funding: This work was funded by National Non-Profit Research Institutions of the Chinese Academy of Forestry (grant numbers: CAFYBB2020ZB008) and by the China Scholarship Council (CSC) (grant numbers: 201903270038). Acknowledgments: We truly appreciate Susan Day for her guidance and thoughtful suggestions. Conflicts of Interest: The authors declare no conflict of interest. Forests 2021, 12, 394 10 of 16

Appendix A

Table A1. List of sampling park names, sizes, ages, tree species, and locations.

Species Location of Number Park Name Size (ha) Age (Year) Conifer Broadleaf Ring Road Salix matsudana 1 Dongfutougou Park 24 8 - 5 to 6 Koidz. Pinus tabuliformis Ailanthus altissima 2 Zhongguancun forest Park 340 14 5 to 6 Carr. (Mill.) Swingle 3 Spring Park 11 12 - Ginkgo biloba L. 5 to 6 4 Dongxiaoying Park 1 8 - Salix matsudana 5 to 6 5 Baiwang Park 9 12 - Ginkgo biloba 5 to 6 Juniperus chinensis Euonymus maackii 6 Mingyuan Park 3 3 5 to 6 Linn. Rupr. Cedrus deodara Platanus 7 Riveside forest Park 431 8 5 to 6 (Roxburgh) G. Don occidentalis L. Metasequoia 8 Huilongyuan 10 20 glyptostroboides Hu Sophora japonica L. 5 to 6 et W. C. Cheng Pinus bungeana 9 Bishuifenghe 3 28 Sophora japonica 5 to 6 Zucc. et Endi Populus tomentosa 10 Zhenggezhuang Park 1 13 - 5 to 6 Carr. 11 Haiyingluo Park 74 5 Pinus bungeana Salix matsudana 5 to 6 Fraxinus chinensis 12 Future technolgy Park 314 5 - 5 to 6 Roxb. 13 Banta country Park 42 5 Juniperus formosana Populus tomentosa 5 to 6 14 Taiping country Park 43 11 - Populus tomentosa 5 to 6 15 Huabohui thesis Park 27 20 - Populus tomentosa 5 to 6 16 Hedi Park 2 6 Pinus tabuliformis Fraxinus chinensis 5 to 6 17 Mananli Park 2 25 - Sophora japonica 5 to 6 18 Yongshiying country Park 21 8 Pinus tabuliformis Salix matsudana 5 to 6 19 624 11 Pinus tabuliformis Ailanthus altissima 4 to 5 20 Heiqiao Park 138 15 - Populus tomentosa 5 to 6 21 Yujin Park 17 15 Juniperus formosana - 5 to 6 22 Riverside Park 1 5 Pinus tabuliformis Sophora japonica 5 to 6 23 Yuwen river Park 16 4 Pinus tabuliformis Salix matsudana 5 to 6 24 Dongba country Park 234 10 - Salix matsudana 5 to 6 25 Entertainment sport Park 7 8 - Salix matsudana 4 to 5 26 Baliqiao music Park 17 10 Pinus tabuliformis Salix matsudana 5 to 6 Forests 2021, 12, 394 11 of 16

Table A1. Cont.

Species Location of Number Park Name Size (ha) Age (Year) Conifer Broadleaf Ring Road 27 Longwangzhuang Park 4 9 - Sophora japonica 5 to 6 Robinia 28 Taihu forest Park 76 5 Pinus tabuliformis 5 to 6 pseudoacacia L. 29 Dongshi Park 20 3 - Fraxinus chinensis 5 to 6 30 Lvfeng Park 23 5 Pinus tabuliformis Sophora japonica 5 to 6 Platanus acerifolia 31 Nanhaizi Park 432 9 Pinus tabuliformis (Aiton) 5 to 6 Willdenow Eucommia 32 Boda Park 16 15 Pinus tabuliformis 5 to 6 ulmoides Oliver 33 Binhe Park 312 2 - Salix matsudana 5 to 6 34 Yizhuang Park 6 4 Pinus tabuliformis Salix matsudana 5 to 6 35 Laojuntang Park 47 11 - Populus tomentosa 4 to 5 36 Huangcun Park 5 35 Juniperus formosana - 5 to 6 Platycladus orientalis 37 Qingyuan Park 124 10 Fraxinus chinensis 5 to 6 (L.) Franco 38 Gaoxin Park 42 10 Platycladus orientalis Salix matsudana 4 to 5 39 Yukang Park 40 11 Pinus tabuliformis Salix matsudana 4 to 5 Robinia 40 Kandan Park 49 10 - 4 to 5 pseudoacacia 41 Century Forest Park 405 11 Juniperus chinensis Sophora japonica 5 to 6 42 Riverside Park 312 16 Pinus tabuliformis Salix matsudana 5 to 6 Huangxinzhuang Green 43 6 10 - Populus tomentosa 5 to 6 Fitness Park 44 University Town Park 6 6 - Populus tomentosa 5 to 6 45 Changyang Park 10 15 Pinus tabuliformis Platanus orientalis 5 to 6 Koelreuteria 46 Changti Park 1 11 - 5 to 6 paniculata Laxm. Koelreuteria 47 Changxindian Park 8 32 Juniperus formosana 5 to 6 paniculata 48 Xiaoyue country Park 37 10 Juniperus formosana Sophora japonica 5 to 6 49 Guozhuang Park 9 20 Pinus bungeana Salix matsudana 4 to 5 50 Small Park 1 20 Platycladus orientalis Sophora japonica 4 to 5 Fengtai Science and Koelreuteria 51 5 22 - 3 to 4 Technology Park paniculata Koelreuteria 52 Fengtai Garden 8 30 - 3 to 4 paniculata 53 Wanfeng Park 24 23 Platycladus orientalis Ginkgo biloba 3 to 4 54 Wukesong Cultural Park 8 30 Pinus tabuliformis Salix matsudana 3 to 4 Forests 2021, 12, 394 12 of 16

Table A1. Cont.

Species Location of Number Park Name Size (ha) Age (Year) Conifer Broadleaf Ring Road 55 Hope Park 9 15 Juniperus chinensis Populus tomentosa 5 to 6 56 Ancient City Park 14 38 Pinus tabuliformis Ginkgo biloba 5 to 6 57 Pine Forest Park 22 70 Platycladus orientalis - 5 to 6 Half-moon popular Koelreuteria 58 3 20 Pinus tabuliformis 4 to 5 science garden paniculata Juniperus formosana 59 Laoshan City Leisure Park 91 11 Salix matsudana 4 to 5 Hayata 60 Sunshine Wednesday 6 16 - Salix matsudana 4 to 5 61 Pingzhuang country Park 44 10 - Ginkgo biloba 4 to 5 62 400 63 Pinus tabuliformis Salix matsudana 5 to 6 Chinese Academy of 63 74 89 Juniperus chinensis Populus tomentosa 5 to 6 Sciences Botanical Garden 64 Yudong Garden 75 11 Juniperus formosana Ginkgo biloba 4 to 5 65 Haidian Park 40 16 - Sophora japonica 4 to 5 66 Shuangyushu Park 1 34 Juniperus formosana Sophora japonica 3 to 4 Dongsheng Bajia 67 101 12 - Salix matsudana 4 to 5 Country Park 68 Zhiyuanzhuang Park 12 6 Juniperus formosana Fraxinus chinensis 5 to 6 International Camping 69 36 16 Pinus bungeana Sophora japonica 4 to 5 Park replacement 70 Nanyuan Park 13 70 Juniperus formosana Populus tomentosa 4 to 5 71 Taoyuan Park 11 18 Juniperus formosana Sophora japonica 4 to 5 72 Wanfang Pavilion 8 29 - Sophora japonica 2 to 3 73 Fengyi Park 10 15 - Populus tomentosa 2 to 3 Koelreuteria 74 Bihai Park 20 16 Pinus tabuliformis 4 to 5 paniculata 75 Breeze Garden 1 22 Juniperus chinensis Salix matsudana 3 to 4 Robinia 76 Haitang Park 34 11 Juniperus chinensis 4 to 5 pseudoacacia 77 Guta Park 56 11 Pinus tabuliformis Sophora japonica 4 to 5 78 Xinglong Park 49 27 Juniperus chinensis - 4 to 5 79 Ditan Park 35 479 Platycladus orientalis Salix matsudana 2 to 3 80 Nanguan Park 3 63 - Sophora japonica 1 to 2 81 Xiangheyuan Park 7 33 - Sophora japonica 2 to 3 Koelreuteria 82 17 21 Pinus tabuliformis 4 to 5 paniculata 83 Wanghe Park 37 4 Pinus tabuliformis Fraxinus chinensis 4 to 5 Forests 2021, 12, 394 13 of 16

Table A1. Cont.

Species Location of Number Park Name Size (ha) Age (Year) Conifer Broadleaf Ring Road Wong Tsao Wan 84 39 10 Pinus tabuliformis Fraxinus chinensis 4 to 5 Country Park 85 Zizhuyuan 47 66 Pinus bungeana Tilia tuan Szyszyl. 2 to 3 Robinia 86 Linglong Park 9 30 - 3 to 4 pseudoacacia 87 Huichengmen Park 3 50 Juniperus chinensis Populus tomentosa 2 to 3 88 Cuifang Garden 1 29 Pinus tabuliformis - 1 to 2 89 Nanlishi Road Park 2 59 Juniperus chinensis Sophora japonica 2 to 3 90 Yuetan Park 8 479 Juniperus formosana Euonymus maackii 2 to 3 Koelreuteria 91 Guanyuan Park 2 19 - 1 to 2 paniculata 92 120 9 Pinus tabuliformis - 1 to 2 93 Park 21 479 Platycladus orientalis Euonymus maackii 2 to 3 94 Tuanjie Lake Park 13 33 Juniperus formosana - 3 to 4 95 Red scarf Park 24 61 Juniperus chinensis Ginkgo biloba 4 to 5 96 210 35 Pinus tabuliformis Populus tomentosa 3 to 4 97 55 67 Juniperus chinensis Sophora japonica 1 to 2 98 Dongdan Park 4 70 - Ginkgo biloba 1 to 2 Huangchenggen 99 8 18 - Ginkgo biloba 1 to 2 Heritage Park 100 19 101 Juniperus chinensis - 1 to 2 101 Xitucheng Park 18 13 - Sophora japonica 2 to 3 102 Rending Lake Park 9 71 - Ginkgo biloba 2 to 3 103 Rose Garden 1 16 Platycladus orientalis Ailanthus altissima 3 to 4 104 Madian Park 8 16 Pinus tabuliformis Ginkgo biloba 3 to 4 105 Arctic Temple Park 5 6 Pinus tabuliformis Ulmus pumila L. 3 to 4 106 Xinglong Park 49 27 - Sophora japonica 4 to 5 107 Xiangheyuan Park 7 33 Juniperus chinensis - 2 to 3

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