Evaluation of the diversity and uniqueness of woody plant communities in park forests transformed form forest patches on urban hills

doi:10.21203/rs.3.rs-3734292/v1

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Evaluation of the diversity and uniqueness of woody plant communities in park forests transformed form forest patches on urban hills

https://doi.org/10.21203/rs.3.rs-3734292/v1

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In the process of urbanization, forest patches on hills are transformed into urban park forests, their functions changing from production to ecological and landscape. The diversity and uniqueness of plant communities support the ecological and landscape functions of parks, but there is a lack of indicators and methods to assess their uniqueness. We propose two evaluation indicators for woody plant community uniqueness in urban parks based on importance value (IV, the average of relative abundance, relative frequency, and relative dominance): the mean non-overlapping IV (MNIV) and the local contribution to beta diversity based on IV (LCBD1). Subsequently, we investigated and evaluated the diversity and uniqueness of woody plant communities in eight hill parks (Parks 1–8) in Qingdao. A total of 101 species were found, with an average of 23.88 ± 5.96 species per park. The Shannon-Wiener and Pielou indices were in the ranges of 1.42–3.13 and 0.48–0.91, respectively. Results from principal coordinate analysis and cluster analysis and the orders of MNIV and LCBD1 suggest that Parks 4 and 8 have the most unique woody plant communities. MNIV was significantly (P < 0.05) positively correlated with Shannon-Wiener and Pielou indexes, while LCBD1 was significantly (P < 0.05) positively correlated with local beta diversity based on species abundance. MNIV and LCBD1 were significantly (P < 0.05) negatively correlated with IV of Pinaceae and significantly (P < 0.05) positively correlated with IVs of Anacardiaceae, Cannabiaceae, and Ginkgoaceae plants. Therefore, diversities of woody plant communities from residual forest patches show great variations. The diversity and IVs of characteristic plants are key factors affecting uniqueness.

urban parks

cultural forests

local contribution to beta diversity

importance value

In the past 45 years, China has experienced a rapid urbanization process (Yang et al., 2021a). This process has resulted in many forests, which were originally used for harvesting forest products, being divided by built-up areas, forming fragmented forest patches (Zhou et al., 2017). These residual patches are crucial for biodiversity conservation (Mitchell et al., 2022), but protecting them is extremely difficult (Nielsen et al., 2013; Lepczyk et al., 2017; Paquette et al., 2023). Transforming residual forest patches into urban parks or other green spaces is a way to protect and utilize these ecological patches (Wang et al., 2021). Urban parks and other urban green spaces serve as the ecological heart of the city, playing a vital role in mitigating urban ecological degradation, preserving native species, and maintaining ecological balance (Chen et al. 2018; Figueroa et al. 2018), thus contributing to the sustainability of urban ecology (White et al. 2013). However, due to the high degree of similarity in tree species composition among different parks and the pressing issue of plant homogenization in green spaces (Li et al. 2023), the uniqueness of urban parks and green spaces has recently gained increasing attention (Chan et al. 2015; Wang et al. 2019).

Due to the lack of evaluation indicators and methods, there are only a few studies on the uniqueness of urban parks. Chan et al. (2015) and Wang et al. (2019) studied the uniqueness of urban parks and green spaces in Hong Kong and Helsinki, respectively. Paquette et al. (2023) studied the uniqueness of plant communities at different sites in a large urban park in Quebec City, Canada. The main evaluation methods for the uniqueness of urban parks and green spaces used in these studies are two: one is to use a questionnaire to conduct a subjective survey of visitors, such as Chan et al. (2015), who designed a survey using the Likert six-point method to interview three types of people - inbound tourists, potential tourists, and local residents - to understand their views on the uniqueness of six types of urban green spaces in Hong Kong. Wang et al. (2019) used a public participation geographic information system to collect coordinates of urban green spaces that people think are unique in Helsinki. However, using interviews to draw conclusions is subjective and influenced by the characteristics of the interviewees. In particular, local people who are familiar with the park tend to evaluate its uniqueness lower (Wang et al. 2019), while outsider tourists often have an un-objective understanding of the park (Chan et al. 2015). The other method is to use beta diversity analysis in ecology to evaluate the uniqueness of plant communities. Specifically, it analyzes the contribution of a certain unit or species to the overall beta diversity, using it as an index of uniqueness for a certain unit area or species (e.g., Paquette et al. 2023). This method is commonly used in ecology to analyze the functional uniqueness of specific sites or species within an ecosystem or community (Xia et al. 2022; Wang et al. 2023). This method can evaluate the uniqueness of different parts within a whole, and its applicability in evaluating and comparing different park green spaces remains to be verified. However, the uniqueness of biodiversity is only one aspect of community uniqueness.

The woody plants in urban green spaces play a crucial role in carbon fixation, air purification, providing habitats for birds, and shading functions (Fini et al. 2023; Villaseñor et al. 2021; Yang et al. 2005; Xiao et al. 2016). Both the diversity of plant species and the spatial size of different species can influence the sensory experience and uniqueness of urban park green spaces. However, in ecological indicators, diversity indexes only reflect species richness and composition (Young and Swiacki 2006) and cannot account for the spatial occupation of species. The species importance value (IV, the average of relative abundance, relative frequency, and relative dominance) is a comprehensive ecological index that takes into consideration species diversity and spatial occupation (Aye and Shibata 2023). Therefore, we suggest the potential use of the IV for calculation to evaluate the uniqueness of urban park green spaces.

Qingdao, a coastal city in eastern China, boasts a typical coastal hilly terrain. The urban area of Qingdao is dotted with numerous hills that are unsuitable for agricultural or construction purposes, resulting in patches of preserved forests. In the process of urban construction and renewal in Qingdao, more than 60 forest patches have been transformed into parks. In this study, we propose two evaluation indicators for woody plant community uniqueness in urban parks based on importance value (IV, the average of relative abundance, relative frequency, and relative dominance): the mean non-overlapping IV (MNIV) and the local contribution to beta diversity based on IV (LCBD1). Subsequently, we investigated and evaluated the diversity and uniqueness of woody plant communities in eight hill parks in Qingdao. We also compared the differences between the results of evaluating park uniqueness using different indicators, and analyzed the relationships between the uniqueness of urban park green spaces and species diversity, park area and age, and the IVs of different plants. This aimed to provide a theoretical basis for the protection and utilization of residual forest patches and the construction of urban park forests.

Natural conditions of Qingdao and locations of the hill parks

As shown in Fig. 1, Qingdao is located in southeastern Shandong Province, with longitudes ranging from 119°30′ to 121°00′ and latitudes ranging from 35°35′ to 37°09′. It faces the Yellow Sea in the east and south and is separated from the Korean peninsula, Japan and other countries by the sea. Qingdao belongs to the temperate monsoon climate with significant marine influence. The average annual temperature is 12°C, and the average annual precipitation is 773 mm. The main soil type in the hills is brown soil, which is equivalent to the primitive soil (Schad 2023) in the world soil reference system. In this study, we investigated 8 parks, including Park 1 Guanhai Hill Park (36°4′0.68″, 120°19′13.18″), Park 2 Guanxiang Hill Park (36°4′13.0″, 120°19′18.98″), Park 3 Xinhao Hill Park (36°3′57.25″, 120°19′35.92″), Park 4 Xiaoyu Hill Park (36°3′31.4″, 120°19′53.29″), Park 5 Zhushui Hill Park (36°4′46.86″, 120°19′55.98″), Park 6 Beiling Hill Park (36°7′8.85″, 120°21′51.3″), Park 7 Laohu Hill Park (36°10′59.6″, 120°24′43.8″), and Park 8 Xianger Hill Park (36°8′40.37″, 120°26′14.25″). Qingdao's history, economy, and urban development conditions are extensively introduced in the paper written by Zhang and Rasiah (2013).

Investigation method of woody plants

In each park, we selected 10 plots of 10 m ×10 m along the path shown in the Supplementary Material 1 for woody plant surveys. The species, ground diameter (GD, measured at 10 cm above ground level), and quantity of all woody plants in each plot were recorded.

Method for analyzing the uniqueness of woody plant communities

IV can effectively reflect the quantity, distribution uniformity, and dominance (relative size of occupied space) of different plants in a given community. Hence, the IV serves as a proxy for assessing the contribution of a particular plant species to the spatial structure of the community. The higher the IV, the more prominent and visible that particular plant species becomes.

Using the IV of woody plants in parks, we propose the mean non-overlapping IV (MNIV), an indicator that captures the overall uniqueness in the diversity and spatial distribution of woody plant communities. The overlapping IV represents the equal contribution of all plants to two communities, whereas the non-overlapping IV (NIV) refers to the distinct contribution of all plants to two communities. NIV quantifies the differences in the observable likelihood of various woody plants across these two communities. The mean NIV (MNIV) computes the average of NIVs for one community (park) compared to other communities, signifying the average level of visual differences within this community relative to others.

Previous studies employed relative abundance or presence-absence binary data in calculating LCBD (Koleff et al. 2003; Legendre and De Caceres 2013). We devise a method to calculate LCBD (LCBD1) using woody plant IV, considering all evaluated parks as a cohesive entity, with each park serving as an evaluation unit. The LCBD1 of each park signifies the contribution of its resident woody plant community to the overall beta diversity of all such communities across all evaluated parks. The beta diversity of IV aptly portrays spatial structure variations between woody plant communities within parks, thus mirroring visual disparities. Hence, LCBD1 effectively depicts the contribution of diverse parks to the overall visual variations in woody plant communities.

In addition, principal coordinate analysis and cluster analysis were conducted on the IVs of various plant species in different hilltop parks. By analyzing the distribution of points in the principal coordinate analysis diagram and the clustering of each park, the uniqueness of each park was studied. The results of principal coordinate analysis and cluster analysis were used to assess the effectiveness of using MNIV or LCBD1 as an indicator of uniqueness.

We also calculated LCBD based on relative abundance (LCBD2) and presence-absence binary data (LCBD3) for comparison with LCBD1 or MNIV. This allowed us to compare and contrast the effectiveness of these different methods in capturing the uniqueness of each park.

To investigate the influencing factors of uniqueness, we analyzed the correlation between each pair of MNIV and different LCBD values, as well as the correlation between uniqueness indicators and plant community diversity indicators (richness, Shannon‒Wiener index, Pielou index), park age, and park area. Woody plants were grouped according to three categories: family in systematic taxonomy, ground diameter (GD) class, and leaf-fall characteristics (evergreen and deciduous). The correlation between the importance value of each woody plant group and MNIV and LCBD1—3 was analyzed. Woody plants were divided into three classes based on GD size: GD ≤ 5 cm, 5 < GD ≤ 15 cm, and GD > 15 cm, corresponding to shrubs, large shrubs & small trees, and large trees, respectively.

Calculations

Calculation of MNIV

The calculation formulas for the relative abundance (RA), relative frequency (RF), and relative dominance (RD) of the i-th plant species in a park are as follows:

RA _i = (A_i/A_t) × 100% (1)

RF _i = (F_i/F_t) × 100% (2)

RD _i = (D_i/D_t) × 100% (3)

In formulas (1–3), RA_i, RF_i, and RD_i represent the relative abundance, relative frequency, and relative dominance of the i-th woody plant species in the park, respectively. A_i, F_i, and D_i represent the abundance, frequency, and basal area of the i-th woody plant species in the park, respectively. A_t, F_t, and D_t represent the total abundance, total frequency, and total basal area of all woody plants in the park, respectively.

IV is the average of RA, RF, and RD (Lan et al., 2021), and its formula is:

IV _i = (RA_i + RF_i + RD_i)/3 × 100% (4)

In formula (4), IV_i is the importance value of the i-th woody plant in the park.

The overlapping IV (OIV) represents the overlapping part of the IV of woody plant species shared by two parks, and the calculation formula is as follows:

$${OIV}_{i-j}={\sum }_{k=1}^{n}\text{M}\text{i}\text{n}\left({IV}_{\text{k}\text{i}},{IV}_{\text{k}\text{j}}\right)$$

In formula (5), OIV_i−j represents the overlapping importance value between park i and park j. IV_ki and IV_kj represent the importance values of the k-th common woody plant species in park i and park j, respectively. n represents the number of common plants shared between park i and park j.

Based on the OIV of woody plants, the MNIV is calculated as follows:

$${MNIV}_{\text{i}}=\frac{{\sum }_{j=1}^{p}{(1-OIV}_{\text{i}-\text{j}} )}{p-1}$$

In formula (6), MNIV_i represents the mean non-overlapping IV of the i-th park, p represents the number of evaluated parks, and OIV_i−j represents the overlapping importance value between the i-th park and the j-th park, where j ≠ i.

Calculation of LCBD

The calculation of LCBD is based on the method of Legendre and De Caceres (2013). Initially, a sampling unit (park) × species matrix (matrix I) was created using IV, relative abundance, or presence-absence values. Subsequently, the mean value for each column of matrix I was computed, resulting in the square of the difference (SD) between each value and the mean value of its respective column. Subsequently, a new matrix (matrix II) was created using SD. The sum of all values in matrix II represents the sum of squares of species composition data (SS_T), while the sum of values in each row of matrix II represents the sum of squares of species composition data for a single park (SS_i). The calculation formula for LCBD for the i-th park is as follows:

LCBD = SS_i/SS_T (7)

Calculation of the diversity indicators

Three indicators, richness, Shannon‒Wiener index, and Pielou index, were used to measure the diversity of woody plant communities in different hill parks. Richness refers to the number of woody plant species. According to the method of Li et al. (2020b), the calculation formulas for the Shannon‒Wiener index and Pielou index are as follows:

$$H=-{\sum }_{i=1}^{S}{RA}_{\text{i}}\text{l}\text{n}{RA}_{\text{i}}$$

In formula (9), H represents the Shannon‒Wiener index, RA_i represents the relative abundance of the i-th plant species in the park, and the calculation formula is given in Eq. (1). S represents the total number of woody plant species in the park.

$$J=H/\left(lnS\right)$$

where J is the Pielou index, H is the Shannon‒Wiener index, and S represents the total number of woody plant species.

Statistical analysis

Based on the Bray‒Curtis distance, principal coordinate analysis was conducted on the IVs of different plant families in the parks. Cluster analysis was carried out using the Euclidean distance, and Pearson's method was used to analyze the aforementioned correlations in 2.3.

IBM SPSS 22.0 software and the Chip graphing website (https://www.chiplot.online/tvbot.html) were used for data analysis and mapping. ArcGIS 10.2 software was used for map production.

Diversity indicators of woody plants

As shown in Table 1, a total of 101 plant species belonging to 77 genera and 38 families were surveyed in the woody plant communities of eight hill parks in Qingdao city. The average number of woody plant species in hill parks was 23.88 species, with Park 5 having the highest species richness (32 species), followed by Park 8, Park 3, Park 6, Park 2, and Park 7. Park 1 had the lowest plant species diversity. The Shannon-wiener index and Pielou index range from 1.42 to 3.13 and 0.48 to 0.91, respectively, suggesting significant variations among different parks.

Table 1

Woody plant diversity in eight hill parks in Qingdao
Park name	Family	Genus	Species	Shannon‒Wiener index	Pielou index
Park1	10	14	15	2.01	0.74
Park2	13	16	20	2.03	0.68
Park3	20	26	27	2.19	0.67
Park4	16	20	22	2.79	0.90
Park5	20	30	32	3.04	0.88
Park6	18	23	25	2.15	0.67
Park7	14	17	19	1.42	0.48
Park8	20	25	31	3.13	0.91
Overall	38	77	101	2.34	0.74

IVs of woody plants and OIVs between different parks

The IVs of different woody plants in the eight hill parks are shown in Fig. 2. Based on family classification, plants of f. Pinaceae and Fabaceae had relatively higher IVs in the eight hill parks surveyed. However, in Park 4, plants of f. Pinaceae have lower IVs, while plants of f. Ginkgoaceae, Cannabinaceae, and Ulmaceae have higher IVs. In Park 8, plants of f. Fabaceae have lower IVs, while plants of f. Sapindaceae and Aristolochiaaceae have higher IVs. Based on GD classification, the woody plant communities in Park 7 had higher IVs for plants with 5 cm < GD ≤ 15 cm, reaching over 0.9, while other parks had higher IVs for plants with GD ≥ 15 cm (0.48‒0.71). Based on leaf characteristics, the evergreen plants in Parks 1, 2, 5, and 6 have higher IVs than deciduous plants, while deciduous plants in Parks 4, 7, and 8 have higher IVs than evergreen plants. In Park 3, the IVs of evergreen and deciduous plants are both close to 0.5.

As shown in Table 2, there is a high (≥ 0.56) OIV between each pair of parks 1, 2, 3, 6, and 7. The OIVs between park 4 and other parks are less than 0.4. The OIV between park 8 and park 5 is 0.42, while the OIVs between park 8 and other parks except park 5 are less than 0.35. The OIV between park 5 and other parks is between 0.3 and 0.5.

Table 2

Overlap importance value (OIV) of woody plant communities between different hill parks
	Park 1	Park 2	Park 3	Park 4	Park 5	Park 6	Park 7	Park 8
Park 1	1.00
Park 2	0.56	1.00
Park 3	0.66	0.59	1.00
Park 4	0.33	0.28	0.35	1.00
Park 5	0.34	0.50	0.49	0.34	1.00
Park 6	0.65	0.52	0.67	0.38	0.43	1.00
Park 7	0.65	0.57	0.62	0.24	0.32	0.56	1.00
Park 8	0.31	0.31	0.37	0.20	0.42	0.33	0.28	1.00

Analysis of the uniqueness of parks and its influencing factors

The analysis of the uniqueness of woody plant communities in Qingdao hill parks is shown in Fig. 3. Using MNIV or LCBD1 as an index, the uniqueness of woody plants in the eight hill parks decreased in the following order: Park 4 > Park 8 > Park 5 > Park 7 > Park 2 > Park 1 > Park 6 > Park 3 (Figs. 3a and 3b). The LCBD2 in different parks decreased in the following order: Park 7 > Park 4 > Park 8 > Park 2 > Park 5 > Park 1 > Park 6 > Park 3 (Fig. 3b). The LCBD3 in different parks decreased in the following order: Park 8 > Park 6 > Park 7 > Park 5 > Park 4 > Park 3 > Park 2 > Park 1 (Fig. 3b). There was no obvious consistency between the order of LCBD1 or MNIV and the orders of LCBD2 and LCBD2. As shown in Fig. 3c and 3d, there are significant differences between Park 4 and Park 8 compared to other parks, making them highly unique. The results of principal coordinate analysis and clustering analysis agree with the results using MNIV or LCBD1 as the comparison indicator.

The results of correlation analysis (Table 3) showed that MNIV was significantly (P < 0.01) positively correlated with LCBD1. LCBD1 was significantly (P < 0.05) positively correlated with LCBD2. LCBD3 was significantly (P < 0.01) positively correlated with park area. MNIV was significantly (P < 0.01) positively correlated with diversity indices Shannon-wiener and Pielou indexes, while LCBD1 was not significantly correlated with Shannon-wiener and Pielou indexes.

Table 3

Correlation between uniqueness indicators, diversity indicators, and park parameters
	Indicators of uniqueness				Indicators of diversity			Park parameters
	MNIV	LCBD1	LCBD2	LCBD3	Richness	Shannon‒Wiener	Pielou	Age	Area
MNIV	1.00	0.94**	0.65	0.42	0.35	0.72*	0.73*	0.21	0.07
LCBD1	0.94**	1.00	0.76*	0.25	0.16	0.54	0.58	0.16	-0.02
LCBD2	0.65	0.76*	1.00	0.34	-0.16	-0.04	-0.04	0.48	0.34
LCBD3	0.42	0.25	0.34	1.00	0.68	0.38	0.16	0.66	0.85*
* indicates significance at P < 0.05. ** indicates significance at P < 0.01.

As shown in Fig. 4, MNIV and LCBD1 were significantly negatively correlated (P < 0.05) with the IV of f. Pinaceae but exhibited a significant positive correlation (P < 0.05) with the IVs of f. Anacardiaceae, Ginkgoaceae, and Cannabinaceae. Additionally, MNIV was significantly positively correlated (P < 0.05) with the IV of f. Sapindaceae, while LCBD1 displayed a significant positive correlation (P < 0.05) with the IV of f. Ulmaceae. LCBD2 exhibited a significant negative correlation (P < 0.05) with the IV of f. Cupressaceae but also displayed a significant positive correlation (P < 0.05) with the IV of f. Anacardiaceae, Ginkgoaceae, and Cannabinaceae plants. Furthermore, LCBD2 was significantly positively correlated (P < 0.05) with the IV of woody plants with a ground diameter between 5 cm and 15 cm. LCBD3 showed a significant positive correlation (P < 0.05) with the IVs of f. Poaceae and Aristolochiaaceae. LCBD1 and LCBD2 were significantly positively (P < 0.05) correlated with the IV of evergreen plants but displayed a significant (P < 0.05) negative correlation with the IV of deciduous plants. Although MNIV was positively correlated with the IV of evergreen plants and negatively correlated with the IV of deciduous plants, these correlations were not significant.

Diversity of woody plants in hill parks

According to research by Jin et al. (2021), there are 331 species of woody landscape plants in Qingdao, ranking only behind Haikou, Guangzhou, and Shenzhen among 36 major cities in China, making it the city with the highest richness of landscape plants among major cities in northern China. The plants surveyed in this study account for approximately one-third of Qingdao's woody landscape plants surveyed by Jin et al. (2021). Yang et al. (2021b) reported that the Shannon‒Wiener index and Pielou index of woody plants in urban forests in Qingdao range from 0.61–1.10 and 0.54–0.80, respectively, which are significantly lower than the Shannon‒Wiener index (1.42–3.13) and Pielou index (0.48–0.91) of woody plants in the hill parks we studied. This is because the richness and Shannon‒Wiener index of woody plants are higher in city parks than in residential and commercial areas (Swan et al. 2017). The results of Yang et al. (2021b) include data from residential and commercial areas.

Burdur (37°N, Türkiye) and Baltimore (39°N, America) are close to the latitude of Qingdao (at 36°N). The highest values of the Shannon‒Wiener index and Pielou index of woody plants in community parks in Burdur are 2.03 and 0.72 (Yücedağ and Aşik 2023), respectively, which are lower than the survey results of Qingdao hill parks in this study. The average richness of woody plants in urban parks in Baltimore is approximately 18 species, with an average Shannon index of 1.23 (Swan et al. 2017), which is also lower than that of Qingdao hill parks in this study. This indicates that the diversities of woody plant communities in Qingdao hill parks are at a high level among urban parks at the same latitude.

Qingdao (36° N, 120° E), Changchun (44° N, 125° E), and Fuzhou (26° N, 119° E) are cities with similar longitudes but different latitudes. Woody plants in urban parks in Changchun showed the highest richness of 22 species and a maximum Shannon‒Wiener index of 2.7 (Ma et al. 2023), which is lower than the diversity level of Qingdao hill parks in this study. In Fuzhou, the species number of woody plants in individual parks in mountainous and riverside parks ranges from 32 to 61 (Lan et al. 2021), which is higher than our findings for Qingdao hill parks. Overall, the diversity of woody plants in urban parks follows the gradient law of lower latitudes having higher richness (Willig 2000; Lyons and Willig 2002).

Uniqueness of woody plant communities in hill parks and its influential factors

Until now, the "uniqueness" of parks or green spaces has been an elusive concept with no clear definitions or evaluation criteria. The perception of uniqueness, obtained through surveys of citizens or visitors using questionnaire methods, is typically defined as perceived uniqueness (Wang et al. 2019). These survey results are often influenced by the social and cultural characteristics of the surveyed group; therefore, it is crucial to consider the grouping of the surveyed population during the survey process (Chan et al. 2015). This may be because different populations have different understandings and standards for the concept of "uniqueness" itself. However, ecological uniqueness calculated using ecological features such as MNIV or LCBD1 proposed in this study and LCBD2 or LCBD3 in previous studies can only indicate the uniqueness of a specific aspect of a park or green space. These indicators struggle to represent people's overall perception of parks or green spaces. We discovered that employing MNIV and LCBD1 as uniqueness indicators of the woody plant communities in hill parks yielded comparable evaluation outcomes, which were in accordance with the results of principal coordinate analysis or cluster analysis.

Robinia pseudoacacia and Pinus nigra are zonal vegetation in Qingdao and are commonly planted in urban green spaces (Liu et al. 2021b; Yang et al. 2023). In this study, MNIV and LCBD1 were significantly negatively correlated with the IV of f. Pinaceae plants (including Pinus nigra) and negatively correlated with the IV of f. Legume plants (including Robinia pseudoacacia) but not significantly. These results indicate that zonal woody plants in Qingdao hill parks have a negative impact on uniqueness. Conversely, MNIV or LCBD1 was significantly positively correlated with the IVs of f. Ginkgoaceae, Sapindaceae, Anacardiaceae, Cannabinaceae, and Ulmaceae plants. These plant families include many species of landscape plants. For example, Ginkgoaceae plants change their leaf pigments to give an impressive golden color in autumn (Li et al. 2020a), while Sapindaceae plants such as Acer rubrum and Anacardiaceae plants such as Pistacia chinensis display bright red colors in autumn (Inoue and Nagai 2015; Liu et al. 2019). The significant positive correlation between MNIV (or LCBD1) and the IV of f. Cannabinaceae plants is mainly due to the extensive planting of Celtis sinensis in Qingdao hill parks. Celtis sinensis is widely distributed in temperate regions and has a wide crown and abundant fruit, providing excellent habitat for birds and mammals (Correll and Johnston 1970). Currently, it is classified as a member of the family Cannabinaceae based on phylogenetic analysis (Yang et al. 2013; Liu et al. 2021a). The significant positive correlation between LCBD1 and the IV of f. Ulmaceae plants is mainly due to the extensive planting of Zelkova serrata in Qingdao hill parks. Zelkova serrata is an important greening plant in East Asia and has special cultural value in some regions of China (Wang et al. 2021; Kimm and Ryu 2015).

Some studies aim to establish a correlation between perceived uniqueness and ecological features (especially species diversity) (Wang et al. 2019) but often arrive at different or even opposite results across various studies. For instance, Wang et al. (2019) mentioned that two studies analyzed the relationship between biodiversity and people's perceptions of parks but yielded contrasting results (Fuller et al. 2007; Qiu et al. 2013). And embellishment: This study found that MNIV is more correlated with plant community diversity than LCBD1. When using MNIV as an indicator of community uniqueness, there is a significant positive correlation between the uniqueness and diversity indices of woody plant communities. Wang et al. (2019) also found a significant correlation between the perceived uniqueness of urban forest patches and community diversity. Therefore, MNIV may better reflect the perceived uniqueness of tourists on plant community uniqueness.

Implications for the protection of residual forest patches and the evaluation of uniqueness

First, the diversity and uniqueness of plant communities are fundamental attributes that support the ecological and landscape functions of park forests (Yücedağ and Aşik, 2023; Goddard et al. 2009; Chan et al. 2015; Wang et al. 2019). The results of this study show that transforming residual forest patches into urban parks can increase the diversity of park woody plant communities compared to general urban parks. By adding some special plants, the uniqueness of these plant communities can be enhanced. Therefore, transforming residual forest patches into urban park forests by adding special plants to the original woody plant communities is a feasible method for protecting and utilizing residual forest patches.

Second, using LCBD1 or MNIV to evaluate the uniqueness of woody plant communities can reflect the influence of the number and distribution of zonal vegetation and characteristic landscape vegetation in the community on uniqueness. The evaluation effects of the two indicators are generally consistent, but MNIV is more closely correlated with plant diversity, so it is recommended that MNIV be used as an evaluation index for the uniqueness of greenbelt woody vegetation communities in future research.

Based on the important values in ecology, this study proposes two evaluation indicators, MNIV and LCBD1, for assessing the uniqueness of park woody plant communities. The diversity and uniqueness of the woody plant communities in eight hill parks in Qingdao transformed from residual forest patches were evaluated. The following conclusions were drawn:

(1) There are great variations in the diversity of woody plant communities among different hill parks.

(2) Using the unique indicators MNIV and LCBD1 based on IV calculations to evaluate the woody plant communities in Qingdao hillside parks, the same results can be obtained, which are consistent with the results obtained by sampling principal coordinate analysis and clustering analysis methods. MNIV is significantly positively correlated with Shannon‒Wiener index and Pielou index.

(3) Correlation analysis between uniqueness indicators and IVs of different plants indicates that plant communities with negative impacts on uniqueness include zonal plants, while plant communities with positive impacts on uniqueness include characteristic landscape plants such as f. Ginkgoaceae and Sapindaceae.

Competing Interests

The authors declare that they have no conflict of interest.

Funding

This work was supported by the Ministry of Housing and Urban‒Rural Development of China and the Chinese Park Association under the Science and Technology Planning Project of the Ministry of Housing and Urban‒Rural Development of China (K20220610).

Author Contributions

Ning Qu, Shilei Zhang, Zhikang Wang contributed to the study conception and design. Zhikang Wang, Chunlin Li, Liangzhen Zhang, Linting Sun performed data collection and analysis. The first draft of the manuscript was written by Ning Qu, Chunlin Li. All authors commented on previous versions of the manuscript.

Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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No competing interests reported.

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Evaluation of the diversity and uniqueness of woody plant communities in park forests transformed form forest patches on urban hills

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Abstract

Figures

Introduction

Material and methods

Natural conditions of Qingdao and locations of the hill parks

Investigation method of woody plants

Method for analyzing the uniqueness of woody plant communities

Calculations

Calculation of MNIV

Calculation of LCBD

Calculation of the diversity indicators

Statistical analysis

Results

Diversity indicators of woody plants

IVs of woody plants and OIVs between different parks

Analysis of the uniqueness of parks and its influencing factors

Discussions

Diversity of woody plants in hill parks

Uniqueness of woody plant communities in hill parks and its influential factors

Implications for the protection of residual forest patches and the evaluation of uniqueness

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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