Optimizing Urban Forest Multifunctionality through Strategic Community Configurations: Insights from Changchun, China

Abstract

The role of forest community configurations in multiple ecosystem functions remains poorly understood due to the absence of quantifiable metrics for evaluating these configurations. This limitation hinders our ability to use forests to enhance urban well-being effectively. This study integrates both observation and experimentation to elucidate the effects of community configurations on the multifunctionality of forests. We examine seven ecosystem functions in Changchun’s urban forests: carbon sequestration, rainwater interception, temperature reduction, humidity increase, particulate matter reduction, noise reduction, and water conservation. Assortment indices, derived from traditional diversity metrics and relative importance values, reveal a negative correlation with multifunctionality. This suggests that improving forest multifunctionality requires a strategically planned species composition rather than simply increasing diversity. Furthermore, the creation of comprehensive configuration indices for evaluating intraspecific configurations has confirmed their beneficial impact on multifunctionality. Our results highlight the significance of intraspecific structural configurations and advocate for using mixed-species plantings in urban forestry practices. We propose practical management strategies to enhance urban forest multifunctionality, including selecting tree species for their functional benefits, implementing uneven-aged plantings, and integrating both shade-tolerant and sun-loving species. Together, our findings underscore the essential role of community configuration in sustaining multifunctionality and strongly support the management of urban forests.

1. Introduction

As urbanization intensifies, cities worldwide confront a range of “urban maladies”, including heat islands, smog, internal flooding, and noise pollution. These challenges significantly degrade the urban living environment. Urban forests are defined as all trees located within a specified urban area [1]. Urban forests, integral to urban ecosystems, contribute substantially by enhancing biodiversity, sequestering carbon, releasing oxygen, regulating microclimates, improving air quality, preventing floods, and reducing noise [2,3,4,5]. Despite a heightened focus on developing urban green infrastructure, the current provision still falls short of meeting all residents’ needs for green spaces and their associated ecological functions, especially in densely populated urban areas. Consequently, implementing nature-based solutions to rectify the disparity between the supply of and demand for urban forest ecological functions has become imperative.

Over the past 25 years, extensive research has underscored the importance of plant diversity in natural forests for multifunctionality [6,7,8]. Numerous studies have elucidated the benefits of mixed-species planting in enhancing forest functionality [9,10,11,12,13]. Despite this, higher species richness in urban areas compared to natural settings does not translate into the anticipated positive impact on multifunctionality observed in natural forests [14,15,16,17,18]. In urban contexts, this biodiversity may not necessarily bolster multifunctionality and could even exhibit a negative correlation [19]. Some scholars contend that species richness alone does not adequately reflect the state of urban forests, as many rare species might contribute to total diversity with minimal impact on forest composition [14]. Meanwhile, the structural attributes of forests, including variations in individual tree size (diameter and/or height) both among and within species, are crucial for maintaining species diversity and supporting effective forest functioning [20]. Consequently, further research is required to ascertain whether species composition or more nuanced aspects of community configuration influence the multifunctionality of urban forests [21].

Forest community configuration refers to the spatial arrangement of trees within a forest at a given time [22,23]. Current research in ecology and forestry on the role of community configuration in forest multifunctionality is inherently constrained by quantification challenges. This body of research generally centers on elementary aspects such as species pairing and forest stratification [24]. In urban forests, particularly, there is a notable lack of understanding regarding how specific spatial arrangements and physical characteristics of trees affect multifunctionality. Moreover, few studies have addressed ecological functions through species composition, occasionally regarding it as a nuisance variable [25,26].

In conclusion, it is vital to gather evidence-based insights on how different community configurations influence the multifunctionality of forest communities. Additionally, these insights are crucial in addressing the ineffectiveness of diversity in enhancing multifunctionality in urban contexts and opening new avenues for exploratory research on multifunctionality. Furthermore, this knowledge can assist forest managers in identifying effective combinations and strategies for tree planting in urban areas. By doing so, it improves the quality of urban living environments, advances sustainable urban ecological practices, and fosters human well-being.

In this study, Changchun—a representative national forest city in Northeast China—served as the research site. We utilized standardized methods for forest community surveys to assess seven key ecological functions impacting residents’ safety and quality of life. These functions encompass carbon sequestration, rainwater interception, temperature reduction, humidity increase, particulate matter reduction, noise reduction, and water conservation. Additionally, we analyzed the structural characteristics of the forest community. Our research aims to (1) quantify the configuration of the urban forest community; (2) examine how this configuration affects its individual functions; (3) explore the influence of this configuration on its multifunctionality; and (4) develop practical, configuration-based strategies to enhance this multifunctionality.

2. Materials and Methods

To provide a comprehensive overview of the research design and methodology, we have included a framework figure (Figure 1) that outlines the key components of our study. This figure illustrates the systematic approach taken, including the specific forest ecosystem functions under investigation and the methodological steps employed.

2.1. Study Area

The study area is located in Changchun City, central Northeast China, positioned between latitudes 43°05′ and 45°15′ North and longitudes 124°18′ and 127°05′ East within the northern temperate zone (Figure A1). Changchun experiences marked seasonal variations in temperature, peaking at an average high of 24.7 °C in July and reaching a low of −13.5 °C in December, with an annual average of 4.6 °C. Precipitation varies between 522 and 615 mm annually, predominantly during the summer, accounting for over 60% of the yearly total. The city has an annual evaporation rate of 1620 mm and a total of 2866 growing degree days above 10 °C. The mean atmospheric pressure is approximately 986.6 hPa. The region typically enjoys a frost-free period of 140–150 days and endures a five-month freezing period annually. Predominant soil types include chernozem, meadow soil, and calcic chernozem. In biodiversity terms, Changchun serves as a confluence between the Changbai and Mongolian plant regions, supporting approximately 800 plant species. The city’s green spaces cover over 180,000 hectares, achieving an urban greening rate of 41%.

2.2. Sampling

Preliminary surveys were conducted in September and October 2021 to evaluate the condition of urban forests and soil quality in Changchun City, ensuring their uniformity for the selection of appropriate sampling sites. The chosen plots were strategically selected to represent a variety of forest types endemic to the region. Formal fieldwork was carried out annually from June to October 2022 and 2023, employing both standard forest community survey methods and the U.S. Department of Agriculture’s urban forest survey techniques [16]. Our field investigations, carried out over two consecutive years, effectively minimized the effects of climatic variations.

A total of 237 plots, each measuring 400 m² (20 m × 20 m), were systematically surveyed to ensure topographical uniformity and maintain homogeneity across plots. For each plot, collected data included the geographical coordinates of the plot center, elevation, and the relative positions of each tree using a Cartesian coordinate system. Parameters recorded for each tree encompassed diameter at breast height (1.3 m), tree height, crown width, first branch height, crown light exposure, and health status [16].

In the understory, three 1 m² quadrats were established per plot of herbaceous plants using stratified sampling techniques. Data collected from these quadrats included plant height, clump number, canopy diameter, and coverage. Soil moisture at each plot was assessed with portable soil sensors using a five-point sampling method during the same period in 2023.

Leaves and branches were collected from each plot, and 1 to 3 trees per species within each plot were selected for on-site fresh weight measurements. In the laboratory, specimens were soaked in water for 12 h and then reweighed after water droplets ceased to fall. The weight difference indicated the water-absorbing capacity of the leaves and branches. Similar procedures (harvest method) were applied to the herbaceous layer. All leaf, branch, and herbaceous specimens were dried at 70 °C for 72 h until they reached a constant weight, after which they were weighed.

2.3. Calculation of the Leaf Area

In this study, we measured the single-leaf area of target trees using a scanner and ImageJ software version 1.8.0. Leaves were placed flat on the scanner bed and scanned at a resolution of 200 DPI. The scanned images, saved in JPEG format, were analyzed with ImageJ to determine leaf area.

Specific leaf area (SLA) was calculated as the ratio of leaf area to leaf dry weight (cm²/g). Similarly, specific twig area (STA) was determined as the ratio of twig surface area to twig dry weight (cm²/g). Total leaf area was estimated by multiplying SLA by the total leaf biomass, while total twig area was calculated by multiplying STA by total twig biomass. Total biomass for both twigs and leaves was estimated using locally relevant allometric equations.

2.4. Instrumentation Setup

Climate variables such as temperature and humidity were measured using HOBO sensors (Onset, Bourne, MA, USA). Concentrations of PM2.5, PM10, and total suspended particulates were measured using a dust meter. These instruments were positioned at a central point within each plot. All sensors were placed at a height of 1.5 m and shielded to reduce the influence of direct sunlight on the readings. Controlled measurements were conducted in an unobstructed open area adjacent to the vegetated plots, employing identical instruments and methods to ensure comparability. Precautionary measures were taken to minimize vibrations and human disturbances, with instruments calibrated before each measurement session. An additional reference point was established at the Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, for further calibration checks and data verification.

Noise-reduction capability was assessed using a handheld sound level meter. A consistent noise was emitted 20 m outside the sample plot. Noise levels were measured at the plot center and at midpoints along the four edges of the forest community.

The operating hours of all instruments spanned from 9:00 a.m. to 5:00 p.m. local time. Measurements were conducted under clear and calm weather conditions.

2.5. Quantification of Functions

2.5.1. Carbon Sequestration

Carbon sequestration was evaluated using the carbon-assimilation method. Literature was reviewed to determine the average daily carbon sequestration rates per unit leaf area for various local tree species. Leaf area was ascertained through direct measurement and subsequent calculations. The annual carbon sequestration for each individual tree was calculated by multiplying the leaf area by the daily sequestration rate per unit leaf area and then by the growth season duration of 120 days. The total annual carbon sequestration per plot represents the cumulative sequestration from all trees and herbs within the plot. For the herbaceous layer, carbon sequestration was estimated by multiplying biomass by a carbon coefficient of 0.45 [27].

2.5.2. Rainwater Interception

This study employed both field measurements and laboratory experiments to assess the water-holding capacity of branches and leaves from greening tree species in Changchun. Interception capacity was calculated by multiplying the average water-holding capacity of the collected branches and leaves with the total tree biomass and then dividing by the biomass of the collected branches and leaves. The total interception for each plot encompasses the combined interception capacities of all trees and the herbaceous layer, which was calculated in a manner analogous to that of the trees.

2.5.3. Temperature Reduction and Humidity Increase

Urban ecological studies often face challenges in synchronizing data collected at various times and locations. To address this, data were normalized using a reference point as a baseline for comparison. The temperature and humidity data from all experimental and control points were normalized using this method. The normalized temperature, T_ni(t), was calculated using the following formula:

T_ni(t) = T_i(t) × T_rea(t)/T_rei(t)

(1)

where T_i(t) represents the temperature recorded at the plot at time t, T_rea(t) is the average temperature at the reference point within the survey period, and T_rei(t) is the temperature recorded at the reference point at time t. This normalization facilitates meaningful comparisons between plots, thereby enhancing the interpretability and analytical capability of the ecological data [28,29].

Subsequently, the normalized data were used to calculate the mean temperature and humidity for each plot across all measurement periods. The temperature reduction rate was calculated by first subtracting the average temperature within the community from the average temperature at the control point and then dividing this difference by the average temperature at the control point. Similarly, the humidity enhancement rate was calculated by first subtracting the average humidity of the control point from the average humidity within the community and then dividing this difference by the average humidity of the control point.

2.5.4. Particulate Matter Reduction

The reduction rates for particulate matter (PM), including PM2.5, PM10, and total suspended particles, were calculated by subtracting the concentrations observed within the community from those at the control point and then dividing these differences by the control point concentrations. The average dust reduction efficiency was then determined by averaging the reduction rates across these particulate sizes.

2.5.5. Noise Reduction

The average noise reduction rate was determined by first subtracting the noise levels at four different points within the community from the community’s maximum noise level and then summing these differences. This sum was then divided by the maximum noise level in the community and finally divided by 4.

2.5.6. Water Conservation

Water conservation functionality was assessed through the measurements of soil moisture content.

2.6. Quantification of Multifunctionality

This study computed the values for various functions. These function indices were then standardized using interval scaling, setting the standardized values between 1 and 2, as per the following formula:

(x − min(x))/(max(x) − min(x)) + 1

(2)

The multifunctionality index was calculated as the mean of these standardized values, using the following formula:

$$M={\displaystyle \frac{1}{F}}{\sum }_{i=1}^{F}fi$$

(3)

where M denotes the multifunctionality index, F is the total number of functions assessed, and f_i represents the standardized value for the i th function. This approach facilitates a comprehensive assessment of ecological performance across diverse functions.

2.7. Quantification of Urban Forest Configuration

2.7.1. Assortment Indices

Contemporary research within the fields of ecology and forestry predominantly concentrates on elucidating the role of biodiversity in determining multifunctionality. However, we have noted that classical diversity indices, which assess species richness and abundance from an individual-based perspective, tend to disproportionately highlight the functional contributions of smaller individuals within a community. Communities are intricate ecological entities, and summarizing them solely with diversity indices can result in substantial information loss. To address this limitation, we propose the use of assortment indices as a more comprehensive measure. These indices are derived from classical diversity indices, but they substitute relative importance values for relative abundance in their calculations. The relative importance values were calculated by averaging four key metrics: relative species abundance, relative height, relative basal area, and relative crown area. The relative importance value’ metric offers a more precise representation of a species’ role and contribution within a community than merely relying on relative abundance. Consequently, the adoption of importance values instead of relative abundance for the computation of indices markedly improves the precision and thoroughness of these assessments.

In this study, we developed “assortment indices” from classical diversity metrics—Shannon, Simpson, and Pielou—to explore their correlation with the multifunctionality of urban forest communities. Assortment indices illustrate the extent to which different species occupy varying amounts of space and resources within a community.

In community configurations, species exhibit varying levels of importance. This prompts an exploration into the relationship between species importance and multifunctionality, specifically addressing whether dominance by a single species enhances functionality more than when multiple species contribute equally. Therefore, we employed the mean and standard deviation of the relative importance values to elucidate interspecies interactions within the communities. The mean importance values of species provide a comprehensive description of species importance. Specifically, these values reflect the average relative importance of each species throughout the community. The standard deviation of the species’ relative importance values highlights the differences in spatial and resource occupancy among the species.

2.7.2. Comprehensive Configuration Indices

Acknowledging the cohesive composition of communities, which encompass diverse species, we developed comprehensive configuration indices that highlight these intraspecific configurations without compromising the overall integrity of the community.

The community’s comprehensive configuration index was computed using the following equation:

$$C=\sqrt{{\sum }_{i=1}^S{\left(PiRi\right)}^2}$$

(4)

where C denotes the comprehensive configuration index; S represents the total number of species in the forest community; P_i is determined by computing the mean and diversity across multiple metrics, including tree height, diameter at breast height, crown width, first branch height, and crown light exposure within the community; and R_i represents a weighted measure using the importance values of different species within their respective plots.

2.8. Statistical Analysis

In this study, all analyses and visualizations were conducted using R version 4.3.3. Data preprocessing employed the “tidyverse” version 2.0.0 and “doBy” version 4.6.22 packages. Community functional clustering was performed using the “factoextra” version 1.0.7 package by K-means cluster analysis. Species-function relationships were depicted using heatmaps created with the “Pheatmap” version 1.0.12 package. The relationship between assortment indices and comprehensive indices of multifunctionality was explored through linear regression analyses conducted using “ggplot2” version 3.5.1, “ggpmisc” version 0.6.0, and “patchwork” version 1.2.0 for both analysis and graphical representation. Cumulative bar plots, illustrating the relative importance of species within these communities, were created using “ggplot2”.

3. Results

3.1. Forest Multifunctionality Classification

Forest plots were categorized into five levels of functional intensity through separate cluster analyses based on either individual functions or multifunctionality. These categories—high (A), adequate (B), moderate (C), limited (D), and low (E)—define a spectrum of functionality and set the stage for further research. The clustering results are shown in

Table 1. Clustering analysis of functionality in urban forest communities. A–E represent functional intensity from strong to weak. Each value represents the percentage of forest plots within each category.

Category	A	B	C	D	E
Multifunctionality	5.91	16.88	9.70	35.86	31.65
Noise reduction	2.53	8.86	21.94	37.55	29.11
PM reduction	7.17	17.30	7.17	27.00	41.35
Water conservation	10.13	28.69	30.38	18.14	12.66
Humidity increase	4.22	8.44	17.72	40.51	29.11
Temperature reduction	4.64	15.19	20.68	36.29	23.21
Rainwater interception	41.77	24.89	17.30	12.24	3.80
Carbon sequestration	50.21	30.80	14.35	3.80	0.84

.

3.2. The Relationship between Species Composition and Forest Functions

Using results from cluster analysis, we assessed the relative importance of each species within designated functional groups. Our examination specifically targeted tree and shrub species unique to communities characterized by high functional intensity (Categories A and B) and identified these species as recommended. We selected tree and shrub species that enhance either individual or multifunctional attributes, as depicted in Figure 2. Furthermore, we compiled tables listing the top 15 species across each functional category, as presented in Appendix A,

Table A1. Top 15 species for carbon sequestration.

	Accepted Name	Category/Frequency
		E	D	C	B	A
Carbon sequestration	Pinus tabulaeformis	17.95%	8.07%	2.48%	2.07%
	Armeniaca mandshurica	8.09%	2.20%
	Betula platyphylla	5.87%		1.84%
	Picea asperata	5.74%	9.60%	3.75%
	Ulmus pumila	5.22%	5.40%	9.22%		0.33%
	Juglans mandshurica	3.90%
	Populus davidiana	3.74%	11.21%	16.50%	41.49%
	Prunus ussuriensis	3.44%
	Padus racemosa	3.42%	5.06%	1.97%	0.55%
	Amygdalus davidiana	2.79%	2.58%
	Populus cathayana	2.48%	2.66%	5.32%	5.77%
	Acer truncatum	2.40%	2.11%
	Quercus mongolica	2.02%	6.75%
	Crataegus pinnatifida	1.94%			1.76%
	Acer triflorum var. triflorum	1.92%
	Salix matsudana		5.81%	10.33%
	Fraxinus mandschurica		3.33%
	Phellodendron amurense		3.08%	2.79%
	Robinia pseudoacacia		2.48%
	Albizia kalkora		2.21%
	Populus alba × P. Berolinensis			16.28%	11.89%
	Salix babylonica			13.20%
	Ulmus densa			3.04%	8.05%
	Populus canadensis			1.79%	16.25%	98.55%
	Catalpa ovata			1.55%
	Tilia mandshurica var. mandshurica			1.25%
	Populus alba				5.06%
	Populus tomentosa				3.65%
	Amygdalus triloba				1.63%
	Spiraea thunbergii				0.81%
	Syringa oblata				0.26%
	Syringa pubescens subsp. microphylla				0.17%
	Juniperus formosana				0.13%
	Sambucus williamsii					0.94%
	Ligustrum quihoui					0.17%

,

Table A2. Top 15 species for rainwater interception.

	Accepted Name	Category/Frequency
		E	D	C	B	A
Rainwater interception	Armeniaca mandshurica	8.41%	3.54%	1.71%
	Betula platyphylla	7.59%		1.31%
	Quercus mongolica	7.40%	3.84%
	Padus racemosa	5.77%	3.62%	2.98%		1.66%
	Amygdalus davidiana	5.68%
	Pinus tabulaeformis	5.68%	10.38%	7.78%	11.74%	13.85%
	Prunus ussuriensis	4.14%
	Fraxinus mandschurica	3.81%
	Maackia amurensis	3.60%
	Juglans mandshurica	3.20%	2.06%			0.24%
	Picea asperata	3.14%	4.63%	10.08%	10.12%
	Populus cathayana	3.05%	5.29%	5.30%
	Albizia kalkora	2.70%
	Ulmus pumila	2.64%	2.84%	8.26%	6.58%	11.66%
	Crataegus pinnatifida	2.40%			1.70%
	Populus davidiana		21.48%	13.33%	18.16%
	Salix matsudana		5.13%	9.54%	4.13%
	Robinia pseudoacacia		3.40%
	Acer triflorum var. triflorum		3.25%
	Populus alba × P. Berolinensis		2.10%	9.15%	13.07%
	Phellodendron amurense		2.05%	3.26%		4.48%
	Malus baccata		1.65%			0.35%
	Salix babylonica			8.46%	7.41%
	Acer truncatum			2.79%	2.19%
	Acer pictum subsp. mono			2.52%
	Populus canadensis			1.83%	7.51%	47.07%
	Ulmus densa				2.45%	6.73%
	Populus alba				2.16%
	Populus tomentosa				1.52%
	Catalpa ovata				1.19%
	Platycladus orientalis				1.16%
	Larix gmelini					6.85%
	Pinus sylvestris var. sylvestriformis					4.72%
	Lonicera maackii					0.89%
	Sambucus williamsii					0.48%
	Euonymus maackii					0.36%
	Syringa oblata					0.24%
	Rosa xanthina					0.16%

,

Table A3. Top 15 species for temperature reduction.

	Accepted Name	Category/Frequency
		E	D	C	B	A
Temperature reduction	Populus davidiana	11.42%	7.28%	21.38%	8.60%	14.50%
	Pinus tabulaeformis	11.03%	7.20%	8.65%	11.04%	11.22%
	Picea asperata	7.52%	3.74%	4.92%	10.81%	0.60%
	Betula platyphylla	7.18%	2.31%
	Populus alba × P. Berolinensis	6.89%	5.36%	3.68%		13.24%
	Populus canadensis	5.87%	8.39%		12.95%
	Fraxinus mandschurica	4.16%
	Salix babylonica	3.71%	5.88%	1.71%	2.44%
	Padus racemosa	3.62%	4.04%	2.58%	2.33%	2.73%
	Quercus mongolica	3.52%	3.37%		4.49%
	Salix matsudana	2.77%	2.35%	6.08%	10.23%
	Acer truncatum	2.55%			1.75%
	Maackia amurensis	2.46%
	Ulmus pumila	2.40%	4.66%	9.10%	5.66%	5.92%
	Acer pictum subsp. mono	2.30%
	Armeniaca mandshurica		5.29%	3.86%	2.59%
	Populus cathayana		5.10%		3.90%	8.05%
	Amygdalus davidiana		2.58%	2.26%
	Phellodendron amurense		2.21%	2.14%		1.42%
	Ulmus densa			4.32%
	Robinia pseudoacacia			3.70%
	Juglans mandshurica			2.37%		1.68%
	Prunus ussuriensis			1.81%
	Crataegus pinnatifida				3.39%
	Pinus sylvestris var. sylvestriformis				2.92%
	Acer triflorum var. triflorum				2.08%
	Albizia kalkora					11.38%
	Larix gmelini					8.80%
	Malus baccata					7.63%
	Malus pumila					3.63%
	Ulmus pumila ‘Tenue’					1.97%
	Ligustrum quihoui					0.79%

,

Table A4. Top 15 species for humidity increase.

	Accepted Name	Category/Frequency
		E	D	C	B	A
Humidity increase	Populus davidiana	16.07%	10.05%	11.97%	9.52%	3.81%
	Pinus tabulaeformis	9.18%	8.61%	15.03%	4.46%
	Picea asperata	6.05%	4.85%	3.43%	18.52%	0.57%
	Populus alba × P. Berolinensis	5.88%	6.33%		8.50%
	Betula platyphylla	5.79%			1.71%
	Populus cathayana	5.63%	2.55%		4.95%
	Salix babylonica	5.10%	3.84%	3.58%
	Populus canadensis	4.32%	7.71%	2.02%		38.84%
	Quercus mongolica	3.60%	2.75%	2.07%	4.87%
	Padus racemosa	3.23%	3.72%	3.69%	2.48%
	Salix matsudana	2.88%	4.00%	4.86%	9.69%	5.95%
	Armeniaca mandshurica	2.49%	4.19%	4.04%	4.18%
	Fraxinus mandschurica	2.25%		2.00%
	Acer truncatum	2.15%		3.61%
	Maackia amurensis	1.98%
	Ulmus pumila		5.07%	12.11%	6.11%	6.09%
	Amygdalus davidiana		4.01%
	Ulmus densa		3.31%
	Phellodendron amurense		2.26%	2.40%		3.12%
	Robinia pseudoacacia			4.84%
	Acer triflorum var. triflorum			1.94%
	Crataegus pinnatifida				6.18%
	Pinus sylvestris var. sylvestriformis				5.91%
	Prunus virginiana				3.49%
	Juglans mandshurica				3.39%	1.61%
	Albizia kalkora					10.98%
	Larix gmelini					8.38%
	Malus baccata					7.38%
	Malus pumila					3.50%
	Ulmus pumila ‘Tenue’					1.90%
	Sambucus williamsii					1.18%
	Ligustrum quihoui					0.68%
	Berberis ferdinandicoburgii					0.51%

,

Table A5. Top 15 species for PM reduction.

	Accepted Name	Category/Frequency
		E	D	C	B	A
PM reduction	Pinus tabulaeformis	13.06%	5.36%	4.03%	4.34%	11.76%
	Populus davidiana	7.67%	17.55%	16.83%	22.31%	4.24%
	Ulmus pumila	7.54%		6.51%	4.78%	8.20%
	Populus canadensis	7.23%	12.37%
	Picea asperata	5.39%	2.91%	22.54%	13.77%	2.95%
	Salix matsudana	4.11%	4.34%	10.02%	3.52%	3.50%
	Salix babylonica	4.01%	2.92%	1.14%	9.25%	2.67%
	Padus racemosa	3.76%	2.10%	3.05%		5.62%
	Phellodendron amurense	3.25%
	Betula platyphylla	2.91%	2.36%			4.24%
	Armeniaca mandshurica	2.45%	4.32%	6.16%		4.42%
	Juglans mandshurica	2.42%			2.33%
	Populus alba × P. Berolinensis	2.40%	8.23%			8.25%
	Amygdalus davidiana	2.22%		1.91%	7.13%
	Fraxinus mandschurica	1.98%			3.26%
	Populus cathayana		5.04%	6.39%	5.53%
	Robinia pseudoacacia		2.80%
	Quercus mongolica		2.77%	2.75%	4.12%	6.10%
	Acer truncatum		2.43%		3.33%
	Larix gmelini		2.30%
	Ulmus densa			5.81%		3.72%
	Ulmus pumila ‘Tenue’			1.75%
	Syringa reticulata var. amurensis			1.55%
	Malus pumila			0.95%
	Prunus ussuriensis				3.23%
	Pinus sylvestris var. mongolica				2.20%
	Crataegus pinnatifida				2.07%
	Maackia amurensis					3.12%
	Pinus sylvestris var. sylvestriformis					2.77%
	Populus alba					2.66%

,

Table A6. Top 15 species for noise reduction.

	Accepted Name	Category/Frequency
		E	D	C	B	A
Noise reduction	Populus davidiana	14.74%	5.08%	20.16%	8.14%	28.08%
	Pinus tabulaeformis	9.80%	6.32%	6.03%	27.27%	11.90%
	Populus alba × P. Berolinensis	7.83%	6.13%
	Picea asperata	6.58%	5.58%	4.35%	3.65%	21.68%
	Salix babylonica	4.20%	3.67%		11.33%
	Betula platyphylla	3.87%			3.52%	15.15%
	Salix matsudana	3.68%	5.61%	4.37%	2.11%
	Padus racemosa	3.51%		6.72%	2.19%
	Amygdalus davidiana	3.48%		2.50%
	Populus cathayana	3.27%	4.57%		2.00%
	Ulmus pumila	3.08%	5.93%	5.35%	11.18%	0.67%
	Ulmus densa	2.87%		1.96%
	Acer triflorum var. triflorum	2.85%
	Juglans mandshurica	2.71%
	Armeniaca mandshurica	2.65%	4.51%	2.71%	2.11%	5.92%
	Populus canadensis		15.20%
	Quercus mongolica		4.64%			1.59%
	Phellodendron amurense		2.54%	2.08%		0.86%
	Acer truncatum		2.42%
	Prunus ussuriensis		1.98%		1.42%
	Fraxinus mandschurica		1.90%
	Robinia pseudoacacia			3.93%
	Malus baccata			3.31%		1.11%
	Albizia kalkora			2.93%
	Acer ginnala subsp. ginnala			2.58%
	Pinus sylvestris var. mongolica			1.96%
	Maackia amurensis				2.98%
	Platycladus orientalis				2.80%
	Syringa reticulata var. amurensis				2.62%
	Armeniaca sibirica				2.11%
	Prunus cerasifera f. atropurpurea					6.75%
	Forsythia mandschurica					1.22%
	Syringa oblata					0.94%
	Xanthoceras sorbifolia					0.91%
	Physocarpus amurensis					0.85%
	Ulmus pumila ‘Jinye’					0.76%

,

Table A7. Top 15 species for water conservation.

	Accepted Name	Category/Frequency
		E	D	C	B	A
Water conservation	Pinus tabulaeformis	14.29%	4.38%	10.79%	6.92%	11.97%
	Populus davidiana	11.35%	21.36%	11.72%	8.32%	8.79%
	Populus alba × P. Berolinensis	9.12%	1.97%	5.80%	3.29%	6.75%
	Picea asperata	8.31%	10.32%	3.40%	6.55%
	Ulmus pumila	5.67%	3.57%	3.72%	8.38%	4.22%
	Armeniaca mandshurica	4.96%	6.25%	2.02%	3.10%	3.01%
	Padus racemosa	4.29%	4.60%		3.20%	5.47%
	Populus cathayana	3.91%	4.44%	2.33%	2.66%	5.85%
	Salix matsudana	3.36%	10.30%	2.48%	4.88%
	Acer truncatum	3.31%			2.92%
	Betula platyphylla	3.28%	3.03%		5.43%
	Armeniaca sibirica	2.77%
	Amygdalus davidiana	2.70%			3.48%
	Amygdalus triloba	1.79%
	Platycladus orientalis	1.75%
	Maackia amurensis		2.62%	1.94%
	Juglans mandshurica		2.32%			2.88%
	Quercus mongolica		1.99%	4.22%	4.30%
	Crataegus pinnatifida		1.89%
	Padus maackii		1.55%
	Populus canadensis			16.30%	4.51%
	Salix babylonica			3.55%	5.18%	9.09%
	Robinia pseudoacacia			2.74%
	Prunus ussuriensis			1.77%
	Acer triflorum var. triflorum			1.66%
	Fraxinus mandschurica					6.52%
	Ulmus densa					6.32%
	Phellodendron amurense					5.80%
	Albizia kalkora					5.61%
	Pinus sylvestris var. sylvestriformis					4.44%
	Larix gmelini					2.14%

and

Table A8. Top 15 species for multifunctionality.

	Accepted Name	Category/Frequency
		E	D	C	B	A
Multifunctionality	Populus davidiana	15.30%	14.31%	6.17%	10.10%
	Pinus tabulaeformis	9.39%	9.01%	10.56%	6.42%	13.21%
	Populus alba × P. Berolinensis	7.54%	4.12%		4.24%	11.96%
	Picea asperata	7.08%		5.72%	12.40%	4.14%
	Armeniaca mandshurica	5.12%	3.24%		4.31%
	Padus racemosa	4.07%		1.73%	5.96%	2.46%
	Salix babylonica	3.56%	5.87%	2.51%	1.87%
	Ulmus pumila	3.50%	3.12%	13.00%	5.82%	6.88%
	Salix matsudana	2.99%	2.57%	9.75%	6.45%	2.00%
	Betula platyphylla	2.91%	2.98%		3.50%	2.56%
	Robinia pseudoacacia	2.90%
	Populus cathayana	2.32%	4.46%		4.30%	7.50%
	Acer truncatum	2.21%
	Quercus mongolica	1.75%	2.69%	2.61%	6.74%
	Tilia mandshurica var. mandshurica	1.63%
	Populus canadensis		6.00%	30.19%
	Juglans mandshurica		2.95%			2.05%
	Amygdalus davidiana		2.93%	1.23%
	Phellodendron amurense		2.76%	3.52%
	Fraxinus mandschurica		2.35%
	Crataegus pinnatifida			3.29%
	Larix gmelini			1.53%		8.24%
	Catalpa ovata			1.39%
	Amygdalus triloba			0.95%
	Ulmus densa				5.76%
	Populus alba				2.71%
	Acer triflorum var. triflorum				2.30%
	Albizia kalkora					10.34%
	Pinus sylvestris var. sylvestriformis					8.36%
	Malus baccata					6.90%
	Malus pumila					3.31%
	Ulmus pumila ‘Tenue’					1.78%

.

In this study, we observed that the proportion of tree species recommended for high-functionality categories is only slightly higher than that for low-functionality categories. Nevertheless, the average number of ecological functions supported by species within high-functionality categories is significantly greater, amounting to 2.73 compared to 1.69 in low-functionality categories (see Figure 3 and Figure 4).

3.3. The Relationship between Assortment Indices and Multifunctionality

Despite the distinct roles that various species play in different functions, we observed that many species, even those with similar levels of importance, fall into widely disparate categories of multifunctionality. This suggests that while species composition influences multifunctionality, it is not the sole determinant; rather, it interacts with additional variables.

We explored the correlation between assortment indices and the multifunctionality of urban forest communities using linear regression. The analysis revealed a negative correlation between these indices and the multifunctionality of these communities (Figure 5). Additionally, we used linear regression to analyze how mean importance values and standard deviation relate to multifunctionality. The results demonstrate that increases in these statistical measures are associated with enhanced multifunctionality in urban forest communities (Figure 5).

Furthermore, we employed partial parameters of relative importance values, namely, relative species abundance, relative height, relative basal area, and relative crown area, in similar calculations. The results align with assortment indices and their relationship with multifunctionality (Figure A2).

3.4. The Relationship between Comprehensive Configuration Indices and Multifunctionality

In our preceding research, we examined the correlation between species importance and multifunctionality within a community. Subsequently, we conducted a more detailed exploration of how interspecies interactions within communities impact multifunctionality. Furthermore, we analyzed the relationship between intraspecific configurations and multifunctionality.

We performed linear regression with comprehensive indices as independent variables and multifunctionality as the dependent variable. The analysis revealed positive correlations between tree height diversity, first branch height diversity, crown width diversity, and multifunctionality. Additionally, the average crown light exposure was positively associated with multifunctionality (Figure 6).

4. Discussion

4.1. Species-Specific Strategies to Enhance Urban Forest Multifunctionality

Urban forests typically develop from either modified natural forests or wholly artificial mass plantings. Human intervention primarily determines the diversity within these ecosystems. Although studies, such as those by Hutt-Taylor and Ziter [30], sometimes report diversity in urban forests that exceeds natural forest levels, their ecological functionality usually does not meet expectations. The relative importance value measures a species’ spatial presence and resource utilization within a community arising from resource distribution and interspecies competition. Research by Winfree et al. [31] and Garnier et al. [32] suggests that a species’ ecological impact is proportional to its prevalence in the community. Species with high importance values are critical for maintaining forest functionality; they form the foundation and deliver essential ecological functionality, as documented by Smith et al. [33] and Smith and Knapp [34]. Therefore, increasing the relative importance value of these key species enhances the overall functionality of the forest community, corroborating Grime’s [35] biomass ratio hypothesis.

Aligning with the existing literature, our findings demonstrate the absence of any “super species” capable of excelling in all ecological functions simultaneously [36,37]. Therefore, we argue that the species composition within a community is pivotal for its overall functionality. The role of species identities in predominantly determining their functional capacities within ecosystems is corroborated by empirical evidence from diverse geographical regions [26,38,39,40,41,42,43]. This includes research on unmanaged mature forest plots in the temperate forests of Central Belgium [38], tropical planted forests in Sardinilla, Panama [40], and field experiments at the Wuhan Botanical Garden in Central China [43]. Collectively, these studies affirm the generalizability of our findings across varied climatic and ecological conditions, highlighting the intrinsic link between species-specific traits and ecosystem functions.

Furthermore, we examined species characterized by high relative importance values, commonly associated with communities exhibiting robust functionality.

These species play a critical role in enhancing various ecological functions. Specifically, for carbon sequestration, we advocate the use of fast-growing Populus paired with native shrubs. For effective rainwater interception, a blend of Pinus and Populus, supplemented with shrubs, is recommended. Additionally, to reduce atmospheric particulate matter, combining Pinus with local medium-sized broadleaf trees is advised. To promote temperature-reduction and humidity-increase effects, the incorporation of large and medium-sized broadleaf trees alongside shrubs is optimal. For water conservation, the primary recommendation includes Salix and Pinus. Regarding noise reduction, shrubs and small trees have been shown to be especially efficacious (refer to Figure 2, Table A1, Table A2, Table A3, Table A4, Table A5, Table A6, Table A7 and Table A8). Overall, species that enhance multifunctionality tend to support several ecological functions simultaneously. In this study, all recommended species are native, reflecting their enhanced ecological functionality compared to non-native counterparts. Despite the potential for high growth rates and aesthetic appeal, non-native species do not consistently offer comparable ecological benefits. Native species demonstrate superior adaptation to local soil, climate, and ecosystem conditions, thereby significantly improving their functionality within local ecosystems. This selective approach can be instrumental in achieving more sustainable urban environments where ecological functions are enhanced.

4.2. Understanding Assortment Indices and Their Impact on Urban Forest Multifunctionality

In our study, we noted that an increase in assortment indices correlates with a decline in multifunctionality. High-functionality communities are generally composed of one to two species with high relative importance values and a moderate variety of other species. Conversely, communities with low functionality are characterized by numerous species with low relative importance values (see Figure 3 and Figure 4). This pattern indicates that a rise in low relative importance values species does not contribute to increased multifunctionality.

Furthermore, this phenomenon can be explained by the biomass ratio hypothesis, ecological niche overlap, and asymmetric competition [44]. In forest communities, higher assortment indices indicate a greater diversity of tree species, which can lead to more complex competition or resource utilization patterns. Such complexity may result in uneven resource distribution or overlapping resource demands, thereby diminishing the overall efficiency of the community. For example, if different tree species exploit similar resources in overlapping manners, this could intensify resource competition, adversely affecting forest functionality. Moreover, in plant competition, species with larger relative importance values often disproportionately dominate the competitive resources, which can inhibit the growth of neighboring plants with smaller relative importance values. This suggests that while the assortment index inappropriately increases, it can lead to reduced multifunctionality due to increased competition and niche overlap. Therefore, urban forest management should focus on the functional compatibility of species to ensure harmony and efficiency.

4.3. Intraspecific Structural Configurations Strategies to Enhance Urban Forest Multifunctionality

Our study introduces comprehensive configuration indices that emphasize the pivotal role of intraspecific structural configurations among high-value species within communities. In forest communities, variations in tree characteristics such as height, diameter at breast height, crown width, and first branch height significantly bolster multifunctionality. This enhancement aligns with recent urban forestry research [45,46]. Urban forestry practices typically involve batch-based plantings of age-similar trees, which curtail intraspecific diversity. Consequently, this practice results in a diversity of three-dimensional traits within the same species, creating complex patterns of size and spatial distribution. These patterns facilitate a stratified forest structure that not only improves light capture and utilization through niche differentiation but also boosts the efficiency of resource use among community woody plants. This includes the optimized use of light, water, and soil, known as niche complementarity effects [47,48,49,50,51,52,53]. Furthermore, Forrester and Bauhus [54] suggest that such complementary effects are maximized in mixed-species forests with relatively uniform species distributions. These observations support our findings of a positive correlation between average relative importance values and our indices, thereby enhancing multifunctionality.

Increased average crown light exposure significantly enhances forest multifunctionality by directly improving light capture and utilization. However, in urban forests characterized by high-density, large-scale plantings, only the peripheries and tops of the tree canopies receive direct sunlight. This configuration results in diminished overall light reception within the community. Therefore, integrating diverse intraspecific configurations can foster a more stratified and functional urban forest, effectively addressing challenges associated with light distribution and resource competition. By varying tree heights, crown widths, and other structural characteristics within the same species, urban forests can develop a more intricate three-dimensional structure. This enhanced complexity promotes improved light penetration and ameliorates microclimates across different forest strata, potentially augmenting ecosystem functions.

4.4. Ecological Insights and Management Strategies for Urban Forests

Additionally, our findings and discussions may provide interconnected evidence for several classical ecological hypotheses. Our results offer some supplementation to the rivet poppers hypothesis, which posits that each species in an ecosystem is like a rivet in an airplane, suggesting that the removal of key species could potentially lead to a systemic collapse [55]. Nonetheless, the degree of impact resulting from species loss varies based on the species’ critical role within the ecosystem. Alternative species may help maintain ecosystem functionality, thus aligning with both the Redundancy Hypothesis and the Insurance Hypothesis, particularly when the removed species is not a key or keystone species.

Drawing from these conclusions, we propose actionable strategies for optimizing community composition in forest management. Initial species selection should prioritize their specific functional contributions. Enhancing a single ecological function necessitates the prioritization of dominant species that are highly effective in that capacity. To achieve multifunctionality, a balanced composition of species is essential, enabling them to complementally deliver various ecological functions. However, it is crucial to note that merely increasing species diversity does not inherently enhance outcomes. Effective management is imperative to foster the balanced growth of beneficial species, optimizing both growth and ecological performance across the community.

Our study presents recommendations for local tree species, as outlined in Figure 2 and Table A1, Table A2, Table A3, Table A4, Table A5, Table A6, Table A7 and Table A8. It is vital to evaluate the adaptability of these species across different geographic areas to ensure their appropriate use. Additionally, intraspecific structural configurations can be improved by integrating trees from various age classes within a single species. Moreover, the combination of shade-tolerant and sun-loving species optimizes light absorption and utilization.

The pursuit of multifunctionality in urban forests is essential for addressing both environmental and social needs. However, it is critical to balance these objectives with the preservation of core ecological functions that sustain the inherent natural value of these ecosystems. To achieve this balance, we recommend the following strategies: (1) Functional Zoning: forest areas segmented into distinct functional zones—including conservation and multifunction zones—enable the safeguarding of core ecological functions while facilitating multifunctionality in designated areas. (2) Comprehensive Assessment: employing rigorous assessment methods to discern and quantify potential trade-offs among forest functions is vital for sustaining fundamental ecological values. This comprehensive evaluation aids in optimizing management decisions that balance ecological sustainability with functional utility, ensuring that functional objectives are met without compromising core ecological values. (3) Long-term Monitoring: the establishment of a robust long-term monitoring framework is crucial for assessing the impacts of forest management on ecosystem health. Such a framework supports adaptive management by providing data-driven insights that guide strategic adjustments in line with ecological health indicators and sustainability objectives. (4) Participatory Management: involving community stakeholders and ecological experts in crafting forest management strategies promotes the integration of a wide range of perspectives. This inclusive approach ensures that both ecological values and multifunctional needs are considered, facilitating ongoing assessments of management effectiveness. Regular evaluations help maintain ecological impacts within acceptable limits, thereby advancing sustainable forest stewardship.

The proposed management strategies for urban forests enhance ecological functions and improve the health of urban ecosystems, positively affecting societal well-being. By selecting species that adapt well to local conditions, these forests can better endure climate change impacts, such as higher temperatures and more frequent storm events, thereby strengthening urban resilience. These improvements stabilize environments by mitigating the urban heat island effect and elevating air quality, which directly benefits public health. Furthermore, well-managed urban forests serve as recreational areas that boost both the mental and physical health of city residents, fostering stronger social bonds and enhancing civic pride. Including native tree species enriches these green spaces, culturally and educationally, turning them into active hubs for studying biodiversity, conservation, and management. Integrating these species and configurations is crucial for ecological sustainability and supports a holistic approach to building resilient urban communities, enhancing both cultural and scientific literacy.

5. Conclusions

In conclusion, our research underscores the critical role of community configurations in enhancing the multifunctionality of urban forests. We introduced assortment indices derived from traditional diversity metrics, revealing that optimizing forest multifunctionality requires a strategic composition of species beyond mere diversity. Communities with a moderate number of species having high relative importance values demonstrate greater ecological functionality than those with many species exhibiting low relative importance values. Furthermore, we developed comprehensive indices to assess internal species configurations within these communities, highlighting the importance of intraspecific structural configurations. Our findings emphasize the complementary effects of mixed-species plantings in urban forestry management. Finally, we recommend integrating ecological theory with practical management strategies to improve the multifunctionality of urban forests. This approach involves selecting tree species based on their functional benefits and complementary potentials. Essential strategies include implementing uneven-aged plantings combining shade-tolerant and sun-loving species.