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Article

Legacy and Luxury Effects: Dual Drivers of Tree Diversity Dynamics in Beijing’s Urbanizing Residential Areas (2006–2021)

by
Xi Li
1,2,
Jicun Bao
1,
Yue Li
1,
Jijie Wang
1,
Wenchao Yan
1 and
Wen Zhang
1,*
1
Sichuan Provincial Forestry and Grassland Engineering Research Center of Natural Forest Protection and Restoration, Sichuan Forestry and Grassland Survey and Planning Institute, Chengdu 610036, China
2
Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
*
Author to whom correspondence should be addressed.
Forests 2025, 16(8), 1269; https://doi.org/10.3390/f16081269
Submission received: 28 June 2025 / Revised: 23 July 2025 / Accepted: 24 July 2025 / Published: 3 August 2025
(This article belongs to the Section Urban Forestry)
Browse Figures
Versions Notes

Abstract

Numerous studies have demonstrated that in residential areas of Western cities, both luxury and legacy effects significantly shape tree species diversity dynamics. However, the specific mechanisms driving these diversity patterns in China, where urbanization has progressed at an unprecedented pace, remain poorly understood. In this study we selected 20 residential settlements and 7 key socio-economic properties to investigate the change trend of tree diversity (2006–2021) and its socio-economic driving factors in Beijing. Our results demonstrate significant increases in total, native, and exotic tree species richness between 2006 and 2021 (p < 0.05), with average increases of 36%, 26%, and 55%, respectively. Total and exotic tree Shannon-Wiener indices, as well as exotic tree Simpson’s index, were also significantly higher in 2021 (p < 0.05). Housing prices was the dominant driver shaping total and exotic tree diversity, showing significant positive correlations with both metrics. In contrast, native tree diversity exhibited a strong positive association with neighborhood age. Our findings highlight two dominant mechanisms: legacy effect, where older neighborhoods preserve native diversity through historical planting practices, and luxury effect, where affluent communities drive exotic species proliferation through ornamental landscaping initiatives. These findings elucidate the dual dynamics of legacy conservation and luxury-driven cultivation in urban forest development, revealing how historical contingencies and contemporary socioeconomic forces jointly shape tree diversity patterns in urban ecosystems.

1. Introduction

Residential green area plays a key role in urban forest, which is an indispensable public place and daily communication space in people’s life, as well as provides habitat for urban animals and plants [1,2]. However, residential green areas especially tree diversity has undergone dramatic changes in past 15 years due to urbanization effects. Understanding how the tree diversity of residential green area changes over time is crucial for sustainable urban forest.
Tree diversity is one of the most important components of residential green area, it provides multiple ecological benefits for human well-being and other creatures in urban areas [3]. Residential green areas with high tree diversity may provide multiple ecosystem services, including improving the ecological environment, regulating the microclimate, beautifying the environment, and maintaining ecological security [4,5]. And more tree diversity in residential green areas could contribute to biodiversity conservation by providing diverse habitat for more animal species and microorganism [6]. In addition, high tree diversity could maintain ecosystem resilience through reducing specific pathogens [7,8]. Urban forest is a human-created mix of native and exotic species, tree diversity is often affected by social and economic factors related to human activities. Two hypotheses have been proposed to help explain the effects of human activities on tree diversity in urban: (1) “luxury effect”: in urban ecosystems, tree diversity is closely related to human wealth, increasing with neighborhood income. In a certain period of time, higher income neighborhoods tend to have more diverse tree species, whereas the poorest neighborhoods tend to have less diverse ones [9,10,11,12,13,14,15]. Proximate drivers of the income-diversity relationship may include lifestyle-associated choices [16], social status maintenance through biodiverse landscape management in affluent neighborhoods [17,18,19], and elevated education levels linked to demand for tree-derived ecosystem services [11,20,21]. Luxury effect have been extensively recognized in numerous large cities, such as Phoenix, AZ, Los Angeles, CA, Vitoria-Gasteiz, BC [11,12,13,15]. (2) “legacy effect”: activities by prior land managers persist have affected current tree composition and diversity in urban areas. Older neighborhoods may have more diverse tree species due to legacies of evolving species availability and city policy [22,23]. Longer development periods enhance tree diversity by allowing extended successional processes, repeated establishment events of varied species by multiple property managers, and sufficient time for long-lived species to attain maturity [10,12,23]. Especially in older cities, relationships between socioeconomics and diversity may be shaped not only by affluence, but also by historical land-use legacies [24]. Since legacy effect can uphold biodiversity patterns driven by luxury effect over time, both luxury and legacy effects may result in additive responses [22].
In addition, population density and distance from an urban centre may reflect variation trend of tree species diversity over time [19]. These factors collectively contribute to the complex dynamics of urban tree diversity. However, we know little about the relative contribution of these factors to tree diversity, which constrains our understanding the variation trend of tree species diversity over time. Especially in China, where urbanization is proceeding at a high speed. Understanding the consequences of luxury and legacy effects on temporal patterns of urban tree diversity may contribute to better management practices and strategies towards environmental justice.
In study, we used data on tree species diversity from residential areas within the Fifth Ring Road of Beijing to evaluate the variation trend of tree species diversity over time and the underlying mechanisms. We analyse how luxury and legacy effects influence the temporal patterns of urban tree diversity in urban landscapes. We test the following hypotheses: the luxury effect hypothesis was stronger than the legacy effect hypothesis in affecting tree species diversity in China.

2. Materials and Methods

2.1. Study Area and Experimental Design

The study area is located in Beijing, the capital of the People’s Republic of China, which encompasses an area of 16,411 km2 and has a population of 21.542 million. Beijing is located in a warm, temperate, semi-humid area and has a monsoon-influenced humid continental climate. The average monthly temperature for the warmest month is 26.9 °C, whereas that for the coolest month is −2.9 °C; average annual precipitation is around 570 mm. The elevation of the city ranges from 20 to 2303 m (average: 43.5 m). Twenty residential settlements were selected using a stratified random approach based on Beijing’s administrative divisions and socioeconomic gradients (Table 1 and Figure 1). We surveyed all trees in each residential green area. Every tree (>2.5 cm diameter at breast height, DBH) was identified at the species level and its DBH, height, species, and species origin were recorded. All trees in the 20 residential settlements were first measured in 2006 and then remeasured in 2021 using the same methods.

2.2. Quantification of Tree Species Diversity

Species richness, Pielou’s species evenness, Simpson’s Index of Diversity, and the Shannon–Wiener index were calculated to describe tree diversity.
1
Species richness (R) indicates the number of species in the community:
R = S
2
Simpson’s Index of Diversity (D) reflects the concentration of species:
D = 1 − ∑ i = 1 s P i 2
3
The Shannon–Wiener index (H) reflects the diversity of species:
H = − ∑ i = 1 s P i ln P i
4
Pielou’s evenness (J) reflects the evenness of the abundance and coverage of different species in the community:
J = − ∑ i = 1 s P i ln P i / ln S
where S is the number of species and P i is the proportional abundance of species i.
Species richness quantifies taxonomic capacity, reflecting foundational biotic resources; the Shannon–Wiener index captures rare species information via entropy weighting, revealing niche differentiation; Simpson’s Index of Diversity focuses on dominant species resilience, diagnosing system stability; Pielou’s evenness measures abundance distribution equity, indicating anthropogenic homogenization. These four metrics constitute an orthogonal complementary framework.

2.3. Data Collection

A total of 7 variables—namely population density, distance from an urban center, housing age, housing prices, floor area ratio, greening rate, and property management fee—were chosen due to being representative of the main socioeconomic characteristics of the study site and having minimal collinearity. Population density is the number of registered residents per square kilometer within a residential community. Housing age refers to the year of construction completion for residential buildings. Housing prices refer to the average transaction price per square meter (CNY) for apartments in each community. Floor area ratio (FAR) is the ratio of total building floor area to the land area of the community. Greening rate indicates the percentage of community land covered by vegetation (grass, shrubs, trees). The property management fee is the monthly fee (CNY/m2) charged to residents for maintenance services. Finally, distance from an urban center refers to the straight-line geographic distance between the centroid of a residential community and the urban core. This distance was calculated using the “Near” tool in ArcGIS Pro 3.0.2, which employs the Euclidean distance algorithm to determine the linear distance between two points.
We used housing prices to estimate neighborhood income. All data came from field surveys administered in 2006 and 2021. Data on population density, housing age, housing prices, floor area ratio, greening rate, and property management fees were derived from 40 questionnaires. Questionnaire surveys were administered to the property management companies (PMCs) of the 20 residential communities (one survey per community) during two key phases: July–September 2006 and May–July 2021. Surveys were conducted at 9:00–11:30 AM on weekdays to ensure PMC staff availability, with each survey requiring approximately 45 min to complete. The template of the questionnaire can be found in the Supplementary Materials.

2.4. Statistical Analysis

A paired-samples t-test was used to evaluate the differences in total, native, and exotic tree species diversity between 2006 and 2021. Stepwise regression analysis was employed to test how population density, distance from an urban center, housing age, housing prices, floor area ratio, greening rate, and property management fees affect tree species diversity. Variance partitioning was used to quantify the relative importance of each factor to variation in tree diversity. All statistical analyses and plots were performed in R 4.4.1 with the ‘vegan’, ‘MASS’, ‘ggplot2′, ‘car’, and ‘relaimpo’ libraries/packages.

3. Results

3.1. Variations in Tree Diversity Between 2006 and 2021

Over the entire study area, of 7030 individual trees recorded in 2006, only 35% were exotic species and 65% were native species. 8233 individual trees recorded in 2021, 47% were exotic species and 53% were native. Of the 17 different tree species recorded between 2006 and 2021, 12 (71%) were exotic species and only 5 (29%) were native species. The newly introduced exotic tree species were mainly ornamental shrubs, especially in wealthier residential areas. Compared with 2006, the proportion of ornamental tree species had increased by 2% in 2012, but the proportion of edible tree species decreased by 22%.
Total, native, and exotic tree species richness was significantly higher (p < 0.05) in 2021 than in 2006 (Figure 2a). Between 2006 and 2021, average total, native, and exotic tree species richness across all residential settlements increased by 36%, 26%, and 55%, respectively. Similarly, the total and exotic tree species Shannon–Wiener index was significantly higher (p < 0.05) in 2021 than in 2006, but no significant difference was found in native tree species (Figure 2b). Between 2006 and 2021, the total and exotic tree species Shannon–Wiener index increased by 10% and 34%, respectively. As for Simpson’s index, we found higher values in 2021 than in 2006 for exotic tree species only (p < 0.05), with an increase of 20% (Figure 2c). However, there were no differences in Pielou’s evenness index for total, native, and exotic tree species in 2006 compared with 2021 for the entire city.

3.2. Key Factors Regulating the Variation in Tree Species Diversity

Temporal variation in total tree diversity across the residential areas within the Fifth Ring Road of Beijing was best explained by a combination of housing prices, housing age, and greening rate (Figure 3a,b). Housing prices as a determinant of total tree diversity were positively related to total species richness and the Shannon–Wiener index, explaining a large proportion of the total variation (Figure 3a,b). Total species richness and the Shannon–Wiener index increased with the greening rate (Figure 3a,b). Additionally, there was no significant correlation between the Shannon–Wiener index, total species richness and housing age (Figure 3a,b).
Housing age was the dominant driver regulating variation in native tree diversity, with higher native species richness at sites with older houses (Figure 3c). Housing prices were also a significant predictor of variation in native tree diversity. Native species richness significantly increased with increasing housing prices (Figure 3c).
Similarly, housing age and housing prices had important impacts on variation in exotic tree diversity: housing prices explained a greater proportion of the variance than housing age (Figure 3d,e). Housing prices had positive effects on exotic species richness and the Shannon–Wiener index (Figure 3,e); conversely, exotic tree species richness and the Shannon–Wiener index were significantly negatively related to housing age (Figure 3d,e). Distance from an urban center also explained a small proportion of the variation in exotic tree species richness, with the latter increasing with increasing distance from an urban center (Figure 3d).

4. Discussion

This study showed the change in tree diversity across residential areas within the Fifth Ring Road of Beijing over the course of a 15-year period, evaluating the role of the luxury effect and legacy effect in regulating temporal variation in total, native, and exotic tree diversity.

4.1. Changes in Tree Diversity over 15 Years

The process of urbanization increased tree species richness in Beijing from 2006 to 2021, as many cultivated species and some native species were imported. Our findings reveal a significant enhancement in total tree species diversity in the residential areas within Beijing’s Fifth Ring Road between 2006 and 2021, with marked increases observed in both species richness and the Shannon–Wiener index. This diversification trend appears predominantly driven by the proliferation of exotic species, as evidenced by significant increases in their species richness, Shannon–Wiener index, and Simpson’s dominance index. In contrast, native tree species exhibited remarkable compositional stability over the 15-year period, maintaining consistent diversity metrics (Shannon–Wiener: Δ + 2.3%, p = 0.34; Simpson’s: Δ + 1.8%, p = 0.41) and evenness (Pielou’s: Δ + 0.7%, p = 0.67), despite fluctuations in species richness.
The temporal dynamics of urban tree diversity within residential areas of Beijing’s Fifth Ring Road, spanning from 2006 to 2021, reveal significant shifts in the composition of species provenance. Over these 15 years, exotic species richness significantly increased, emerging as the dominant driver of tree diversity dynamics. Exotic species were imported and planted primarily for ornamental purposes. These findings align with global urbanization patterns, demonstrating an increase in diversity dominated by exotic species, as documented in Southern California’s cultivated landscapes, the New York metropolitan region [25,26], and vacant inner-city lots [27]. In recent years, urban forestation programs have shown a preference for exotic species over native ones, especially in large cities [4,28,29]. The preference for exotic species is commonly linked to rapid growth rates, aesthetics, nursery stocks, and the perception of greater tolerance to urban stress and pests [30,31]. In our study, exotic species such as Koelreuteria paniculata, Prunus serrulata, and Platanus × acerifolia were found to be popular in residential areas because they can quickly adapt to the local environment, have a relatively fast growth rate, and possess high ornamental value. Exotic trees could also play a crucial role in providing ecosystem services and promoting greater diversity in urban forests [32,33,34,35]. Using examples from Northern and Central Europe, previous studies show that, in certain regions, the catalogue of native tree species may be too limited to fulfill ecosystem services and maintain resilience in harsh urban environments. It has also been indicated that urban forests can contribute to fulfilling ecosystem services in certain regions where there are few native tree species suitable for urban environments. Especially under global climate change, urban forests are facing a series of pressures, such as drought, environmental pollution, and soil degradation [36,37,38,39].
Nevertheless, there is substantial evidence that cautions against ecological risks associated with the dominance of exotic species, including their invasive potential [40], biodiversity homogenization [41], and native species displacement [42,43]. Growing evidence also suggests that planning an urban forest with a preference for native species might enhance resilience against pests and diseases, conserve local tree species, and provide more diverse habitats for native species in the urban environment [31,44,45]. However, the perceived advantages of exotic species, such as rapid growth rates, ornamental traits, or stress tolerance, may inadvertently drive their disproportionate introduction, thereby increasing the risk of extinction for native taxa through competitive exclusion [46,47]. Therefore, management should pay more attention to balancing the proportion of exotic and native tree species in future urban forest planning.
In Chinese cities, such as our study area, urban tree planning and management, largely driven by developers and constrained by municipal greening policies, often prioritizes rapid aesthetic greening over ecological resilience. This approach has inadvertently accelerated the dominance of ornamental non-native species, which exhibit lower pest resistance and narrower ecosystem service capacities compared to native alternatives. To mitigate this imbalance, management frameworks should explicitly prioritize native species propagation while integrating quantitative assessments of species-specific benefits, including air purification efficiency, carbon sequestration potential, and socioeconomic value.
By grounding planting criteria in empirical ecosystem service data, rather than solely compliance with superficial greening metrics, urban forestry policies could transition from developer-dominated, short-term aesthetics toward long-term ecological functionality. Such evidence-based reforms would enhance urban forest resilience, align green infrastructure with biodiversity conservation goals, and ensure sustainable delivery of critical ecosystem services under climate change pressures.

4.2. Luxury and Legacy Effects on Tree Diversity in Beijing’s Urban Residential Areas

As hypothesized, the luxury effect predominates in shaping total and exotic tree diversity patterns. Our analyses demonstrate significant positive correlations between housing prices, total species richness, and the Shannon–Wiener index, as well as between exotic species richness and the Shannon–Wiener index. Among socioeconomic variables, housing prices emerged as the most statistically significant predictor, indicating a robust association between residents’ economic capacity and greening investment intensity within Beijing’s Fifth Ring Road residential areas. Our results align with studies conducted in the West, e.g., in Queensland, Australia, and Salt Lake County, Utah, U.S., where tree diversity is positively related to local household income and where wealthier urban neighborhoods typically exhibit greater tree diversity [48,49,50]. High-priced residential properties typically reflect the socioeconomic status and lifestyle aspirations of affluent residents, often entailing substantial investments in landscape design, which provide access to a wider selection of tree species for greening purposes [51,52]. Particularly for exotic species, housing price is the most critical factor affecting their diversity, with exotic species richness demonstrating a significant positive correlation with property value increments [11]. This pattern suggests that affluent residential regions exhibit a stronger preference for exotic species.
Conversely, exotic tree species richness and the Shannon–Wiener index were significantly negatively related to housing age, with higher exotic species diversity at sites with younger housing. Housing age emerges as the primary determinant of native tree species diversity, with increasing housing age showing a marked positive correlation with native species richness and the Shannon–Wiener index. This means the legacy effect primarily shapes the diversity of native tree species, with older residential neighborhoods exhibiting a greater abundance of indigenous tree populations. A similar pattern has been found in Shanghai, China, and Halifax, Canada, where native species are more prevalent in residential areas with older neighborhoods compared to newer ones [44,53]. However, it is crucial to note that the “luxury effect” and “legacy effect” hypotheses only partially explain the species diversity of certain taxonomic groups in Beijing’s urban flora. This discrepancy arises from fundamental differences in research contexts, as Western residential areas primarily consist of detached houses or private gardens. In these settings, household income and personal preferences exert a substantial influence on the biodiversity of residential areas [15,23,54]. In contrast, urban residential areas in China predominantly include high-density apartment complexes with communal green spaces that are managed collectively [32,55,56]. Here, the composition and maintenance of vegetation in residential green spaces are delegated to design firms and property management companies. Consequently, the tree diversity of residential green spaces becomes more susceptible to various factors, including the design preferences of landscape architects, the prevailing market availability of ornamental species, and the budgetary limitations imposed on green infrastructure projects.

4.3. Change in Tree Functional Types over 15 Years

In addition to tree taxonomic diversity, there were notable differences in tree functional types between 2006 and 2021 [57]. Following the 2008 Olympics, Beijing substantially increased the introduction of exotic species, with ornamental and adaptive exotic species gaining prominence over traditional native ones in affluent residents [56,58]. In recent years, ornamental exotic species such as Prunus serrulata var. lannesiana (Carri.) Makino and Acer palmatum Thunb. have become particularly popular in upscale neighborhood; they are highly regarded for their rapid growth, abundant spring blooms, and vibrant autumn colors. Furthermore, our analyses revealed that deciduous broad-leaf exotic species show a disproportionately high representation in high-value properties compared to their evergreen counterparts. This phenomenon may be attributable to deciduous species’ superior capacity to provide summer shading while allowing enhanced winter sunlight penetration relative to evergreen species [59,60,61,62]. Notably, exotic species in residential areas are primarily composed of shrubs with strong adaptability and compact growth habits, which may be attributed to the limited green spaces [63]. As a result, vertical stratification emerges, with native canopy trees dominating the upper strata and exotic species occupying understory niches.
Similarly, prior to 2006, older residential areas predominantly featured edible native species such as Prunus persica L., Punica granatum L., and Ziziphus jujuba Mill. After 15 years, our investigations revealed an increased prevalence of native tree species with high ornamental value, enhanced adaptability, and compact growth habits within residential landscapes, as exemplified by Forsythia suspensa (Thunb.) Vahl or Hibiscus syriacus Linn. This shift from a preference for edible species to ornamental species may be attributed to an improvement in local residents’ living standards and socioeconomic development [64]. Additionally, it is presumably the result of residential preferences for shade trees and plants with showy flowers. This has implications for urban ecosystem function and derived services, such as ecosystem cooling and beauty. This transition from edible to ornamental native species has nuanced implications for urban ecosystem functionality and service delivery: for instance, while smaller-canopied ornamentals may reduce ecosystem cooling efficiency compared to larger edible trees, their prolonged flowering periods enhance aesthetic value through seasonal color diversity, directly shaping residents’ perception of the “beauty” of green spaces.

5. Conclusions

Our analyses indicate that there were significant increases (p < 0.05) in the total, native, and exotic tree species richness between 2006 and 2021. Housing prices emerged as the key determinant of total and exotic tree diversity, exhibiting strong positive correlations with both metrics. Conversely, a significant positive correlation was observed between native tree diversity and housing age. These dynamics highlight the unique socio-ecological drivers in residential green spaces: older communities act as biodiversity reservoirs, while wealthier areas prioritize ornamental exotic species, often neglecting other ecological functions. To address climate adaptation challenges, future urban forest planning should (1) leverage the legacy effect by integrating native species templates from older neighborhoods into new developments to sustain biodiversity, (2) regulate the luxury effect through policies mandating climate-resilient native species in high-income areas, balancing aesthetic preferences with climate regulation functions, and (3) strengthen developer design standards to prioritize ecological functionality, supported by environmental impact assessments. By aligning these strategies with our findings, planners can foster urban forests that harmonize community aspirations with climate resilience.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f16081269/s1.

Author Contributions

Conceptualization, X.L.; data curation, X.L. and J.B.; investigation, X.L. and Y.L.; methodology, X.L. and W.Z.; resources, X.L. and J.W.; validation, X.L. and J.B.; visualization, X.L. and W.Y.; writing—original draft, X.L. and J.B.; writing—review and editing, X.L. and W.Z.; project administration, W.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Internal Research Fund of Sichuan Academy of Forestry and Grassland Survey and Planning (Project: Mechanisms Underlying the Effects of Vegetation Restoration Models on Multiple Ecosystem Functions in Rocky Desertification Ecosystems of the Chishui River Basin).

Data Availability Statement

The data presented in this study are available on request from the corresponding author. Due to privacy concerns, the data is not publicly available.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Survey map of study area.
Figure 1. Survey map of study area.
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Figure 2. Variation in total, native, and exotic tree species diversity between 2006 and 2021. (a) Species richness. (b) Shannon–Wiener index. (c) Simpson’s index. (d) Pielou’s evenness index. Error bars are for standard errors. Different letters indicate significant differences between stand types (α = 0.05).
Figure 2. Variation in total, native, and exotic tree species diversity between 2006 and 2021. (a) Species richness. (b) Shannon–Wiener index. (c) Simpson’s index. (d) Pielou’s evenness index. Error bars are for standard errors. Different letters indicate significant differences between stand types (α = 0.05).
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Figure 3. Relative effects of multiple predictors of total species richness (a), Shannon–Wiener index of total species (b), native species richness (c), exotic species richness (d), and Shannon–Wiener index of exotic species (e). The estimated average parameters (regression coefficients) of the model predictors with their associated 95% confidence intervals are shown, as is the relative importance of each predictor, quantified as the percentage of explained variance. The graph represents the optimal model selected.
Figure 3. Relative effects of multiple predictors of total species richness (a), Shannon–Wiener index of total species (b), native species richness (c), exotic species richness (d), and Shannon–Wiener index of exotic species (e). The estimated average parameters (regression coefficients) of the model predictors with their associated 95% confidence intervals are shown, as is the relative importance of each predictor, quantified as the percentage of explained variance. The graph represents the optimal model selected.
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Table 1. Basic information on the sample plots by region.
Table 1. Basic information on the sample plots by region.
NumberResidential AreaArea/(m2)Year of ConstructionResidential Green Area Attribute
1Anzhen Li West community45071988public
2Beilin relative’s courtyard30,0001990public
3Xin’anxili community41,0611990public
4Xin’annanili community45,0681981public
5Zixin road community37,5001989public
6Weigong village community80,4561982public
7North Third Ring Road on the 10th community20,0001980public
8Chinese Academy of Sciences Huangzhuang community39,6501963public
9Anxiangli community43,8751964public
10Sizhu community40,0001981public
11Beidadisanli community21,2751981public
12Shiliuyuannanli community25,7851996public
13Fushou apartment38001992public
14Qingnianhu community38,8501987public
15Shilibaobeili community39,0001980public
16Shuabglong community336,0001994public
17Huajiadinanli community336,0001990public
18Huajiadixili community52,5001996public
19Kangfu community25,5001972public
20Nanlishi Road, No. 40 Light Industry community70001984public
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Li, X.; Bao, J.; Li, Y.; Wang, J.; Yan, W.; Zhang, W. Legacy and Luxury Effects: Dual Drivers of Tree Diversity Dynamics in Beijing’s Urbanizing Residential Areas (2006–2021). Forests 2025, 16, 1269. https://doi.org/10.3390/f16081269

AMA Style

Li X, Bao J, Li Y, Wang J, Yan W, Zhang W. Legacy and Luxury Effects: Dual Drivers of Tree Diversity Dynamics in Beijing’s Urbanizing Residential Areas (2006–2021). Forests. 2025; 16(8):1269. https://doi.org/10.3390/f16081269

Chicago/Turabian Style

Li, Xi, Jicun Bao, Yue Li, Jijie Wang, Wenchao Yan, and Wen Zhang. 2025. "Legacy and Luxury Effects: Dual Drivers of Tree Diversity Dynamics in Beijing’s Urbanizing Residential Areas (2006–2021)" Forests 16, no. 8: 1269. https://doi.org/10.3390/f16081269

APA Style

Li, X., Bao, J., Li, Y., Wang, J., Yan, W., & Zhang, W. (2025). Legacy and Luxury Effects: Dual Drivers of Tree Diversity Dynamics in Beijing’s Urbanizing Residential Areas (2006–2021). Forests, 16(8), 1269. https://doi.org/10.3390/f16081269

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