Urban plants with different seed dispersal modes have convergent response but divergent sensitivity to climate change and anthropogenic stressors

doi:10.21203/rs.3.rs-3865539/v1

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Urban plants with different seed dispersal modes have convergent response but divergent sensitivity to climate change and anthropogenic stressors

https://doi.org/10.21203/rs.3.rs-3865539/v1

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Spontaneous plants are crucial components of urban biodiversity. The distribution of spontaneous plants can be profoundly affected by their seed dispersal mode and environmental factors in urban systems. Since a comprehensive investigation into the drivers of successful seed dispersal modes of spontaneous plants is still lacking, we explored the impacts of natural factors, dispersal limitation, and habitat quality factors on the diversity pattern of spontaneous plants. We assessed the diversity patterns of four seed dispersal modes in 16 major cities in Yunnan province, the most biodiverse province in China. A total of 1,744 spontaneous plants of 916 genera and 175 families were recorded in 893 green patches. The dominating seed dispersal mode of urban spontaneous plants in most cities (13 out of 16) was autochory (33.5–38.7%), with hydrochory being least frequent (4.3–10.9%). Our research highlights spontaneous plants in heavily disturbed anthropogenic ecosystems, such as urban areas, tend to adopt convergent strategies to address environmental stressors. Their richness was significantly higher in colder and humid climates. However, as dispersal limitations (measured by distance to city boundary, city size and urbanization rate) increased and decrease in habitat quality (as expressed by patch area), the richness of all dispersal modes experienced a reduction. However, the sensitivities among different dispersal modes to these factors are divergent. Hydrochory exhibited the strongest sensitivity to habitat quality and climate factors. Whereas autochory demonstrated a strongest sensitivity, and anemochory showed a weakest sensitivity to dispersal limitation. These results suggest that include improving habitat quality or creating green corridors to mitigate dispersal limitation between urban areas and surrounding mountains will be valuable additions to urban biodiversity conservation efforts.

Biological sciences/Ecology/Urban ecology

Biological sciences/Ecology/Biodiversity

Biological sciences/Plant sciences/Plant ecology

Biological sciences/Ecology/Biogeography

Biodiversity hotspots

seed dispersal strategies

urban spontaneous plants

urban ecosystems

species richness

The mode of seed dispersal is a crucial trait that determines the response of species to environmental changes and is therefore considered a main driver of biodiversity patterns (Howe and Smallwood, 1982). Urban areas are complex systems characterized by a myriad of environmental factors that affect the distribution of plant species (Vargas-Hernández et al., 2023). The land use and disturbance characteristics of urban regions are complex, and the highly intricate factors that influence the seed dispersal of species, such as climate, altitude and urbanization, makes the pattern even more complex (Di Musciano et al., 2023; Gorton and Shaw, 2023; Niemelä et al., 2011). Moreover, the response of a species to these environmental factors is affected by its mode of seed dispersal (Zhang et al., 2023). For example, plants with self-dispersal modes (including gravity and explosive dispersal, here after autochory) are typically more sensitive to habitat fragmentation and urbanization than species that rely on wind dispersal (i.e., anemochory) (Garrard et al., 2012). This disparity may lead to anemochorous species having a higher potential to become widespread than species that rely on autochory (Damschen et al., 2014; Niu et al., 2023; Smith and Fellowes, 2014). Additionally, plant species that are highly reliant on specific habitats, such as water dispersers (i.e., hydrochory) in wetlands, are particularly susceptible to habitat loss (Soomers et al., 2013), while species dispersed by animals (i.e., zoochory) are affected by local fauna populations and diversity (Farwig et al., 2017; Niu et al., 2021). Given these disparate responses, dispersal mode is considered a main driver of biodiversity patterns (Bowler and Benton, 2005; Gorton and Shaw, 2023). Nonetheless, we lack a comprehensive understanding of the diversity patterns and drivers of plants with different dispersal modes in urban environments.

Beyond dispersal capability, successful dispersal to a new habitat is also affected by environmental factors (Piano et al., 2023). Several classic theories and framework have postulated that dispersal is affected by the size of the habitat [the species-area relationships, (Arrhenius, 1921; De Candolle, 1855)], the distance between habitat islands and species pools [the island biogeography theory, (MacArthur and Wilson, 1967)], the degree of isolation [metapopulation theory, (Hanski and Gilpin, 1991)], urban-rural gradient (McDonnell and Pickett, 1990), edge effects and matrix properties [the patch-matrix perspective, (Laurance, 2008)]. Furthermore, the response of plants to environmental changes may exist “lag effect”, resulting in “extinction debt” or “invasion debt” (Essl et al., 2011; Hahs and McDonnell, 2014; Tilman et al., 1994), therefore the historical process can also affected biodiversity (Gao et al., 2023). In addition, at the regional scale, species dispersal is also affected by climatic and topographic factors, such as temperature, precipitation and elevation (Di Musciano et al., 2023). While numerous studies have assessed factors that affect dispersal, it remains unknown how these factors interact, and a comprehensive investigation in urban ecosystems is lacking (Aranda et al., 2013; Cote et al., 2017; Coutts et al., 2011; Piano et al., 2023; Walentowitz et al., 2022).

Urban spontaneous plants form an important part of urban vegetation. They are not-intentionally planted yet are also not part of the remaining natural vegetation, as they have established throughout urban environments on their own accord (Prach et al., 2001). These plants can rapidly respond to disturbances, making them effective indicators for the urbanization process (Chen et al., 2014). In recent years, urban spontaneous plants have gained increasing attention as the optimal material and life landmark for enhancing resilience and sustainable of native plant community (Chang et al., 2022; Chen et al., 2021; Hu et al., 2021; Hu et al., 2022; Li et al., 2019). To help elucidate the mechanisms of species dispersal, we use urban spontaneous plants as a model species group, as they integrate urbanization characteristics and climatic factors in urban ecosystems. Using a survey of spontaneous plants in 16 prefecture-level cities in Yunnan Province, the most biodiverse province of China, we systematically analyze the relative contribution of three types of environmental drivers (natural factors, dispersal limitation and habitat quality, Fig. 1) to diversity patterns of spontaneous plants with different dispersal modes. Our aims were: 1) clarify the composition of the life form and seed dispersal modes of urban spontaneous plants in 16 prefecture-level cities in Yunnan Province; 2) to explore the differences in sensitivity among spontaneous plants with different dispersal modes to the same environmental factor; 3) disentangle the environmental factors affecting and its relative contribution to diversity patterns of spontaneous plants with different dispersal modes.

Across the 16 major cities surveyed in Yunnan province, we recorded 1,744 spontaneous plants species of 916 genera and 175 families. Of all species, 1,135 were herbaceous (65.1%) and 609 were woody plants (34.9%). Autochory was the most frequent seed dispersal mode (604 species, 34.6%), followed by zoochory (599, 34.4%), anemochory (429, 24.6%), and hydrochory (112, 6.4%). Plant species richness among cities varied from 180 in Zhaotong city to 479 in Dali city. The species composition of Chuxiong city (34.3%), Dehong city (34.5%) and Qujing city (35.1%) was dominated by zoochory, while other cities were all dominated by autochory (≥ 33.5%). In addition, anemochory varied between 23.5% and 27.6%, and hydrochorous species constituted less than 10.9% of all species in all cities (Fig. 2).

The final model encompassed natural factors (i.e. MAT and MAP), dispersal limitation factors (BD, Sealed₂₅, city size and UR) and habitat quality variables (patch area and LSI). The final model included interaction terms with dispersal mode and patch area, LSI, BD, city size, UR, MAP and MAT, indicating substantial evidence for disparate responses of various dispersal modes to these variables. The interactions of seed dispersal mode and Sealed₂₅ was not retained indicating similar response of different dispersal modes to Sealed₂₅ (Table 1).

Table 1

Model summary of the final generalized linear mixed-effects model including the main effects of habitat quality, dispersal limitation and climate factors, and their interactions with seed dispersal modes of spontaneous plant species.
	Main effects and interactions	Chisq	Df	Pr(> Chisq)
Interaction	Dispersal modes	4181.03	3	< 0.001***
Habitat quality	Area	1561.18	1	< 0.001***
	Dispersal modes: Area	49.43	3	< 0.001***
	LSI	2.01	1	0.15
	Dispersal modes: LSI	7.68	3	0.05.
Dispersal limitation	BD	7.26	1	0.007**
	Dispersal modes: BD	13.62	3	0.003**
	Sealed₂₅	4.35	1	0.04*
	City size	18.06	1	< 0.001***
	Dispersal modes: City size	9.00	3	0.03*
	UR	6.72	3	0.009**
	Dispersal modes: UR	17.05	3	< 0.001***
Climate factors	MAP	31.61	1	< 0.001***
	Dispersal modes: MAP	25.91	3	< 0.001***
	MAT	7.57	1	< 0.001***
	Dispersal modes: MAT	17.79	3	< 0.001***

Note: Area: patch area; LSI: landscape shape index; BD: distance of patches from city boundary; Sealed₂₅: the proportion of impervious surface within radii of 25 m around the patch; UR: urbanization rate; MAP: mean annual precipitation; MAT: mean annual temperature; factors with bold face present this factor significantly correlated (p < 0.05) with richness; n.s. present non-significance.

Results of contrasts showed that the effect of patch area differed significantly among all seed dispersal modes, except for no significance between zoochory and autochory (Fig. 3). Specifically, increase in patch area showed the strongest positive effects on the richness of hydrochory, and the least effects on anemochory. The effect of LSI showed more negative effects on the richness of hydrochory than that of zoochory and anemochory. The effect of BD on richness was significantly more negative for hydrochory compared to the other three modes (zoochory, autochory and anemochory), while there were no significant differences observed among the other three modes. The effect of city size on richness exhibited significant variations between zoochory and autochory, as well as between anemochory and autochory. Notably, autochory demonstrated the more negative response to city size compared to both zoochory and anemochory. The effect of UR on richness showed weakest negative effects on the anemochory than any other modes. MAP was more strongly positively associated with hydrochory than with the other modes. Lastly, hydrochory showed a highest significant negative association with MAT compare to other three dispersal modes, followed autochory, anemochory and zoochory (Fig. 3).

Detailed assessment of the separate GLMM for different seed dispersal modes showed that the combination of habitat quality, dispersal limitation, and climate factors explained the richness of each seed dispersal mode of spontaneous plants well (\({R}_{m}^{2}\) ranges between 0.46–0.59). Anthropogenic factors were the dominant drivers for the richness of all categories, accounting for over 32.7% of the fixed effects for each seed dispersal mode. Habitat quality (expressed as patch area and LSI) explained 31.1% (anemochory), 28.3% (autochory), 20.7% (hydrochory) and 28.4% (zoochory) of the variation in the richness of each seed dispersal modes. In contrast, anthropogenically driven dispersal limitation (expressed as BD, Sealed₂₅, city size and UR) explained the variance 7.5%-anemochory, 17.3%-autochory, 12.0%-hydrochory and 12.5%-zoochory, respectively. Natural factors (MAP and MAT) explained 14.3% (anemochory), 13.0% (autochory), 13.8% (hydrochory) and 8.6% (zoochory), respectively (Table 3 and Fig. 4). The random effects explained 9.2%, 8.0%, 11.0% and 10.5% variation in the averaged models for anemochory, autochory, hydrochory and zoochory, respectively.

Table 3

Results of the separate generalized linear mixed-effects models for spontaneous species richness of different seed dispersal modes
Factors		Anemochory		Autochory		Hydrochory		Zoochory
Factors		Estimate	z value	Estimate	z value	Estimate	z value	Estimate	z value
Habitat quality	Log (Area)	0.42	22.68	0.46	24.80	0.66	16.01	0.52	20.26
Habitat quality	LSI	0.01	0.82	0.00	0.10	-0.01	0.31	0.02	0.70
Dispersal limitation	BD	-0.01	0.38	-0.02	0.80	-0.15	2.86	-0.04	1.05
	Sealed₂₅	0.00	0.29	0.00	0.27	-0.01	0.33	0.00	0.19
	Log (City size)	-0.09	3.42	-0.16	4.98	-0.10	1.34	-0.11	2.59
	Population	-	-	-	-	-	-	-	-
	UR	-	-	-0.10	3.34	-0.13	2.18	-0.09	2.43
Natural factors	MAP	0.15	4.90	0.12	3.80	0.28	4.32	0.16	4.16
	MAT	-0.05	1.67	-0.09	2.69	-0.16	2.58	0.00	0.20
	Elevation	-	-	-	-	-	-	-	-
\({R}_{m}^{2}{/R}_{c}^{2}\)		0.53/0.62		0.59/0.67		0.46/0.58		0.49/0.60

Despite increased attention on urban spontaneous plants in urban ecosystem studies, and the importance of seed dispersal in biodiversity patterns generally, our knowledge on the distribution pattern and drivers of spontaneous plants with different seed dispersal modes is still lacking. Our study investigated the diversity patterns of spontaneous plants with different seed dispersal modes in the sixteen prefectural-level cities of Yunnan province, China, and the importance of drivers related to natural factors, dispersal limitation and habitat quality. Our study highlights that the richness of species with different dispersal modes is generally correlated with a similar combination of external drivers, but that there are various sensitivities among different dispersal modes to these factors.

Given the varying properties of species with different dispersal modes, including their dispersal distance and seed mass, seed quantity and seed morphology (Cote et al., 2017), it may be expected that some are more susceptible to fragmentation than others (Gorton and Shaw, 2023; Niu et al., 2023; Piano et al., 2023). For example, self-dispersed species (autochory) have a more limited dispersal distance than wind-dispersed species (anemochory), and hence one may expect that autochorous species are more sensitive to fragmentation. Despite the highly fragmented landscape in urban systems, we found that species exhibiting autochory dispersal dominated the flora in most of the studied cities (13/16), while anemochory accounted for a relatively lower proportion in all cities. As expected, we found the lowest proportion of hydrochory in the urban floras, presumably due to urbanization resulting in wetland habitat loss in urban areas (Soomers et al., 2013).

Habitat quality determines to which extent plants can obtain resources from the focal patches. Our results showed that larger patches contained higher richness for all groups of plants, following many other studies (Arrhenius, 1921; MacArthur and Wilson, 1967; Matthies et al., 2015). However, trends differed significantly among the various groups, suggesting that the influence of patch area varies with different seed dispersal modes, and showing the strongest positive effects for species exhibiting hydrochory, followed by zoochory, autochory and, lastly, anemochory. Additionally, zoochorous species and anemochory species responded more positively to LSI (landscape shape index) than hydrochorous ones. The relative importance of (i.e. variance explained by) habitat quality was the lowest contribution for the richness of species exhibiting hydrochory, compared with species with other dispersal modes. Hence, while hydrochorous species appear most responsive to patch area, the explanatory power of this relationship was low. This disparity indicates the importance of conducting a comprehensive analysis, considering not only the driving factors and their relative contributions.

Given that the studied cities expand in concentric rings and are encompassed by natural mountains, our measure of distance to city boundary (BD) conveyed not only the intensity of urbanization but also the difficulty of accessing the natural species pool (Gao et al., 2023). As hydrochorous species heavily relies on wet habitats(Gorton and Shaw, 2023; Howe and Smallwood, 1982; Van der Pijl, 1982), species exhibiting hydrochory showed decreased richness with increasing BD compared to other dispersal modes. The diminishing presence of water bodies as we move towards city centers and the stronger dispersal barriers between water bodies in increasingly highly urbanized areas may contribute to the lower richness of hydrochory in urbanized regions. That is, the larger cities (bigger city size in our case) potentially construe increased barriers to species dispersal into the city center due to increasingly higher urbanization levels and more intense human disturbance (Ceplová et al., 2017). Indeed, spontaneous plants with different dispersal modes showed significantly decreased richness with increasing city size. Moreover, autochorous species demonstrated a more negative response to city size in comparison to zoochory and anemochory, suggesting an elevated sensitivity to urbanization. As urbanization intensifies, there is a concurrent rise in fragmentation levels, indirectly affecting species with short dispersal range, such as autochorous species (Cruz et al., 2013; Wilson et al., 2020), thereby potentially amplifying their sensitivity to city size. Additionally, Higher urbanization rate (UR) may have led to more frequent and unstable disturbances, potentially giving rise to “extinction debt” or “invasion debt” issues and affect biodiversity (Essl et al., 2011; Tilman et al., 1994). Therefore, current biodiversity pattern also is jointly affected by the present statues of urbanization and historical process of urbanization (Gao et al., 2023). Collectively, our findings highlight the predominant role of dispersal limitation in shaping the richness of autochory compared to species with other dispersal modes. In contrast, its impact was least pronounced in the case of anemochory, which boasts a longer dispersal range, typically produces a larger quantity of seeds and long been regarded as possessing the capacity for sustained resistance to adverse environmental stressors, making them appear less sensitive to the current and historical urbanization process (Lososová et al., 2023; Zhang et al., 2023). In conclusion, our study underscores the significant impact of dispersal limitation on the diversity of spontaneous plants with different seed dispersal modes. We suggest that augmenting the number of wetland habitats in urban areas holds the potential to boost the richness of hydrochorous species. Additionally, the creation of corridors between urban and natural areas to reduce dispersal limitation emerges as a critical strategy for enhancing and conserving urban biodiversity.

Through acting as the first level environmental filter, climate factors influence the regional species pool and ultimately affect the pattern of biodiversity (Francis and Currie, 2003; García-Palacios et al., 2018). The results of our study demonstrate significant positive impacts of precipitation on all groups of spontaneous plants. Similar trends were also found in research on the diversity of urban vegetations in 7 cities in the USA (Wheeler et al., 2017). Higher precipitation was also associated with increased urban plant richness across 10 cities in Kazakhstan (Vakhlamova et al., 2022), 18 cities in China (Ouyang et al., 2023), and 117 urban yards from 6 metropolitan areas in the USA (Padullés Cubino et al., 2019). However, our study showed a significant negative impact of temperature on the richness of autochory and hydrochory. In contrast to mean annual precipitation (MAP), species diversity reduced in cities with high mean annual temperature (MAT), presumably due to accompanying drought conditions (Aronson et al., 2014; Wheeler et al., 2017). In addition, the divergent trends in climate factors revealed that hydrochory showed the strongest positive relationship with MAP and strongest negative relationship with MAT compared to plants with other seed dispersal modes. Evidently, lower precipitation and higher temperature jointly contribute to more arid environments, consequently impacting plants richness, particularly in term of hydrochorous plants which heavily rely on wet habitats. Furthermore, we infer that due to the restricted of dispersal distance, autochory faces challenges in achieving long-distance migrations across climatic zones, rendering it more susceptible to the influence of climate compared to anemochory and zoochory. The findings of our study indicate that the ongoing rise in global temperatures could exacerbate the decline of plant diversity in urban areas, and that hydrochory and autochory are more sensitive than others. This underscores the need to implement effective conservation measures to preserve urban biodiversity in the face of climate change.

The study revealed significant interactions between dispersal modes and environmental factors. The divergent trends of factors among the different seed dispersal modes, indicating that different plant groups exhibit varied degrees of sensitivity to environment. However, a more in-depth analysis of spontaneous plants with different seed dispersal modes showed that the drivers of diversity patterns within each group are identicality, except for UR was not retained on the model of anemochory. The natural factors (like climate) and anthropogenic factors together shape the diversity pattern of urban spontaneous plants with different dispersal modes, however anthropogenic factors such as habitat quality and dispersal limitation, played an overwhelming role. In a study of 110 cities globally, urban plants diversity patterns were also found to be dominated by anthropogenic features (Aronson et al., 2014). Additionally, the combinations of factors affecting different group of plants in urban areas aligns with observations in other studies, such as those conducted in Kunming city of China (Gao et al., 2021), and a study on vascular plants in sixty European cities (Kalusová et al., 2019). These results indicated that spontaneous plants in heavily disturbed artificial ecosystems like urban areas tend to develop convergent strategies to cope with environmental stressors, nevertheless, that there are various sensitivities among different dispersal modes to these factors. Therefore, to maximize the conservation of urban biodiversity, in addition to improving habitat quality and reducing dispersal limitations, we should also take into account the characteristics of different species.

Study area

Our study was carried out in Yunnan province, China (N 21°8'32'' - N 29°15'8'', E 97°31'39'' - E 106°11'47''), which has complex topography with significant variation in altitude (77 m − 6740 m), temperature (average annual temperature 5 ℃ − 23.8 ℃) and precipitation (total annual precipitation 580 mm − 2700 mm) across its territory (Yunnan Yearbook Editorial Committee, 2019). The diverse natural environmental conditions contributes makes Yunnan province the most biodiverse province in China, accounting for more than 51.6% (≥ 18,000 species) of all higher plants recorded in China, despite occupying only 4.1% of the total territory area of China (Qian et al., 2020; Yang et al., 2004). Additionally, the unique terrain and climate conditions of each city in Yunnan province have also led to distinct urbanization processes. These extensive and diverse environmental gradients present an exceptional place to study urban ecology. Spontaneous plants were sampled across all 16 prefecture level cities, which are widely distributed along natural and urbanization gradients. In each city, sampling sites with a 500 m radius were established at about 2 km intervals from the city center to the outskirts. At each sampling site, more than 3 accessible green patches were randomly selected for a species richness survey (Fig. 5 and Table S1). In total, 893 patches were surveyed in this study, with the mean patch area ranging from 0.15 ha to 2.9 ha in each city.

Data collection

Field surveys of spontaneous plant species were conducted during the growing season between April and October in 2017–2018 (one city), 2019 (eight cities) and 2022 (seven cities), following the sampling protocol by Gao et al., (2023; 2021). Our analysis focused solely on spontaneous plant species within each patch. In this context, naturally occurring herbaceous plants and seedlings of woody plants in all urban man-made green patches were categorized as spontaneous species (Gao et al., 2023; Gao et al., 2021). Notably, fully grown trees were omitted from analysis due to the challenge of determining whether they were intentionally planted or occurred spontaneously, except for perhaps those growing on abandoned lands, roofs and walls (Huang et al., 2019).

The seed dispersal modes of species are typically classified into four categories: anemochory, autochory, hydrochory or zoochory (Van der Pijl, 1982). Firstly, we determined the seed dispersal modes of the plant species by referring to published literature (Guo and Zheng, 2017; Howe and Smallwood, 1982; Yu et al., 2018), International Network for Seed Based Restoration and Royal Botanic Gardens Kew (https://ser-sid.org). If a species was not found in the literature or the database, its seed dispersal mode was determined based on fruit type and characteristics. For instance, species with fleshy fruits and nuts will attract birds and other fruit-eating animal (such as bats and rodents) and are subsequently (accidently) dispersed; these were categorized as zoochory. Seeds or fruits with special structures, such as mucilage or hooks, that adhere to animals for dispersion, as observed in Bidens pilosa and Xanthium strumarium, were also classified as zoochory. Adaptations representing wind-dispersal include dust-like seeds (e.g., the small and lightweight seeds, such as most of species in Asteraceae family), balloon-like structures (e.g. genus Koelreuteria), winged structures (e.g. genus Acer), or seeds with a tuft of hairs (as found in Taraxacum genus). Species with mechanisms like seed pod explosion (where seeds are propelled when the fruit, often a capsule, bursts open, as seen in the Ecballium genus) or seeds simply dropping to the ground due to their weight (as genus Cocos, were assigned to autochory. The seeds of hydrochorous species can float in water, allowing them to be carried to different locations by rivers and other water bodies. If multiple dispersal modes were addressed for the one species, only the main dispersal mode was used in our analysis. For instance, the seeds of Cocos nucifera can be dispersed by both gravity and water, but observations in urban areas suggest their dispersal there is mainly by gravity, resulting in an autochory classification in our research.

The potential environmental drivers of species richness were divided into three groups: natural factors, dispersal limitation and habitat quality (see Fig. 1). The natural environmental factors were sourced from the National Earth System Science Data Center (mean value of 1960–2021, resolution = 30 m, www.geodata.cn) and WorldClim website (http://www.worldclim.com, resolution = 30 m), including mean annual temperature (MAT), mean annual precipitation (MAP), mean annual duration of sunshine (MSD), the coldest month mean temperature of year (CMT) and elevation. Habitat quality was represented by patches’ properties, including patch area, patch perimeter, perimeter-area ratio (PA) and landscape shape index (\(LSI=Perimeter/2\sqrt{\pi \times Area}\)), which calculated by ArcGIS 10.3 (ESRI, 2015). Dispersal limitation factors are mainly characterized by urbanization index, including urbanization intensity and urbanization rate (Gao et al., 2023). As urban areas are in rapid expansion, the frequency of habitat disturbances increases, subsequently intensifying species' dispersal limitations. To represent current urbanization intensity, we used Sealed₂₅ (the proportion of impervious surface within a radius of 25 m around the patch, please see Gao et al., 2021), BD (distance to city boundary), city population and city size. City size was calculated by moving windows (1 km × 1 km, threshold = 50%, 2020); Population data were collected from “Chinese population census yearbook 2020”. To estimate the Sealed₂₅, the Maximum Likelihood Classification approach (a supervised classification in ArcGIS 10.3) was used to classify high-resolution aerial orthophotos of Google Earth (RGB, 0.14 m resolution, 2018). We represented urbanization rate by quantifying the sealed surface expansion rate of each city (i.e., urbanization rate, UR) between 1990 and 2019 using land cover data with a resolution of 30 meters from Yang and Huang, (2021).

Data analysis

To check whether the responses of spontaneous plant richness to driving factors differ among dispersal modes, generalized linear mixed effect models (GLMMs) were used to analyze the driving factors of the richness of species with the four seed dispersal modes: anemochory, autochory, hydrochory and zoochory. We fitted a model with species richness as the response variable, using a negative binomial distribution and “glmer.nb” function within R package lme4 (Bates et al., 2009) and site ID nested within city ID as random effect. As fixed effects, we used all natural factors, dispersal limitation, and habitat quality described above, as well as the interaction between these factors and seed dispersal modes (hereafter so called full model).

The “dredge” function in the R package MuMIn was used on the full model, which created a suite of models with all possible combinations of the initial variables and sorted them according to the Akaike Information Criterion (AIC). We then conducted model averaging on all models with ΔAIC < 2 using the function “model.avg” (Anderson and Burnham, 2004). Since key diagnostic and summary functions described below cannot directly accommodate averaged models, we refitted a model with the same parameters for trend evaluation (referred to hereafter as the “final model’). A comparison of the estimated coefficients of this final refitted model and the averaged model showed minimal coefficient changes, indicating similar interaction trends (Table S2 and Table S3). Residual diagnostics of the final model were then checked using the “simulateResiduals” function in R package DHARMa (Hartig, 2020). The “Anova” function in the R package car was employed to assess both the main effects and interaction effects in the final model. To assess differences in environmental responses between dispersal modes, we used the “emtrends” function in the R package emmeans estimate and compared trend estimates of interaction factors for the different dispersal modes (Lenth et al., 2019).

To further explore the driving factors within a dispersal mode, we separately ran the same GLMM analysis described above for each group. We used the “r.squaredGLMM” function in the R package MuMIn to calculate R², then manually performed variance decomposition to assess the relative contribution of each driving factor and the random effects (García-Palacios et al., 2018; Gross et al., 2017). We ran all analyses using the software R 3.5.1 (R Core Team, 2015).

Data and code availability

The data and codes for the analysis will available once accepted.

Acknowledgement

This research was funded by Major Program for Basic Research Project of Yunnan Province (202101BC070002), the Ministry of Science and Technology of China (2015FY210200), ECNU Academic Innovation Promotion Program for Excellent Doctoral Students (YBNLTS2019), National Key Research and Development Program of China (2023YFF1305800). Yingji Pan acknowledges the Innovation Team Project of Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences (2023CXTD03). We thank Professor Xiaoya Yu from Qiannan Normal University for Nationalities for helping with the species identification.

Author contribution

Zhiwen Gao: field survey, data collection, analysis, interpretation and drafting the manuscript; Tian Wu, Mingming Zhuge, Tiyuan Xia, Yuandong Hu: field survey, collecting data and revising the manuscript; Ellen Cieraad, Kun Song and Yingji Pan: data analysis, interpretation of data, conception and revising the manuscript critically; Kun Song and Liangjun Da: funding, conception, revising the manuscript and final approval of the manuscript. All authors have read and agreed to the published version of the manuscript.

Conflict of interest statement

The authors declare no conflict of interest.

Anderson, D., Burnham, K., 2004, Model selection and multi-model inference, Second. NY: Springer-Verlag 63(2020):10.
Aranda, S. C., Gabriel, R., Borges, P. A. V., Santos, A. M. C., Hortal, J., Baselga, A., Lobo, J. M., 2013, How do different dispersal modes shape the species-area relationship? Evidence for between-group coherence in the Macaronesian flora, Global Ecology and Biogeography 22(4):483-493.
Aronson, M. F. J., La Sorte, F. A., Nilon, C. H., Katti, M., Goddard, M. A., Lepczyk, C. A., Warren, P. S., Williams, N. S. G., Cilliers, S., Clarkson, B., Dobbs, C., Dolan, R., Hedblom, M., Klotz, S., Kooijmans, J. L., Kuhn, I., MacGregor-Fors, I., McDonnell, M., Mortberg, U., Pysek, P., Siebert, S., Sushinsky, J., Werner, P., Winter, M., 2014, A global analysis of the impacts of urbanization on bird and plant diversity reveals key anthropogenic drivers, Proceedings of the Royal Society B: Biological Sciences 281(1780):20133330-20133330.
Arrhenius, O., 1921, Species and area, Journal of Ecology 9(1):95-99.
Bates, D., Maechler, M., Bolker, B., Walker, S., Christensen, R. H. B., Singmann, H., Dai, B., Scheipl, F., Grothendieck, G., Green, P., 2009, Package ‘lme4’, URL http://lme4. r-forge. r-project. org.
Bowler, D. E., Benton, T. G., 2005, Causes and consequences of animal dispersal strategies: relating individual behaviour to spatial dynamics, Biological reviews 80(2):205-225.
Ceplová, N., Kalusová, V., Lososová, Z., 2017, Effects of settlement size, urban heat island and habitat type on urban plant biodiversity, Landscape and Urban Planning 159:15-22.
Chang, M., Luo, X., Zhang, Y., Pang, Y., Li, M., Liu, J., Da, L., Song, K., 2022, Land-use diversity can better predict urban spontaneous plant richness than impervious surface coverage at finer spatial scales, Journal of Environmental Management 323:116205.
Chen, C., Lu, Y., Jia, J., Chen, Y., Xue, J., Liang, H., 2021, Urban spontaneous vegetation helps create unique landsenses, International Journal of Sustainable Development & World Ecology:1-9.
Chen, X., Wang, W., Liang, H., Liu, X., Da, L., 2014, Dynamics of ruderal species diversity under the rapid urbanization over the past half century in Harbin, Northeast China, Urban Ecosystems 17(2):455-472.
Cote, J., Bestion, E., Jacob, S., Travis, J., Legrand, D., Baguette, M., 2017, Evolution of dispersal strategies and dispersal syndromes in fragmented landscapes, Ecography 40(1):56-73.
Coutts, S. R., van Klinken, R. D., Yokomizo, H., Buckley, Y. M., 2011, What are the key drivers of spread in invasive plants: dispersal, demography or landscape: and how can we use this knowledge to aid management? Biological Invasions 13(7):1649-1661.
Cruz, J. C., Ramos, J. A., Da Silva, L. P., Tenreiro, P. Q., Heleno, R. H., 2013, Seed dispersal networks in an urban novel ecosystem, European journal of forest research 132:887-897.
Damschen, E. I., Baker, D. V., Bohrer, G., Nathan, R., Orrock, J. L., Turner, J. R., Brudvig, L. A., Haddad, N. M., Levey, D. J., Tewksbury, J. J., 2014, How fragmentation and corridors affect wind dynamics and seed dispersal in open habitats, Proceedings of the National Academy of Sciences 111(9):3484-3489.
De Candolle, A., 1855, Géographie botanique raisonnée ou exposition des faits principaux et des lois concernant la distribution géographique des plantes de l'époque actuelle, V. Masson.
Di Musciano, M., Ricci, L., Di Cecco, V., Bricca, A., Di Martino, L., Frattaroli, A. R., 2023, Elevational patterns of plant dispersal ability in Southern Europe, Plant Biosystems - An International Journal Dealing with all Aspects of Plant Biology 157(1):71-79.
ESRI, 2015, ArcGIS 10.3. 1, ESRI Redlands, California.
Essl, F., Dullinger, S., Rabitsch, W., Hulme, P. E., Hulber, K., Jarosik, V., Kleinbauer, I., Krausmann, F., Kuhn, I., Nentwig, W., Vila, M., Genovesi, P., Gherardi, F., Desprez-Loustau, M. L., Roques, A., Pysek, P., 2011, Socioeconomic legacy yields an invasion debt, Proceedings of the National Academy of Sciences 108(1):203-207.
Farwig, N., Schabo, D. G., Albrecht, J., 2017, Trait‐associated loss of frugivores in fragmented forest does not affect seed removal rates, Journal of Ecology 105(1):20-28.
Francis, A. P., Currie, D. J., 2003, A globally consistent richness-climate relationship for angiosperms, The American Naturalist 161(4):523-536.
Gao, Z., Pan, Y., Van Bodegom, P. M., Cieraad, E., Xing, D., Yang, Y., Xia, T., Luo, X., Song, K., Da, L., Malkinson, D., 2023, Beta diversity of urban spontaneous plants and its drivers in 9 major cities of Yunnan province, China, Landscape and Urban Planning 234:104741.
Gao, Z., Song, K., Pan, Y., Malkinson, D., Zhang, X., Jia, B., Xia, T., Guo, X., Liang, H., Huang, S., Da, L., M. Van Bodegom, P., Cieraad, E., 2021, Drivers of spontaneous plant richness patterns in urban green space within a biodiversity hotspot, Urban Forestry & Urban Greening 61:127098.
García-Palacios, P., Gross, N., Gaitán, J., Maestre, F. T., 2018, Climate mediates the biodiversity–ecosystem stability relationship globally, Proceedings of the National Academy of Sciences 115(33):8400-8405.
Garrard, G. E., McCarthy, M. A., Vesk, P. A., Radford, J. Q., Bennett, A. F., 2012, A predictive model of avian natal dispersal distance provides prior information for investigating response to landscape change, Journal of Animal Ecology 81(1):14-23.
Gorton, A. J., Shaw, A. K., 2023, Using theoretical models to explore dispersal variation and fragmentation in urban environments, Population Ecology 65(1):17-24.
Gross, N., Le Bagousse-Pinguet, Y., Liancourt, P., Berdugo, M., Gotelli, N. J., Maestre, F. T., 2017, Functional trait diversity maximizes ecosystem multifunctionality, Nature ecology & evolution 1(5):1-9.
Guo, Z., Zheng, J., 2017, Predicting modes of seed dispersal using plant life history traits, Biodiversity Science 25(09):966-971.
Hahs, A. K., McDonnell, M. J., 2014, Extinction debt of cities and ways to minimise their realisation: a focus on Melbourne, Ecological Management & Restoration 15(2):102-110.
Hanski, I., Gilpin, M., 1991, Metapopulation dynamics: brief history and conceptual domain, Biological Journal of the Linnean Society 42(1-2):3-16.
Hartig, F., 2020, DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models, R package version 0.3 3(5).
Howe, H. F., Smallwood, J., 1982, Ecology of seed dispersal, Annual review of ecology and systematics 13(1):201-228.
Hu, L., Qin, D., Lu, H., Li, W., Shang, K., Lin, D., Zhao, L., Yang, Y., Qian, S., 2021, Urban growth drives trait composition of urban spontaneous plant communities in a mountainous city in China, Journal of Environmental Management 293:112869.
Hu, S., Jin, C., Huang, L., Huang, J., Luo, M., Qian, S., Jim, C. Y., Song, K., Chen, S., Lin, D., 2022, Characterizing composition profile and diversity patterns of spontaneous urban plants across China's major cities, Journal of Environmental Management 317:115445.
Huang, L., Qian, S., Li, T., Jim, C. Y., Jin, C., Zhao, L., Lin, D., Shang, K., Yang, Y., 2019, Masonry walls as sieve of urban plant assemblages and refugia of native species in Chongqing, China, Landscape and Urban Planning 191:103620.
Kalusová, V., Čeplová, N., Chytrý, M., Danihelka, J., Dřevojan, P., Fajmon, K., Hájek, O., Kalníková, V., Novák, P., Řehořek, V., 2019, Similar responses of native and alien floras in European cities to climate, Journal of Biogeography 46(7):1406-1418.
Laurance, W. F., 2008, Theory meets reality: How habitat fragmentation research has transcended island biogeographic theory, Biological Conservation 141(7):1731-1744.
Lenth, R., Singmann, H., Love, J., Buerkner, P., Herve, M., 2019, Package ‘emmeans’, R package version 1(3.2).
Li, X., Fan, S., Guan, J., Zhao, F., Dong, L., 2019, Diversity and influencing factors on spontaneous plant distribution in Beijing Olympic Forest Park, Landscape and Urban Planning 181:157-168.
Lososová, Z., Axmanová, I., Chytrý, M., Midolo, G., Abdulhak, S., Karger, D. N., Renaud, J., Van Es, J., Vittoz, P., Thuiller, W., 2023, Seed dispersal distance classes and dispersal modes for the European flora, Global Ecology and Biogeography 32(9):1485-1494.
MacArthur, R. H., Wilson, E. O., 1967, Island biogeography, Princeton.
Matthies, S. A., Rüter, S., Prasse, R., Schaarschmidt, F., 2015, Factors driving the vascular plant species richness in urban green spaces: using a multivariable approach, Landscape and Urban Planning 134:177-187.
McDonnell, M. J., Pickett, S. T., 1990, Ecosystem Structure and Function along Urban-Rural Gradients: An Unexploited Opportunity for Ecology, Ecological Society of America Stable 71(4):1232-1237.
Niemelä, J., Breuste, J. H., Guntenspergen, G., McIntyre, N. E., Elmqvist, T., James, P., 2011, Urban ecology: patterns, processes, and applications, OUP Oxford.
Niu, H., Rehling, F., Chen, Z., Yue, X., Zhao, H., Wang, X., Zhang, H., Schabo, D. G., Farwig, N., 2023, Regeneration of urban forests as influenced by fragmentation, seed dispersal mode and the legacy effect of reforestation interventions, Landscape and Urban Planning 233:104712.
Niu, H., Wang, X., Wu, S., Xing, J., Peng, C., Chen, Z., Li, Y., Zhang, H., 2021, Granivorous rodent loss poses greater threats to oak trees with large acorns than those with small ones in urban forests, Urban Forestry & Urban Greening 62:127185.
Ouyang, Y., Chen, Y., Yang, G., Ren, Y., Yu, M., Chang, J., Ge, Y., 2023, Homogenization of trees in urban green spaces along the moisture gradient in China, Urban Forestry & Urban Greening 83:127892.
Padullés Cubino, J., Cavender-Bares, J., Hobbie, S. E., Pataki, D. E., Avolio, M. L., Darling, L. E., Larson, K. L., Hall, S. J., Groffman, P. M., Trammell, T. L. E., Steele, M. K., Grove, J. M., Neill, C., 2019, Drivers of plant species richness and phylogenetic composition in urban yards at the continental scale, Landscape Ecology 34(1):63-77.
Piano, E., Bonte, D., De Meester, L., Hendrickx, F., 2023, Dispersal capacity underlies scale‐dependent changes in species richness patterns under human disturbance, Ecology:e3946.
Prach, K., Bartha, S., Joyce, C. B., Pyšek, P., Van Diggelen, R., Wiegleb, G., 2001, The role of spontaneous vegetation succession in ecosystem restoration: a perspective, Applied Vegetation Science 4(1):111-114.
Qian, L., Chen, J., Deng, T., Sun, H., 2020, Plant diversity in Yunnan: Current status and future directions, Plant Diversity 42(4):281-291.
R Core Team, 2015, R version 3.5. 1.
Smith, L. S., Fellowes, M. D. E., 2014, The grass-free lawn: Management and species choice for optimum ground cover and plant diversity, Urban Forestry & Urban Greening 13(3):433-442.
Soomers, H., Karssenberg, D., Soons, M. B., Verweij, P. A., Verhoeven, J. T., Wassen, M. J., 2013, Wind and water dispersal of wetland plants across fragmented landscapes, Ecosystems 16:434-451.
Tilman, D., May, R. M., Lehman, C. L., Nowak, M. A., 1994, Habitat destruction and the extinction debt, Nature 371(6492):65-66.
Vakhlamova, T., Wagner, V., Padullés Cubino, J., Chytrý, M., Lososová, Z., 2022, Urban plant diversity in Kazakhstan: Effects of habitat type, city size and macroclimate, Applied Vegetation Science 25(3):e12679.
Van der Pijl, L., 1982, Principles of dispersal in higher plants, Springer.
Vargas-Hernández, J. G., Pallagst, K., Zdunek-Wielgołaska, J., 2023, Urban green spaces as a component of an ecosystem, in: Sustainable Development and Environmental Stewardship: Global Initiatives Towards Engaged Sustainability, Springer, pp. 165-198.
Walentowitz, A., Troiano, C., Christiansen, J. B., Steinbauer, M. J., Barfod, A. S., 2022, Plant dispersal characteristics shape the relationship of diversity with area and isolation, Journal of Biogeography 49(9):1599-1608.
Wheeler, M. M., Neill, C., Groffman, P. M., Avolio, M., Bettez, N., Cavender-Bares, J., Roy Chowdhury, R., Darling, L., Grove, J. M., Hall, S. J., Heffernan, J. B., Hobbie, S. E., Larson, K. L., Morse, J. L., Nelson, K. C., Ogden, L. A., O Neil-Dunne, J., Pataki, D. E., Polsky, C., Steele, M., Trammell, T. L. E., 2017, Continental-scale homogenization of residential lawn plant communities, Landscape and Urban Planning 165:54-63.
Wilson, M. C., Hu, G., Jiang, L., Liu, J., Liu, J., Jin, Y., Yu, M., Wu, J., 2020, Assessing habitat fragmentation's hierarchical effects on species diversity at multiple scales: the case of Thousand Island Lake, China, Landscape Ecology 35(2):501-512.
Yang, J., Huang, X., 2021, The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019, Earth System Science Data 13(8):3907-3925.
Yang, Y., Tian, K., Hao, J., Pei, S., Yang, Y., 2004, Biodiversity and biodiversity conservation in Yunnan, China, Biodiversity & Conservation 13(4):813-826.
Yu, X., Li, Y., Yang, G., 2018, Fruit types and seed dispersal modes of plants in different communities in Shilin Geopark, Yunnan, China, Chinese Journal of Plant Ecology 42(06):663-671.
Yunnan Yearbook Editorial Committee, 2019, Yunnan Yearbook.
Zhang, M., Fan, S., Li, X., Li, K., Xing, X., Hao, P., Dong, L., 2023, How urban riparian corridors affect the diversity of spontaneous herbaceous plants as pollination and dispersal routes - a case of the Wenyu River- North Canal in Beijing, China, Ecological Indicators 146:109869.

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supplymentaryinformation.docx
urban plants with different seed dispersal modes have convergent response but divergent sensitivity to climate change and anthropogenic stressors
Intercationmodel.r
Dataset 1
Interactionmodels.csv
Dataset 1A
Sankeydiagram.txt
Dataset 2
Sankeydiagram.csv
Dataset 2A
Seperatemodel.txt
Dataset 3
Seperatemodels.csv
Dataset 3A
Variancecomposition.r
Dataset 4
Variancecomposition.xlsx
Dataset 4A
Specieslist.csv
Dataset 5

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Urban plants with different seed dispersal modes have convergent response but divergent sensitivity to climate change and anthropogenic stressors

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