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

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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 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction 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. Results 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, Sealed 25 , 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 Sealed 25 was not retained indicating similar response of different dispersal modes to Sealed 25 (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** Sealed 25 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; Sealed 25 : 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, Sealed 25 , 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 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 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 Sealed 25 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 Discussion 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. Methods 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 Sealed 25 (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 Sealed 25 , 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 R 2 , 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 ). Declarations 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. References 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? 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Supplementary Files 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 Cite Share Download PDF Status: Published Journal Publication published 11 Dec, 2024 Read the published version in Nature Cities → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3865539","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":274835772,"identity":"d1c5def9-c24d-4e2c-936c-e3e8133f6ba6","order_by":0,"name":"Kun Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACCTBZwYwkdIAoLWdI1sLYRooWyf7Dxx5+nWctJ9/AfPBzYRuDHN+NBMbPBXi0SEukpRvLbks3NjjAliw9s43BWPJGArP0DDxa5CR4zKQltx1O3MDAY8bM28aQuOFGAhszDz4t/GeAWuYcrp/fwP8NpKWeoBZphhwzyY8NhxMYDvCwgbQkGBDSIjkjLU2a4Vi64YbDbMbSPOckDGeeedgsjU+LxPnDxyR/1FjLy7c3P/zMU2Yjz3c8+eBnfFpAAOIMSNSA4omxgYAGoJIfBJWMglEwCkbBiAYAu/FCDPo8qakAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-8019-9707","institution":"East China Normal University","correspondingAuthor":true,"prefix":"","firstName":"Kun","middleName":"","lastName":"Song","suffix":""},{"id":274835773,"identity":"637040d7-0654-4dc0-962e-737caa8b6e11","order_by":1,"name":"Zhiwen Gao","email":"","orcid":"","institution":"East China Normal University","correspondingAuthor":false,"prefix":"","firstName":"Zhiwen","middleName":"","lastName":"Gao","suffix":""},{"id":274835774,"identity":"b5f1323a-6ded-46a6-a389-3fb8604c8f90","order_by":2,"name":"Yingji Pan","email":"","orcid":"https://orcid.org/0000-0002-8203-3943","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yingji","middleName":"","lastName":"Pan","suffix":""},{"id":274835775,"identity":"83e4f918-d1a9-478a-b405-720eb215444b","order_by":3,"name":"Mingming Zhuge","email":"","orcid":"","institution":"School of Landscape Architecture, Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Mingming","middleName":"","lastName":"Zhuge","suffix":""},{"id":274835776,"identity":"bf3b8779-2ba2-4983-8c5f-e0aef7a91abb","order_by":4,"name":"Tian Wu","email":"","orcid":"","institution":"College of Agriculture and Life Sciences, Kunming University","correspondingAuthor":false,"prefix":"","firstName":"Tian","middleName":"","lastName":"Wu","suffix":""},{"id":274835777,"identity":"f8abf7a2-f6ed-4e3a-bd10-d3905edb9c26","order_by":5,"name":"Tiyuan Xia","email":"","orcid":"","institution":"College of Agriculture and Life Sciences, Kunming University","correspondingAuthor":false,"prefix":"","firstName":"Tiyuan","middleName":"","lastName":"Xia","suffix":""},{"id":274835778,"identity":"4a234549-e7a7-4188-b5d6-1fab0271207a","order_by":6,"name":"Yuandong Hu","email":"","orcid":"","institution":"School of Landscape Architecture, Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Yuandong","middleName":"","lastName":"Hu","suffix":""},{"id":274835779,"identity":"008f1806-467e-4db8-8062-413db29ca6c1","order_by":7,"name":"Liangjun Da","email":"","orcid":"","institution":"Xi’an University of Architecture and Technology","correspondingAuthor":false,"prefix":"","firstName":"Liangjun","middleName":"","lastName":"Da","suffix":""},{"id":274835780,"identity":"49e6f467-4abe-428c-8e8d-2f113853bb3e","order_by":8,"name":"Ellen Cieraad","email":"","orcid":"","institution":"Nelson Marlborough Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Ellen","middleName":"","lastName":"Cieraad","suffix":""}],"badges":[],"createdAt":"2024-01-15 06:00:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3865539/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3865539/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s44284-024-00169-8","type":"published","date":"2024-12-11T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51690880,"identity":"af0ee5a2-b128-47a7-a9ba-bfd2e94341bf","added_by":"auto","created_at":"2024-02-27 09:46:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":204594,"visible":true,"origin":"","legend":"\u003cp\u003eThe conceptual framework of urban spontaneous plants of different seed dispersal modes and potential corresponding drivers of species richness. MAT: mean annual temperature; MAP: mean annual precipitation; MSD: mean annual duration of sunshine; CMT: the coldest month mean temperature of year; BD: distance of patches from city boundary; Sealed\u003csub\u003e25\u003c/sub\u003e: the proportion of impervious surface within radii of 25 m around the patch; Area: patch area; Perimeter: patch perimeter; PA: perimeter-area ratio; LSI: landscape shape index.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3865539/v1/155046e22d0af5b957d45627.png"},{"id":51690888,"identity":"68bf13e9-1e1f-4a92-b5dc-17a2352fe091","added_by":"auto","created_at":"2024-02-27 09:46:46","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":620579,"visible":true,"origin":"","legend":"\u003cp\u003eSankey chart of species composition in 16 cities surveyed in Yunnan province, China. The number represent spontaneous species richness.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3865539/v1/8de738a8a4e93604e4939b30.jpeg"},{"id":51690881,"identity":"4350b1b4-e987-4d5d-a556-3649655f1253","added_by":"auto","created_at":"2024-02-27 09:46:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":142216,"visible":true,"origin":"","legend":"\u003cp\u003eContrast interactions estimates (with 95% confidence interval) between different seed dispersal modes. Black lines represent significant differences between two seed dispersal modes, while grey lines indicate non-significant differences between two seed dispersal modes. The red dashed line is serves as a reference (contrast = 0); the closer the contrast estimate is to this line, the smaller the difference in the trend between the two dispersal modes.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3865539/v1/e1f58253e4b0a6ae256d022d.png"},{"id":51690884,"identity":"f18d66f1-333b-48eb-9133-df1afc6edbf1","added_by":"auto","created_at":"2024-02-27 09:46:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":161325,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3865539/v1/be2ee7ca875c93d84ebf4b55.png"},{"id":51690890,"identity":"bf054679-d0e2-4c2b-8226-99b082122417","added_by":"auto","created_at":"2024-02-27 09:46:46","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":533252,"visible":true,"origin":"","legend":"\u003cp\u003eThe sampling sites and patches in the 16 prefecture-level cities in Yunnan province, China. 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with different seed dispersal modes have convergent response but divergent sensitivity to climate change and anthropogenic stressors\u003c/p\u003e","description":"","filename":"supplymentaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3865539/v1/f50991a5e39b0d94774db0d9.docx"},{"id":51690883,"identity":"647fc636-8cad-4c67-b288-315e5f42ebf4","added_by":"auto","created_at":"2024-02-27 09:46:45","extension":"r","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20992,"visible":true,"origin":"","legend":"Dataset 1","description":"","filename":"Intercationmodel.r","url":"https://assets-eu.researchsquare.com/files/rs-3865539/v1/7fa87648b64f17f30fae4220.r"},{"id":51690892,"identity":"72ae1cdc-f865-421a-b076-b69942dfcb48","added_by":"auto","created_at":"2024-02-27 09:46:46","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":765886,"visible":true,"origin":"","legend":"\u003cp\u003eDataset 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2A\u003c/p\u003e","description":"","filename":"Sankeydiagram.csv","url":"https://assets-eu.researchsquare.com/files/rs-3865539/v1/972cd21e3047dd42c1594be7.csv"},{"id":51690894,"identity":"ab09c41a-1286-4575-bcf6-2aebb274f7e7","added_by":"auto","created_at":"2024-02-27 09:46:46","extension":"txt","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":15902,"visible":true,"origin":"","legend":"\u003cp\u003eDataset 3\u003c/p\u003e","description":"","filename":"Seperatemodel.txt","url":"https://assets-eu.researchsquare.com/files/rs-3865539/v1/97699e839abdc8a03cd41aad.txt"},{"id":51691041,"identity":"b29a1e0d-4117-43e1-b453-d1e33072f9fd","added_by":"auto","created_at":"2024-02-27 09:54:46","extension":"csv","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":192791,"visible":true,"origin":"","legend":"\u003cp\u003eDataset 3A\u003c/p\u003e","description":"","filename":"Seperatemodels.csv","url":"https://assets-eu.researchsquare.com/files/rs-3865539/v1/289dbb8397a1d92835adf7fc.csv"},{"id":51690887,"identity":"d77e40db-3d41-4704-968e-20a09d2a334d","added_by":"auto","created_at":"2024-02-27 09:46:46","extension":"r","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":9492,"visible":true,"origin":"","legend":"\u003cp\u003eDataset 4\u003c/p\u003e","description":"","filename":"Variancecomposition.r","url":"https://assets-eu.researchsquare.com/files/rs-3865539/v1/41074dbbedad17407fa5736c.r"},{"id":51690891,"identity":"008db8cb-7d4c-4127-8cbe-5d1bb74b87d3","added_by":"auto","created_at":"2024-02-27 09:46:46","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":15642,"visible":true,"origin":"","legend":"\u003cp\u003eDataset 4A\u003c/p\u003e","description":"","filename":"Variancecomposition.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3865539/v1/668904fe588e012f015bcbd7.xlsx"},{"id":51690893,"identity":"7ff2135a-ca41-424b-b38a-46e8304eb8a0","added_by":"auto","created_at":"2024-02-27 09:46:46","extension":"csv","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":106120,"visible":true,"origin":"","legend":"Dataset 5","description":"","filename":"Specieslist.csv","url":"https://assets-eu.researchsquare.com/files/rs-3865539/v1/c62183fe996a9aef513489f1.csv"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Urban plants with different seed dispersal modes have convergent response but divergent sensitivity to climate change and anthropogenic stressors","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe 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, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). Urban areas are complex systems characterized by a myriad of environmental factors that affect the distribution of plant species (Vargas-Hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). 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., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gorton and Shaw, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Niemel\u0026auml; et al., 2011). Moreover, the response of a species to these environmental factors is affected by its mode of seed dispersal (Zhang et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). 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., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This disparity may lead to anemochorous species having a higher potential to become widespread than species that rely on autochory (Damschen et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Niu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Smith and Fellowes, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). 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., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), while species dispersed by animals (i.e., zoochory) are affected by local fauna populations and diversity (Farwig et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Niu et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Given these disparate responses, dispersal mode is considered a main driver of biodiversity patterns (Bowler and Benton, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Gorton and Shaw, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Nonetheless, we lack a comprehensive understanding of the diversity patterns and drivers of plants with different dispersal modes in urban environments.\u003c/p\u003e \u003cp\u003eBeyond dispersal capability, successful dispersal to a new habitat is also affected by environmental factors (Piano et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Several classic theories and framework have postulated that dispersal is affected by the size of the habitat [the species-area relationships, (Arrhenius, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1921\u003c/span\u003e; De Candolle, 1855)], the distance between habitat islands and species pools [the island biogeography theory, (MacArthur and Wilson, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1967\u003c/span\u003e)], the degree of isolation [metapopulation theory, (Hanski and Gilpin, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1991\u003c/span\u003e)], urban-rural gradient (McDonnell and Pickett, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), edge effects and matrix properties [the patch-matrix perspective, (Laurance, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)]. Furthermore, the response of plants to environmental changes may exist \u0026ldquo;lag effect\u0026rdquo;, resulting in \u0026ldquo;extinction debt\u0026rdquo; or \u0026ldquo;invasion debt\u0026rdquo; (Essl et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hahs and McDonnell, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tilman et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), therefore the historical process can also affected biodiversity (Gao et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). 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., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). 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., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Cote et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Coutts et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Piano et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Walentowitz et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUrban 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., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). These plants can rapidly respond to disturbances, making them effective indicators for the urbanization process (Chen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). 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., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) 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.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAcross 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 (\u0026ge;\u0026thinsp;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.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe final model encompassed natural factors (i.e. MAT and MAP), dispersal limitation factors (BD, Sealed\u003csub\u003e25\u003c/sub\u003e, 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 Sealed\u003csub\u003e25\u003c/sub\u003e was not retained indicating similar response of different dispersal modes to Sealed\u003csub\u003e25\u003c/sub\u003e (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eModel 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.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMain effects and interactions\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eChisq\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDf\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePr(\u0026gt;\u0026thinsp;Chisq)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInteraction\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDispersal modes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4181.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eHabitat quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eArea\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1561.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDispersal modes: Area\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e49.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLSI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDispersal modes: LSI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.05.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eDispersal limitation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.007**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDispersal modes: BD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSealed\u003csub\u003e25\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.04*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCity size\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e18.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDispersal modes: City size\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.03*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.009**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDispersal modes: UR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eClimate factors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMAP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e31.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDispersal modes: MAP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e25.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMAT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDispersal modes: MAT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: Area: patch area; LSI: landscape shape index; BD: distance of patches from city boundary; Sealed\u003csub\u003e25\u003c/sub\u003e: 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 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with richness; n.s. present non-significance.\u003c/p\u003e\n\u003cp\u003eResults 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.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). 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.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eDetailed 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 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}_{m}^{2}\\)\u003c/span\u003e\u003c/span\u003e ranges between 0.46\u0026ndash;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, Sealed\u003csub\u003e25\u003c/sub\u003e, 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\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). 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.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eResults of the separate generalized linear mixed-effects models for spontaneous species richness of different seed dispersal modes\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eFactors\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eAnemochory\u003c/span\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eAutochory\u003c/span\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eHydrochory\u003c/span\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eZoochory\u003c/span\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEstimate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ez value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEstimate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ez value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEstimate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ez value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEstimate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ez value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHabitat quality\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLog (Area)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.42\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.46\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.66\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.52\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLSI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.70\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDispersal\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003elimitation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-0.15\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSealed\u003csub\u003e25\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLog (City size)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-0.09\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-0.16\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-0.10\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-0.11\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.59\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePopulation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-0.10\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-0.13\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-0.09\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.43\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNatural\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003efactors\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMAP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.15\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.12\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.28\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.16\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMAT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-0.09\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-0.16\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eElevation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}_{m}^{2}{/R}_{c}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.53/0.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.59/0.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.46/0.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.49/0.60\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite 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.\u003c/p\u003e \u003cp\u003eGiven the varying properties of species with different dispersal modes, including their dispersal distance and seed mass, seed quantity and seed morphology (Cote et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), it may be expected that some are more susceptible to fragmentation than others (Gorton and Shaw, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Niu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Piano et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). 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., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHabitat 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, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1921\u003c/span\u003e; MacArthur and Wilson, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; Matthies et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eGiven 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., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As hydrochorous species heavily relies on wet habitats(Gorton and Shaw, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Howe and Smallwood, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Van der Pijl, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1982\u003c/span\u003e), 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 (Ceplov\u0026aacute; et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). 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., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wilson et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), 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 \u0026ldquo;extinction debt\u0026rdquo; or \u0026ldquo;invasion debt\u0026rdquo; issues and affect biodiversity (Essl et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tilman et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Therefore, current biodiversity pattern also is jointly affected by the present statues of urbanization and historical process of urbanization (Gao et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). 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 (Lososov\u0026aacute; et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eThrough acting as the first level environmental filter, climate factors influence the regional species pool and ultimately affect the pattern of biodiversity (Francis and Currie, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Garc\u0026iacute;a-Palacios et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). 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., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Higher precipitation was also associated with increased urban plant richness across 10 cities in Kazakhstan (Vakhlamova et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), 18 cities in China (Ouyang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and 117 urban yards from 6 metropolitan areas in the USA (Padull\u0026eacute;s Cubino et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). 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., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wheeler et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eThe 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., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). 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., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and a study on vascular plants in sixty European cities (Kalusov\u0026aacute; et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). 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.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy area\u003c/h2\u003e\n\u003cp\u003eOur study was carried out in Yunnan province, China (N 21\u0026deg;8'32'' - N 29\u0026deg;15'8'', E 97\u0026deg;31'39'' - E 106\u0026deg;11'47''), which has complex topography with significant variation in altitude (77 m \u0026minus;\u0026thinsp;6740 m), temperature (average annual temperature 5 ℃ \u0026minus;\u0026thinsp;23.8 ℃) and precipitation (total annual precipitation 580 mm \u0026minus;\u0026thinsp;2700 mm) across its territory (Yunnan Yearbook Editorial Committee, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The diverse natural environmental conditions contributes makes Yunnan province the most biodiverse province in China, accounting for more than 51.6% (\u0026ge;\u0026thinsp;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., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yang et al., \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). 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.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cstrong\u003eTable \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). 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.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eData collection\u003c/h2\u003e\n\u003cp\u003eField surveys of spontaneous plant species were conducted during the growing season between April and October in 2017\u0026ndash;2018 (one city), 2019 (eight cities) and 2022 (seven cities), following the sampling protocol by Gao et al., (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gao et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe seed dispersal modes of species are typically classified into four categories: anemochory, autochory, hydrochory or zoochory (Van der Pijl, \u003cspan class=\"CitationRef\"\u003e1982\u003c/span\u003e). Firstly, we determined the seed dispersal modes of the plant species by referring to published literature (Guo and Zheng, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Howe and Smallwood, \u003cspan class=\"CitationRef\"\u003e1982\u003c/span\u003e; Yu et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), International Network for Seed Based Restoration and Royal Botanic Gardens Kew (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ser-sid.org\u003c/span\u003e\u003c/span\u003e). 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 \u003cem\u003eBidens pilosa\u003c/em\u003e and \u003cem\u003eXanthium strumarium\u003c/em\u003e, 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 \u003cem\u003eKoelreuteria\u003c/em\u003e), winged structures (e.g. genus \u003cem\u003eAcer\u003c/em\u003e), or seeds with a tuft of hairs (as found in \u003cem\u003eTaraxacum\u003c/em\u003e genus). Species with mechanisms like seed pod explosion (where seeds are propelled when the fruit, often a capsule, bursts open, as seen in the \u003cem\u003eEcballium\u003c/em\u003e genus) or seeds simply dropping to the ground due to their weight (as genus \u003cem\u003eCocos\u003c/em\u003e, 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 \u003cem\u003eCocos nucifera\u003c/em\u003e 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.\u003c/p\u003e\n\u003cp\u003eThe potential environmental drivers of species richness were divided into three groups: natural factors, dispersal limitation and habitat quality (see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The natural environmental factors were sourced from the National Earth System Science Data Center (mean value of 1960\u0026ndash;2021, resolution\u0026thinsp;=\u0026thinsp;30 m, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://ser-sid.org\" target=\"_blank\"\u003ewww.geodata.cn\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e) and WorldClim website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.worldclim.com\u003c/span\u003e\u003c/span\u003e, resolution\u0026thinsp;=\u0026thinsp;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\u0026rsquo; properties, including patch area, patch perimeter, perimeter-area ratio (PA) and landscape shape index (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(LSI=Perimeter/2\\sqrt{\\pi \\times Area}\\)\u003c/span\u003e\u003c/span\u003e), which calculated by ArcGIS 10.3 (ESRI, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Dispersal limitation factors are mainly characterized by urbanization index, including urbanization intensity and urbanization rate (Gao et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). 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 Sealed\u003csub\u003e25\u003c/sub\u003e (the proportion of impervious surface within a radius of 25 m around the patch, please see Gao et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), BD (distance to city boundary), city population and city size. City size was calculated by moving windows (1 km \u0026times; 1 km, threshold\u0026thinsp;=\u0026thinsp;50%, 2020); Population data were collected from \u0026ldquo;Chinese population census yearbook 2020\u0026rdquo;. To estimate the Sealed\u003csub\u003e25\u003c/sub\u003e, 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, (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eData analysis\u003c/h2\u003e\n\u003cp\u003eTo 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 \u0026ldquo;glmer.nb\u0026rdquo; function within R package \u003cem\u003elme4\u003c/em\u003e (Bates et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) 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).\u003c/p\u003e\n\u003cp\u003eThe \u0026ldquo;dredge\u0026rdquo; function in the R package \u003cem\u003eMuMIn\u003c/em\u003e 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 \u0026Delta;AIC\u0026thinsp;\u0026lt;\u0026thinsp;2 using the function \u0026ldquo;model.avg\u0026rdquo; (Anderson and Burnham, \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). 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 \u0026ldquo;final model\u0026rsquo;). A comparison of the estimated coefficients of this final refitted model and the averaged model showed minimal coefficient changes, indicating similar interaction trends (\u003cstrong\u003eTable S2\u003c/strong\u003e and \u003cstrong\u003eTable S3\u003c/strong\u003e). Residual diagnostics of the final model were then checked using the \u0026ldquo;simulateResiduals\u0026rdquo; function in R package \u003cem\u003eDHARMa\u003c/em\u003e (Hartig, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The \u0026ldquo;Anova\u0026rdquo; function in the R package \u003cem\u003ecar\u003c/em\u003e 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 \u0026ldquo;emtrends\u0026rdquo; function in the R package \u003cem\u003eemmeans\u003c/em\u003e estimate and compared trend estimates of interaction factors for the different dispersal modes (Lenth et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTo further explore the driving factors within a dispersal mode, we separately ran the same GLMM analysis described above for each group. We used the \u0026ldquo;r.squaredGLMM\u0026rdquo; function in the R package MuMIn to calculate \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e, then manually performed variance decomposition to assess the relative contribution of each driving factor and the random effects (Garc\u0026iacute;a-Palacios et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gross et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). We ran all analyses using the software R 3.5.1 (R Core Team, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData and code availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and codes for the analysis will available once accepted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhiwen 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnderson, D., Burnham, K., 2004, Model selection and multi-model inference, \u003cem\u003eSecond. NY: Springer-Verlag\u003c/em\u003e \u003cstrong\u003e63\u003c/strong\u003e(2020)\u003cstrong\u003e:\u003c/strong\u003e10.\u003c/li\u003e\n\u003cli\u003eAranda, 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? 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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\u0026ndash;38.7%), with hydrochory being least frequent (4.3\u0026ndash;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.\u003c/p\u003e","manuscriptTitle":"Urban plants with different seed dispersal modes have convergent response but divergent sensitivity to climate change and anthropogenic stressors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-27 09:46:41","doi":"10.21203/rs.3.rs-3865539/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-cities","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natcities","sideBox":"Learn more about [Nature Cities](https://www.springer.com/journal/44284)","snPcode":"44284","submissionUrl":"","title":"Nature Cities","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"28ad012e-f0b9-4673-9f84-f6bae7302f23","owner":[],"postedDate":"February 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28959937,"name":"Biological sciences/Ecology/Urban ecology"},{"id":28959938,"name":"Biological sciences/Ecology/Biodiversity"},{"id":28959939,"name":"Biological sciences/Plant sciences/Plant ecology"},{"id":28959940,"name":"Biological sciences/Ecology/Biogeography"}],"tags":[],"updatedAt":"2024-12-12T08:07:17+00:00","versionOfRecord":{"articleIdentity":"rs-3865539","link":"https://doi.org/10.1038/s44284-024-00169-8","journal":{"identity":"nature-cities","isVorOnly":false,"title":"Nature Cities"},"publishedOn":"2024-12-11 05:00:00","publishedOnDateReadable":"December 11th, 2024"},"versionCreatedAt":"2024-02-27 09:46:41","video":"","vorDoi":"10.1038/s44284-024-00169-8","vorDoiUrl":"https://doi.org/10.1038/s44284-024-00169-8","workflowStages":[]},"version":"v1","identity":"rs-3865539","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3865539","identity":"rs-3865539","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}