Next Article in Journal
Spatiotemporal Coupling Analysis of Street Vitality and Built Environment: A Multisource Data-Driven Dynamic Assessment Model
Previous Article in Journal
The Influence of Spiritual Behavior in Sustainable Performance: A Confirmatory Factor Analysis
Previous Article in Special Issue
The Ecological Efficiency of Green Materials in Sustainable Urban Planning—A Model for Its Measurement
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 17
Issue 21
10.3390/su17219516
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Vegetation Management Changes Community Assembly Rules in Mediterranean Urban Ecosystems—A Mechanistic Case Study

by
Vincenzo Baldi
1,2,
Alessandro Bellino
1,*,
Mattia Napoletano
1 and
Daniela Baldantoni
1,2
1
Department of Chemistry and Biology “Adolfo Zambelli”, University of Salerno, Via Giovanni Paolo II, 132, Fisciano, 84084 Salerno, Italy
2
National Biodiversity Future Center (NBFC), Piazza Marina, 61, 90133 Palermo, Italy
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(21), 9516; https://doi.org/10.3390/su17219516 (registering DOI)
Submission received: 8 September 2025 / Revised: 20 October 2025 / Accepted: 24 October 2025 / Published: 26 October 2025
(This article belongs to the Special Issue Urban Landscape Ecology and Sustainability—2nd Edition)
Browse Figures
Versions Notes

Abstract

Urban ecosystems are structurally and functionally distinct from their natural counterparts, with anthropogenic management potentially altering fundamental ecological processes such as seasonal community dynamics and impairing their sustainability. However, the mechanisms through which management filters plant diversity across seasons remain poorly understood. This study tested the hypothesis that management acts as an abiotic filter, dampening seasonal community variations and increasing biotic homogenization in urban green spaces. In this respect, through an intensive, multi-seasonal case study comparing two Mediterranean urban green spaces under contrasting management regimes, we analysed plant communities across 120 plots over four seasons. Results reveal a contingency cascade under management: while the species composition remains relatively stable (+26% variability, p < 0.001), the demographic success becomes more contingent (+41%, p < 0.001), and the ecological dominance becomes highly stochastic (+90%, p < 0.001). This hierarchy demonstrates that management primarily randomizes which species achieve dominance, in terms of biomass and cover, from a pool of disturbance-tolerant generalists. A 260% increase in alien and cosmopolitan species and persistent niche pre-emption dominance–diversity patterns also indicate biotic homogenization driven by management filters (mowing, trampling, irrigation, and fertilization) that favors species resistant to mechanical stresses and induces a breakdown of deterministic community assembly. These processes create spatially and temporally variable assemblages of functionally similar species, explaining both high structural variability and persistent functional redundancy. Conversely, seasonally structured, niche-based assemblies with clear dominance–diversity progressions are observed in the unmanaged area. Overall, findings demonstrate that an intensive management homogenizes urban plant communities by overriding natural seasonal filters and increasing stochasticity. The study provides a mechanistic basis for sustainable urban green space management, indicating that reduced intervention can help preserve the seasonal dynamics crucial for sustaining biodiversity and ecosystem functioning.

1. Introduction

World population is rapidly increasing and concentrating in ever enlarging urban environments, 70% of which is expected to live in cities by 2050 [1]. From an ecological point of view, urban environments represent heterotrophic and dissipative ecosystems [2], where the majority of human activities take place. The dysfunctional energy budget and organic matter cycle that characterize these ecosystems cause, irrespective of their still limited global coverage (less than 3% [3]), environmental pressures acting both at local and global scales, such as various forms of pollution, soil erosion and landscape fragmentation, ultimately impacting biodiversity and the provision of ecosystem services. Several authors have tried to quantify anthropogenic impacts on ecological systems, mainly focusing on proxies such as land use conversion [4,5], carbon dioxide emissions [6], or ecosystem processes from which we draw benefits [7,8]. Despite all these efforts, there is still no solid way to define or quantify the effects of multiple anthropogenic stressors [9] on ecological systems, especially at small spatial and temporal scales.
A way to mitigate these issues and, at the same time, improve the provision of ecosystem services by urban ecosystems, is the introduction of green areas within the urban tissue [10]. Indeed, despite their limited extension and connectivity, urban green areas are able to improve the overall functioning and stability of the urban ecosystem, support biodiversity, and provide material, non-material and control nature contributions to people [11]. However, these areas are subject to significant anthropogenic pressures [12,13,14,15], which can severely affect the biodiversity of less mobile species such as plants [16,17,18,19] and small animals [20,21,22]. For instance, the management of urban green areas (e.g., mowing and irrigation) can select species with specific functional traits, thus shaping plant community structure and dynamics [14,23,24].
These aspects are particularly relevant in regions such as the Mediterranean [25], where anthropogenic disturbances threaten the invaluable biodiversity comprising the 20% of global flowering plant and fern species [26], i.e., nearly 22,500 plant species, with 11,700 endemics [27,28]. In this area, wide annual oscillations in temperature and precipitations determine clear seasonal dynamics in community assemblages [29]. However, how anthropogenic disturbances modify these seasonal dynamics remains a remarkably understudied topic, despite the potential implications for trophic webs and overall diversity.
Although previous studies on urban vegetation have often focused on structural diversity at a single point in time [30,31], or on functional traits without tracking their seasonal dynamics [32,33], our study aims at going beyond this by integrating structural and functional diversity metrics throughout multiple seasons. This combined approach is crucial to fully understand how management practices alter not only the composition, but also the functional response and seasonal dynamics of plant communities [34,35].
We hypothesize that anthropogenic pressures from management practices alter the amplitude of seasonal community variations by filtering functional traits. In order to test this hypothesis, we compare the seasonal vegetation dynamics in two urban green areas subjected to different management regimes (mowing, irrigation, and fertilization) but similar in location, climate and land-use history. Through intensive seasonal sampling (120 samplings over 4 seasons), multiple abundance measures, and a wide spectrum of functional traits, this study sheds light on the complex interactions between anthropogenic activities and ecological dynamics in urban ecosystems.

2. Materials and Methods

2.1. Study Areas

The study was carried out over 4 seasons from October 2023 to July 2024, in two urban green spaces within the municipalities of Avellino (SAV, Avellino province, 40°54′22.71″ N, 14°48′19.73″ E, 358 m a.s.l.) and Fisciano (SSA, Salerno province, 40°46′10.49″ N, 14°47′25.07″ E, 251 m a.s.l.), Italy (Figure 1). Both areas exhibit a Mediterranean climate [36], with monthly mean temperatures of 15.9 °C (SAV) and 17.4 °C (SSA), and total monthly precipitation of 81.8 mm (SAV) and 110.2 mm (SSA) over the period 1 September 2023 to 28 August 2024 (data from Avellino Genio-Civile and Serino stations for SAV and from Baronissi, Mercato San Severino and Pizzolano stations for SSA—https://centrofunzionale.regione.campania.it/, accessed on 31 August 2024). Information on landscape characteristics and history of the two areas was retrieved from aerial photogrammetry coverage and satellite imagery (https://sit2.regione.campania.it/, accessed on 1 January 2024, and the desktop application of Google Earth Pro v7.3.6).
SAV is an unkempt peri-urban green space (around 160 × 114 m2) of artificial derivation. Prior to the construction of the first buildings around 2010 and the creation of the urban green space, the area was predominantly covered by orchards. After land-use conversion, efforts to enhance the fruition of the area with tree cover of Quercus ilex L. and Tilia cordata Mill. proved unsuccessful due to the lack of irrigation and fertilization. In fact, water availability for the vegetation in the area still depends exclusively on rainfall. Mowing is practiced twice a year during spring and summer, to avoid excessive vegetation growth. Trampling is negligible in the area and limited to the occasional passage of people, mainly during mowing.
SSA is also an urban green space (around 130 × 48 m2) of artificial origin with recreational use. Before the construction of the first buildings, the area hosted agricultural fields and orchards. According to satellite imagery, land-use conversion to the actual destination occurred between 1984 and 1998, with the construction of a warehouse, sidewalks, and managed gardens. Further details regarding the area are reported in Baldi et al. [19]. Vegetation in the area is irrigated for 30 min daily and mowing is carried out every two weeks. Urea (40 g/m2) is also applied as a fertilizer twice a year. Regular trampling occurs due to daily pedestrian use and recreational activities, except during holidays and weekends.

2.2. Phytosociological Surveys

Seasonally, 15 phytosociological surveys (50 × 50 cm2) were carried out per study area, with their positions randomly selected, resulting in a total of 120 samplings over the course of one year. Each sampling campaign was completed within one week per area in order to avoid appreciable vegetation changes during the analyses. Herbaceous plants in each relevée were identified at the species level and their relative abundance was measured in terms of (i) coverage, (ii) number of plants and (iii) dry biomass [37]. In the field, species were preliminary identified through expert judgment and their coverage was estimated using the Braun–Blanquet method [38]. Individual plants of each species were then collected in paper bags, with the number of individuals recorded, and stored at 75 °C for the dry biomass measurements. In the laboratory, species identification was revised using a M165C stereomicroscope (Leica Microsystems, Wetzlar, Germany) and the dichotomous keys of Pignatti et al. [39], while dry biomass was measured using a XT-220A analytical balance (Precisa, Dietikon, Switzerland). Species functional traits were also retrieved, i.e., family, Raunkiær life forms and chorology [39], as well as the Ellenberg indices [40].

2.3. Data Analysis

Data on species composition (thereafter referred to as P/A-presence-absence) and abundances were used to derive information on vegetation structural biodiversity, and were combined with the functional traits to investigate community functional biodiversity. Specifically, coverage data were transformed through the equation x ′ = x y , where x is the coverage and y was chosen to be equal to 0.25 according to Wildi [38]. Chorotypes from Pignatti et al. [39] were grouped according to Di Biase et al. [41] to simplify their interpretation. The combined structural and functional data were used to derive functional metrics, allowing for a comprehensive evaluation of the multiple facets of functional diversity, i.e., dissimilarity, divergence, evenness, richness and Rao’s quadratic entropy [42].
Seasonal changes in community structural and functional biodiversity were evaluated by means of non-metric multidimensional scaling (NMDS) based on 2 axes and either the Bray–Curtis dissimilarity metrics (for structural diversity) or the Euclidean distances (for functional diversity). Convex hulls and standard error ellipses (for α = 0.05 ) enclosing observations belonging to each season were superimposed onto the NMDS spaces to evaluate potential differentiations among seasons. Species projections onto the NMDS spaces were computed using weighted average scores to evaluate the contribution of each variable in the differentiation of seasonal groups. The relative turnover of different families, chorotypes, and life forms across the seasons were further tested using χ 2 tests and estimated by the seasonal coefficient of variation, calculated as C V = σ / x ¯ , where x ¯ indicates the mean of the total abundance of each functional trait in the different seasons and σ the respective standard deviation. In order to test the hypothesis of management-induced damping of seasonal shifts, the seasonal differentiation among communities was estimated using both the analysis of multivariate homogeneity of group dispersions (PERMDISP) and permutational multivariate analysis of variance (PERMANOVA). Specifically, the average distance of individual relevées to the group centroid was computed in Bray–Curtis spaces (for structural diversity) and Euclidean spaces (for functional diversity) for each area and season, with respect to the different variable types (species abundances vs z-score transformed functional metrics). The differences in dispersion between SAV and SSA were then estimated by permutational tests. In PERMANOVA, the overall differentiation of relevées in relation to area and season was sequentially estimated using the same distance spaces used in PERMDISP.
To test the hypotheses of management-induced biotic homogenization, the differences in the incidence of combined alien and cosmopolitan species in SAV and SSA were evaluated using a generalized linear model with a binomial family, including area and season as fixed factors. The same approach was also adopted to test the hypothesis of management promoting the incidence of species resistant to mechanical stresses such as trampling and mowing, by focusing on the combined incidence of Asteraceae and Poaceae.
For each season, all major rank abundance distribution (RAD) models of dominance–diversity relationships (niche pre-emption, log-normal, McArthur’s broken stick, Zipf, and Zipf–Mandelbrot [43]) were fitted to the abundance data, expressed as the number of individuals per species, in order to select the model that best represents the observed dominance–diversity patterns. Model selection was carried out using the Akaike’s information criterion (AIC). Furthermore, the differences in the proportions of functional groups between SAV and SSA were assessed.
All the analyses were carried out within the R 4.4.1 programming environment [44] with the functions of the “climaemet” [45], “fundiversity” [46], “ggplot2” [47], “ggpubr” [48], “ggtext” [49], “gridExtra” [50], “tidyr” [51] and “vegan” [52] packages.

3. Results

SAV and SSA show similar trends in temperature and rainfall, with characteristic dry conditions during summer but slightly higher total rainfall in the latter (Figure 2). Overall species richness is comparable between the areas, with 47 species (Table 1) in SAV and 49 species (Table 2) in SSA. Areas are also characterized by comparable species richness in winter (SAV: 13 ± 2 and SSA: 10 ± 4), autumn (SAV: 6 ± 3 and SSA: 6 ± 2) and summer (SAV: 7 ± 2 and SSA: 5 ± 1) but not in spring, when 41% fewer species are observed in SSA than in SAV (10 ± 4 and 17 ± 2, respectively).
Species in SAV mostly belong to the families Fabaceae ( 28 ± 8 %), Plantaginaceae ( 17 ± 7 %) and Poaceae ( 13 ± 3 %) (Figure 3), whereas in SSA (Figure 3) they are mostly represented by Poaceae ( 26 ± 8 %, e.g., C. dactylon and D. glomerata) and, to a lesser extent, by Asteraceae ( 10 ± 4 %), Convolvulaceae ( 10 ± 3 %), Fabaceae ( 15 ± 3 %) and Rosaceae ( 10 ± 3 %). Overall, the combined number of species belonging to Poaceae and Asteraceae is 33% higher in SSA than in SAV (p < 0.001). Both areas show significant seasonal changes in family composition (both p < 0.001) and comparable association strengths between families and seasons (Cramér’s V effect sizes equal to 0.304 and 0.312 in SAV and SSA, respectively), but substantially less family turnover ( χ 2 = 188 in SAV vs. χ 2 = 135 in SSA) in the managed area than in the unmanaged one. The variations in SAV are mostly associated with 4 families (Caryophyllaceae, Hypericaceae, Rubiaceae, and Brassicaceae) undergoing near complete seasonal turnover ( 1.01 ≤ C V ≤ 0.96 ), whereas the others, including the dominant ones (Fabaceae, Poaceae), show low to moderate seasonality ( 0.47 ≤ C V ≤ 0.28 ). In contrast, SSA show compressed seasonality dynamics ( 0.89 ≤ C V ≤ 0.25 ) and a more equitable distribution of CV values, with seasonal variations mainly involving several families represented by few species (Plantaginaceae, Geraniaceae, Malvaceae, Oxalidaceae, Caryophyllaceae, Asteraceae, and Ciperaceae).
In terms of chorological spectra, SAV and SSA differ in the dominance of specific chorotypes and their seasonal dynamics. In particular, SAV is mainly characterized by paleotemperate and eurymediterranean chorotypes all over the year ( 36 ± 4 % and 29 ± 13 % respectively; Figure 3), whereas SSA communities are dominated by paleotemperate, cosmopolitan and alien chorotypes ( 28 ± 7 %, 23 ± 2 % and 18 ± 6 % respectively; Figure 3), with eurymediterranean species accounting for only 16 ± 9 % of the taxa. Remarkably, the increase in the combined abundance of alien and cosmopolitan species in SSA compared to SAV amounts to 260% (p < 0.001). In terms of seasonal dynamics, the chorological variations are similar to those observed for families, with significant changes in both areas (both p < 0.001) and comparable, modest association strengths between chorotypes and seasons (Cramér’s V effect sizes equal to 0.299 and 0.237 in SAV and SSA, respectively). In terms of chorology, however, the reduction in chorotype turnover in SSA relative to SAV amounts to 57% ( χ 2 = 187 in SAV vs. χ 2 = 77.7 in SSA) compared with the 28% observed for families. Chorotype seasonal variations in SAV result mainly from european, stenomediterranean and euro-montane species undergoing complete seasonal turnover ( 1.44 ≤ C V ≤ 1.04 ), as well as from boreal and eurymediterranean species ( C V = 0.85 and C V = 0.70 ), whereas paleotemperate, cosmopolitan, euro-asiatic and alien chorotypes show low to modest seasonal variations ( 0.37 ≤ C V ≤ 0.09 ). Conversely, coefficient of variation values in SSA show both restricted dynamic range ( 0.88 ≤ C V ≤ 0.29 ) and a more equitable distribution, with seasonal variations of alien species reaching 5.5× higher values in SSA than in SAV ( C V = 0.52 vs. C V = 0.09 ).
In terms of Raunkiær life forms, SAV and SSA are dominated by therophytes, particularly scapose species, and by geophytes/hemicryptophytes (Figure 3), respectively. Although seasonal variations are significant in both areas (both p < 0.001), life forms exhibit reduced seasonal turnover in respect to families and chorology, with values 13% lower in SSA than in SAV ( χ 2 = 99.4 vs. χ 2 = 86.3 in SSA). Cramér’s V effect sizes also indicate low and comparable association strengths between life forms and seasons (0.299 and 0.237 in SAV and SSA, respectively). Seasonal coefficients of variation show similar dynamic ranges in SAV and SSA ( 0.76 ≤ C V ≤ 0.06 and 0.73 ≤ C V ≤ 0.04 , respectively) and comparable distributions of values.
The overall variations in community structural and functional diversity across the seasons in SAV and SSA are summarized in Table 3, as well as in Figure 4 and Figure 5.
Community seasonal variations were consistently significant in SAV when considering the P/A data and all abundance measures for both structural and functional biodiversity (always p < 0.001), whereas they were significant in SSA only for structural biodiversity (always p < 0.001) and for functional diversity calculated from P/A data (p < 0.01). SSA invariably exhibits significant damping (always p < 0.001) of seasonal variations in both structural and functional diversity relative to SAV, with comparable effect sizes (3.0–5.5 × reduction in PERMANOVA F-values; Table 3) among P/A, number, biomass and cover data. PERMDISP values (Table 3) further indicate that the reduction in seasonal differentiation in SSA is accompanied by significant increases (always p < 0.001) in within-season variance with respect to SAV, with management inducing different effect sizes on the P/A and abundance data in the case of structural and functional diversity (Table 3). Indeed, the sequence of effect size values for structural diversity, i.e., P / A < number < biomass , is reversed in the case of functional diversity. The variations in SAV are mainly associated with therophytes such as A. cretica, A. thaliana, B. media, P. hieraciodes, S. halepense and V. carinata, with increase in abundance according to their specific phenologies (Figure 4). Conversely, in SSA, the reduced seasonal variations are mainly associated with the abundance of perennial species such as C. dactylon, O. corniculata, P. reptans and T. repens, which dominate the community throughout the year (Figure 4).
The different seasonal dynamics in species composition between SAV and SSA are reflected in the seasonal variations of the community dominance–diversity structure (Figure 6). Indeed, remarkable variations were observed in the dominance–diversity structure of the SAV community, regardless of the abundance measure adopted, whereas SSA presented similar structures across most of the seasons, albeit with varying evenness (Figure 6).
The models best describing the dominance–diversity relationships, fitted on the number of plants abundance data, vary in SAV from niche pre-emption in winter, to log-normal in spring and autumn, to Zipf–Mandelbrot in summer (AIC reduction as compared to the second best model: autumn 42.6%, spring 16.1%, summer 8.7%, winter 47.6%). In SSA, dominance–diversity relationships were consistently best described by the pre-emption and Zipf–Mandelbrot (with comparable AIC values) in all seasons, except in summer, when the Zipf–Mandelbrot model scores lower AIC values (reduction as compared to the second best model: autumn 0.2‰, spring 0.01‰, summer 49.6%, winter 0.4‰). Overall, the slope of the dominance–diversity curves in SAV decreases according to the order autumn > summer > winter > spring, with variations greater than those observed in SSA, where the main differentiation appears between autumn/summer and spring/winter for cover data (Figure 6).

4. Discussion

Overall, the obtained results form several lines of evidence that consistently demonstrate strong environmental filtering inducing dramatic shifts from niche-based to stochastic-based community assembly rules under management. The main ones include (i) the promotion of biotic homogenization, (ii) the induction of (inverted) contingency cascades on structural and functional diversities, (iii) the constrained seasonal dynamics in dominance–diversity relationships, (iv) the seasonal signal attenuation, and (v) the hierarchical filtering on species traits.
First, the substantial increases in alien and cosmopolitan species [54,55], as well as in species resistant to mowing and trampling [19,56], provide unequivocal evidence of management that induces biotic homogenization by promoting specific functional traits [57,58,59], while reducing overall functional diversity [60,61,62]. Environmental filtering in the study system is primarily exerted through mechanical damage to reproductive structures caused by mowing and trampling, and through the promotion of species that exploit high nutrient availability with rapid population dynamics. These processes account for the increased incidence and dominance of Poaceae and Asteraceae species in SSA, most of which are also alien or cosmopolite [56]. These species can easily preempt or outcompete native populations, becoming dominant within a short time [59] as exemplified by the autumnal outbreak of P. dilatatum, a weed with allelopathic properties that proliferates in environments with high nitrogen availability [63]. The year-round availability of water and nutrients also promotes the dominance of species with long life cycles [24,64], especially in conjunction with mowing and trampling. Indeed, the persistence of gemmae in or at the soil level allows geophytes and hemicryptophytes to resist these pressures and be competitive in managed urban ecosystems [65]. For instance, by spreading horizontally, they can access patchily distributed resources or spaces left vacant by other plants [66,67]. Such a hypothesis is supported by the results of Kelemen et al. [68], who observed that in managed urban lawns, litter accumulation following the cessation of mowing tends to increase the dominance of grasses and reduce the presence of prostrate plants.
The differential variance inflation in P/A, number and biomass data implies a contingency cascade, in which initially stochastic differences in species establishment are amplified over time, leading to highly divergent and path-dependent outcomes in ecological dominance. This applies for both structural and functional diversity, although the inverted contingency cascades indicate that management generates two parallel yet complementary assembly processes, where stochastic establishment coexists with deterministic functional outcomes. At the structural level, the local species pool remains relatively stable, but population sizes become more contingent on local factors and species randomly achieve ecological dominance (the seemingly contradictory cover results can be explained by the strong variance-stabilizing transformation–30× variance compression–making the 21% increase a conservative estimate of management’s true effect on spatial occupancy patterns). At the functional level, different functionally equivalent species randomly establish across plots, yet pervasive environmental filtering ensures that only a narrow subset of trait combinations can achieve ecological dominance–a mechanism coherent with a “functional redundancy saturation” hypothesis. This process may be further amplified by abundance-decoupling and priority effects, where initial colonists preempt resources within a constrained functional space [19,69,70], a hypothesis supported by the observed dominance–diversity patterns across seasons in SAV and SSA.
Indeed, the consistent niche pre-emption dominance–diversity patterns across most seasons in SSA indicate that a few dominant species, favored by management-induced filtering but contingently selected by local factors, disproportionately control resources through strong asymmetric competition. The shift to a Zipf–Mandelbrot model during the summer drought indicates that the signal from natural environmental drivers, typically overridden by management, briefly becomes strong enough to be discernible from the anthropogenic noise. In this season, the balance between the contributions of anthropogenic filtering and drought stress tips sufficiently to allow a more complex assemblage of subordinate species. Conversely, the niche pre-emption model observed in SAV in winter, when harsh climate favors few dominant species, gives way to models indicating complex biotic interactions in spring and autumn, and to intermediate stages in summer with both abiotic (drought) and biotic control [43,71,72]. Remarkably, the same seasonal variations were observed in coastal lagoons by Bellino et al. [73], who suggested that the winter pre-emption model indicates a buildup phase in algal communities culminating in spring under optimal environmental conditions, followed by a phase of exogenous control of community structure by summer temperatures, returning to an autumnal pre-emption model. As a cautionary note, since slightly more than 20 species were seasonally observed in SSA, the dominance–diversity patterns can be equally well described by Zipf–Mandelbrot models [74]. However, even in this case the interpretation would remain substantially the same, differing only in the extent of environmental control vs endogenous niche-structuring processes [43,73,74,75,76,77]. The stark comparison between areas indicates that management overweighs the natural environmental and biotic controls, replacing responsive, seasonally adapted communities with others characterized by muted seasonal dynamics.
In terms of seasonal dynamics, the 4–7× compression of the seasonal signal in SSA is remarkable and further demonstrates that management overrides natural environmental rhythms, creating functionally static assemblages in which the same strategies dominate year round. Furthermore, the compression of the seasonal signal unevenly affects functional traits and establishes a hierarchy of management impacts from the broadest to the finest ecological adaptations, i.e., from muted effects on life forms to the dramatic collapse of chorological specializations. Indeed, while life forms, representing broad solutions to survival, demonstrate remarkable resistance to management, the chorological affinities, which reflect fine-tuned evolutionary adaptations to specific regional climates and historical biogeographic contexts, are rendered useless by the novel and homogenized conditions of managed urban ecosystems [35,78,79]. In other words, management filters primarily against the deepest ecological specializations, favoring instead a pool of generalist species whose broad strategies remain viable across a wide range of disturbed environments [31,80].

5. Conclusions

The obtained findings demonstrate that management through mowing, irrigation, and fertilization induces regime shifts characterized by strong biotic homogenization and markedly muted seasonal dynamics. This shift establishes management as the dominant ecological force, increasing stochasticity and creating a cascade of contingency that affects species composition, demography, and resource control, while functionally constraining the traits that dominate the communities. The impact of this filtering is hierarchical, with severe consequences for specialized adaptations like chorology, while broader ecological strategies, such as life forms, remain relatively unaffected. Collectively, this evidence points toward a comprehensive rewiring of urban plant communities and a fundamental change in community assembly rules, where local contingency and anthropogenic filters override natural abiotic and biotic drivers. From a practical perspective, although specific management thresholds inducing regime shifts remain to be quantified across different systems, reducing management intensity and frequency–and aligning management schedules with natural seasonal dynamics–would mitigate anthropogenic filtering. Such an approach is essential for preserving viable, niche-based community assemblies and the ecological functions they support in urban ecosystems.

Author Contributions

Conceptualization, A.B. and D.B.; methodology, A.B., D.B. and V.B.; validation, A.B., D.B. and V.B.; formal analysis, A.B. and V.B.; investigation, M.N. and V.B.; resources, D.B.; data curation, A.B., D.B. and V.B.; writing–original draft preparation, M.N. and V.B.; writing, reviewing and editing, A.B., D.B., M.N. and V.B.; supervision, A.B. and D.B.; project administration, D.B. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4—Call for tender No. 3138 of 16 December 2021, rectified by Decree n. 3175 of 18 December 2021 of the Italian Ministry of University and Research funded by the European Union—NextGenerationEU; Award Number: Project code CN_00000033, Concession Decree No. 1034 of 17 June 2022 adopted by the Italian Ministry of University and Research, CUP, H43C22000530001 Project title “National Biodiversity Future Center—NBFC”.

Data Availability Statement

Dataset available on request from the authors.

Acknowledgments

The authors are grateful to the master degree students of the Ecology Lab at the University of Salerno, Paola Senatore and Alfonsina Palomba, whose passion and tireless help eased the burden of the field and lab activities, allowing flawlessly carrying out the research. A special thanks goes also to the family of the first author (Emanuele, Luisa and Francesco) for the constant support and love, and to the families of all the authors for the inspiration and the patience–notwithstanding the long hours subtracted to their care. For the linguistic review of this research article, the authors acknowledge Marianna Ciuccio, a certified Cambridge English C2 Level specialist, whose expertise was invaluable in polishing the final draft.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
SAVStudy area in the municipality of Avellino
SSAStudy area in the municipality of Fisciano
P/APresence-Absence
NMDSNon-metric multidimensional scaling
PERMDISPAnalysis of multivariate homogeneity of group dispersions
PERMANOVAPermutational multivariate analysis of variance
RADRank abundance distribution
AICAkaike’s information criterion

References

  1. Ouyang, X.; Luo, X. Models for assessing urban ecosystem services: Status and outlooks. Sustainability 2022, 14, 4725. [Google Scholar] [CrossRef]
  2. Teixeira, C.; Fernandes, C. Novel ecosystems: A review of the concept in non-urban and urban contexts. Landsc. Ecol. 2020, 35, 23–29. [Google Scholar] [CrossRef]
  3. Maibritt, P. Devising urban biodiversity habitat provision goals: Ecosystem services analysis. Forests 2019, 10, 391. [Google Scholar] [CrossRef]
  4. Etter, A.; McAlpine, C.; Seabrook, L.; Wilson, K. Incorporating temporality and biophysical vulnerability to quantify the human spatial footprint on ecosystems. Biol. Conserv. 2011, 144, 1585–1594. [Google Scholar] [CrossRef]
  5. Dai, X.; Zheng, H.; Yang, Y.; Meng, N.; Yang, Q.; Zhu, J.; Yan, D.; Li, Z.; Li, R. A new method to quantify the impacts of human activity on soil conservation service. J. Environ. Manag. 2024, 368, 122257. [Google Scholar] [CrossRef]
  6. Shi, Y.; Yuan, X.; Tang, Y.; Li, Y.; Wang, Q.; Ma, Q.; Zuo, J.; Liu, H. Localized regional life cycle model research for the impacts of carbon dioxide on human health and ecosystem. Sustain. Prod. Consum. 2022, 29, 36–45. [Google Scholar] [CrossRef]
  7. Zhang, D.; Huang, Q.; He, C.; Wu, J. Impacts of urban expansion on ecosystem services in the Beijing-Tianjin-Hebei urban agglomeration, China: A scenario analysis based on the Shared Socioeconomic Pathways. Resour. Conserv. Recycl. 2017, 125, 115–130. [Google Scholar] [CrossRef]
  8. Napoletano, M.; Bellino, A.; Baldantoni, D. Depletion and recovery of soil organic matter: Ecological, economic and social implications. Ecol. Civiliz. 2025, 2, 10002. [Google Scholar] [CrossRef]
  9. Becher, J.; Englisch, C.; Griebler, C.; Bayer, P. Groundwater fauna downtown—Drivers, impacts and implications for subsurface ecosystems in urban areas. J. Contam. Hydrol. 2022, 248, 104021. [Google Scholar] [CrossRef] [PubMed]
  10. Fasolino, I.; Cicalese, F.; Bellino, A.; Grimaldi, M.; del Caz-Enjuto, M.R.; Baldantoni, D. The ecological efficiency of green materials in sustainable urban planning—A model for its measurement. Sustainability 2023, 15, 16038. [Google Scholar] [CrossRef]
  11. Chen, X.; Di, Q.; Liang, C. The mechanism and path of pollution reduction and carbon reduction affecting high quality economic development—Taking the Yangtze River Delta urban agglomeration as an example. Appl. Energy 2024, 376, 124340. [Google Scholar] [CrossRef]
  12. White, P.; Pickett, S. Chapter 1—Natural Disturbance and Patch Dynamics: An Introduction. In The Ecology of Natural Disturbance and Patch Dynamics; Pickett, S.T.A., White, P.S., Eds.; Academic Press: San Diego, CA, USA, 1985; pp. 3–13. [Google Scholar] [CrossRef]
  13. Gámez, M.; López, I.; Shamandy, A. Open- and closed-loop equilibrium control of trophic chains. Ecol. Model 2010, 221, 1839–1846. [Google Scholar] [CrossRef]
  14. Newman, E. Disturbance Ecology in the Anthropocene. Front. Ecol. Evol. 2019, 7, 147. [Google Scholar] [CrossRef]
  15. Boldt, J.; Hazen, E.; Hunsicker, M.; Fu, C.; Perry, R.; Shan, X. Quantifying ecosystem responses to environmental and human pressures in the marine ecosystem off the west coast of Vancouver Island. Ecol. Indic. 2021, 132, 108232. [Google Scholar] [CrossRef]
  16. Portugal, A.; Carvalho, F.; de Macedo Carneiro, P.; Rossi, S.; de Oliveira Soares, M. Increased anthropogenic pressure decreases species richness in tropical intertidal reefs. Mar. Environ. Res. 2016, 120, 44–54. [Google Scholar] [CrossRef] [PubMed]
  17. Leung, T.; So, K.; Shum, B.; Hau, B. Optimal mowing regime in enhancing biodiversity in seasonal floodplains along engineered channels. Sustainability 2022, 14, 4002. [Google Scholar] [CrossRef]
  18. Toledo-Garibaldi, M.; Gallardo-Hernández, C.; Ulian, T.; Toledo-Aceves, T. Urban forests support natural regeneration of cloud forest trees and shrubs, albeit with limited occurrence of late-successional species. For. Ecol. Manag. 2023, 546, 121327. [Google Scholar] [CrossRef]
  19. Baldi, V.; Bellino, A.; Baldantoni, D. Small-scale land use effects on plant communities in Mediterranean urban ecosystems. Ecol. Indic. 2025, 170, 113051. [Google Scholar] [CrossRef]
  20. Milano, V.; Cortet, J.; Baldantoni, D.; Bellino, A.; Dubs, F.; Nahmani, J.; Strumia, S.; Maisto, G. Collembolan biodiversity in Mediterranean urban parks: Impact of history, urbanization, management and soil characteristics. Appl. Soil Ecol. 2017, 119, 428–437. [Google Scholar] [CrossRef]
  21. Milano, V.; Maisto, G.; Baldantoni, D.; Bellino, A.; Bernard, C.; Croce, A.; Dubs, F.; Strumia, S.; Cortet, J. The effect of urban park landscapes on soil Collembola diversity: A Mediterranean case study. Landsc. Urban Plan. 2018, 180, 135–147. [Google Scholar] [CrossRef]
  22. Bellino, A.; Baldantoni, D.; Milano, V.; Santorufo, L.; Cortet, J.; Maisto, G. Spatial patterns and scales of Collembola taxonomic and functional diversity in urban parks. Sustainability 2021, 13, 13029. [Google Scholar] [CrossRef]
  23. Ratajová, A. Study on the dynamics of grass microgametophytes from urban vegetation. Environ. Sci. Pollut. Res. 2014, 21, 6218–6220. [Google Scholar] [CrossRef]
  24. Jakobsson, S.; Bernes, C.; Bullock, J.; Verheyen, K.; Lindborg, R. How does roadside vegetation management affect the diversity of vascular plants and invertebrates? A systematic review. Environ. Evid. 2018, 7, 17. [Google Scholar] [CrossRef]
  25. Krajter Ostoić, S.; Salbitano, F.; Borelli, S.; Verlič, A. Urban forest research in the Mediterranean: A systematic review. Urban For. Urban Green. 2018, 31, 185–196. [Google Scholar] [CrossRef]
  26. Aurelle, D.; Thomas, S.; Albert, C.; Bally, M.; Bondeau, A.; Boudouresque, C.; Cahill, A.; Carlotti, F.; Chenuil, A.; Cramer, W.; et al. Biodiversity, climate change, and adaptation in the Mediterranean. Ecosphere 2022, 13, e3915. [Google Scholar] [CrossRef]
  27. Bellino, A.; Bellino, L.; Baldantoni, D.; Saracino, A. Evolution, ecology and systematics of Soldanella (Primulaceae) in the southern Apennines (Italy). BMC Evol. Biol. 2015, 15, 158–173. [Google Scholar] [CrossRef] [PubMed]
  28. Buira, A.; Fernández-Mazuecos, M.; Aedo, C.; Molina-Venegas, R. The contribution of the edaphic factor as a driver of recent plant diversification in a Mediterranean biodiversity hotspot. J. Ecol. 2021, 109, 987–999. [Google Scholar] [CrossRef]
  29. Braz Pires, M.; Kougioumoutzis, K.; Norder, S.; Dimopoulos, P.; Strid, A.; Panitsa, M. The future of plant diversity within a Mediterranean endemism centre: Modelling the synergistic effects of climate and land-use change in Peloponnese, Greece. Sci. Total. Environ. 2024, 947, 174622. [Google Scholar] [CrossRef] [PubMed]
  30. Knapp, S.; Kühn, I.; Mosbrugger, V.; Klotz, S. Do protected areas in urban and rural landscapes differ in species diversity? Divers. Distrib. 2008, 14, 622–630. [Google Scholar] [CrossRef]
  31. Aronson, M.F.J.; La Sorte, F.A.; Nilon, C.H.; Katti, M.; Goddard, M.A.; Lepczyk, C.A.; Warren, P.S.; Williams, N.S.G.; Cilliers, S.; Clarkson, B.; et al. A global analysis of the impacts of urbanization on bird and plant diversity reveals key anthropogenic drivers. Proc. R. Soc. B 2014, 281, 20133330. [Google Scholar] [CrossRef]
  32. Williams, N.S.G.; Schwartz, M.W.; Vesk, P.A.; McCarthy, M.A.; Hahs, A.K.; Clemants, S.E.; Corlett, R.T.; Duncan, R.P.; Norton, B.A.; Thompson, K.; et al. A conceptual framework for predicting the effects of urban environments on floras. J. Ecol. 2009, 97, 4–9. [Google Scholar] [CrossRef]
  33. LaPaix, R.; Freedman, B. Vegetation structure and composition in urban habitats in Halifax, Nova Scotia, Canada. Environ. Manag. 2020, 65, 842–855. [Google Scholar] [CrossRef]
  34. Li, X.; Fan, S. Seasonal variation in plant functional traits and its implications for ecosystem functioning in a subtropical urban forest. Urban For. Urban Green. 2020, 55, 126824. [Google Scholar] [CrossRef]
  35. Yang, J.; Luo, X.; Peng, F.; Liu, H.; Sun, S. Temporal dynamics of plant functional traits in response to urbanization. Ecol. Indic. 2021, 129, 107976. [Google Scholar] [CrossRef]
  36. Lara, W.; Bravo, F.; Maguire, D. Modeling patterns between drought and tree biomass growth from dendrochronological data: A multilevel approach. Agric. For. Meteorol. 2013, 178–179, 140–151. [Google Scholar] [CrossRef]
  37. Baldantoni, D.; Bellino, A.; Morra, L.; Alfani, A. Compost amendment enhances natural revegetation of a Mediterranean degraded agricultural soil. Environ. Manag. 2015, 56, 946–956. [Google Scholar] [CrossRef] [PubMed]
  38. Wildi, O. Data Analysis in Vegetation Ecology, 2nd ed.; Wiley: Oxford, UK, 2013; p. 330. [Google Scholar]
  39. Pignatti, S.; Guarino, R.; La Rosa, M. Flora d’Italia, 2nd ed.; Edagricole: Milan, Italy, 2017. [Google Scholar]
  40. Pignatti, S. Indicator Values of the Vascular Plants of the Flora of Italy; Università degli Studi di Camerino: Macerata, Italy, 2005; p. 97. [Google Scholar]
  41. Di Biase, L.; Pace, L.; Mantoni, C.; Fattorini, S. Variations in plant richness, biogeographical composition, and life forms along an elevational gradient in a Mediterranean mountain. Plants 2021, 10, 2090. [Google Scholar] [CrossRef]
  42. Villéger, S.; Mason, N.; Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 2008, 89, 2290–2301. [Google Scholar] [CrossRef]
  43. Whittaker, R. Dominance and diversity in land plant communities: Numerical relations of species express the importance of competition in community function and evolution. Science 1965, 147, 250–260. [Google Scholar] [CrossRef]
  44. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2023. [Google Scholar]
  45. Pizarro, M.; Hernangómez, D.; Fernández-Avilés, G. climaemet: Climate AEMET Tools; R Foundation for Statistical Computing: Vienna, Austria, 2021. [Google Scholar] [CrossRef]
  46. Grenié, M.; Gruson, H. Fundiversity: Easy Computation of Functional Diversity Indices, R Package Version 1.1.1; R Foundation for Statistical Computing: Vienna, Austria, 2023.
  47. Wickham, H. ggplot2: Elegant Graphics for Data Analysis; Springer: New York, NY, USA, 2016. [Google Scholar]
  48. Kassambara, A. ggpubr: ’ggplot2’ Based Publication Ready Plots, R Package Version 0.6.0; R Foundation for Statistical Computing: Vienna, Austria, 2023.
  49. Wilke, C.; Wiernik, B. ggtext: Improved Text Rendering Support for ’ggplot2’, R Package Version 0.1.2; R Foundation for Statistical Computing: Vienna, Austria, 2022.
  50. Auguie, B. gridExtra: Miscellaneous Functions for “Grid” Graphics, R Package Version 2.3; R Foundation for Statistical Computing: Vienna, Austria, 2017.
  51. Wickham, H.; Vaughan, D.; Girlich, M. tidyr: Tidy Messy Data, R Package Version 1.3.1; R Foundation for Statistical Computing: Vienna, Austria, 2024.
  52. Oksanen, J.; Simpson, G.; Blanchet, F.; Kindt, R.; Legendre, P.; Minchin, P.; O’Hara, R.; Solymos, P.; Henry, M.; Stevens, H.; et al. vegan: Community Ecology Package, R Package Version 2.6-4; R Foundation for Statistical Computing: Vienna, Austria, 2022.
  53. Ellenberg, H.; Mueller-Dombois, D. A key to Raunkiær plant life forms with revised subdivisions. Ber. Geobot. Inst. Eth Stift RüBel 1965, 37, 56–73. [Google Scholar] [CrossRef]
  54. Narcizo, A.; Braga, J.; Sartori, R. Impact of exotic tree species on the natural regeneration of an urban restinga forest. Trees 2023, 37, 1643–1655. [Google Scholar] [CrossRef]
  55. Lubarda, B.; Rat, M.; Petronić, S.; Marić, N.; Maksimović, T.; Anačkov, G. Urban floristic diversity in Bosnia and Herzegovina—The reflection of nature. Urban Ecosyst. 2024, 27, 1067–1083. [Google Scholar] [CrossRef]
  56. Hayasaka, D.; Akasaka, M.; Miyauchi, D.; Box, E.O.; Uchida, T. Qualitative variation in roadside weed vegetation along an urban–rural road gradient. Flora 2012, 207, 126–132. [Google Scholar] [CrossRef]
  57. Fornal-Pieniak, B.; Stangierska-Mazurkiewicz, D.; Kamionowski, F.; Widera, K.; Żarska, B.; Latocha, P. Preferences of adults for synanthropic flora in the sustainable development of Polish cities’ green areas. Sustainability 2024, 16, 3610. [Google Scholar] [CrossRef]
  58. Moszkowicz, L.; Krzeptowska-Moszkowicz, I.; Porada, K.; Zieliński, M. The potential impact of changes in soil and climate conditions on development of the herb layer vegetation of public parks in Krakow (Southern Poland). Sustainability 2024, 16, 451. [Google Scholar] [CrossRef]
  59. Pianta, M.; Calbi, M.; Dagnino, D.; Turcato, C.; Roccotiello, E. Peri-urban Mediterranean plant communities are shaped by chronic anthropogenic disturbances. Urban For. Urban Green. 2024, 95, 128333. [Google Scholar] [CrossRef]
  60. Dylewski, L.; Banaszak-Cibicka, W.; Maćkowiak, L.; Dyderski, M. How do urbanization and alien species affect the plant taxonomic, functional, and phylogenetic diversity in different types of urban green areas? Environ. Sci. Pollut. Res. 2023, 30, 92390–92403. [Google Scholar] [CrossRef]
  61. He, R.; Li, L.; Wang, G.; Cao, L.; Xiong, G.; Yang, F. Plant diversity value of informal green spaces in tropical coastal urban areas: An empirical study of species, functional, and phylogenetic diversity. Sci. Total Environ. 2024, 955, 176741. [Google Scholar] [CrossRef]
  62. Zheng, B.; Su, L.; Hui, N.; Jumpponen, A.; Kotze, D.J.; Lu, C.; Pouyat, R.; Szlavecz, K.; Wardle, D.A.; Yesilonis, I.; et al. Urbanisation shapes microbial community composition and functional attributes more so than vegetation type in urban greenspaces across climatic zones. Soil. Biol. Biochem. 2024, 191, 109352. [Google Scholar] [CrossRef]
  63. Hassan, M.; Mohamed, H. Allelopathic interference of the exotic naturalized Paspalum dilatatum Poir. threatens diversity of native plants in urban gardens. Flora 2020, 266, 151593. [Google Scholar] [CrossRef]
  64. Du, M.; Fan, J.; Liu, M.; Niu, X.; Wang, S. Mowing and phosphorus affect plant diversity and soil carbon and nitrogen storage under nitrogen enrichment in the semi-arid alpine steppe. CATENA 2022, 217, 106458. [Google Scholar] [CrossRef]
  65. Benvenuti, S. Weed dynamics in the Mediterranean urban ecosystem: Ecology, biodiversity and management. Weed Res. 2004, 44, 341–354. [Google Scholar] [CrossRef]
  66. Cornelissen, J.; Lavorel, S.; Garnier, E.; Díaz, S.; Buchmann, N.; Gurvich, D.; Reich, P.; Ter Steege, H.; Morgan, H.; Van Der Heijden, M.; et al. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust. J. Bot. 2003, 51, 335–380. [Google Scholar] [CrossRef]
  67. Zeeman, B.; McDonnell, M.; Kendal, D.; Morgan, J. Biotic homogenization in an increasingly urbanized temperate grassland ecosystem. J. Veg. Sci. 2017, 28, 550–561. [Google Scholar] [CrossRef]
  68. Kelemen, A.; Török, P.; Valkó, O.; Deák, B.; Miglécz, T.; Tóth, K.; Ölvedi, T.; Tóthmérés, B. Sustaining recovered grasslands is not likely without proper management: Vegetation changes after cessation of mowing. Biodivers. Conserv. 2014, 23, 741–751. [Google Scholar] [CrossRef]
  69. Loreau, M.; de Mazancourt, C. Biodiversity and ecosystem stability: A synthesis of underlying mechanisms. Ecol. Lett. 2013, 16, 106–115. [Google Scholar] [CrossRef]
  70. Fukami, T. Historical contingency in community assembly: Integrating niches, species pools, and priority effects. Annu. Rev. Ecol. Evol. Syst. 2015, 46, 1–23. [Google Scholar] [CrossRef]
  71. Shen, M.; Tang, Y.; Chen, J.; Yang, X.; Wang, C.; Cui, X.; Yang, Y.; Han, L.; Li, L.; Du, J.; et al. Earlier-season vegetation has greater temperature sensitivity of spring phenology in northern hemisphere. PLoS ONE 2014, 9, 88178. [Google Scholar] [CrossRef]
  72. Nothaaß, D.; Huth, A. Community recomposition caused by species extinction in the colonization-competition trade-off model for vegetation. Ecol. Model 2025, 499, 110906. [Google Scholar] [CrossRef]
  73. Bellino, A.; Mangano, M.; Baldantoni, D.; Russell, B.; Mannino, A.; Mazzola, A.; Vizzini, S.; Sarà, G. Seasonal patterns of biodiversity in Mediterranean coastal lagoons. Divers. Distrib. 2019, 25, 1512–1526. [Google Scholar] [CrossRef]
  74. Wilson, J.; Gitay, H.; Steel, J.; King, W. Relative abundance distributions in plant communities: Effects of species richness and of spatial scale. J. Veg. Sci. 1998, 9, 213–220. [Google Scholar] [CrossRef]
  75. Chiarucci, A.; Wilson, J.; Anderson, B.; De Dominicis, V. Cover versus biomass as an estimate of species abundance: Does it make a difference to the conclusions? J. Veg. Sci. 1999, 10, 35–42. [Google Scholar] [CrossRef]
  76. Steel, J.; Wilson, J.; Anderson, B.; Lodge, R.; Tangney, R. Are bryophyte communities different from higher-plant communities? Abundance relations. Oikos 2004, 104, 479–486. [Google Scholar] [CrossRef]
  77. Spatharis, S.; Tsirtsis, G. Zipf–Mandelbrot model behavior in marine eutrophication: Two way fitting on field and simulated phytoplankton assemblages. Hydrobiologia 2013, 714, 191–199. [Google Scholar] [CrossRef]
  78. Knapp, S.; Dinsmore, L.; Fissore, C.; Hobbie, S.E.; Jakobsdottir, I.; Kattge, J.; King, J.Y.; Klotz, S.; McFadden, J.P.; Cavender-Bares, J. Phylogenetic and functional characteristics of household yard floras and their changes along an urbanization gradient. Ecology 2012, 93, S83–S98. [Google Scholar] [CrossRef]
  79. Knapp, S.; Li, L.; Ladouceur, E.; Wang, J.; Yuan, C.; Wolters, V.; Hackländer, K.; Haddaway, N.R. A role for artificial night-time lighting in plant community dynamics in urban ecosystems. Front. Ecol. Environ. 2021, 19, 150–156. [Google Scholar] [CrossRef]
  80. McKinney, M.L. Urbanization as a major cause of biotic homogenization. Biol. Conserv. 2006, 127, 247–260. [Google Scholar] [CrossRef]
Sustainability 17 09516 g001
Figure 1. Aerial views of the study areas (SAV, SSA). Meteorological stations from which climate data were retrieved are indicated with yellow stars. Background images from ESRI Satellite imagery and inlays from Google Earth Pro.
Figure 1. Aerial views of the study areas (SAV, SSA). Meteorological stations from which climate data were retrieved are indicated with yellow stars. Background images from ESRI Satellite imagery and inlays from Google Earth Pro.
Sustainability 17 09516 g001
Sustainability 17 09516 g002
Figure 2. Walter–Lieth climatic diagrams for SAV (left panel) and SSA (right panel) areas. Mean temperatures are indicated in red and total precipitation in blue. Light blue boxes on the x axes indicate probable frost periods evaluated according to Pizarro et al. [45]. Temperature and precipitation data of September–December refer to 2023, whereas those of the other months refer to 2024. Data from https://centrofunzionale.regione.campania.it/, accessed on the 31 August 2024.
Figure 2. Walter–Lieth climatic diagrams for SAV (left panel) and SSA (right panel) areas. Mean temperatures are indicated in red and total precipitation in blue. Light blue boxes on the x axes indicate probable frost periods evaluated according to Pizarro et al. [45]. Temperature and precipitation data of September–December refer to 2023, whereas those of the other months refer to 2024. Data from https://centrofunzionale.regione.campania.it/, accessed on the 31 August 2024.
Sustainability 17 09516 g002
Sustainability 17 09516 g003
Figure 3. Functional trait abundances in the different seasons in SAV (first row) and SSA (second row), represented through stacked bar plots. Specifically, the family, chorology, Raunkiær life form (abbreviated according to Ellenberg [53]) general class, subclass and type are reported.
Figure 3. Functional trait abundances in the different seasons in SAV (first row) and SSA (second row), represented through stacked bar plots. Specifically, the family, chorology, Raunkiær life form (abbreviated according to Ellenberg [53]) general class, subclass and type are reported.
Sustainability 17 09516 g003
Sustainability 17 09516 g004
Figure 4. NMDS biplots relative to P/A (A,B), number of plants (C,D), dry mass (E,F) and cover (G,H) in SAV (left) and SSA (right). The convex hulls grouping the observations in the four seasons and the respective standard confidence ellipses at α = 0.05 are also shown. Species are indicated with Roman numerals according to Table 1 and Table 2. Biplots are drawn with equal scales to simplify comparisons.
Figure 4. NMDS biplots relative to P/A (A,B), number of plants (C,D), dry mass (E,F) and cover (G,H) in SAV (left) and SSA (right). The convex hulls grouping the observations in the four seasons and the respective standard confidence ellipses at α = 0.05 are also shown. Species are indicated with Roman numerals according to Table 1 and Table 2. Biplots are drawn with equal scales to simplify comparisons.
Sustainability 17 09516 g004
Sustainability 17 09516 g005
Figure 5. NMDS biplots based on functional diversity indices (richness: ric, evenness: eve, dispersion: dis, divergence: div, Rao’s quadratic entropy: rao) with community weighted means calculated from P/A (A,B), number of plants (C,D), dry mass (E,F) and cover (G,H) in SAV (left) and SSA (right). The convex hulls grouping the observations in the four seasons and the respective standard confidence ellipses at α = 0.05 are also shown. Species are indicated with Roman numerals according to Table 1 and Table 2. Biplots are drawn with equal scales to simplify comparisons.
Figure 5. NMDS biplots based on functional diversity indices (richness: ric, evenness: eve, dispersion: dis, divergence: div, Rao’s quadratic entropy: rao) with community weighted means calculated from P/A (A,B), number of plants (C,D), dry mass (E,F) and cover (G,H) in SAV (left) and SSA (right). The convex hulls grouping the observations in the four seasons and the respective standard confidence ellipses at α = 0.05 are also shown. Species are indicated with Roman numerals according to Table 1 and Table 2. Biplots are drawn with equal scales to simplify comparisons.
Sustainability 17 09516 g005
Sustainability 17 09516 g006
Figure 6. Seasonal rank–abundance curves based on total number of plants, total biomass and relative cover of each species from SAV (A,C,E) and SSA (B,D,F). The former were described through dominance–diversity models: pre-emption (SAV: winter; SSA: autumn, winter, spring), log-normal (SAV: autumn, spring) and Zipf–Mandelbrot models (summer in both areas).
Figure 6. Seasonal rank–abundance curves based on total number of plants, total biomass and relative cover of each species from SAV (A,C,E) and SSA (B,D,F). The former were described through dominance–diversity models: pre-emption (SAV: winter; SSA: autumn, winter, spring), log-normal (SAV: autumn, spring) and Zipf–Mandelbrot models (summer in both areas).
Sustainability 17 09516 g006
Table 1. Species identified in SAV listed in Roman numerals, with indication of their families, chorology, biological forms, Ellenberg indices (L: light, T: temperature, C: continentality, U: moisture, R: soil reactivity, N: nutrients, S: salinity) and the seasons in which they were found (Aut: autumn, Spr: spring, Sum: summer, Win: winter). Abbreviations of life forms according to Ellenberg and Mueller-Dombois [53].
Table 1. Species identified in SAV listed in Roman numerals, with indication of their families, chorology, biological forms, Ellenberg indices (L: light, T: temperature, C: continentality, U: moisture, R: soil reactivity, N: nutrients, S: salinity) and the seasons in which they were found (Aut: autumn, Spr: spring, Sum: summer, Win: winter). Abbreviations of life forms according to Ellenberg and Mueller-Dombois [53].
SpeciesFamilyFormChorotypeLTCURNSSeason
IAnchusella cretica (Mill.) Bigazzi, E. Nardi & SelviBoraginaceaeT scapStenomediterranean7863560Spr
IIArabidopsis thaliana (L.) Heynh.BrassicaceaeT scapPaleotemperate6 54540Spr, Win
IIIArtemisia vulgaris L.AsteraceaeH scapBoreal9784 50Spr
IVAvena barbata Pott ex LinkPoaceaeT scapEurymediterranean8853720Spr, Sum. Win
VBriza media L.PoaceaeH caespEuro-Asiatic6 4 20Sum
VICalepina irregularis (Asso) Thell.BrassicaceaeT scapEurymediterranean8843530Spr
VIICapsella bursa-pastoris (L.) Medik.BrassicaceaeH bienneCosmopolitan7 55540Spr, Win
VIIICardamine hirsuta L.BrassicaceaeT scapCosmopolitan7853540Win
IXCarex distachya Desf.CyperaceaeH caespStenomediterranean6642450Win
XCerastium glomeratum Thuill.CaryophyllaceaeT scapEurymediterranean7 55550Spr, Win
XICota tinctoria (L.) J. GayAsteraceaeH bienneEuropean8652640Aut, Win
XIICrepis neglecta L.AsteraceaeT scapEuro-Montane7634630Sum
XIIICrepis sancta L.AsteraceaeT scapEurymediterranean11962 20Aut, Spr, Win
XIVCynodon dactylon L.PoaceaeG rhizCosmopolitan8854 40Aut, Spr, Sum, Win
XVDaucus carota L. subsp. carotaApiaceaeH biennePaleotemperate8654540Aut, Spr, Sum, Win
XVIDigitaria sanguinalis (L.) Scop.PoaceaeT scapCosmopolitan7753640Aut
XVIIEquisetum telmateja Ehrh.EquisetaceaeG rhizBoreal5748850Sum
XVIIIErigeron sumatrensis Retz.AsteraceaeT scapAlien8853 70Spr, Sum
XIXErodium cicutarium (L.) L’HérGeraniaceaeT scapCosmopolitan8753530Spr
XXHypericum perforatum L.HypericaceaeH scapPaleotemperate786 0Aut, Spr, Sum, Win
XXIHypochaeris radicata L.AsteraceaeH rosEuropean9842 10Spr
XXIILamium purpureum L.LamiaceaeT scapEuro-Asiatic7754550Spr, Win
XXIIILolium perenne L.PoaceaeH caespBoreal8545 70Sum
XXIVMedicago arabica (L.) Huds.FabaceaeT scapEurymediterranean9952 20Aut, Spr, Sum, Win
XXVMedicago lupulina L.FabaceaeT scapPaleotemperate75 4870Spr, Sum
XXVIMinuartia hybrida (Vill.) ShischkCaryophyllaceaeT scapPaleotemperate7753620Spr
XXVIIMyosotis ramosissima Rochel subsp. ramosissimaBoraginaceaeT scapEuropean9852430Spr
XXVIIIOrnithogalum umbellatum L.AsparagaceaeG bulbEurymediterranean5655750Spr, Win
XXIXOrnithopus compressus L.FabaceaeT scapEurymediterranean11952210Aut, Spr, Sum, Win
XXXPapaver rhoeas L.PapaveraceaeT scapEuro-Montane66557 0Spr
XXXIPetrorhagia prolifera (L.) P.W. Ball & HeywoodCaryophyllaceaeT scapEurymediterranean8552 20Sum
XXXIIPicris hieraciodes L.AsteraceaeH scapEuro-Asiatic8 54840Sum
XXXIIIPlantago lanceolata L.PlantaginaceeH rosEuro-Asiatic675 0Aut, Spr, Sum, Win
XXXIVPoa annua L.PoaceaeT caespCosmopolitan7 56 80Spr, Win
XXXVRaphanus raphanistrum L.BrassicaceaeT scapEurymediterranean1155 450Aut, Sum
XXXVISenecio vulgaris L.AsteraceaeT scapEurymediterranean7 5 80Spr
XXXVIISetaria pumila (Poir.) Roem & Schult.PoaceaeT scapCosmopolitan7754560Aut, Sum
XXXVIIISherardia arvensis L.RubiaceaeT scapEurymediterranean8655850Aut, Spr, Sum, Win
XXXIXSilene gallica L.CaryophyllaceaeT scapEurymediterranean8953210Spr
XLSorghum halepense (L.) Pers.PoaceaeG rhizCosmopolitan88 7880Aut, Sum
XLITrifolium arvenseFabaceaeT scapPaleotemperate8552210Spr
XLIITrifolium campestre Schreb.FabaceaeT scapPaleotemperate8554 30Spr, Sum
XLIIITrifolium pratense L.FabaceaeCh pulvEuro-Asiatic7 4 0Aut, Spr, Sum, Win
XLIVTrifolium repens L.FabaceaeCh reptPaleotemperate8 70Aut, Spr, Sum, Win
XLVValerianella carinata Loisel.ValerianaceaeT scapEurymediterranean78548 0Spr, Win
XLVIVeronica arvensis L.PlantaginaceeT scapPaleotemperate55556 0Aut, Spr, Win
XLVIIVicia sativa L.FabaceaeT scapEurymediterranean556 0Aut, Spr, Sum, Win
Table 2. Species identified in SSA listed in Roman numerals, with indication of their families, chorology, biological forms, Ellenberg indices (L: light, T: temperature, C: continentality, U: moisture, R: soil reactivity, N: nutrients, S: salinity) and the seasons in which they were found (Aut: autumn, Spr: spring, Sum: summer, Win: winter). Abbreviations of life forms according to Ellenberg and Mueller-Dombois [53].
Table 2. Species identified in SSA listed in Roman numerals, with indication of their families, chorology, biological forms, Ellenberg indices (L: light, T: temperature, C: continentality, U: moisture, R: soil reactivity, N: nutrients, S: salinity) and the seasons in which they were found (Aut: autumn, Spr: spring, Sum: summer, Win: winter). Abbreviations of life forms according to Ellenberg and Mueller-Dombois [53].
SpeciesFamilyFormChorotypeLTCURNSSeason
IArtemisia vulgaris L.AsteraceaeH scapBoreal9784 50Aut, Spr, Sum, Win
IIAvena barbata Pott ex LinkPoaceaeT scapEurymediterranean8853720Spr
IIIBromus hordeaceus L.PoaceaeT scapCosmopolitan765 0Spr
IVCardamine hirsuta L.BrassicaceaeT scapCosmopolitan7853540Spr, Win
VCarex distachya Desf.CyperaceaeH caespStenomediterranean6642450Win
VICatapodium rigidum (L.) C.E. Hubb.PoaceaeT scapEurymediterranean8852540Spr
VIICerastium glomeratum Thuill.CaryophillaceaeT scapEurymediterranean7 55550Spr, Win
VIIIConvolvolus arvensis L.ConvolvulaceaeG rhizPaleotemperate7754550Aut, Spr, Sum
IXCrepis bursifolia L.AsteraceaeH scapEndemic9643820Win
XCrepis neglecta L.AsteraceaeT scapEuropean7634630Win
XICynodon dactylon L.PoaceaeG rhizCosmopolitan8854 40Aut, Spr, Sum, Win
XIICyperus rotundus L.CyperaceaeG rhizCosmopolitan81056850Aut
XIIIDactylis glomerata L.PoaceaeH caespPaleotemperate7654560Spr, Sum, Win
XIVDichondra micrantha Urb.ConvolvulaceaeG rhizAlien5856320Aut, Spr, Sum, Win
XVDigitaria sanguinalis (L.) Scop.PoaceaeT scapCosmopolitan7753640Aut
XVIErigeron canadiensis L.AsteraceaeT scapAlien8853 70Aut
XVIIFestuca ciliata GouanPoaceaeT caespEurymediterranean8952420Spr
XVIIIGeranium rotundifolium L.GeraniaceaeT scapPaleotemperate7853630Aut, Spr, Win
XIXHypochaeris radicata L.AsteraceaeH rosEuropean9842 10Aut, Spr, Sum, Win
XXLamium purpureum L.LamiaceaeT scapEuro-Asiatic7754550Spr, Win
XXIMalva sylvestris L.MalvaceaeH scapEuro-Asiatic8644 80Aut, Spr, Sum, Win
XXIIMedicago arabica (L.) Huds.FabaceaeT scapEurymediterranean9952 20Aut, Spr, Win
XXIIIMedicago lupulina L.FabaceaeT scapPaleotemperate75 4870Aut, Spr, Sum
XXIVMedicago sativa L.FabaceaeH scapEuro-Asiatic8573930Sum
XXVMedicago tenoreana Ser.FabaceaeT scapEuropean11962 20Win
XXVIOrnithogalum umbellatum L.AsparagaceaeG bulbEurymediterranean5655750Win
XXVIIOxalis corniculata L.OxalidaceaeCh reptEurymediterranean7704 60Aut, Spr, Sum, Win
XXVIIIPaspalum dilatatum Poir.PoaceaeH caespAlien 8 10880Aut, Sum, Win
XXIXPlantago lanceolata L.PlantaginaceaeH rosEuro-Asiatic675 0Spr, Sum, Win
XXXPoa annua L.PoaceaeT caespCosmopolitan7 56 80Spr, Win
XXXIPolygonum aviculare L.PolygonaceaeT reptCosmopolitan7753610Spr, Sum
XXXIIPortulaca oleracea L.PortulacaceaeT scapCosmopolitan7854770Aut
XXXIIIPotentilla reptans L.RosaceaeH rosPaleotemperate6656750Aut, Spr, Sum, Win
XXXIVPrunella vulgaris L.LamiaceaeH scapBoreal76464 0Spr
XXXVRanunculus arvensis L.RanunculaceaeT scapPaleotemperate66548 0Spr
XXXVIRaphanus raphanistrum L.BrassicaceaeT scapEurymediterranean1155 450Spr
XXXVIIRumex crispus L.PolygonaceaeH scapCosmopolitan7556 50Spr, Win
XXXVIIISenecio vulgaris L.AsteraceaeT scapEurymediterranean7 5 80Win
XXXIXSherardia arvensis L.RubiaceaeT scapEurymediterranean8655850Aut, Spr, Win
XLSilene dioica (L.) Clairv.CaryophillaceaeH scapPaleotemperate7556780Spr, Sum, Win
XLISonchus oleraceus L.AsteraceaeT scapEuro-Asiatic75 Aut, Spr, Sum, Win
XLIISorghum halepense (L.) Pers.PoaceaeG rhizCosmopolitan88 7880Aut, Spr, Sum
XLIIIStellaria media (L.) Vill.CaryophillaceaeT reptCosmopolitan6 4780Spr, Sum, Win
XLIVSymphyotrichum squamatum (Spreng.) G.L. NesomAsteraceaeT scapAlien8854770Aut
XLVTrifolium campestre Schreb.FabaceaeT scapPaleotemperate8554 30Spr
XLVITrifolium repens L.FabaceaeCh reptPaleotemperate8 70Aut, Spr, Sum, Win
XLVIITrifolium resupinatum L.FabaceaeT reptPaleotemperate8855 50Spr
XLVIIIVeronica persica Poir.PlantaginaceaeT scapAlien8755560Aut, Win, Spr
XLIXVicia sativa L.FabaceaeT scapEuro-Asiatic556 0Spr, Win
Table 3. PERMANOVA F-ratios (seasonal signal strength) and PERMDISP average distances to median (within-season variability) for structural and functional diversity across four abundance measures. ϱ values represent percentage changes in the managed area (SSA) relative to the unmanaged area (SAV). All ϱ values are significant at α = 0.001 . Asterisks indicate significant seasonal effects according to PERMANOVA (for α = 0.05 ).
Table 3. PERMANOVA F-ratios (seasonal signal strength) and PERMDISP average distances to median (within-season variability) for structural and functional diversity across four abundance measures. ϱ values represent percentage changes in the managed area (SSA) relative to the unmanaged area (SAV). All ϱ values are significant at α = 0.001 . Asterisks indicate significant seasonal effects according to PERMANOVA (for α = 0.05 ).
PERMANOVAPERMDISP
SeasonSAVSSAϱ (%)SAVSSAϱ (%)
StructuralP/A32.6 *7.43 *−77.20.05590.070325.7
Number16.3 *3.87 *−76.20.2910.40940.6
Biomass19.5 *6.28 *−67.80.1940.36990.0
Cover25.3 *4.99 *−80.30.1060.12820.9
FunctionalP/A13.3 *2.96 *−77.71.282.0660.9
Number8.50 *1.90−77.61.281.8846.9
Biomass8.72 *1.27−85.41.541.8721.4
Cover8.81 *1.94−78.01.272.0359.8
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Baldi, V.; Bellino, A.; Napoletano, M.; Baldantoni, D. Vegetation Management Changes Community Assembly Rules in Mediterranean Urban Ecosystems—A Mechanistic Case Study. Sustainability 2025, 17, 9516. https://doi.org/10.3390/su17219516

AMA Style

Baldi V, Bellino A, Napoletano M, Baldantoni D. Vegetation Management Changes Community Assembly Rules in Mediterranean Urban Ecosystems—A Mechanistic Case Study. Sustainability. 2025; 17(21):9516. https://doi.org/10.3390/su17219516

Chicago/Turabian Style

Baldi, Vincenzo, Alessandro Bellino, Mattia Napoletano, and Daniela Baldantoni. 2025. "Vegetation Management Changes Community Assembly Rules in Mediterranean Urban Ecosystems—A Mechanistic Case Study" Sustainability 17, no. 21: 9516. https://doi.org/10.3390/su17219516

APA Style

Baldi, V., Bellino, A., Napoletano, M., & Baldantoni, D. (2025). Vegetation Management Changes Community Assembly Rules in Mediterranean Urban Ecosystems—A Mechanistic Case Study. Sustainability, 17(21), 9516. https://doi.org/10.3390/su17219516

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop