Urban planning and landscape greening management drive plant diversity in five tropical cities of China

doi:10.21203/rs.3.rs-3112826/v1

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Urban planning and landscape greening management drive plant diversity in five tropical cities of China

https://doi.org/10.21203/rs.3.rs-3112826/v1

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Sustainable green space is essential in urban areas to maintain and improve human well-being. However, to better understand the impact of environmental and socioeconomic changes on the sustainability of tropical urban green space landscapes, work is needed to explore the patterns of plant diversity and its drivers in urban green spaces. We explore urban floristic diversity patterns based on samples from 826 urban functional units located in five cities in the tropical coastal region of China. Field surveys were conducted to obtain data on plant diversity, land use types, socioeconomic characteristics, and environmental characteristics within these units. Plant diversity within the cities varied significantly among land use types, with high-rise housing, parks and universities exhibiting higher diversity. The diversity of cultivated plants and regional economy showed significant positive correlation, while an increase in the diversity of spontaneous plants and native species was linked to building age. Management also affected plant diversity, with watering frequency being positively associated and maintenance frequency. Urban plant diversity patterns result from a combination of multiple factors. Socio-economic factors are the primary driver of the patterns of urban plant diversity, as space management and maintenance determine what can grow in different areas. The implications of the study have important practical significance for policymakers and managers in the development and management of urban green spaces more sustainably.

Urban plant

Urban functional units

Distribution patterns

Driving mechanisms

Socioeconomic factors

Land use types

Urban green space management

Urban plant diversity is an important element of urban ecology, social function, and the well-being of residents, serving as the green infrastructure and life support system of urban ecosystems (Ma and Wang, 1984; Wang and Liu, 2010; Hobbie and Grimm, 2020). However, rapid urbanization has resulted in environmental changes, particularly altered land use patterns and the introduction of exotic plants, consequently reducing the provision of urban ecosystem services and homogenizing the species composition of urban areas (Elmqvist et al., 2013; Muller et al., 2010). Urban green spaces are important ecological infrastructure and resource for mitigating urbanization issues such as heat reduction and brownfield space restoration (Escobedo et al., 2019; Semeraro et al., 2021). Although there is growing research on urban plant diversity in temperate regions, little is known about the distribution patterns and driving mechanisms of plant diversity in the global tropics (Cheng et al., 2022), particularly in the Chinese tropics where urban plant conservation issues are not well understood (Liu et al., 2017). To conserve urban plant diversity, maintenance, and Sustainability, it is essential to understand the spatial distribution patterns of plant diversity, including cultivated and spontaneous plants, native and non-native plants, and the effects of natural and anthropogenic activities (Lepczyk et al., 2017; Chang et al., 2021). Therefore, this study investigated the spatial distribution patterns of plant diversity and their drivers in five tropical regions of China, namely Zhanjiang, Haikou, Danzhou, Sanya, and Yazhou.

Urban green spaces can include a combination of natural and cultivated plants, and these may be planted or grow naturally (or spontaneously). Cultivated species, include trees, shrubs, and groundcover herbaceous species, are typically cultivated in nurseries and planted in urban green spaces, thereby requiring significant resources such as time and money (Hope et al., 2003; Avolio et al., 2018; Avolio et al., 2020). Conversely, spontaneously germinating plants that arise unintentionally from human activities are increasingly perceived as an important constituent of urban vegetation (Cervelli et al., 2013). The emergence of spontaneous plants does not entail economic investments and provide valuable ecosystem services that benefit urban residents (Bonthoux et al., 2019). These services include the regulation of microclimates, and provision of plant resources for pollinators (Larson et al., 2014; Yang et al., 2019), including more native species (Huang et al., 2019). Moreover, plants which are good at spreading and can grow spontaneously and exploit many conditions for growth can adapt quickly to urbanization (Smith and Fellowes, 2014). A better comprehension of the drivers of diverse types of plant diversity in urbanizing regions could enhance the management and conservation of biodiversity in urban areas worldwide and provide a solid foundation for achieving sustainable cities.

We adopted the definition of urban functional units (UFUs) proposed by Wang et al. (2013), in which they represent unique land cover and land use with distinct economic and social functions in an urban matrix. Affluence and urban plant diversity are positively correlated (Hope et al., 2003), and urban history could influence the structure and composition of urban green spaces due to differences in plant species accumulation, growth times, and past management decisions (Lubbe et al., 2010; Clarke et al., 2013; Larson et al., 2017; Roman et al., 2018). Our study combines remote sensing with field surveys to collect data on plant diversity, land use types, socioeconomic factors, and environmental factors in the study area, to understand the determinants of urban plant diversity, to enable proactive management to increase urban plant diversity.

2.1. Study area

The study area encompassed five cities within the tropical coastal region of China, namely Zhanjiang (109.67°~ 110.97°, 20.22°~21.95°), Haikou (110.12°~110.71°, 19.53°~20.08°), Danzhou (108.93°~109.77°, 19.18°~19.87°), Sanya (108.94°~109.81°, 18.16°~18.62°), and Yazhou (108.93°~109.80°, 18.15°~18.62°) (Fig. 1). While the four cities of Zhanjiang, Haikou, Danzhou, and Sanya are situated in varying latitudinal gradients, Yazhou and Sanya lay within the same latitudinal region. Yazhou was selected as one of the study areas due to the construction of Yazhou Bay Science and Technology City in recent years, which has led to rapid urbanization and different socio-economic conditions and greening management levels when compared to the other four cities. All five cities are located on the southern coast of mainland China and the northern, northwestern, and southern coasts of Hainan Province, respectively. The vegetation types in these regions are predominantly evergreen broad-leaved forests, and the climate is regulated by the oceanic climate throughout the year.

2.2 Data Collection

2.2.1. Field survey

We conducted field surveys of plants between June and October in 2021 and 2022. Sampling areas were established in five study areas following approaches used in previous studies (Wang et al., 2020; Zhang et al., 2022) as shown in Fig. 1. areas the total area of each city was different we divided each into different sized grids to ensure the same number of points could be representatively placed in each city. Zhanjiang was divided into 0.65 × 0.65 km grids, Haikou into 1 × 1 km grids, Danzhou into 0.5 × 0.5 km grids, Sanya into 2 × 2 km grids, and Yazhou into 0.5 × 0.5 km grids (Fig. 1).

In this study, we selected representative UFUs in each grid (Fig. 1). UFUs differ in land use type, green space coverage and maintenance strategies (i.e., greening strategies), and socioeconomic characteristics (Wang et al., 2013). To select UFUs, we focused on the most common land use types in each grid. For example, in Sanya Yalong Bay, commercial and residential are the most common land use types. Therefore, we selected UFUs such as resort hotels and food plazas, including the Phoenix Island Resort (Fig. 2, A1). In addition to common land use types, we also selected UFUs with unique cultural significance and long history, such as the Bell Tower in Haikou (Fig. 2, B1), Yazhou Ancient City (Fig. 2, C1), and Shuinan Village (Fig. 2, A2), one of the four major villages in Hainan. We also considered UFUs with important ecological significance, function, or value, such as Zhanjiang Southland Tropical Garden (Fig. 2, B2) and Danzhou Huaguoshan Park (Fig. 2, C2). This study uses the U.S. urban forestry classification system (Anderson, 1976) and the UFU classification from previous studies in China (Wang et al., 2015; Nizamani et al., 2021). To avoid duplicate sampling, we ensured that all major land use types were sampled, and each UFU represented only one land use type (Wang et al., 2015).

During the field survey, we established 1–3 plant samples for each UFUs according to the size of the green area of each UFUs, consisting of large tree samples measuring 20 m × 20 m, accompanied by 5 shrub samples measuring 5 m × 5 m, positioned at the four corners and center of the tree samples, as well as 5 herbaceous samples measuring 1 m × 1 m located within the shrub samples. For the purposes of this study, trees were defined as woody perennials with a single main stem and a distinct canopy, while shrubs were defined as perennial woody plants without a distinct trunk or canopy, usually ranging from 0.5-5 m in height (FAO, 1998, 2004; UNECE/FAO, 2000). Herbaceous plants were categorized as vascular plants that are not trees or shrubs, including some ferns. It should be noted that the same plant may belong to different categories of trees, shrubs, or herbs depending on its morphological characteristics. To provide a visual representation of the plants observed in tropical cities during our field survey, we include a collection of characteristic plant photographs in Fig. 2.

During our field surveys, we collected information on species, primarily focusing on plant species and their respective numbers. We identified each species manually, seeking assistance from plant experts or utilizing the Aiplants application (www.aiplants.cn) for photo identification if necessary. To ensure accuracy, we cross-checked all species with images from the Chinese Natural Herbarium (www.cfh.ac.cn). Each species was classified as either cultivated or spontaneous, with some plants being both depending on the UFU. For species whose establishment could not be directly determined, we consulted with the green space manager for confirmation. We further subdivided the plants into native and non-native species based on the Flora of Hainan (Chen, 1964), Exotic Plants of China (He, 2012), and the List of Invasive Plants of China (Ma et al., 2013).

2.2.2 Environment Variables

Previous studies have shown that plant diversity at the regional scale is often correlated with temperature, precipitation, wind speed, solar radiation and elevation (Irl et al., 2015; Kessler et al., 2011). Our study aimed to investigate whether these environmental factors have the same effect on each UFU greenfield in the tropics. We therefore selected data from the 30s resolution Bioclim2 dataset, which includes four variables (Annual Mean Temperature, Max Temperature of Warmest Month, Min Temperature of Coldest Month and Annual Precipitation) from the WorldClim database (http://www.worldclim.org), as well as the solar radiation, wind speed, and elevation dataset at the same resolution. The data presented in these datasets represent a 30-year average from 1970–2000 (Fick and Hijmans, 2017). We processed the data using Arcgis 10.8 software and extracted them into each UFU, resulting in seven environmental variables: annual mean temperature, max temperature of warmest month, min temperature of coldest month, annual precipitation, monthly mean solar radiation, monthly mean wind speed, and elevation.

2.2.3 Socioeconomic variables

We differentiated UFUs into two levels of detail: primary and detailed UFUs, as outlined in Table 1 (refer to section 2.2 for details on the classification process). To evaluate the socioeconomic characteristics of UFUs, we employed a set of indicators including house prices, building age, population density, and maintenance factors (such as trimming frequency, fertilization, and watering), as provided in Appendix 1. House prices were sourced from either the sample field surveys, or house sale websites (https://anjuke.com, https://www.58.com, or https://esf.fang.com) for the year of the sample survey. We gathered information on the age of construction through consultation with administrative sources and maintenance personnel. We used the methods introduced by Wang et al., 2015 and Nizamani et al., 2021 to ascertain the number of dwellings per building and floor, and to estimate the population in each UFU by multiplying these numbers. The population was then divided by the area of each UFU (in km²) to obtain the population density (in thousands of persons per km²). Trimming (times/year), fertilization (times/year), and watering frequency (times/year) were selected as maintenance measures for urban green spaces. We collected data on these measures through interviews with five local maintenance staff members, and averaged their responses to minimize memory errors.

2.3 Data Analysis

We used data from five distinct cities and regions to analyze the drivers of plant species richness for each UFU using a generalized linear mixed-effects model. To ensure consistency among variables, we standardized and centered the independent variables following Gao et al., (2023). Additionally, we avoided covariance between variables by retaining only one variable in the model when the correlation coefficient between independent variables was greater than 0.7. We checked the variance inflation factor (VIF) of the model and removed any variables with a VIF greater than 5 until the VIF of all variables was ≤ 5 (Menard, 2002; Vittinghoff et al., 2006; Gareth et al., 2013). With urban ID as a random effect, fixed effects were categorized into three groups: land use type, natural environment variables such as mean annual temperature, hottest month temperature, coldest month temperature, annual precipitation, mean monthly solar radiation, mean monthly wind speed, and elevation, and socioeconomic characteristics variables that included house price, population density, building age, and three variables associated with green space management and maintenance (trimming, fertilization, and watering). We fitted the model using REML's R package "lme4" (Bates et al., 2015) and conducted T-tests using Satterthwaite's method ['lmerModLmerTest']. Finally, we selected the best model based on the Akaike Information Criterion (AIC).

In order to demonstrate and compare the distribution of plant species numbers among various types of primary and detailed UFUs across different cities, we utilized the R package "ggplot 2" (Wickham, 2016) to create two sets of box plots. The first set depicted the number of cultivated, spontaneous, and total species within each type of UFU (Fig. 3), while the second set showed the number of spontaneous native and non-native species (Fig. 4). All statistical analyses were conducted using R 4.1.2 software.

3.1 Major UFUs distribution

Land use categories such as agricultural land, forest land, utilities and infrastructure, mining areas, and undeveloped areas seldom appear within the grid and are not the prevailing land use types in the cells where they exist. As a result of its limited sample size and our focus on urban environments, we have chosen to exclude this UFU type from our research.

A total of 826 UFUs were selected from five cities or regions, as presented in Annex 1. Five primary UFU types were identified, which included traffic areas in Zhanjiang (47, 26.3%), institutional business service areas in Haikou (63, 33.7%), residential areas in Danzhou (74, 46.3%), residential areas in Sanya (39, 26.0%), and institutional business service areas in Yazhou (49, 32.7%) (Annex 1). Additionally, we identified 13 detailed UFU types, with the most prevalent being road UFUs in Zhanjiang (44, 24.6%), high-rise residential UFUs in Haikou (42, 22.5%), high-rise residential UFUs in Danzhou (69, 43.1%), commercial area UFUs in Sanya (35, 23.3%), and high-rise residential UFUs in Yazhou (26, 17.3%) (Annex 1).

3.2 Plant diversity Distribution Patterns

The study focuses on UFUs, and how the respective greening strategies and maintenance management patterns of primary and detailed UFUs contribute to the distribution of various plant diversity types. Thus, this study investigates the differences in the types and distribution of plant diversity (e.g., total species richness, cultivated and spontaneous plant diversity, native and non-native species richness) among UFUs in different cities. It describes the distribution pattern of diversity by analyzing the differences in plant diversity among various types of UFUs. The mean and standard deviation (N_AVG±SD) of species number within each type of urban UFU or the total number of species occurring within each type of urban UFU (N) are used to express the specific richness values.

Zhanjiang had a higher total number of species in both the Institutional Business Service Area-Institute (N = 51) and Institutional Business Service Area-University (N_AVG±SD=48.25 ± 4.65). Haikou displayed a greater number of total species in the Institutional Business Service Area-University (N_AVG±SD=18 ± 5.33). In Danzhou, both the institutional commercial service area-university (N = 34) and the recreational area-plaza and sports center (N_AVG±SD=27.91 ± 10.95) had a higher total number of species. Finally, Sanya showed a higher total number of species in the Institutional Commercial Service Area-University (N_AVG±SD=42.8 ± 10.38) and the Recreation Area-Park (N_AVG±SD=39.57 ± 9.5). Of all types of UFUs, total species richness was higher within the Transportation Area-Transportation Hub (N = 27) and Residential Area-High Rise Housing (N_AVG±SD=17.88 ± 5.34) in Yazhou (Fig. 3).

Zhanjiang and Sanya had a greater number of cultivated species in Residential Areas-High Rise Residential (N_AVG±SD=33.17 ± 7.4, N_AVG±SD=30.6 ± 6.89) and Institutional Commercial Service Areas-University (N_AVG±SD=29.5 ± 6.18, N_AVG±SD=32 ± 6.93). Haikou displayed a higher number of cultivars in the Institutional Commercial Service Area-University (N_AVG±SD=14 ± 3.22), while Danzhou presented more cultivated species in Institutional Commercial Service Areas-Government Departments (N_AVG±SD=16.09 ± 10.51). Lastly, Yazhou contained more cultivated species in Residential Areas-High Rise Residential (N_AVG±SD=11.38 ± 3.69) (Fig. 3).

Zhanjiang had a greater number of spontaneously germinated species in Institutional Business Service Area-Institute (N = 24) and Residential Area-Low-Rise Residential (N_AVG±SD=21.28 ± 7.84). Haikou showed a high number of spontaneous species in Residential Areas-Low-Rise Residential (N_AVG±SD=4.5 ± 3.84). Danzhou exhibited a higher number of spontaneous species in Transportation Areas-Transportation Hubs (N_AVG±SD=13.33 ± 6.6) and Recreation Areas-Plazas and Sports Centers (N_AVG±SD=13.45 ± 6.6). Sanya presented a higher number of spontaneous species in the Institutional Commercial Service Area-Government Departments (N_AVG±SD=15 ± 11.47). Yazhou contained more spontaneous species in Transportation Areas-Transportation Hubs (N = 18) and Institutional Business Service Areas-Hospitals (N_AVG±SD=9±2.94) (Fig. 3).

Zhanjiang showed higher numbers of native species in Institutional Business Service Areas-Institute (N = 30), Institutional Business Service Areas-University (N_AVG±SD=23 ± 2.24) and Residential Area-Low Rise Residential (N_AVG±SD=20.17 ± 6.43). The Residential Area-High Rise Residential (N_AVG±SD=26.75 ± 4.58) and Institutional Commercial Service Area-University (N_AVG±SD=25.25 ± 4.55) had a higher number of non-native species. Haikou exhibited a higher number of native species in Residential Areas-Low-Rise Residential (N_AVG±SD=6.75 ± 0.83) and a higher number of non-native species in Institutional Business Service Areas-University (N_AVG±SD=12.8 ± 4.4). Danzhou contained more native species in Institutional Business Service Areas-University (N = 16) and Residential Area-Low Rise Residential (N_AVG±SD=10.5 ± 1.8), and more non-native species in Recreation Areas-Squares and Sports Centers (N_AVG±SD=18.27 ± 7.72). The recreation area-park (N_AVG±SD=17.35 ± 5.03) had a higher number of native species in Sanya, and the Institutional Commercial Service Area-University (N_AVG±SD=25.8 ± 5.34) had more non-native species. Finally, Yazhou showed more native species in Transportation Areas-Transportation Hubs (N = 14) and Residential Areas-Low Rise Residential (N_AVG±SD=8.06 ± 2.7), and more non-native species in Residential Areas-High Rise Residential (N_AVG±SD=11.25 ± 2.97) (Fig. 4).

Zhanjiang and Sanya had a higher total number of species than the other three cities, as depicted in Fig. 3. Although some differences in species richness were observed among the five cities, certain trends emerged. Institutional commercial service areas, such as universities, and residential areas with high-rise residences tended to have more cultivated species, while spontaneous species were typically concentrated in residential areas with low-rise residences and transportation areas, such as transportation hubs. Interestingly, residential areas with low-rise housing were identified as a hotspot for native plant diversity, whereas residential areas with high-rise housing tended to host more non-native species.

Table 1

Analysis of the factors influencing plant diversity in five cities (Zhanjiang, Haikou, Danzhou, Sanya and Yazhou) of China. We constructed a Generalized Linear Mixed-effects Model. The minimum AIC values of the equation help determine whether to include the variable in the model, with "-” indicating exclusion from the models. Signif. codes: 0 “***” 0.001 “**” 0.01 “*” 0.05 “.” 0.1 “ ” 1.
	Total		Cultivated			Spontaneous			Native			Non-native
Random effects
Groups Name	Variance	Std.Dev.		Variance	Std.Dev.		Variance	Std.Dev.		Variance	Std.Dev.		Variance	Std.Dev.
City (Intercept)	0.40	0.63		0.43	0.65		0.33	0.56		0.34	0.59		0.43	0.66
Residual	0.40	0.63		0.46	0.68		0.44	0.66		0.41	0.64		0.48	0.69
Number of obs: 733, groups: City, 5
Fixed effects
	β coefficient		β coefficient			β coefficient			β coefficient			β coefficient
(Intercept)	-0.13		-0.07			-0.09			-0.17			-0.04
Primary UFU - Institutional business service area	0.07		0.07			-0.01			0.11			-0.03
Primary UFU - Leisure and entertainment area	0.19*		0.11			0.21*			0.26**			0.10
Primary UFU - Residential area	0.19**		0.20*			0.07			0.28***			0.07
Primary UFU - Traffic area	-0.40***		-0.43***			-0.18*			-0.31***			-0.41***
Annual Mean Temperature	-		-			-0.21			-			-
Annual wind speed	-		-			0.12.			-			-
Construction age (year)	-		-			0.12**			0.13**			-
Housing price (Yuan)	-		0.15*			-0.16*			-			-
Trimming frequency (times/year)	0.09		0.25***			-0.11.			-			0.23***
Watering frequency (times/year)	0.15***		0.18***			-			0.07.			0.19***
AIC	1441.6		1547.3			1517.7			1453.4			1570.9

3.3 The factors affecting urban plant diversity

The results obtained from the fitting of the generalized linear mixed-effects model clearly show significant differences in plant diversity across various primary UFUs (Table 1, Fig. 5). Specifically, the fitting results for total species number indicate that the diversity of total species number in residential and recreational areas was significantly higher than that of industrial and commercial areas, while the diversity of total species number in transportation areas was significantly lower than that of industrial and commercial areas (Table 1, Fig. 5). The drivers of plant diversity for each specific type were analyzed as follows: total species richness was influenced by land use (i.e., type of UFU) and had a highly significant positive correlation with watering frequency (β coefficient = 0.15, p-value < 0.001) (Table 1, Fig. 5). Cultivated species richness was jointly influenced by land use, house price, Trimming frequency, and watering frequency, and was significantly and positively correlated with house price (β coefficient = 0.15, p-value < 0.05), particularly with Trimming frequency and watering frequency (Trimming: β coefficient = 0.25, p-value < 0.001; watering: β coefficient = 0.18, p-value < 0.001) (Table 1, Fig. 5). Spontaneous species richness was influenced by land use and significantly positively correlated with years of establishment (β coefficient = 0.12, p-value < 0.01), but negatively correlated with house prices (β coefficient = 0.16, p-value < 0.05) (Table 1, Fig. 5). The abundance of native and non-native species was also influenced by land use, where the abundance of native species was mainly related to the year of establishment (β coefficient = 0.13, p-value < 0.01), and the abundance of non-native species was highly significantly and positively correlated with Trimming frequency and watering frequency (Trimming: β coefficient = 0.23, p-value < 0.001; watering: β coefficient = 0.19, p-value < 0.001) (Table 1, Fig. 5).

4.1 Socioeconomic factors dominate urban plant diversity

Urban biota is influenced by a range of environmental, biological and anthropogenic factors that determine the species composition and richness across different spatial scales (Aronson et al., 2016). In most circumstances plant diversity is primarily driven by climatic and geographic factors (La Sorte et al., 2014; Liu et al., 2017). Only spontaneous plant abundance was weakly correlated with mean annual temperature and wind speed, suggesting that temperature affects the growth of spontaneous plants while wind speed contributes to their spread (Table 1). Unlike other studies conducted in regions with greater temperature and wind speed variation between cities (Harris et al., 2017; Cheval et al., 2020; McFadden et al., 2019; Li et al., 2020; Korell et al., 2021), our study was undertaken in China's tropical coastal region where such variation is relatively limited. Our predicted results indicate that environmental factors are not a key driver of urban plant diversity in this region. However, it is possible that environmental factors have a screening effect on the species and composition of urban plants (Kendal et al., 2012), which warrants further investigation in future research.

We found a positive and significant correlation between house prices and the abundance of cultivated species (Table 1), similar findings have previously been recorded (Hope et al., 2003). Wealth or municipalities with financial resources often have the means to promote biodiversity management, or areas with a high level of biodiversity are simply more attractive (Leong et al., 2018). Developers and designers of commercial and residential high-rise buildings also strive to increase plant diversity through continuous green space management, to attract consumers.

We also show that during initial planning and design stages, prior structures and natural greenery are often fully removed and replaced with different, often non-native flora, resulting in a loss of original vegetation (Walker et al., 2009; Szlavecz et al., 2011). Cultivated plants, including various tree and shrub species along with individual herbs, are transplanted from nurseries to the specially designated green spaces, with maintenance protocols such as pruning and watering being commonplace in order to improve plant survival (Faeth et al., 2005). Cultivated plants and non-native species are more common in areas with frequent watering, though this may relate more to the level of maintenance or even the management budget, whereas there was weak correlation observed between native species richness and watering frequency (Table 1). Additionally, our results demonstrate a positive and significant correlation between cultivated plants and non-native species and mowing/maintenance frequency, while spontaneous plants showed a negative correlation (Table 1). This can be attributed to the human manipulation of urban green spaces to promote certain cultivated species and increase aesthetic value through pruning and maintenance practices, resulting in the selective removal of spontaneous plants, or "weeds", to maintain consistency within the space.

The results of the study demonstrate a significant and positive correlation between the diversity of spontaneous and native species and the age of buildings (Table 1). These results support the legacy effect hypothesis as proposed by Dow (2000), Yang et al. (2017), and Helm et al. (2019). The construction age of urban green spaces reflects the history of urban development and its impact on such spaces. As buildings age, there is a greater opportunity for plants to germinate and grow from seeds in the vicinity. It is important to acknowledge that the taxonomic diversity, which was the focus of our study, does not completely reflect the level of plant diversity in this city. The plant functional diversity should be considered as a more effective measure for understanding the response of plant community assembly patterns. Such analysis could potentially influence some of our results in some regions.

4.2 Effect of land use type on plant diversity

The findings of this study indicate notable disparities in the distribution of plant diversity among the unique UFUs. Primary UFU's residential and recreational areas exhibited higher diversity in the total number of species than the commercial and industrial sectors, while the transportation zones displayed substantially lower diversity in the total number of species than the commercial and industrial areas (Table 1). High-rise residential areas, parks, and institutional commercial service areas such as universities exhibited greater plant diversity.

High-rise residential communities typically have well-planned green spaces that are established early in their development. Builders allocate significant funds toward creating and maintaining green areas in order to satisfy the aesthetic preferences of their customers. Likewise, residents are willing to pay for premium landscaping and management of these areas. As observed by earlier research studies (Larson and Brumand, 2014; Larson et al., 2016), individuals favor a variety of plant life and manicured, weed-free lawns as part of an aesthetically pleasing landscape, thus affluence is known to relate to non-native plant diversity in many situations.

The ecological and landscape functions, such as biodiversity, of urban green spaces are influenced by their size, shape, and distribution (Zhang, 2017). Among the different functional units, universities and parks with larger green space patches demonstrated a higher plant diversity. Likely because these public green spaces are subject to more frequent and extensive management practices (Ramer and Nelson, 2020). Furthermore, these areas were initially designed to enhance the urban vegetation landscape and provide greater ecological service value to urban regions (Grafius et al., 2018).

Plant community composition in university and park areas can be more complex and irregular, particularly in inaccessible areas. The creation of parks has proved to be an effective safeguarding mechanism for the maintenance of native diversity within cities (Braun et al., 2010; Hilborn and Sinclair, 2021). Unfortunately, the concern is that as metropolises continue to expand, parks are being transformed (Hilborn and Sinclair, 2021). Regrettably, green space planners often prioritize human needs over ecological value, designing parks first and foremost around human preferences, entertainment and safety, and then create regulations that facilitate visitor movement, such as installing densely packed walkways to enable visitors to access the whole park, and may not prioritize native diversity. Thus, facilitating the preservation of native diversity in less accessible and developed areas may be the easiest solution

Plant diversity and its distribution are influenced by a combination of factors. 1) Land-use type or urban planning significantly impacts plant diversity, with higher overall plant diversity observed in high-rise housing, parks, and universities. 2) The richness of cultivated species is positively correlated with the regional house prices, suggesting a potential role for economics in plant diversity management. 3) Construction age positively correlates with the richness of spontaneous species and native species, implying that different planning strategies for green spaces can have a significant impact on plant diversity. Therefore, the historical context of urbanization must be taken into account when studying urban plant diversity. 4) Human management of green spaces has both positive and negative impacts on plant diversity, with ornamental and non-native species being more adaptable to maintenance while spontaneous plants decline. Overall, this study highlights the complex interplay between various factors that influence plant diversity in urban ecosystems and underscores the need for holistic strategies to manage and conserve urban plant diversity.

To enhance our understanding of urban plant diversity and its drivers, we recommend additional long-term observations of sampling sites that can expand spatial and temporal coverage and better reflect management and other factors, as well as expanding to other dimensions of diversity, such as that of pollinators. However, we show the delicate interplay of different factors which influence diversity, and the potential to increase the use of native species in developments to maximise their survival prospects, reduce costs, and support other elements of urban biodiversity. This approach promotes a healthier environment by protecting vital ecosystem services with a view to improving overall human well-being.

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and / or company that could be construed as influencing the position presented in , or the review of , the manuscript entitled Distribution Patterns of Plant diversity and Its Drivers in Five Representative Cities in Tropical China.

CRediT authorship contribution statement

Jian-Peng Cui: Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - original draft, Writing - review & editing. Mei-Hui Zhu: Formal analysis, Investigation, Writing - review & editing, Data curation. Lin-Yuan Guo: Formal analysis, Investigation, Methodology, Software, Writing - original draft, Writing - review &editing. Hai-Li Zhang: Validation, Visualization, Writing - review & editing. Alice C. Hughes: Formal analysis, Writing - original draft, Writing - review & editing. Hua-Feng Wang: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing.

Acknowledgements: This study was supported by National Natural Science Fundation of China (32160273), the Project of Sanya Yazhou Bay Science and Technology City (SCKJ-JYRC-2022-83), an open funding from East China Normal University (SHUES2021A08, SHUES2022A06), the PhD Scientific Research and Innovation Foundation of Sanya Yazhou Bay Science and Technology City (HSPHDSRF-2023-12-010). And funding from Collaborative Innovation Center of Nanfan and High-Efficiency Tropical Agriculture, Hainan University (XTCX2022NYB09).

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Urban planning and landscape greening management drive plant diversity in five tropical cities of China

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1. Study area

2.2 Data Collection

2.2.1. Field survey

2.2.2 Environment Variables

2.2.3 Socioeconomic variables

2.3 Data Analysis

3. Results

3.1 Major UFUs distribution

3.2 Plant diversity Distribution Patterns

3.3 The factors affecting urban plant diversity

4. Discussion

4.1 Socioeconomic factors dominate urban plant diversity

4.2 Effect of land use type on plant diversity

5. Conclusions

Declarations

References

Appendix 1

Additional Declarations

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