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. 2026 May 16;48(5):964–973. doi: 10.1016/j.pld.2026.05.004

Tougher leaves under global urbanization

Yiyong Li a,b,⁎,1, Jianan Wang a,b,1, Zhuxuan Tan a, Longfei Li a, Zihao Man a, Cheng Zhang a, Hui Liu c,⁎⁎
PMCID: PMC13615779  PMID: 42801136

Abstract

Urban environments expose resident organisms to intense, multifaceted selective pressures. Studies employing intraspecific responses of functional traits have proven particularly valuable for understanding vegetation ecological strategy under urbanization. Here, we compiled a dataset of ten leaf functional traits of 1051 observations of 322 species from 104 cities to assess the impacts of urbanization. Urban-related moderators (climatic region, air quality and build up area), phylogenetic group, leaf habits and study design were considered. Results showed that leaf thickness (LT) and leaf dry matter content (LDMC) significantly increased, while the photosynthetic rate under saturating light (Asat), specific leaf area (SLA), chlorophyll content (Chl), and leaf area (LA) significantly decreased with urbanization. This pattern suggests that urbanization has driven plants to reorient their resource investment-return strategies toward a more conservative ecological strategy. Greater responses to urbanization were observed for angiosperm and deciduous species compared to gymnosperm and evergreen species. We also found leaf trait responses to urbanization are not uniform but depend on the interplay between anthropogenic (e.g., population density, built-up area) and natural (e.g., aridity, MAT) gradients. Regardless of whether this variation arises from genetically based local adaptation or phenotypic plasticity, the results of this study provide key insights into how vegetation responds to urbanization.

Keywords: Meta-analysis, Leaf functional traits, Urban-rural gradient, Intraspecific trait variation, Literature review

Highlights

  • •

    A dataset of 10 leaf traits from 322 species (1051 observations) across 104 cities was compiled.

  • •

    Urbanization altered leaf traits: LT and LDMC increased; Asat, SLA, Chl and LA decreased.

  • •

    Trait changes show urbanization pushes plants toward a conservative resource strategy.

  • •

    Angiosperms and deciduous species respond more strongly to urbanization than gymnosperms and evergreens.

  • •

    Leaf trait responses to urbanization are uneven, shaped by anthropogenic and natural gradients.

1. Introduction

Urbanization ranks among one of the most prominent global drivers of biodiversity loss and ecosystem change (Urban et al., 2024; Li et al., 2022). Cities nowadays have been characterized by habitat loss and fragmentation, increased impervious surfaces, the urban heat-island effects and high level of environmental pollutants (e.g., air and soil pollution, anthropogenic light and noise) (Grimm et al., 2008; Tian et al., 2024; Wang et al., 2021). To mitigate the adverse impact of urbanization, governments across the world are increasingly focusing on the nature-based solutions (Cook et al., 2025; Anderegg et al., 2025; Ozturk et al., 2024), such as construction and preservation of urban parks, urban forests, and urban green spaces. However, huge changes in environmental conditions inevitably bring considerable influence on individual adaptability, life-history characteristics, population dynamics, interspecific relationships, community organization, and ecosystem processes (Pena et al., 2023; Liang et al., 2023; Silvina Fenoglio et al., 2020; Jung and Threlfall 2018). Despite occupying only 0.3–0.6% of the global land surface, urban areas produce most of the world's CO2 emissions (Shan et al., 2018). As global urban areas continue to expand, particularly in developing regions, there is a growing concern within the scientific community, the general public, and political spheres regarding how to manage cities in ways that foster biodiversity conservation, mitigate emissions, and promote ecological restoration, thereby achieving a sustainable future.

The adaptability of plant species to urbanization is largely represented by their functional traits (Siefert et al., 2015). Various abiotic and biotic environmental factors characteristic of urbanized settings drive the selection for varied ecological strategies exhibited by conspecific individuals. Such adaptive approaches are reflected in the characteristic change patterns of species-specific leaf physiological and morphological attributes (Sides et al., 2014). For instance, elevated temperatures associated with the urban heat-island effect have been demonstrated to lower the leaf turgor loss point and enhance leaf hydraulic safety, thereby improving drought tolerance in urban trees (Esperon-Rodriguez et al., 2020; Tordoni et al., 2022). Increased inputs of heavy metal contaminants in urban soils often lead to alterations in the morphological structure of plant leaves. Changes in impervious surface coverage and soil hydrology, on the other hand, commonly cause shifts in stomatal density, leaf thickness, and specific leaf area, reflecting adjustments in water-use strategies (Bini et al., 2012; Zhang et al., 2023). Increasing empirical evidence supports the notion that intraspecific functional variation—attributable to phenotypically plastic responses or local adaptation—exerts a substantial and meaningful influence on species properties and ecosystem function, such as carbon sequestration, soil fertility (Midolo et al., 2019; Zhang et al., 2023). In this context, numerous studies have examined individual plant species along urbanization gradients to reveal consistent patterns of leaf trait adjustment to concurrent changes in biotic and abiotic environmental conditions.

The influences of urbanization on plant functional traits have been widely investigated within cities, whereas the prevalent approach has been to compare plant traits along urban–rural gradients (Qian et al., 2020; Salamanca-Fonseca et al., 2024) or green spaces with different land-use types (Chi et al., 2022; Zhang et al., 2022). A way forward is to synthesize local studies to examine the magnitude and direction of urbanization influence on plant traits at broader spatial scales. Although many of urbanization-related factors (e.g., air and soil pollution, drought and spatial isolation) have detrimental effects on plant growth and function (Wu et al., 2025), urban environments also have higher rates of nutrient deposition (Blake and Templer 2023; Decina et al., 2018), longer growing season (Zipper et al., 2016; Li et al., 2017), warmer temperatures (urban heat-island effect) (Patriota et al., 2024) and increased CO2 concentrations-factors that often, but not invariably, enhance plant growth (Pretzsch et al., 2017; Zhang et al., 2024b; Zhao et al., 2016).

There are presumably differences at multiple levels that account for these varied effects. Firstly, plant functional types such as phylogenetic group, leaf habits could influence the responses of plant traits to urbanization. Several studies found that the responses of plant traits to urbanization differed significantly between woody and herb species, gymnosperm and angiosperm, or shrubs and trees (Xiao et al., 2021; Brice et al., 2016; Galán Díaz et al., 2023). Secondly, the effects of urbanization on plants could vary among urban areas with different climatic and geographic conditions (e.g., latitude, temperature, precipitation, aridity). In arid and semi-arid climatic regions, urbanization renders the environment more humid through the implementation of irrigation and the creation of wetlands, ponds, and other anthropogenic water bodies (Luo et al., 2021). Nevertheless, in humid regions, the phenomenon of urbanization results in a drier environment in comparison to adjacent areas, primarily due to increased runoff and evapotranspiration enhanced by urban development (Hao et al., 2018). Third, socioeconomic characteristics (e.g., population density, air quality, and build-up area) may also be expected to influence the responses of plant traits to urbanization. More developed cities are anticipated to generate stronger negative impacts due to their accumulation of a higher degree of fragmentation (Irwin and Bockstael 2007). Fourth, the varying effects of urbanization on plant traits could be attributed to variations in methodological approaches across studies, such as those employing intra-urban gradients or urban–rural comparisons (Williams et al., 2009). Overall, these factors make it difficult to summarize global patterns and highlight the need for multilevel meta-analytical approaches that account for species-specific responses and local climatic conditions.

While systematic reviews exist regarding the impacts of urbanization on biodiversity (McDonald et al., 2020; Aronson et al., 2014), to our knowledge, meta-analytical synthesis of published studies focusing on the effects of urbanization on plant functional traits remains scarce. When determining the direction and magnitude of plant responses to urbanization, the roles played by various aspects of cities, plant traits, and research methodologies remain unclear. In this paper, we compiled a dataset of ten leaf functional traits of 1051 observations of 322 species from 104 cities. These traits were selected based on the leaf economics spectrum framework, as they represent key trade-offs between resource acquisition and conservation (Wright et al., 2004). To be specific, we intended to answer the three subsequent questions. (1) How do leaf functional traits respond to urbanization at the global scale (2) How do different plant functional types (i.e., deciduous versus evergreen, gymnosperm versus angiosperm) differ in their responses to urbanization? (3) Do these variation patterns correlate with urbanization characteristics, such as latitude, built-up area, and green coverage? Based on previous research, we propose the following hypotheses: (I) Urban environments are typically characterized by high temperatures and limited water availability, which drive leaf traits to shift toward a resource-conservative strategy (Cao et al., 2024). Specifically, plants in urban environments tend to invest more in tissue density and structural defense (e.g., lower specific leaf area (SLA) and higher leaf dry matter content (LDMC)) to cope with environmental stressors; (II) Based on established differences in resource-use strategies and life-history traits among plant functional types (Reich 2014; Wright et al., 2004). The magnitude of responses to urbanization will be greater in deciduous trees than in evergreen trees, and greater in angiosperm than in gymnosperm; (III) Urbanization intensity varies spatially. High vegetation coverage can mitigate microclimatic stress, whereas high population density is often associated with intense anthropogenic disturbance (Bowler et al., 2010; Grimm et al., 2008). We hypothesize that photosynthetic trait responses to urbanization are positively associated with urban vegetation index and negatively associated with population density (Fig. 1).

Fig. 1.

Fig. 1

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Conceptual diagram illustrating the effects of urbanization on leaf functional traits and plant economics strategy.

2. Materials and methods

2.1. Data screening

We investigated the intraspecific variation of 10 leaf functional traits, including specific leaf area (SLA), leaf area (LA), leaf length (LL), leaf thickness (LT), leaf dry matter content (LDMC), leaf nitrogen concentration (LN), leaf phosphorus concentration (LP), photosynthetic rate under saturating light (Asat), chlorophyll content (Chl), and relative water content (RWC). A literature search was performed using the Web of Science and CNKI databases (as of September 15, 2023), combining different keywords based on the research questions. To search for literature on the impact of urbanization on plant traits, we used the keywords ["urban" OR "city" OR "cities" OR "town"] AND ["SLA" OR "photosynthesis" OR "photosynthetic rate" OR "leaf size" OR "specific leaf area" OR "leaf dry matter" OR "leaf nitrogen" OR "leaf phosphorus" OR "leaf N" OR "leaves traits" OR "leaf trait" OR "leaf traits"]. This resulted in a sample of 1788 publications.

According to the following two criteria, screening was conducted by reading the titles, abstracts, and full texts of the literature: (1) The study must report data for any of the following indicators along an urbanization gradient or under different urbanization intensities (including intra-urban or urban-rural comparisons): specific leaf area, photosynthetic rate, leaf nitrogen concentration, leaf phosphorus concentration, leaf size; (2) Numerical data must be provided, along with the mean values, measures of variation, and sample sizes for different comparison categories, or provide regression coefficients or correlation coefficients for studies along an urbanization gradient. A total of 131 articles met our screening criteria.

One third of the studies were carried out along urban-rural gradients (38 studies, 380 observations), and the remaining studies were carried out using comparison between different land-use types within urban (93 studies, 671 observations). Most studies focused on angiosperm (982 observations), while a small number of studies involved gymnosperm (69 observations). There was little difference in the number of studies on evergreen (484 observations) and deciduous (567 observations) plants.

2.2. Data collection

We extracted the average value, standard deviation (SD), and sample size (n) for each trait documented at each site. Data available only in graphical form were extracted using Web Plot Digitizer (https://apps.automeris.io/wpd); data provided in tables or text were obtained directly from those sources. Species names were checked in www.theplantlist.org/across the datasets to standardize species synonyms. When standard error (SE) was reported instead of SD, the latter was derived through:

SD=SEn

If the research report does not provide SD or SE, we derived missing SD through multiplying the reported means by the average coefficient of variation of the entire dataset (Hou et al., 2020). Where sample sizes were not provided, the median sample size of our full dataset was used for assignment.

According to previous meta-analysis studies concerning urbanization (Liang et al., 2023; Silvina Fenoglio et al., 2020), we categorized them into two types based on research design: intra-urban comparisons (i.e., comparisons of green spaces with varying degrees of impervious surface cover) and urban–rural comparisons (i.e., comparisons between urban sites and rural or suburban sites). To assess whether the effects of urbanization vary by plant taxa, we classified the studied plant species according to phylogenetic groups (gymnosperm versus angiosperm) and leaf habit (deciduous versus evergreen). Based on the latitude of the study locations, each study was categorized as tropical (< 23°26′13.4″) or non-tropical (> 23°26′13.4″). Utilizing the geographic coordinates of each sampled city, we evaluated the spatial distribution of the research projects alongside their climatic distribution patterns; wherein, for datasets encompassing multiple cities, only designated control points from corresponding regions were selected as stand-ins to streamline data processing.

Environmental factors (mean annual temperature [MAT], latitude, longitude) were obtained from the literature, with missing MAT data extracted from the WorldClim database using the sites' geographical coordinates. The aridity index was determined as the ratio of annual precipitation (estimated from the WorldClim database) to potential evapotranspiration (obtained from the CGIAR-CSI Geo-Portal), with higher values indicating lower aridity. Climate data were extracted using the raster package in R, with a spatial resolution of 30 arcseconds (corresponding to approximately 0.93 × 0.93 km2 at the equator).

We utilized the City Population database (https://citypopulation.de) to obtain population data during the study period. We also extracted the Normalized Difference Vegetation Index (NDVI), build up area, GDP from GHS-BUILT database (Florczyk et al., 2019). Per capita GDP was calculated as the total GDP divided by the average annual population. The pollution level in cities was determined through the World Air Quality Index (https://www.aqicn.org/). This includes official data from environmental protection agencies of various countries, which is based on the concentrations of PM2.5, PM10, ozone, NO2, SO2 and CO concentrations.

2.3. Data analysis

The urbanization effect size for each observation is estimated using the response ratio (lnRR) after natural logarithm transformation:

lnRR=lnXtXc

where Xt and Xc are the means of the respective treatment groups (urban sites in urban-rural/natural comparison studies, or high impervious sites in within-urban studies) and controls (natural, rural or suburban sites in urban-rural/natural comparison studies, or low impervious sites in within-urban studies), respectively. The variance (v) of response ratios was calculated as follows:

v=SDt2ntXt2+SDc2ncXc2

where nt and nc denote the number of replicates for the treatment group and the control group, respectively, with SDt and SDc representing the respective standard deviations of the treatment and control groups.

Firstly, we employed a random effects model using the ‘rma.mv’ function in the R package metafor (Viechtbauer et al., 2010) to examine the aggregate effect sizes of plant functional traits. Some studies report multiple effect sizes, which may lead to issues of pseudoreplication. Therefore, we conducted a hierarchical meta-analysis that allows for the nesting of effects within studies, specifically using the following structure: ∼1|Reference ID/ID, ∼1|Species. Subgroup analysis was also conducted across different study types (urban–rural vs within urban), phylogenetic groups (gymnosperm vs angiosperm), leaf habits (evergreen vs deciduous), and climatic regions (tropical vs non-tropical). Secondly, we performed a multimodel inference analysis quantifying the importance of climatic variables and plant functional types to moderate trait variation starting from the full model including each predictor. For each response variable, we used the "glmulti" function in the R package glmulti (Calcagno and de Mazancourt, 2010) to rank models based on the small-sample-corrected Akaike Information Criterion (AICc) and evaluated the relative importance of each predictor by summing the Akaike weights (AICcW) across all models that included it. For each response variable, the smallest subset of models with cumulative Akaike weights ≥ 0.95 was selected to construct a confidence set of models. The unconditional average estimates of the predictors were then calculated based on this model set. Continuous variables were log-transformed if they exhibited a positively skewed distribution prior to modeling and were standardized. There was no significant collinearity (ρ < 0.8) among all continuous predictor variables. We also fitted meta-regression models by considering MAT, aridity, air quality, Per capita GDP, city level NDVI, population densities as the continuous moderators. The influence of moderators on the effects of urbanization was examined using residual heterogeneities (Qm). Egger's regression test was used to assess potential publication bias by examining the asymmetry of funnel plots. All the analyses were performed in R statistical software (R Core Team, 2021).

3. Results

The geographical distribution of the studies exhibited a notable imbalance (Fig. 2). Over three-quarters of the research was conducted across just two continents, with Asia accounting for 39.1% (n = 89) and Europe making up 36.8% (n = 48). The remaining research were distributed across North America (12.8%, n = 6), South America (6%, n = 5), and Africa (3%, n = 1). Furthermore, 104 (80.6%) studies were conducted in non-tropical regions and only 25 (19.4%) were conducted in tropical regions. Egger's regression test indicated no consistent evidence of publication bias across most traits, although significant asymmetry was detected for a small subset of traits (Table S1) (Egger et al., 1997).

Fig. 2.

Fig. 2

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Geographical distribution of studies selected in this study (131 publications in 106 cities). Circle size corresponds proportionally to the count of studies in each city.

In general, urbanization produced a positive effect on LT (0.12; [0.01, 0.22]; p = 0.027) and LDMC (0.10; [0.03, 0.17]; p = 0.008), and a negative effect on SLA (−0.10; [−0.16, −0.03]; p = 0.005), Chl (−0.20; [−0.27, −0.13]; p < 0.001), LA (−0.22; [−0.32, −0.12]; p < 0.001) and Asat (−0.28; [−0.44, −0.13]; p < 0.001), with no significant effect on LN, LP and LL (Fig. 3). Moreover, there is a marginally negative effect of urbanization on RWC (−0.04; [−0.08, −0.01]; p = 0.052) (Fig. 3).

Fig. 3.

Fig. 3

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Overall effects of urbanization on the leaf functional traits. Error bars represent 95% confidence intervals. The orange scatters indicate the effect value of the original data. Vertical dashed lines are the reference of the response ratio of zero. ∗, p < 0.05; ∗∗, p < 0.01; ∗∗∗, p < 0.001. LA, leaf area; SLA, specific leaf area; LL, leaf length; LT, leaf thickness; LDMC, leaf dry matter content; RWC, relative water content; LN, leaf nitrogen concentration; LP, leaf phosphorus concentration; Chl, chlorophyll content; Asat, photosynthetic rate under saturating light.

Except for LT, the study design type failed to account for the heterogeneity in how urbanization impacts all other leaf functional traits (Table 1). Nevertheless, in research adopting a within-urban study design, urbanization exerted significant effects on LDMC, LT, and SLA, with no significant effects in urban-rural gradient studies (Fig. 4a). The effects of urbanization on LDMC, Asat, LT and SLA depended on the climatic regions, with significant effects detected just in non-tropical regions (Fig. 4b). We also carried out subgroup analyses for different plant functional types. Deciduous and evergreen plants showed different effect sizes in their response ratios of RWC and LDMC (Fig. 4c), which were significantly influenced for deciduous species, but not for evergreen species. For phylogenetic group, significantly responses were detected in LA, Chl and SLA of angiosperm species, but not in gymnosperm species (Fig. 4d).

Table 1.

Summary table of meta-analysis with tests of moderators with corresponding residual heterogeneities Qm. Moderators.

SLA LL LT LA LN LP LDMC Asat Chl RWC
Continuous moderators Air quality 1.86 4.03a 1.68 1.18 0.02 0.96 0.50 2.59 0.04 1.08
NDVI 1.66 0.39 2.41 0.26 0.66 7.67b 0.17 0.82 0.00 0.06
Built up area 0.13 0.76 3.35 0.05 0.00 0.01 0.19 0.29 1.28 1.38
Population density 0.65 4.38a 0.00 0.15 0.18 0.82 0.29 3.88a 2.96 1.14
Per capita GDP 0.12 1.89 0.53 0.01 1.19 0.84 0.00 0.13 2.25 0.07
MAT 2.07 8.89b 0.05 0.91 0.72 1.03 2.57 0.27 0.26 0.26
Aridity 3.25 6.35a 1.73 1.77 0.31 1.25 0.71 6.18a 2.63 0.65
Categorical moderators Study design 0.82 1.75 3.88a 0.12 0.00 0.13 1.76 1.67 1.75 0.33
Phylogenetic group 0.08 0.02 – 1.73 0.00 0.16 0.79 – 0.99 0.42
Climatic region 0.04 0.08 0.20 5.43a 0.00 0.40 0.98 0.14 0.00 0.84
Leaf habit 0.22 0.02 0.01 2.13 0.28 1.90 11.20c 0.58 0.02 1.41
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Note: Significant moderators are indicated in bold.

a

p < 0.05.

b

p < 0.01.

c

p < 0.001.

Fig. 4.

Fig. 4

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Effects of urbanization on the leaf functional traits depending on (a) study design; (b) deciduous versus evergreen species; (c) tropical versus non-tropical; (d) gymnosperm versus angiosperm species. The blue and red scatters indicate the effect value of the original data. Vertical dashed lines are the reference of the response ratio of zero. ∗, p < 0.05; ∗∗, p < 0.01; ∗∗∗, p < 0.001. LA, leaf area; SLA, specific leaf area; LL, leaf length; LT, leaf thickness; LDMC, leaf dry matter content; RWC, relative water content; LN, leaf nitrogen concentration; LP, leaf phosphorus concentration; Chl, chlorophyll content; Asat, photosynthetic rate under saturating light.

We used model selection analyses to explore the importance of factors in the dataset that may influence plant functional trait responses to urbanization (Fig. 5). Model-selection analysis suggested that NDVI was the most important variables explaining variations of SLA, LA, LN, LP, Asat and Chl. Results also revealed the importance of air quality on the response of SLA and RWC. Responses of LL, Chl and LT were best predicted by aridity and build up area.

Fig. 5.

Fig. 5

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Averaged estimates and 95% confidence intervals (CIs) for fixed-effect parameters in the confidence set of models explaining intraspecific leaf trait variation's response ratios (lnRR) observed under urbanization are reported. Continuous predictor variables were standardized to unify the magnitudes of estimated values. Relative importance (AICcW) is the sum of AICc weights from models that include a specific predictor. MAT, mean annual temperature; NDVI, Normalized Difference Vegetation Index.

Meta-regression results indicated that aridity showed significant positive correlations with lnRR of Asat, SLA, and LL (Fig. 6). NDVI had a positive effect on lnRR of Asat but a negative association with lnRR of LP. Population density negatively affected the lnRR of Asat. Built-up area weakly reduced the lnRR of LT, while MAT was negatively correlated with the lnRR of LL.

Fig. 6.

Fig. 6

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Intraspecific leaf trait variation (lnRR) under urbanization of Asat, LP, LT and LL in response to aridity, population density, NDVI, built up area, air quality and MAT of each study sites. Significance levels (∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001) are provided for the slope estimate. Symbol size depicts the observation weight.

4. Discussion

4.1. Overall intraspecific responses of leaf traits to urbanization

With the growing ubiquity of urban areas, a more in-depth mechanistic and functional comprehension of plant responses to urbanization will facilitate sustainable landscape planning and the delivery of ecosystem services (Percival 2023; Ankaya 2025). Our results support the Hypothesis I that at the global scale, urbanization led to smaller and tougher leaves (lower LA and SLA, higher LT and LDMC) with lower photosynthetic capacity (lower Asat and Chl), but did not affect leaf nutrients (LN and LP). Thicker leaves (or modified SLA) may enhance physical defense against urban stressors like pollution or extreme temperatures (Zhang et al., 2024a), aligning with the “stress-tolerance” hypothesis (Saihanna et al., 2018). LDMC and LA show strong responses, underscoring urbanization's impact on leaf structure and resource allocation. Reduced LA in urban environments, for example, may minimize heat absorption and water loss (Tang et al., 2025), while enhanced LDMC reflects changes in the leaf carbon investment—key adaptations to urban drought or nutrient scarcity (Kramp et al., 2022). According to ‘leaf economics spectrum’, plants adopting resource-acquisitive strategies generally exhibit greater nutrient concentration, LA and SLA, and tend to grow in environments where light, water and nutrients are abundant (Wright et al., 2004). Species employing resource conservation strategies possess smaller LA, SLA, and leaf nutrient concentration, and are preferentially selected in environments characterized by low light availability, water deficit, or limited nutrient supply. This means that in the drier and hotter urban environments, plants exhibit significant phenotypic plasticity even with artificial management, shifting from the fast-return, resource-acquiring end of the leaf economics spectrum toward a slow-growth (photosynthetic) and resource-conservative strategy, which aligns with previous observations (Tan et al., 2025). Furthermore, this result is further corroborated by manipulative experiments where trees subjected to individual stressors such as drought, heat, or pollution exhibited analogous trait shifts (Song et al., 2022; Visakorpi et al., 2023), underscoring the generality of this physiological adaptation mechanism.

Meanwhile, we found that traits related to leaf photosynthesis and carbon construction exhibited higher plasticity, while nutrient-related traits did not show significant responses. This contrasts with a previous meta-analysis which found that leaf chemical traits tended to have greater intraspecific variation compared to leaf morphological traits (Siefert et al., 2015). This difference may reflect the trade-off strategy of plants in allocating energy under urbanization stress. Leaf morphology and photosynthetic structures are highly plastic traits that can directly and rapidly respond to short-term changes in physical environments such as light, temperature, and water availability to optimize carbon acquisition (Wang et al., 2022). In contrast, leaf nitrogen (LN) and phosphorus (LP), key components of the leaf economics spectrum, are more strongly constrained by internal physiological regulation and long-term adaptive strategies, which may limit their short-term responsiveness to urban environmental gradients (Reich and Oleksyn, 2004). Their adjustment involves overall metabolic pathways and exhibits lower plasticity. In resource-limited stressful environments, plants may prioritize allocating resources to the construction of photosynthetic organs that provide immediate carbon gains, rather than increasing tissue nutrient concentrations.

Our results of leaf trait variation suggested that in urban environment, despite resource availability like N increased, under the other environment stress such as drought, soil compaction, plants shift their ecological strategies toward a more conservative direction. Future urban aridification and warming will intensify environmental stress on plant communities, with species exhibiting low phenotypic plasticity facing heightened mortality risks due to their limited capacity to adjust functional traits in response to rapid drought and heat shifts. In contrast, traits with high plasticity-such as Chl and SLA-can serve as reliable proxies for pre-evaluating plant stress tolerance. High plasticity in these traits allows plants to rapidly optimize the trade-off between carbon acquisition and water loss: adjusting SLA regulates the transpirational surface area, while modulating Chl content balances light harvesting with photoprotection under stress. Incorporating plasticity-based trait assessments into urban vegetation planning will enable targeted selection of resilient species, enhancing the stability and sustainability of urban green spaces under future climate change. This approach not only mitigates the loss of vulnerable plant populations but also optimizes the ecosystem services provided by urban vegetation, such as heat mitigation and carbon sequestration, amid intensifying climate stressors.

4.2. Moderators affecting the intraspecific responses of leaf traits

For different study designs, larger effect sizes of LA, LDMC, LT and SLA were observed in within-urban studies than urban-rural gradient studies, indicating that urban intensification and impervious surfaces—factors triggering habitat loss and fragmentation—serve as the key driving forces. Given that 71% of the studies in our dataset were within-urban comparison, our results highlighted that study design was an important moderator to quantify plant responses to urbanization.

Consistent with our Hypothesis II, our results detected that the effects of urbanization varied among plant types. Our study identified significant differences in the variation magnitude of RWC and LDMC among deciduous and evergreen species. Our results indicated larger responses in deciduous species in intraspecific RWC and LDMC with urbanization compared to evergreen species. These findings support the general hypothesis that fast-growing deciduous plants exhibit greater capacity for phenotypic plasticity in response to environmental fluctuations relative to evergreen species (Boehnke and Bruelheide 2013; Arenas-Navarro et al., 2021). As a partial compensation for the increased construction costs per unit dry mass (Vincent 2006), the longevity of evergreen leaves is likely to exert a detrimental influence on phenotypic plasticity (Stotz et al., 2022). Furthermore, angiosperm species showed a greater response in LA, Chl and SLA than gymnosperm species. This was consistent with previous studies which found that broadleaved species have higher capacity to responds to different resource availabilities than conifers (Bastias et al., 2018). Notably, greater emphasis should be placed on the role of distinct functional groups when investigating intraspecific trait variability under urbanization, given that divergent functional groups may exhibit disparate magnitudes of intraspecific variation.

Urbanization acts as a composite stressor, interacting with natural gradients to reshape leaf trait responses. As stated in Hypothesis III, urban climatic and socioeconomic variables played key roles in influencing the relationships between plant functional traits and urbanization. Model selection results indicated that NDVI played important roles in regulating response of many leaf traits to urbanization. NDVI—a proxy for vegetation cover—explains trait variation in contrasting ways. For Asat, higher NDVI (more vegetation) correlates with reduced urban - induced Asat decline, implying that vegetated urban areas buffer plants from photosynthetic stress. Our meta-regression results also revealed that the NDVI of urban areas had a pronounced negative impact on the extent of LP responses to urbanization. One potential explanation is that phosphorus has been widely acknowledged as an important limiting nutrient in subtropical and tropical regions (Ellsworth et al., 2022), which have higher NDVI irrespective of the urbanization level (Huete et al., 2002). In addition, urban areas are characterized by a substantial proportion of built-up lands and high building density, consequently leading to relatively low NDVI values (Gascon et al., 2016). Urbanization can promote additional inputs of P from anthropogenic activities into the soil (Li et al., 2018; Xia et al., 2022), which may lead to the negative relationship between LP responses and NDVI. Furthermore, higher NDVI usually indicates denser vegetation coverage and more intense interspecific/intraspecific competition, which would enhance plant demand for P. Under such conditions, urbanization-induced changes in soil P availability would be offset by the increased P demand driven by high NDVI, thereby leading to a weaker extent of LP responses to urbanization. The absence of significant impacts in LN, LP, and their complex correlations with environmental factors, may be attributed to the confounding effects of atmospheric deposition, soil management, and taxonomic differences (e.g., legumes vs. non-legumes). Additionally, the relatively small sample size for chemical traits in our dataset limits our ability to generalize these patterns. Future studies with broader sampling are required to clarify how urbanization alters plant nutrient stoichiometry. Built - up area explains LT variation, with more impervious surfaces correlating to reduced leaf thickness. This may reflect trade - offs: in highly built environments, plants minimize leaf biomass to cope with heat and drought, sacrificing physical stress tolerance. Population density strongly drives Asat responses, with higher density correlating to greater Asat reduction. Dense urban areas intensify pollution, heat, and resource scarcity, directly impairing photosynthetic function.

Parallel to latitudinal gradients (De Frenne et al., 2013) and elevational gradients (Read et al., 2014), urbanization gradients provide a robust tool for inferring plant responses to climate change. The negative response of LL to urbanization were inclined to be more pronounced in cities located in warmer macroclimates. Interestingly, we found that the response of Asat to urbanization was positively influenced by the aridity of the city. Within regions characterized by dry climates, urbanization mitigates environmental aridity through irrigation practices and the construction of anthropogenic water sources such as wetlands and ponds. In contrast, in areas with humid climates, urbanization can result in an environment drier than the adjacent surroundings, driven by increased runoff generation and evapotranspiration rates.

These patterns underscore that leaf trait responses to urbanization are not uniform but depend on the interplay between anthropogenic (e.g., population density, built-up area) and natural (e.g., aridity, MAT) gradients. For cities in arid regions, the combined effects of aridity and urbanization on Asat and SLA may reduce carbon sequestration potential, impacting urban microclimates. In densely populated cities, the strong Asat - population density relationship suggests heightened risks to urban green infrastructure functionality, as photosynthetic capacity declines.

4.3. Uncertainties and future directions

To our knowledge, this is the first quantitative global synthesis focusing on the consequences of urbanization for leaf characteristics. There are several uncertainties in our analysis. First, some bias may arise from uneven distribution of study sites, with a dominance of studies in Asia, Europe and other areas in the temperate zone. However, there were few studies available for Africa and Oceania, especially in tropical regions. Second, effect size of urbanization on plant traits might depend on spatial scale of the study, such as varying plot size, magnitude of the urban-rural gradient, which may lead to the non-significant results and should be incorporated in future studies. Lastly, existing research often provides vague information about the sample plots, especially the field meteorological factors. Thus, we had to use the most commonly used land use classifications (urban-rural gradient and within-urban type) in this study. However, the definitional standards for land use classification can vary significantly across studies, employing diverse criteria such as population density, built-up area, and land cover types. This methodological heterogeneity may compromise the generalizability of meta-analytical findings. It is essential to clarify the roles of multiple abiotic drivers (e.g., environmental factors, management information), which will allow us to identify and quantify the key drivers of the urbanization leading to plant changes.

5. Conclusion

Our global-scale meta-analysis clarified that urbanization produced negative effects on lnRR of Asat, Chl, SLA, LA, but positive effects on LT and LDMC. Climate - vegetation metrics (aridity, NDVI, MAT) of different cities were the key factors influencing multiple leaf functional trait (e.g., Asat, SLA, LL) responses to urbanization, highlighting the interplay among climate conditions, human activity, and plant lineage (angiosperms vs. gymnosperms) in shaping leaf trait expression. Anthropogenic factors (population density, built - up area, air quality) also have pivotal impacts on leaf traits (e.g., Asat, LT, RWC), with however, no universal drivers dominated all traits, emphasizing trait - specific controls. Continuous moderators (e.g., air quality) and categorical moderators (e.g., leaf habit) shape trait responses, highlighting synergistic climate - human - evolutionary controls. Overall, we identified key drivers of different leaf functional traits, advancing understanding of plant adaptation to human - modified environments and informing future research on ecosystem resilience amid global change.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This research received funding from National Natural Science Foundation of China (Grant Number: 32371935) and Anhui Province Young and Middle-aged Teacher Training - Outstanding Young Teacher Cultivation Project (Grant Number: YQZD2024004).

Footnotes

Peer review under the responsibility of Editorial Office of Plant Diversity.

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.pld.2026.05.004.

Contributor Information

Yiyong Li, Email: yiyonglee@ahau.edu.cn.

Hui Liu, Email: hui.liu@scbg.ac.cn.

Appendix A. Supplementary data

The following is the Supplementary data to this article.

Multimedia component 1
mmc1.docx (16.6KB, docx)

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