How Well Do Three Tree Species Adapt to the Urban Environment in Guangdong-Hongkong-Macao Greater Bay Area of China Regarding Their Growth Patterns and Ecosystem Services?

Abstract

It is predicted that global change combined with urbanization will impact increasingly on the society and terrestrial ecosystem in the Guangdong-Hongkong-Macao Greater Bay Area of China (GBA). In this context, the cities in GBA began to plant a variety of urban trees since 2000 which are considered to play an important role in fixing carbon, improving air quality, reducing noise and providing other ecosystem services. However, data on the growth patterns and ecosystem services of the planted trees remains scarce, which hampers a comprehensive understanding of how well the planted trees adapt to the local urban environment. Therefore, we selected three widely planted tree species in Foshan, one of the core cities in GBA and investigated their tree growth and ecosystem services via a harvest campaign and soil analysis. With the same, fast tree growth as natural forests and the greatest above- and below-ground biomass among the three tree species, Ml (Mytilaria laosensis Lec.) showed a distinguished adaption to the local urban environment in terms of growth patterns, carbon fixation, stabilization against typhoon risk and water uptake capacity against potential drought risk in the future. Although Cf (Chinese fir) showed reduced diameter at breast height (DBH) and volume development, it significantly increased the total and available potassium in soils to improve the soil quality. The DBH growth of Sp (Slash pine) decreased between six and 12 years old while it recovered at the age of 12 years, probably suggesting its adaptation might take a longer time. Our results indicated that different trees had different growth patterns and ecosystem services after they were planted in cities. In a harsh urban environment under climate change, precise and comprehensive data on urban trees is necessary, helping to provide different perspectives for urban managers to select appropriate tree species and make policies.

1. Introduction

Urban areas around the world are expanding rapidly and will have more than 60 percent of the estimated world population by the year 2030 [1,2], and in association with which, the impacts of global change have become more serious than in previous decades in cities [3,4]. It is widely acknowledged that urban trees can be a key component in the adaptation of cities to climate change and provide ecosystem services accompanying rapid urbanization [5,6,7], including improving urban air quality, reducing noise, attenuating storm-water flooding and conserving energy, which have drawn increasing interest from researchers [8,9,10,11,12]. For example, Nowak et al. estimated the carbon sequestration rate of urban trees in the USA [13], and Grote et al. reported that urban trees helped to improve air quality by facilitating widespread deposition of various gases and particles through the provision of large surface areas as well as through their influence on the microclimate and air turbulence [14]. Besides, urban trees could contribute enormously to thermal comfort because heat loads caused by densely built structures could be reduced via evapotranspiration and shading [15]. Furthermore, Gardiner et al. found that urban trees could change at scales from the cell to the whole plant to acclimate to the wind [16].

There is an increasing demand for data on urban trees; however, multiple factors might prevent researchers from obtaining profound knowledge of the growth and ecosystem services of urban trees. Compared to forest stands, urban trees grow in harsh environments with high temperatures [17], limited rooting space [18], reduced aeration due to impervious pavement [19], and less access to soil moisture and nutrients [20], which might have a complex influence on their growth and ecosystem services. For example, Zhang et al. reported that limited water availability in cities could strongly reduce the fine root development of some urban trees in both vertical and horizontal levels [21]. Pretzsch et al. found that urban climate can both accelerate and slow down tree growth, depending on the climatic zone of the given city [22]. In addition, human activities could not be ignored as these disturbances could affect urban tree growth in different ways [23]. Therefore, it is essential to acquire detailed and accurate information for urban trees which may benefit enormously urban management and planning such as providing guidelines for the selection of appropriate tree species in cities.

With an area over 56,000 km² and a population over 70 million, the Guangdong-Hongkong-Macao Greater Bay Area of China (GBA) is one of the most economic and open regions around the world [23]. In the context of global change and rapid urbanization, GBA is facing increasingly serious environmental challenges and positive actions have to be adopted to ameliorate the negative effects. Foshan, one of the core cities in GBA, started to implement a green city policy, aiming to become a forest city since 2000 [24]. Through planting a variety of tree species in the urban area, the forest coverage rate of Foshan reached 40% in 2010 [25]. However, there still existed some problems which hampered our comprehensive understanding of how well the planted trees adapt to the local environment [26]. Because of the strict protection, i.e., the planted urban trees were usually prevented from researches, information on detailed tree growth was still lacking which resulted in a vague understanding of how they grew and allocated above- and below-ground biomass. Besides, whether the planted trees fit the local edaphic condition was rarely researched which was closely related to how to apply and develop fertilizer policy.

Therefore, with the support of the urban forest management bureau of Foshan, we selected three tree species Slash pine (Pinus elliottii Engelm. var. elliottii), Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) and Mytilaria laosensis Lec. as our research objects which were widely planted in the city [27]. We launched a harvest campaign for the three tree species to measure their detailed growth patterns and to analyze their above- and below-ground biomass allocation. Furthermore, how the selected trees impact the soil’s physical and chemical properties was investigated as well. Hence, the specific questions addressed in this study were: (1) How well do the three tree species adapt to urban environment in terms of their volumes, heights and DBH (diameter at breast height)? (2) What are their specific biomass allocation between above- and below-ground compartments? (3) Do they have different impacts on the physical and chemical properties of the soil?

2. Materials and Methods

2.1. Study Site and Trees Selection

This study was performed in the urban green space (22°45′21’’–22°46′43’’N, 112°37′46’’–112°39′34’’E) of Gaoming district, Foshan, China, which is under a subtropical humid monsoon climate [28]. With the area around 15000 m² and each tree being planted more than 3 m away from the others to avoid the mutual effect, this site shows little variation in terms of microclimatic conditions and is ideal for research. Slash Pine (Sp), Chinese fir (Cf) and Mytilaria laosensis (Ml) are three widely-planted tree species in GBA and each of them had over 20 individuals in this green space (Figure 1). Aiming to select the most representative trees of the three species in this area, the diameters at breast height (DBH) and the heights of all the trees were measured with the help of a diameter tape and a clinometer (Haglof Vertex IV, Sweden) during the summer of 2013. The mean DBH and height of all the investigated trees were calculated, and based on which, the three most representative sample trees of Sp, Cf and Ml (nine trees in total) were selected for further analysis.

2.2. Measurements of Climate Variables

Climate variables, including highest, lowest and mean temperature (°C), precipitation (mm) and max wind speed (m·s⁻¹) were sampled every 10 min with a weather station (WP3103 mesoscale automatic weather station, China) located at an unshaded site approximately 500 m away from the study site. The data were stored in the logger and copied to our laboratory to produce monthly data. For the past decade, with the mean annual precipitation 3416 mm, the rainfall varied monthly and usually reached peaks in summer which could be over 250 mm. Additionally, the mean and the highest temperature in summer was over 28 °C and 35 °C, while in winter the mean and lowest temperature was 13.5 °C and 4.4 °C, which showed a moist and warm environment for trees. The max wind speed fluctuated in different months and ranged from 7.5 to 15.2 m·s⁻¹ generally, which showed a windy circumstance (Figure 2).

2.3. Measurements of Soil Variables

Soil sampling was launched for the three tree species in November using a soil wreath knife (200 cm³) which aimed to analyze the physical and chemical properties. For each tree, three corings were taken in the distance of 50, 100 and 150 cm to the trunk for replications. For every coring in detail, firstly a 100-cm depth hole was dug by a spade and it was divided into four layers on average, i.e., 0–25, 25–50, 50–75 and 75–100 cm. The soil physical properties were measured according to Li et al. [29]. Soil bulk densities (BD, g·cm⁻³) were determined by the mass of soil per unit volume (sum of solids and pore space) and the soil cores were oven-dried at 105 °C to obtain field capacity (FC, %). Each soil core was put into a salver to absorb water via filter paper until it reached a steady weight, usually 12 h later to measure saturation moisture content (SMC, g·kg⁻¹). Then, the soil cores were put on a sand salver and allowed to drain for 2 h in order to calculate soil capillary water content (Scwc). Hence, soil capillary porosity (Scp, %) was calculated according to the equation as:

$$Scp=0.1\times Scwc\times ds$$

(1)

where ds is the soil density (mg·m⁻³). And soil non-capillary porosity (Sncp, %) and soil total porosity (Stp, %) were calculated as:

$$Sncp=0.1\times \left(Smc-Scwc\right)\times ds$$

(2)

$$Stp=Scp+Sncp$$

(3)

For soil chemical properties, soil pH was determined in 1:2.5 soil–water slurry using a combination glass electrode. The soil organic matter (SOM, g·kg⁻¹) was determined by the oil bath-K₂Cr₂O₇ titration method. The total nitrogen (TN, g·kg⁻¹) was determined by the semi-micro Kjeldahl method. The available nitrogen (AN, mg·kg⁻¹) was determined by a micro-diffusion technique after alkaline hydrolysis. The total phosphorus (TP, g·kg⁻¹) was determined colorimetrically after wet digestion with H₂SO₄ + HClO₄. The available phosphorus (AP, mg·kg⁻¹) was exacted with 0.5 mol l⁻¹ NaHCO₃ solution (pH 8.5). The total potassium (TK, g·kg⁻¹) was determined by the Cornfield method. The available potassium (AK, mg·kg⁻¹) was determined by the CH₃COONH₄ extraction method [30].

2.4. Measurements of Biomass Allocation and Tree Growth

In December, a harvest campaign was launched in which all the sample trees were excavated carefully with a tree digger and divided into four parts: stem, branch, leaf and root system. Then, the fine root (<2 mm) and coarse root (>2 mm) were separated and washed cautiously to remove the soils and stones on the surface. All these compartments were dried at 65 °C for 72 h and weighed using a balance with an accuracy of up to two decimal places to obtain the dry weight.

A complete stem analysis was carried on for all the felled trees which involved measurements at a series of positions along the stem to deduce their heights and diameters. The sample disks were taken at the nearest representative point below spring branch whorls on trees that had a regular branching pattern. We oriented each disc in the same direction and measured two lines that went through the pith and were at 90 degrees to each other. The mean diameters were calculated and recorded with the height, which were used to produce Height: Age and DBH: Age curves. In addition, tree volumes (V, m³) were calculated on the basis of species, DBH and height, i.e.,

$$V=C\times {10}^{-5}\times DB{H}^a\times H^b$$

(4)

where C, a and b are coefficients related to specific tree species [31,32,33].

2.5. Statistical Analysis

The software package R (Version 3.4.4) was used for statistical analysis [34]. To investigate the difference between means, two-sampled t-test and analysis of variance (ANOVA) with Tukey’s HSD test were used. In all the cases, statistical significance was detected at p < 0.05. Where necessary, data were log or power transformed in order to correct for data displaying heteroscedasticity.

3. Results

3.1. Soil Physical and Chemical Properties

It was red soil in the study site, exposed to common visitors. Six variables were measured to describe the soil physical properties of the three tree species (

Table 1. Measured soil physical properties including bulk density (BD, g·cm⁻³), field capacity (FC, %), saturation moisture content (SMC, g·kg⁻¹), soil non-capillary porosity (Sncp, %), soil capillary porosity (Scp, %) and soil total porosity (Stp, %) in 0–25, 25–50, 50–75, 75–100 and 0–100 cm layers. The symbol ‘*’ indicates significant differences (p < 0.05) between different tree species in the same layers.

Species	Soil Layers (cm)	n	BD (g·cm−3 ± SD)	FC [% ± SD]	SMC (g·kg−1 ± SD)	Sncp (% ± SD)	Scp (% ± SD)	Stp (% ± SD)
Sp	0–25	3	1.48 ± 0.09	18.26 ±3.14	284.68 ± 39.85	3.78 ± 0.71	38.09 ± 2.84	41.87 ± 3.49
	25–50	3	1.49 ± 0.12	18.01 ± 6.12	274.93 ± 54.46	2.43 ± 0.32	38.06 ± 5.47	40.48 ± 5.49
	20–75	3	1.43 ± 0.15	21.33 ± 6.11	303.64 ± 63.99	3.41 ± 0.85	39.39 ± 6.02	42.80 ± 5.64
	75–100	3	1.46 ± 0.08	23.32 ± 1.43	308.74 ± 7.64	3.26 ± 1.38	41.89 ± 1.50	45.15 ± 1.30
	0–100	3	1.47 ± 0.06	20.21 ± 2.22	1171.99 ± 95.41	3.22 ± 0.28	39.36 ± 2.00	42.58 ± 1.86
Cf	0–25	3	1.42 ± 0.08	21.40 ± 5.25	316.32 ± 26.21	2.23 ± 1.18	42.58 ± 2.98	44.81 ± 4.05
	25–50	3	1.41 ± 0.12	22.19 ± 4.32	342.66 ± 17.47	4.10 ± 1.17	44.12 ± 5.15	48.23 ± 4.45
	20–75	3	1.54 ± 0.13	17.06 ± 4.38	258.37 ± 54.12	2.78 ± 0.57	36.63 ± 5.47	39.41 ± 4.97
	75–100	3	1.62 ± 0.05 *	18.37 ± 5.42	265.98 ± 57.76	3.78 ± 1.02	39.11 ± 7.44	42.89 ± 8.17
	0–100	3	1.50 ± 0.03	20.36± 2.14	1183.33 ± 42.92	3.22 ± 0.21	40.61 ± 2.20	43.84 ± 2.42
Ml	0–25	3	1.22 ± 0.14	26.22 ± 8.05	397.94 ± 83.94	3.10 ± 0.27	44.91 ± 5.23	48.00 ± 5.49
	25–50	3	1.49 ± 0.14	24.65 ± 1.56	315.90 ± 28.20	3.11 ± 0.74	43.59 ± 0.78	46.70 ± 0.59
	20–75	3	1.55 ± 0.02	26.28 ± 2.73	321.78 ± 14.80	2.94 ± 0.92	46.91 ± 0.91	49.86 ± 1.83
	75–100	3	1.41 ± 0.03	28.50 ± 1.49 *	351.13 ± 11.50	3.15 ± 1.67	46.23 ± 0.86	49.38 ± 1.26
	0–100	3	1.42 ± 0.03	26.19 ± 1.20	1386.75 ± 68.34	3.07 ± 0.42	45.41 ± 0.73	48.49 ± 1.14

). Additionally, we not only analyzed the overall soil layer (0–100 cm) but also classified it into four layers on average for significant differences between the three tree species. Cf had the highest BD (1.50 ± 0.03 g·cm⁻³) and lowest Scp (40.61 ± 2.20%), while Ml had the highest FC (26.19 ± 1.20%) and lowest Sncp (3.07 ± 0.42%). Except for BD and Scp, Sp had the lowest values of other indexes in comparison to Cf and Ml. However, except for BD of Cf in 75–100 cm (p = 0.008) and FC of Ml in 75–100 cm (p = 0.019) which were significantly higher, there were no significant differences between the three tree species in all the corresponding soil layers (p > 0.05).

Eight variables were measured to describe the soil chemical properties in different layers of the three tree species (

Table 2. Measured soil chemical properties including pH values, soil organic matter (SOM, g·kg⁻¹), total nitrogen (TN, g·kg⁻¹), total phosphorus (TP, g·kg⁻¹), total potassium (TK, g·kg⁻¹), available nitrogen (AN, mg·kg⁻¹), available phosphorus (AP, mg·kg⁻¹), available potassium (AK, mg·kg⁻¹) in 0–25, 25–50, 50–75, 75–100 and 0–100 cm layers. The symbol ‘*’ indicates significant differences (p < 0.05) between different tree species in same layers.

Species	Soil Layers (cm)	n	pH	SOM (g·kg−1 ± SD)	TN (g·kg−1 ± SD)	TP (g·kg−1 ± SD)	TK (g·kg−1 ± SD)	AN (mg·kg−1 ± SD)	AP (mg·kg−1 ± SD)	AK (mg·kg−1 ± SD)
Sp	0–25	3	3.93 ± 0.22	24.30 ± 10.69	1.14 ± 0.45	0.31 ± 0.04	19.47 ± 5.43	92.90 ± 25.20	1.33 ± 0.51	31.00 ± 19.31
	25–50	3	4.50 ± 0.42	15.70 ± 1.97	0.76 ± 0.04	0.24 ± 0.06	18.00 ± 6.02	61.07 ± 15.85	1.13 ± 0.61	56.67 ± 54.85
	50–75	3	4.36 ± 0.26	12.17 ± 1.96	0.61 ± 0.08	0.29 ± 0.02	16.37 ± 3.21	43.60 ± 4.28	1.04 ± 0.34	21.00 ± 6.56
	75–100	3	4.19 ± 0.25	14.63 ± 3.70	0.62 ± 0.11	0.31 ± 0.02	16.77 ± 2.10	56.07 ± 18.32	1.00 ± 0.27	22.00 ± 17.44
	0–100	3	4.32 ± 0.15 *	16.04 ± 2.69	0.77 ± 0.08	0.28 ± 0.01	17.05 ± 4.22	59.83 ± 9.14 *	1.12 ± 0.42	30.42 ± 8.18 *
Cf	0–25	3	4.06 ± 0.07	23.13 ± 1.10	1.28 ± 0.19	0.34 ± 0.02	30.80 ± 4.36 *	115.70 ± 15.70	1.74 ± 0.45	76.00 ± 16.52 *
	25–50	3	4.02 ± 0.03	16.53 ± 0.55	0.82 ± 0.10	0.32 ± 0.06	27.63 ± 5.35	83.23 ± 19.24	1.13 ± 0.44	56.67 ± 7.64
	50–75	3	4.07 ± 0.06	15.63 ± 3.57	0.77 ± 0.25	0.33 ± 0.02	29.43 ± 3.73 *	65.13 ± 12.44	1.08 ± 0.27	60.00 ± 13.23 *
	75–100	3	3.97 ± 0.06	10.28 ± 4.36	0.59 ± 0.26	0.28 ± 0.02	28.50 ± 3.93 *	51.43 ± 18.43	0.91 ± 0.31	40.00 ± 8.66
	0–100	3	4.03 ± 0.02	16.40 ± 1.98	0.87 ± 0.19	0.31 ± 0.03	29.09 ± 4.27 *	78.88 ± 6.99	1.21 ± 0.23	58.17 ± 9.21 *
Ml	0–25	3	3.94 ± 0.10	33.80 ± 7.01	1.58 ± 0.37	0.34 ± 0.08	15.63 ± 1.04	141.13 ± 30.89	1.78 ± 0.35	26.33 ± 9.81
	25–50	3	3.97 ± 0.08	17.80 ± 5.37	0.84 ± 0.25	0.28 ± 0.02	15.97 ± 2.47	86.00 ± 9.40	1.13 ± 0.32	15.33 ± 4.51
	50–75	3	3.81 ± 0.27	9.97 ± 0.22	0.55 ± 0.02	0.26 ± 0.04	13.80 ± 3.00	50.20 ± 9.21	1.07 ± 0.17	17.33 ± 4.04
	75–100	3	3.84 ± 0.28	9.84 ± 1.79	0.48 ± 0.08	0.33 ± 0.04	15.40 ± 1.51	47.53 ± 2.89	0.77 ± 0.07	14.00 ± 1.73
	0–100	3	3.89 ± 0.18	17.85 ± 0.64	0.86 ± 0.04	0.30 ± 0.04	15.20 ± 1.79	81.21 ± 6.16	1.19 ± 0.03	18.25 ± 2.38 *

and Figure 3), among which SOM, TN, TP and AP showed no significant differences in all the layers. Sp had a significantly higher pH value (4.32 ± 0.15, p = 0.043) and significantly lower AN (59.83 ± 9.14 mg·kg⁻¹, p = 0.025) in the overall 0–100 cm soil layer. For TK, Cf was significantly higher than Sp and Ml in all the layers (p < 0.01) except for the layer of 25–50 cm. Additionally in terms of AK, Cf was significantly higher in 0–25 cm (76.00 ± 16.52 mg·kg⁻¹), 50–75 cm (60.00 ± 13.23 mg·kg⁻¹) and overall 0–100 cm (58.17 ± 9.21 mg·kg⁻¹).

3.2. Development of DBH, Height and Volume

Growth of height, DBH and volume were analyzed for the three tree species for the entire growing period (Figure 4). For Ml, it showed the highest values of height, DBH and volume among the three tree species (p < 0.05). Besides, its fastest development of height and DBH increment occurred from zero to four years old which was the same as the other two tree species. Sp exhibited the medium growth of DBH and volume but the lowest height and height increment. Cf had a distinctly strong growth of height but probably tended to reach a peak of 10 m. For all the tree species, the height increment speed was significantly reduced after four years old (p < 0.05).

3.3. Biomass Allocation and Root:Shoot Ratio

Generally, stem biomass had the largest proportion of the tree, followed by the root system, and branch and leaf had the lowest share (

Table 3. Height (m ± SD), DBH (cm ± SD) and biomass data (kg ± SD) of the three tree species from the harvest campaign in November, 2013. The above-ground biomass consists of stem, branch and leaf while the below-ground biomass consists of the coarse and fine root.

Species	n	Age	H (m ± SD)	DBH (cm ± SD)	Biomass (kg ± SD)
					Above-Ground			Below-Ground
					Stem	Branch	Leaf	Coarse Root	Fine Root
Sp	3	18	14.10 ± 0.89	16.67 ± 0.15	144.53 ± 7.94	11.67 ± 4.02	10.97 ± 6.61	24.83 ± 5.27	13.80 ± 4.91
Cf	3	12	9.50 ± 0.72	11.77 ± 0.67	48.27 ± 8.34	6.97 ± 4.97	7.10 ± 2.07	13.33 ± 7.41	4.47 ± 2.68
Ml	3	12	13.23 ± 1.03	15.27 ± 0.80	129.60 ± 22.65	44.73 ± 26.04	16.87 ± 6.17	46.70 ± 6.09	20.23 ± 3.23

). With a height of 14.10 ± 0.89 m and DBH of 16.67 ± 0.15 cm, Sp had the largest biomass of stem (144.53 ± 7.94 kg) which was the oldest among the three tree species. For Cf, its biomass of all the compartments was way less than the other two species. With the same age as Cf but younger than Sp, Ml exhibited the highest below-ground biomass (coarse root: 46.70 ± 6.09 kg; fine root: 17.63 ± 4.02 kg). Meanwhile, the highest sum of above-ground biomass was noted for Ml in the three tree species (Ml: 191.2 kg; Sp: 167.17 kg; Cf: 62.34 kg).

Three types of root:shoot ratio were calculated: (1) fine root:leaf biomass ratio, (2) coarse root:branch biomass ratio and (3) below-ground:above-ground biomass ratio (Figure 5). All the root:shoot ratios were significantly different (p < 0.05) and Ml was the highest, which had a larger proportion of root system than Sp and Cf. Among the three ratios, the below-ground:above-ground biomass ratio of Ml showed the greatest difference (p < 0.01).

4. Discussion

4.1. Growth Patterns of the Three Tree Species

Information on growth patterns for individual trees is an essential tool for forest management [35]. Especially for tree species planted in cities, how growth patterns develop has raised various expectations and concerns [36]. In GBA, the climate was warm and windy, under which the urban trees might face potential drought stress and wind damage as well as other urban disturbances. In our research, Ml exhibited steady development over time which was in line with Chen’s result in a natural forest [27], and it showed significantly higher growth than the other two species in terms of height, DBH and volume. Besides, it grew rapidly in the early stage and the annual DBH and height growth reached peaks in around four years after planting. Moreover in two years later, volume and volume increment started to obtain substantial development which was consistent with previous studies [37,38]. Overall, these results proved Ml to be a distinguished fast-growing tree species with vigorous growth within the whole growing period, which probably implied that the local urban environment had a minor impact on it.

It was reported that Cf’s growth relied on site conditions to a great extent [39]. In southern subtropical China, Zheng et al. found that Cf’s DBH and height development reached a peak rapidly and kept steady for over four years under limited disturbance, while under disadvantaged circumstances, its growth could be strongly weakened [40]. In this study, Cf had a fast and great development of height as Ml but the lowest level of DBH increment among the three species, which resulted in the lowest tree volume. Compared to the previous study [40], Cf in a natural forest outperformed the trees in our research in terms of the DBH and the volume, implying Cf in the urban area is failing to fulfill the growth expectations under a splendid growing environment.

For Sp, it exhibited a stable and moderate development of height within the whole period and vigorous growth of DBH in its fast-growing period, which was in line with the results of Zhao’s research [41]. Nevertheless, the growing speed of DBH in a natural forest showed a gradually decreasing trend after six years old [42], while that in our research reduced substantially at the same time, which was the most significant difference for Sp between the two conditions, probably due to the urban limited environmental conditions. However, the DBH increment recovered and a sharp growth was observed at the age of 12 years. As it was reported that different tree species might take several years to adapt to its surroundings in cities [43], it could possibly explain that Sp regained DBH growth as natural forest stands at the age of 12 years, showing a longer progress of adaptation than Ml.

4.2. Biomass Allocation and Ecosystem Services

Biomass allocation within a plant, particularly the distribution of biomass among the various organs, is strongly affected by species characteristics, ontogeny and the environment [44]. In this study, various weights of above- and below-ground compartments were quantified including leaf, branch, stem, coarse and fine root and calculated three types of root:shoot ratios, which promoted to understanding not only the different capacities of carbon fixation but also the strategies of biomass allocation of the three tree species. Ml had the largest total tree biomass, followed by Sp, and Cf had the least one. In addition, although Ml trees in this study were only 12 years which were younger than Sp of 18 years, it could be speculated that Ml would have the largest biomass when all the three tree species were at the same age of 18 years on the basis of Guo’s and Chen’s results that Ml’s highest annual volume increment occurred between 15 to 17 years after planting [26,35]. All of this might make us believe that Ml could be an adaptive tree species in cities as it could play a better role as a carbon sink under the global change [45]. Furthermore, combined with its rapid and vigorous growth of volume, Ml might attract more attention on the short- and long-term ecological and economic benefits [46,47].

It was reported that China has a high frequency of typhoons with a spatial peak in GBA every year [48,49]. The resistance of urban trees to breakage or overturning in windy climates largely relied on structural modifications to obtain mechanical strength, and the biomass allocation to the root system had a positive impact on tree stability [50]. Besides, as demonstrated by global warming and rapid urbanization in GBA [51,52], it was essential for urban trees to develop competitive root systems to cope with this trend [53]. Furthermore, Zhang et al. pointed out that sufficient development of fine root biomass could be crucial for urban trees to cope with extreme drought events as they would grow in vertical and horizontal directions to absorb water [21]. In our research, Ml had way higher below-ground biomass of coarse and fine root than Cf and Sp (Table 3, Figure 3), which might help to guarantee its adaptation to the urban environment. On one hand, the greatest coarse root biomass of Ml tended to be a site-specific advantage to avoid the overturning risk, and on the other hand, its largest fine root biomass might have the greatest possibilities of adapting to the local urban environment under a warmer background.

4.3. Impact on Soil Properties

Generally, soil physical properties acted as important soil quality indicators [54]. For the overall 0–100 cm soil layer, no significant differences were found between the three tree species. Nevertheless, our results showed that Sp had a lower FC and SMC than Cf which was in line with the result of Tian et al. that Sp would have more severe soil water loss than Cf [55]. Furthermore, the highest FC and SMC of Ml among the three trees, suggesting that a higher water content in soils could help to explain why Ml had a more prosperous development of below-ground biomass, as soil water would promote the distribution and growth of root system [56,57]. Cassel et al. also stated that a high water capacity usually meant that adequate water could be used by plants [58], which could contribute to alleviating drought stress [53]. As the soil is usually very dense and compact in cities [59], the capacity of holding water in soils could be a specific advantage of urban tree species in GBA to cope with potential water shortage under the global change.

For soil chemical properties, Cf had significantly higher TK and AK while significantly lower AN and AK were observed for Sp and Ml, respectively. Contrary to the result of Selvalakshmi et al. that Cf had a decrease of TK in the natural forest [60], Cf was observed to increase the amount of K in soils than the other two tree species in our research, which could have resulted from the difference between the natural environment and urban area. Unlike the compact space in the natural forest, city trees usually did not encounter various interspecific competition therefore they could have adequate growth, especially the fine root growth [22,61], which probably changed the release patterns of TK in soils [62]. Sp had less AN compared to Ml and Cf, which could be explained by its small range of ecosystem functions, providing a less diverse array of N-related substrates and nutrients to microbial communities, which was also proved by Wang et al. [63]. For TP and AP, no significant difference was found among the three tree species, which was probably due to the inactive nature of phosphorus, especially in acidic conditions [64]. This information could help to provide guidelines for city managers that, on one hand, fertilization should take the impact of species on soil properties into account to maximize the effect; on the other hand, it was better to launch soil measurements regarding soil nutrients before planting new trees in order to select more adaptive tree species.

5. Conclusions

It is predicted that global change combined with urbanization will impact increasingly on the society and terrestrial ecosystem in GBA. In our study, Ml had the greatest tree growth and total biomass, which was same as the patterns in natural forests, proving its well adaptation to the urban environment and strong capacity of fixing carbon. Besides, Ml’s higher root:shoot ratios, combined with great root biomass, suggested that it might cope better with the local environment in GBA, that its strong coarse root helped to reduce the overturning risk and vigorous fine root could alleviate drought stress. Cf, a tree species relying strongly on the quality of the growing environment, had a decreased growth of DBH and volume compared to natural forests, suggesting being negatively affected by the urban environment. However, Cf significantly increased TK and AK in the soil while Sp and Ml had a reduced AN and AK, respectively. This indicated that Cf could be considered to be planted in the areas lacking TK and AK to improve the soil quality. For Sp, it had a vigorous development in its fast-growing period while the growth of DBH was reduced between six and 12 years. However, it recovered the DBH increment after 12 years which probably suggested it needed to take a longer time than other tree species to adapt to urban environment.

Overall, different tree species may have different adaptation strategies to urban surroundings and their adaptation can be various and vary with time accordingly. Detailed and precise information on the growth patterns and ecosystem services are extremely necessary for the appropriate selection of urban trees, which will promote a better understanding of urban ecosystems.