Diversity of pollinator communities along urban environmental gradients in Merida, Yucatan, a tropical city in Mexico

Diversity of pollinator communities along urban environmental gradients in Merida, Yucatan, a tropical city in Mexico | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Diversity of pollinator communities along urban environmental gradients in Merida, Yucatan, a tropical city in Mexico Sebastián Gay Escalante, Richard Evan Feldman, Rosalina Rodríguez, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8329681/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Urbanization transforms landscapes and alters plant and pollinator biodiversity and their interactions. In tropical cities, where species richness is high and interspecific interactions are numerous, how urban development reshapes plant–pollinator communities remains not well understood. We studied diurnal pollinator diversity along urban environmental gradients in Merida, Yucatan, a rapidly growing tropical city. Across 14 urban and peri-urban natural areas, we characterized land cover using satellite images and measured vegetation structure complexity, ground cover complexity, and plant diversity through field surveys. Overall, we recorded 302 pollinator species and morphospecies across nine taxonomic orders, observed on 202 flowering plant species, allowing for a comprehensive community composition assessment. Using generalized linear mixed models, we examined the effects of three land cover gradients (impervious surface, vegetation type, water cover) as well as local habitat features (vegetation structure complexity, ground cover complexity, plant diversity) on pollinator diversity measured with Hill numbers ( q ). Plant diversity had a strong positive impact on pollinator species richness ( q = 0) and moderate but consistently positive effects on Shannon diversity ( q = 1) and Simpson diversity ( q = 2). Sites with tall woody vegetation showed reduced pollinator diversity, while the impervious surface and water cover gradients, along with vegetation and ground cover complexity, had limited explanatory power. These findings highlight that promoting herbaceous floral diversity, rather than dense woody cover, better supports pollinator communities. Overall, our results emphasize the role of vegetation composition and vertical structure in shaping pollinator diversity in tropical cities. biodiversity birds flower visitors ground cover complexity impervious surfaces insects PCA vegetation complexity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Urbanization transforms the original natural vegetation of an area, increases the coverage of impervious surfaces, introduces non-native species, and changes the availability of resources and the abundance of consumers, reshaping ecological communities (Aronson et al., 2015 ; de Barros Ruas et al., 2022 ; Harrison & Winfree, 2015 ; McKinney, 2008 ; Rebele, 1994 ; Theodorou, 2022 ; Theodorou et al., 2020 ). However, urban systems like cities are not without ecological function. These systems can support communities of urban-tolerant plants and animals and, in some cases, even serve as biodiversity hotspots (Adams et al., 2020 ; Kantsa et al., 2013 ) or refuges for endangered species (Planchuelo et al., 2019 ). Vegetation in urban systems is crucial for supporting animal diversity, as it provides essential food resources (Campos-Silva & Piratelli, 2021 ; Christie et al., 2010 ). Of vegetation food resources, floral nectar and pollen drive the foraging behaviors and interactions of pollinators. Flowers also provide resources, such as shelter and hunting grounds, which further influence the structure and activity of pollinator communities (Chien, 1998 ; Sponsler et al., 2023 ). In particular, the introduction of non-native plant species in urban areas in some cases can partially compensate for vegetation loss and provide new floral rewards for native pollinators, but often fails to support the original diversity of pollinators (Kovács-Hostyánszki et al., 2022 ; Moroń et al., 2009 ), functionally simplifying plant-pollinator communities. In addition to vegetation loss and the introduction of non-native plants, urbanization reduces ecological connectivity by restricting the movement of pollinators, thereby constraining pollinator diversity among urban vegetation patches (Xiao et al., 2016 ). However, it is possible that enhancing and preserving certain components of habitat quality may mitigate pollinator diversity loss. Enhancing native plant diversity and structural complexity within urban green spaces creates a mosaic of microhabitats, expanding niche availability and supporting diverse pollinator communities. Varying canopy layers, understory vegetation types, and natural ground cover can provide additional nesting substrates, feeding grounds, and other valuable resources (de Lima et al., 2020 ; Dusza et al., 2020 ; Knuff et al., 2020 ). These finer-scale improvements may enable pollinator persistence by buffering against the broader negative impacts of urban development and vegetation loss, promoting the coexistence of diverse species within urban ecosystems. Research on the effects of urbanization on plant and animal diversity has largely focused on temperate areas, overlooking tropical and subtropical urban areas where biodiversity is greater, and plant resources are more finely partitioned (Harrison & Winfree, 2015 ; Silva et al., 2023 ). In tropical and subtropical climates, consistent warmth and humidity with minimal seasonality, enable year-round pollinator activity, unlike temperate regions (Abrahamczyk et al., 2011 ; Genini et al., 2021 ). There are also notable taxonomic biases in the knowledge of pollinator diversity in urban systems. Much of the literature has focused on specific groups, such as bees, which are often considered emblematic pollinators due to their ecological importance and ease of study (Silva et al., 2023 ; Skaldina & Blande, 2025 ). This narrow focus overlooks the broader pollinator community, which includes diverse taxa such as beetles, flies, ants, birds, and even specialized predators like spiders that may occasionally transport pollen among flowers, all of which play integral roles in urban ecosystems. With these ideas in mind, we focus on Merida, a rapidly growing subtropical city in the political state of Yucatan, southeastern Mexico. The urban landscape of this city has undergone significant transformation over the last 30 years, marked by rapidly increasing impervious surface cover, native vegetation loss and fragmentation (Valdiviezo, 2014 ). As urban expansion in Merida is expected to accelerate over the next decade, it is crucial to document the diversity of plant and animal communities. We also need to understand how environmental gradients in the urban area affect these communities, which are vital for human well-being. The objective of our study is to evaluate species diversity of the broader plant–pollinator communities responding to urban environmental gradients in Merida. Specifically, we aim to disentangle the effects on pollinator communities of land cover gradients associated with urbanization from other local habitat attributes that include variables representing plant diversity, vegetation structure complexity, and ground cover complexity on pollinator communities. We hypothesize a decline in pollinator diversity as urbanization-related gradients increase, as the pollinator communities are likely to become increasingly constrained to a subset of species that can tolerate reduced vital resources and challenging abiotic conditions in urban environments. However, we also expect that increases in plant diversity, together with greater vegetation structure complexity and more complex ground cover, will act as buffering factors, promoting higher pollinator diversity despite urban pressures. Methods Urban natural areas selection The city of Merida (20.58° N, 89.37° W) is the capital of the state of Yucatan. Located in the northwest of the Yucatan Peninsula, Mexico, Merida is the largest urban center on the Peninsula, with a population of nearly one million inhabitants (INEGI, 2023 ). Due to real estate speculation and its status as one of the safest cities in the country, this city is undergoing rapid growth, with urban development replacing former agricultural and natural areas (Aguilar et al., 2025). Merida was founded in 1542 on the ruins of the important pre-Hispanic Mayan city of T’hó (Barteet, 2015 ), surrounded by a dry forest landscape. The area is located at an elevation of 10 masl and is characterized by a predominantly flat limestone platform formed by karstic processes. There are no above-ground rivers in this region, and the soils are shallow, rocky, and have a fine to medium-textured soil, making them easily erodible when vegetation cover is lost (Lugo-Hubp et al., 1992 ). The area has a typical Tropical climate, marked by warm, humid summers and mild winters. The mean annual temperature is 26.3°C, with average maximum values of 32.8°C and minimum values of 21.0°C. Rainfall is strongly seasonal, with an annual total of 1,079 mm; monthly values range from 9.6 mm in March to 187.2 mm in September (Servicio Meteorológico Nacional, 2021). To effectively capture the diversity of urban environmental gradients in Merida, we focused on areas identified by local authorities and the community as natural spaces. These regions include remnants of native vegetation, parks, and public gardens, thus reflecting the variety of land cover types and vegetation management practices in the area. Then we selected sites that were accessible, and were spaced at least 1 km apart to avoid spatial overlap and better capture the range of urbanization processes across the city. Using these criteria, we identified 14 urban natural areas (UNAs) distributed across the four cardinal points of the city (Fig. 1 ). Land cover composition gradient Each UNA was characterized by land cover within a 1000 m radius using satellite images from ESA WorldCover project 2021 datasets (Zanaga et al., 2021 ). The data layers were obtained from Sentinel-1 and Sentinel-2 satellite imagery with a spatial resolution of 10 meters (Copernicus Sentinel data 2021 processed by the ESA WorldCover consortium). Produced by the European Space Agency, WorldCover is recognized for its high reliability and sensitivity to capturing heterogeneous land cover types, making it a robust tool for landscape analysis and particularly suited for rapidly changing environments such as tropical cities (Venter et al., 2022 ; Xu et al., 2024 ). For each UNA, we counted the number of pixels of each land cover type: impervious surface, tree cover, grassland, shrubland, cropland, bare ground, water bodies, and wetlands. To ensure that land cover types with larger pixel counts did not disproportionately influence the analysis, we standardized the values. We then applied a Principal Component Analysis (PCA) using R v4.5 (R Core Team, 2024 ) and the FactoMineR package (Lê, Josse, & Husson, 2008 ) to reduce the dimensionality of the multiple land cover types and identify the land cover gradients. This approach increased interpretability while minimizing information loss regarding the distribution of land covers across sites. Using the same land cover pixel counts, we calculated proportional values in percentage for each cover type to visualize the composition of each site as stacked bars. To determine the number of principal components to retain for subsequent analysis of environmental urban gradients, we applied a broken-stick model criterion (Jackson, 1993 ), which compares observed eigenvalues to those expected under random distributions (Legendre & Legendre, 2012 ). Vegetation structure and ground cover gradients To estimate the complexities of ground cover and vegetation structure, we utilize the point-centered quarter method (Mitchell, 2010 ) by setting up a 50 m long transect in each urban natural area. We sampled vegetation along these transects at randomly established points separated by at least 5 m. At each point, we ran an imaginary line perpendicular to the transect dividing the space into four quarters. In each quarter, we identified the tree nearest to the central point and registered its distance from the central point, treetop cover, and trunk diameter. For the ground cover, we recorded the percentage of ground types within a 1m x 1m square in each quarter at each point along the transects. Ground types registered were classified as sand-dominated ground, clay-dominated ground, rocky ground, vegetation, leaf litter, logs, impervious and debris surfaces. To analyze vegetation and reduce multiple variable measurements into a few key variables that reflect a gradient of vegetation structure, we conducted a PCA, and then to obtain a single complexity metric per urban natural area, we used a surface roughness metric that measures structural heterogeneity by assessing the degree of deviation of individual observations from the mean (McGarigal et al., 2009 ). We calculated surface roughness as the sum of the differences between each tree’s PC1 score and the mean PC1 score for the urban natural area, providing an index of within-site variability in the complexity of vegetation structure. We calculated surface roughness using the geodiv package (Smith et al., 2021 ) in R v 4.5 (R Core Team, 2024 ). To analyze ground cover types, we also applied a PCA to summarize variation in the relative proportion of each ground type recorded for each urban natural area. Because proportional data are constrained to a constant sum, they do not meet the assumptions of PCA, which requires variables to be unconstrained and vary independently in Euclidean space (Aitchison, 1982 ; Egozcue et al., 2003 ). Hence, we used a centered log-ratio (CLR) transformation that projects compositional data from a space constrained by constant totals (i.e., proportions that must sum to one) into regular, unconstrained space where standard statistical techniques can be validly used (like PCA). The CLR transformation achieves this by taking the logarithm of the ratio between each component and the geometric mean of all components in a given composition, effectively removing the unit-sum constraint while preserving relative information among components. Before applying the transformation, we replaced zero values using the count zero multiplicative (CZM) method from the zCompositions package (Palarea-Albaladejo & Martín-Fernández, 2015 ). This step is necessary because the CLR transformation involves logarithmic operations, which cannot be performed on zeros. The CZM method estimates small positive values for zero components while preserving the internal structure of the proportions, allowing the dataset to be safely log-transformed. We then used the pcaCoDa() function from the robCompositions package (Templ et al., 2011 ), which applies the CLR transformation and performs PCA in the resulting euclidean space. As with the vegetation structure complexity data, we used the scores of the first PC for each urban natural area and calculated surface roughness as a complexity metric using the geodiv package. Plant and pollinator sampling We conducted sampling from January 23, 2023, to October 23, 2024, with each urban natural area visited eight times at intervals of two to three months. Our sampling encompassed the typical weather patterns in Yucatan, covering both the dry months (November – May) and wet months (June – October). This approach allowed us to obtain a comprehensive snapshot of the plant-pollinator communities across the year. In each UNA, we recorded the pollinators visiting flowers by setting up a starting point to walk, usually near a trail or vegetation clearing. We walked along the trail or clearing, stopping at each of the first 17 plants or plant clusters in bloom we encountered. We observed individual plants only if they had five or more open flowers or inflorescences at the time of sampling. We observed clusters of the same plant species, each measuring 50 x 50 cm, specifically for those species that typically produce one or a few flowers per individual. We excluded any plant or clusters that did not meet these criteria and ensured there was at least 2.5 m between points to approach independence among recordings. We identified the plant species at each sampling point for estimating total plant diversity at the urban natural area level. Although the flowering periods of the plant species varied across sampling visits, compiling records from all visits allowed us to create a more complete representation of the local blooming plant community within each UNA. At each individual floral cluster, we registered pollinators at flowers for five minutes, starting observations between 9:00 AM and 9:30 AM and ending around 12:00 PM every sampling day. Visits were conducted under sunny or slightly cloudy conditions to maximize the likelihood of observing the typical pollinator activity. Three field technicians trained in entomological and botanical identification assisted in identifying both plant and pollinator species. The team worked either directly in the field or by using photographs taken during sampling, as well as by comparing samples with herbarium specimens. To support taxonomic identification, we uploaded photographs to iNaturalist . In instances where we could not confidently identify the species, individuals were collected for later identification. All specimens were identified to the finest taxonomic level possible. For samples that could not be reliably assigned to a species level, we used the next confident taxonomic rank, which was typically genus, and classified them as morphospecies. A record was deemed valid for this study when a flower visitor, referred to as a pollinator, made contact with any part of the inner side of the flower, whether petals, anthers, or pistil, thereby potentially transporting pollen. If a pollinator visited the flower multiple times, it was recorded only once, provided it remained in view of the observer. Diversity analysis The diversity of plants and pollinators across the 14 UNAs was evaluated using Hill numbers ( q ) implemented in the iNEXT package (Hsieh et al. 2025 ), which computes species accumulation curves based on the rarefaction and extrapolation framework. First, we calculated sample completeness to an endpoint slightly larger than the most abundant site. Sample completeness represents the proportion of individuals expected to belong to species already observed, providing a measure of sampling effort effectiveness. Using a common endpoint allowed us to assess whether sampling effort was sufficient and to compare sites on equal terms. We also calculated diversity at three values of q for each urban natural area: the first, species richness ( q = 0), represents the total number of species in a community; the second, the exponential of Shannon entropy ( q = 1), also referred to as Shannon diversity, reflects the effective number of common species in a community; and the third, the inverse of Simpson concentration, is referred to as Simpson diversity ( q = 2), and indicates the effective number of dominant species (Chao et al., 2014 ). Sample completeness was calculated to estimate the proportion of individuals in the community that are expected to belong to species already observed in the sample (Chao & Jost, 2012 ) and allow comparability across sites. For plants, we focused specifically on Shannon diversity ( q = 1) as a predictor variable, as it reflects the effective number of common species in a community and provides a balanced measure between rare and dominant species. Species richness ( q = 0), although widely used, can obscure important ecological patterns because it treats all species equally, overemphasizing the contribution of rare taxa that may have little influence on community functioning or interaction dynamics (Fletcher et al., 2025 ). This limitation is particularly relevant in urban contexts, where variation in sampling completeness and habitat heterogeneity can amplify such biases. In contrast, Simpson diversity ( q = 2) disproportionately weights the most abundant species, potentially masking variation among moderately abundant and ecologically meaningful taxa. Shannon diversity ( q = 1), calculated as the exponential of Shannon entropy, integrates both species presence and relative abundance, yielding a metric that is sensitive to changes in community composition while remaining robust to extremes in species Simpson diversity (Chao & Jost, 2012 ; Chao et al., 2014 ; Roswell et al., 2021 ). Effect of urban environmental gradients on pollinator diversity To assess the relationship between pollinator diversity and urban environmental gradients across urban natural areas and obtain effect coefficients, we fitted a generalized linear mixed model (GLMM) to the data using the glmmTMB package (Brooks et al., 2017 ) in R v.4.5 (R Core Team, 2024 ). We built separate models for pollinator species richness ( q = 0), Shannon diversity ( q = 1), and Simpson diversity ( q = 2) as response variables. As predictor variables, we utilized the urban environmental gradients identified through surface image analysis of land covers in Merida, and gradients of ground cover and vegetation structure complexity, as well as plant diversity (Shannon diversity) at each site. We modeled the visits as a random effect denoting UNAs intrinsic variation in pollinator diversity due to repeated sampling of plants and pollinators. Diversity data typically has a right-skewed distribution; therefore, we set a negative binomial error distribution and a logarithmic link function in our model. Predictor variables were standardized using z-transformation, allowing the obtaining of comparable effect coefficients. We tested the assumptions of normality, overdispersion, zero-inflation, and collinearity of model residuals with the performance package (Lüdecke et al., 2021 ). To compare the impact of predictor variables on parameters of pollinator diversity, we extracted the coefficients and their confidence intervals and visualized them as forest plots using the broom.mixed package (Bolker and Robinson, 2025 ). Results Land cover composition gradients The 14 urban natural areas in Merida varied in land cover composition. Most sites were dominated by a mix of impervious surfaces, tree cover, and grassland, with higher impervious cover generally corresponding to lower vegetation and vice versa. Minor land cover types, including shrubland, cropland, water bodies, and wetlands, contributed variably across sites (Fig. 2 ) Sites like Parque Arqueoecológico (ARQ) and Kai Luum (KLM) exhibited small fractions of cropland cover. Grasslands were present and relatively abundant across all sites, particularly in mid-disturbance peripheral areas such as MAPSA, KLM, and Las Américas (LAM). Shrubland cover varied among sites, with the highest fractions found in Dzoyaxché (DYX), Tixcuytún (TIX), and PCTY, which also had the greatest tree cover. Bare ground was generally rare, most noticeable in KLM, MAPSA, and LAM. Water bodies were limited, occurring mainly in Acuaparque (AQP), ARQ, and MAPSA, all of which also contained some wetland cover. Overall, the sites with the most extensive vegetation cover were DYX, TIX, and PCTY, contrasting with those dominated by impervious surfaces, particularly Itzimná (ITZ), La Iberica (LIB), and the Roger Orellana Botanical Garden (JBRRO), with the first grouping located on the outskirts, far from the city core, and the second grouping embedded within the main urban mass. Land cover pixel counts by UNA are in the Supplementary Material. The first three principal components from the PCA explained 89.9% of the variation in land cover types, representing the most informative environmental urban gradients for the city of Merida. PC1 explained 35.5% of environmental variation, indicating a gradient of impervious surface, ranging from fully built-up areas on one end to extensive vegetation cover, including tree cover, grassland, and shrubland, on the other. In Fig. 3 a, we flipped the axis for better interpretation so that higher PC1 values reflect more urbanized landscapes. PC2 accounted for 31.1% of the variation and represents a vegetation type gradient, separating tall and woody areas with trees and shrubs from areas with low height vegetation or open ground, either with cropland, grassland, or bare ground. Areas with concrete, asphalt, and other artificial structures were near the center of this PC axis, reflecting an urban landscape of scattered greenery between buildings. PC3 accounted for 23.3%, featuring a gradient of water cover that ranged from small wetlands and water bodies at one end to drier upland cover, including cropland, and shrubland, at the other end (Fig. 3 a & 3 b). Vegetation structure and ground covers gradients Vegetation structure and ground cover complexity followed similar unidimensional gradients (Fig. 3 c & 3 d). Summary values for each site are presented in the Supplementary Material. For vegetation structure, PC1 explained 83.7% of the total variance, with higher values corresponding to greater tree height, trunk diameter, and canopy cover in both north-to-south and east-to-west orientations. PC1 for the ground cover complexity gradient explained 44.7% of the variance, ranging from impervious surfaces at higher values to sites with more diverse ground covers, including vegetation, rocks, leaf litter, and, to a lesser extent, logs and debris, at lower values. In both cases, most of the variation was concentrated along PC1, which was used as the main complexity axis in subsequent analyses. Pollinator and Plant Diversity Total pollinator visits to flowers differed among urban natural areas. Primary pollinators, defined based on previous reports as taxa that regularly visit flowers and actively contribute to pollen transfer, included Apodiformes (hummingbirds), Coleoptera, Hymenoptera, Lepidoptera, and Diptera, and contributed the majority of visits. Incidental visitors included Araneae, Orthoptera, Hemiptera, and Passeriformes (orioles), which are groups for which pollination has been less consistently or only rarely reported. In general, visit number seems to fluctuate across sites rather than increase with urbanization. Incidental visitors were slightly more common in less populated urban natural areas, though their contribution to total visits remained low overall (Fig. 4 a). We registered a total of 302 pollinator species and morphospecies, spanning 9 taxonomic orders and 79 families. Of these, 93 were classified as morphospecies, with most identified at the genus level. In this study, we refer to both identified species and morphospecies collectively as species to facilitate the interpretation of results. Invertebrates accounted for the vast majority of pollinator diversity, with 289 species distributed among Hymenoptera (82), Lepidoptera (97), Diptera (48), Coleoptera (31), Hemiptera (18), Araneae (8), and Orthoptera (5) (Fig. 4 b). Vertebrate visitors were less diverse, comprising only 8 species: 4 Apodiformes and 4 Passeriformes (see Supplementary Material for detailed list of species and morphoespecies registered). Sample completeness at the site level was consistently high for pollinators, with an average sample coverage of 0.96 ± 0.005 across sites. This indicates that, on average, 96% of pollinator individuals present in the urban natural areas likely belonged to species detected during sampling, suggesting that most species richness was well represented (Fig. 5 a). Sample completeness was slightly lower for plants, averaging 0.84 ± 0.06 across sites, with the lowest site-level coverage at 0.71 (Fig. 5 b). These values suggest that, while not as thoroughly captured as pollinators, the most common plant species were reasonably well represented at each site. Diversity metrics for pollinators and plants varied across urban natural areas (Table 1 ). Pollinator richness ( q = 0) ranged from 55 species at ITZ to 118 at DYX, while the effective number of common species ( q = 1) varied between 18.17 at ITZ and 54.80 at KLM. When dominant species were weighted more heavily ( q = 2), diversity ranged from 6.75 at LAM to 32.81 at KLM. Plant richness ( q = 0) ranged from 26 species at ITZ to 65 at DYX, with effective diversity ( q = 1) spanning from 16.36 at ITZ to 49.58 at DYX, and dominant-weighted diversity ( q = 2) ranging from 12.01 at LAM to 38.53 at DYX. Table 1 Hill numbers calculated for each urban natural area ( q = 0, 1, 2), summarizing both pollinator and plant species richness, Shannon diversity, and Simpson diversity across the entire sampling period. Urban natural area Pollinators Plants q = 0 q = 1 q = 2 q = 0 q = 1 q = 2 AQP 88 24.39 9.71 38 24.53 18.42 ARQ 84 41 20.80 46 33.43 25.57 DYX 118 43.29 23.15 65 49.58 38.53 DZB 88 37.33 14.89 46 30.14 21.26 FDP 96 31.67 16.68 46 32 22.92 ITZ 55 18.17 9.28 26 16.36 12.44 JBRRO 78 27.38 16.34 50 33.59 23.23 KLM 109 54.80 32.81 43 28.18 19.74 LAM 79 19.88 6.75 42 21.79 12.01 LHR 88 35.92 18.26 51 32.95 21.78 LIB 75 36.57 22.34 42 29.71 22.04 MAPSA 109 44.62 21.54 39 24.26 17.15 PCTY 80 27.08 10.27 35 23.05 15.44 TIX 83 37.55 21.82 37 25.32 17.36 In the case of plants, we recorded an assemblage of 202 flowering plant species, spanning 29 taxonomic orders and 55 families (Fig. 6 ). A total of 1,822 individual plants were observed across all sites, representing both native (172) and non-native species (30). The most species-rich orders were Fabales (34 species), Lamiales (25), and Asterales (22). At the family level, Fabaceae (34 species), Asteraceae (22), and Euphorbiaceae (14) contributed substantially to overall richness. Regarding growth forms, the most abundant were herbs (924 individuals), followed by trees (423), shrubs (370), vines (81), and aquatic plants (23) (see Supplementary Material for detailed list of species and morphoespecies registered). Effects of urban environmental gradients on pollinator diversity We found similar trends in the effects of the urban environmental gradients on pollinators for all three diversity indices (species richness, Shannon diversity and Simpson diversity; Fig. 7 ). Complete model summaries are included in the Supplementary Material. The gradient of impervious surface (PC1) had a weak effect, with overlapping confidence intervals across all indices. Additionally, the gradients of water cover (PC3) and ground cover complexity did not show any significant effect in any of the models. Although, there was a slight positive trend in the effect of vegetation complexity, it was not statistically significant. In contrast, the gradient of vegetation cover type (PC2) had a negative effect on all diversity metrics (Positive PC2 values indicate UNAs with more trees and shrubs, and less low vegetation and open ground). This negative trend was particularly pronounced for pollinator species richness, followed by the Shannon diversity and Simpson diversity. Plant diversity had a positive effect on pollinators, resulting in a significant increase in species richness. Specifically, there was a 16% increase in pollinator species richness for each unit increase in plant diversity (Fig. 7 ). Discussion Our study assessed the influence of different land cover and ecological complexity properties as drivers of pollinator diversity across urban green spaces in a tropical city. Among the six variables we assessed, local plant diversity and landscape-scale vegetation type were the most consistent predictors of three diversity indices: species richness, Shannon diversity, and Simpson diversity. At the same time, the effects of other habitat characteristics such as the impervious surface gradient, water cover gradient and both vegetation cover and ground cover complexities were weak or inconclusive. Despite expectations regarding the effect of impervious surfaces on pollinator diversity, we did not detect a strong influence of built infrastructure. While high impervious surface cover has been documented to negatively affect pollinator diversity (Bennett & Lovell, 2019 ; Geslin et al., 2016 hätalo et al., 2024), other studies have found that factors such as the quality and connectivity of vegetation areas may outweigh the broader urban landscape context (Bates et al., 2011 ; Graffigna et al., 2024 ), especially when urbanization allows the existence of vegetation areas relatively interconnected at moderate levels (Fortel et al., 2014 ; Martins et al., 2017 ). In the case of tropical systems, the climatic and biotic conditions in urban areas might encourage vegetation to grow spontaneously and continuously, and form structurally and compositionally rich habitats in the matrix surrounding urban natural areas. Further, lack of maintenance of urban infrastructure frequently allows native flowering plants to grow along sidewalks, vacant lots, or roadsides. These informal green spaces may enhance connectivity by offering critical foraging resources and maintaining spatiotemporal resource continuity across otherwise heavily urbanized areas (Cheng et al., 2022 ; He et al., 2024 ). Sites characterized by higher values along the gradient of vegetation types, represented by PC2 axis values from the PCA of land cover types, were associated with lower pollinator diversity parameters (species richness, Shannon diversity, Simpson diversity). This gradient reflects a shift from open areas to sites dominated by trees and shrubs. Although increased vertical vegetation structure with layers, such as tree canopy, understory shrubs, and herbs, is assumed to enhance pollinator diversity by increasing habitat heterogeneity, resource availability, and buffering against microclimate extremes (da Silva et al., 2021 ; Xing et al., 2023 ), these benefits may not extend to pollinator groups in urban environments. In our study, urban areas with denser woody cover limited light penetration, thereby reducing herb and floral availability and foraging opportunities for pollinators that depend on clearing areas (Bozek et al. 2023). This result is consistent with findings from other tropical ecosystems, where increased tree canopy has been linked to a decline in understory floral diversity due to reduced light and space (Chazdon et al., 1996 ; Radhamoni et al., 2023 ). At the other end of the vegetation type gradient (shorter vegetation), the positive effect of light in cleared areas on the availability of flowers at the herb strata may help explain the higher pollinator diversity. This pattern has been observed in canopy gaps and forest edges, where floral resources can be more accessible and diverse (Ammann et al., 2024 ; Coulin et al., 2019 ; Proctor et al., 2012 ; Mathis et al., 2021 ). Water features, such as ponds, wetlands, or riparian zones, can influence pollinator communities by moderating local microclimates and enhancing habitat heterogeneity, particularly through increased humidity and the support of moisture-adapted plant species (Riis et al., 2020 ). Some studies have found positive associations between proximity to water and pollinator diversity, particularly for bees and hoverflies (Dylewski et. al, 2024 ), likely due to indirect benefits such as greater floral availability in surrounding vegetation. However, aquatic elements do not provide direct foraging or nesting resources for most pollinators, making it difficult to gauge their overall contribution to pollinator communities. In our study, the water cover gradient did not emerge as a strong predictor of pollinator diversity, likely due to the limited variation in this feature. Only a few sites contained permanent or semi-permanent water bodies, reducing our ability to detect potential ecological effects. In this study, the gradient of vegetation structure complexity, measured from transect estimates of tree and shrub height, diameter, and canopy cover, did not show a strong relationship with pollinator diversity. This result contradicts the expectation that vertical vegetation complexity is a promoting factor for species richness by increasing habitat and microenvironmental heterogeneity (Tews et al., 2004 ; Torresani et al., 2024 ). One possible explanation is that, despite the varying levels of urbanization traits we measured, all our study sites maintained a baseline level of structural complexity in the vegetation sufficient to support a moderate diversity of pollinators, thereby limiting detectable variation in our models. Consistent with this interpretation, Tavares Brancher et al. ( 2024 ) reported that medium-sized urban areas can sustain relatively high pollinator diversity because vegetation characteristics reach levels adequate to support diverse communities. Alternatively, our measure of vegetation structure complexity might not fully reflect the various layers of vegetation that are most relevant to pollinators. For example, we did not consider the variation in herbaceous vegetation height, which may play a larger role in shaping resource accessibility and, consequently, foraging frequency at the lower strata of vegetation (Klecka et al., 2018 ). Finer-scale measures of vegetation layering, such as flower stratification or patchiness in canopy gaps, could also produce a more subtle characterization of vegetation complexity and its impact on pollinator diversity. Vertical distribution of floral resources can influence pollinator foraging behavior, as different taxa exhibit preferences based on floral height (Diniz et al., 2019 ; Hernández-Villa et al., 2020 ; Klecka et al., 2018 ). Closely linked to this is the concept of three-dimensional microhabitat availability, where structural complexity creates vertical layers with varying light, temperature, and humidity conditions (Tews et al., 2004 ; De Smedt et al., 2019 ). These vertical gradients generate ecological niches that may be differentially exploited depending on species traits such as flight ability, thermal tolerance, or foraging height preferences (Xing et al., 2023 ). Without metrics capturing this kind of fine-scale habitat variation, structurally mediated ecological filtering may have gone undetected. The gradient of ground cover complexity also failed to show a significant effect on pollinator diversity in our study. This may be partly due to the limitations of our measurements. The first component of PCA of ground cover types captured only 44.7% of the total variation. This suggests that much of the heterogeneity in ground cover types, which pollinators can respond to, occurs at a broader scale than we measured. Furthermore, pollinator responses to different ground cover types should depend on the pollinator nesting guilds (Cane et al., 2006 ; Neame et al., 2013 ); however, we did not differentiate between ground-nesting pollinators and other guilds. In contrast to the vegetation structure complexity, plant diversity showed a robust and positive relationship with the three parameters of pollinator diversity. This relationship was strongest with species richness, followed by Shannon diversity and Simpson diversity, in that order. This result aligns with a broad body of work emphasizing the foundational role of floral diversity in structuring pollination networks (Kantsa et al., 2018 ; Gómez-Martínez et al., 2022 ). Although this relationship appears to be dependent on the density and diversity of floral resources at a small spatial scale (Hegland & Boeke, 2006 ), overall evidence points out that increasing plant diversity tends to promote pollinator taxonomic and functional diversity (Orford et al., 2016 ; Steffan-Dewenter & Tscharntke, 2001 ; Venjakob et al., 2016 ). The mechanisms proposed include both a direct increase in the number of foraging niches (Ebeling et al., 2008 ; Steffan-Dewenter & Tscharntke, 2001 ) and an expansion of spatio-temporal niche opportunities that facilitate species coexistence (Venjakob et al., 2016 ). The stronger effect observed under richness ( q = 0), where all species are weighted equally regardless of abundance, suggests that more diverse plant assemblages increase the likelihood of supporting rare pollinators, probably benefiting specialists with narrow floral requirements. In addition to expanding niche space, diverse floral assemblages may enhance the nutritional quality and attractiveness of foraging sites, with pollinators relying on a mixture of floral resources to meet complex physiological demands (Filipiak, 2019 ; Stephen et al., 2024 ; Wäckers et al., 2007 ). Variation in floral morphology, phenology, scent, and color may further improve resource appeal and detectability across pollinator taxa, promoting both generalist and specialist species (Burkle & Runyon, 2019 ; Hornung-Leoni et al., 2013 ; Ornai & Keasar, 2020 ) and therefore influencing species richness, Shannon diversity, and Simpson diversity of the species abundances within the pollinator communities. These factors likely contribute to the positive relationship observed between plant diversity and pollinator diversity (Kantsa et al., 2018 ). The positive association between plant diversity and pollinator diversity strongly suggests that promoting and enhancing the herbaceous layer within urban green spaces should be a conservation priority. Increasing the species richness, abundance, and presence of herbaceous flowering plants can promote overlapping blooming periods within and across urban green areas. This will help sustain pollinator diversity as urbanization continues to advance. Nevertheless, the contributions of trees and shrubs must also be recognized, as they provide critical resources for pollinators that do not forage at the ground level. Urban habitat design should thus aim for a heterogeneous mosaic, balancing open herbaceous patches with strategically placed woody vegetation, while avoiding excessive vertical closure that could suppress understory floral diversity and abundance. Conclusion Our findings offer practical guidance for urban planning and conservation of pollinator diversity in urban environments. Both plant diversity and vegetation type emerged as key drivers, with plant diversity exerting the strongest influence on species richness, and vegetation type shaping all diversity parameters. Sites dominated by tall woody cover supported lower pollinator diversity, underscoring the importance of maintaining open herbaceous layers within urban green spaces. By contrast, impervious surface cover, which we expected to shape community composition, showed only limited effects, likely mitigated by the presence of spontaneous and unmanaged vegetation that sustains floral resources even in heavily built-up areas. Together, these results highlight that conservation strategies in tropical cities must prioritize diverse and heterogeneous vegetation assemblages, balancing herbaceous and woody components, to support the full spectrum of pollinator taxa to safeguard the ecological functions they provide. Declarations Funding SGE was supported by a graduate scholarship from the Secretaría de Ciencia, Humanidades, Tecnología e Innovación (SECIHTI). Competing Interests The authors declare that they have no competing interests. Author Contributions All authors contributed to study conception and design. SGE led the study, including field data collection, species identification, data analysis, and writing of the original draft. RRR contributed to field data collection and species identification. AC and REF advised on analytical approaches and provided critical feedback during manuscript revisions. All authors reviewed and commented on previous versions of the manuscript and approved the final version. References Abrahamczyk S, Kluge J, Gareca Y, Reichle S and Kessler M (2011) The influence of climatic seasonality on the diversity of different tropical pollinator groups. PLoS One 6(11):e27115. https://doi.org/10.1371/journal.pone.0027115 Adams BJ, Li E, Bahlai CA, Meineke EK, McGlynn TP and Brown BV (2020). Local‐and landscape‐scale variables shape insect diversity in an urban biodiversity hot spot. Ecol Appl 30(4):e02089. https://doi.org/10.1002/eap.2089Aguilar AG, Flores-Espinosa M and Hernández J (2025). Metropolización, dinámica inmobiliaria y segregación socio-territorial. El caso de Mérida, Yucatán. EURE 51(153):1-27. https://doi.org/10.7764/EURE.51.153.03 Aitchison J (1982). The statistical analysis of compositional data. J R Stat Soc Series B Stat Methodol 44(2):139-160. https://doi.org/10.1111/j.2517-6161.1982.tb01195.x Ammann L, Bosem-Baillod A, Herzog F, Frey D, Entling MH and Albrecht M (2024) Spatio-temporal complementarity of floral resources sustains wild bee pollinators in agricultural landscapes. Agric Ecosyst Environ 359:108754. https://doi.org/10.1016/j.agee.2023.108754 Aronson MFJ, Handel SN, La Puma IP, and Clemants SE (2015) Urbanization promotes non-native woody species and diverse plant assemblages in the New York metropolitan region. Urban Ecosyst 18:31-45. https://doi.org/10.1007/s11252-014-0382-z Barteet C (2015). Representing Tihó-Mérida in colonial Yucatán: maya and spanish considerations of a colonial capital. South Central Review 32(2):21-47. http://www.jstor.org/stable/44016890 Bates AJ, Sadler JP, Fairbrass, AJ, Falk SJ, Hale JD and Matthews TJ (2011) Changing bee and hoverfly pollinator assemblages along an urban-rural gradient. PLoS One 6(8):e23459. https://doi.org/10.1371/journal.pone.0023459 Bennett AB and Lovell S (2019). Landscape and local site variables differentially influence pollinators and pollination services in urban agricultural sites. PLoS One 14(2):e0212034. https://doi.org/10.1371/journal.pone.0212034 Bolker B, Robinson D (2025) broom.mixed: Tidying methods for mixed models (Version 0.2.9.7) https://github.com/bbolker/broom.mixed Brooks ME, Kristensen K, van Benthem KJ, Magnusson A, Berg CW, Nielsen A, Skaug HJ, Maechler M, Bolker BM (2017) glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. RJ 9 (2):378-400. https://doi:10.32614/RJ-2017-066 Bożek M, Denisow B, Strzałkowska-Abramek M, Chrzanowska E and Winiarczyk K (2023) Non-forest woody vegetation: A critical resource for pollinators in agricultural landscapes: a review. Sustainability 15(11): 8751. https://doi.org/10.3390/su15118751 Burkle LA and Runyon JB (2019) Floral volatiles structure plant–pollinator interactions in a diverse community across the growing season. Funct Ecol 33(11): 2116-2129. https://doi.org/10.1111/1365-2435.13424 Campos-Silva LA and Piratelli AJ (2021) Vegetation structure drives taxonomic diversity and functional traits of birds in urban private native forest fragments. Urban Ecosyst 24(2):375-390. https://doi.org/10.1007/s11252-020-01045-8 Cane JH, Minckley RL, Kervin LJ, Roulston TAH and Williams NM (2006) Complex responses within a desert bee guild (Hymenoptera: Apiformes) to urban habitat fragmentation. Ecol Appl 16(2):632-644. https://doi.org/10.1890/1051-0761(2006)016[0632:CRWADB]2.0.CO;2 Chao A, Gotelli NJ, Hsieh TC, Sander EL, Ma KH, Colwell RK and Ellison AM (2014) Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol Monogr 84(1):45-67. https://doi.org/10.1890/13-0133.1 Chao A and Jost L. (2012). Coverage‐based rarefaction and extrapolation: standardizing samples by completeness rather than size.Ecol 93(12): 2533-2547. https://doi.org/10.1890/11-1952.1 Chazdon RL, Pearcy RW, Lee DW and Fetcher NED (1996). Photosynthetic responses of tropical forest plants to contrasting light environments. In: Mulkey SS, Chazdon RL, Smith AP (eds) Tropical forest plant ecophysiology. Springer Boston, MA pp. 5-55 https://doi.org/10.1007/978-1-4613-1163-8_1 Cheng XL, Cubino JP, Balfour K, Zhu ZX and Wang HF (2022) Drivers of spontaneous and cultivated species diversity in the tropical city of Zhanjiang, China. Urban For Urban Green 67: 127428. https://doi.org/10.1016/j.ufug.2021.127428 Chien SA and Morse DH (1998) The roles of prey and flower quality in the choice of hunting sites by adult male crab spiders Misumena vatia (Araneae, Thomisidae). J Arachnol 26:238-243. https://doi.org/10.5281/zenodo.16240103 Christie FJ, Cassis G and Hochuli DF (2010) Urbanization affects the trophic structure of arboreal arthropod communities. Urban Ecosyst 13: 169-180. https://doi.org/10.1007/s11252-009-0115-x Coulin C, Aizen MA and Garibaldi LA (2019) Contrasting responses of plants and pollinators to woodland disturbance. Austral Ecol 44(6):1040-1051. https://doi.org/10.1111/aec.12771 da Silva CI, de Souza Pacheco Filho AJ, Toppa RH, Arena MVN and de Oliveira PE (2021) Seasonal and vertical distribution of floral resources and its implications for the conservation of pollinators. Flora 278:151787. https://doi.org/10.1016/j.flora.2021.151787 de Barros Ruas R, Costa LMS and Bered F (2022) Urbanization driving changes in plant species and communities: A global view. Glob Ecol Conserv 38: e02243. https://doi.org/10.1016/j.gecco.2022.e02243 de Lima KB, Ferreira PA, Groppo M, Goldenberg R, Pansarin ER, Barreto RC, Coelho GP, Barros-Souza Y and Boscolo D (2020) Does landscape context affect pollination-related functional diversity and richness of understory flowers in forest fragments of Atlantic Rainforest in southeastern Brazil? Ecol Process 9:1-12. https://doi.org/10.1186/s13717-020-00261-6 De Smedt P, Vangansbeke P, Bracke R, Schauwvliege W, Willems L, Mertens J and Verheyen K (2019) Vertical stratification of moth communities in a deciduous forest in Belgium. Insect Conserv Divers 12(2):121-130. https://doi.org/10.1111/icad.12320 Diniz UM, Domingos-Melo A, & Machado, I. C. (2019). Flowers up! The effect of floral height along the shoot axis on the fitness of bat-pollinated species. Ann Bot 124(5):809-818 https://doi.org/10.1093/aob/mcz116 Dusza Y, Kraepiel Y, Abbadie L, Barot S, Carmignac D, Dajoz I, Gendreau E, Lata JC, Meriguet J, Motard E and Raynaud X (2020) Plant-pollinator interactions on green roofs are mediated by substrate characteristics and plant community composition. Acta Oecol 105:103559. https://doi.org/10.1016/j.actao.2020.103559 Dylewski Ł, Białas JT, Szymysł A and Banaszak-Cibicka W (2024) Pollinator assemblages in grasslands along river valleys depend on the urban matrix and local habitat scale variables. Ecol Indic 159:111687. https://doi.org/10.1016/j.ecolind.2024.111687 Ebeling A, Klein AM, Schumacher J, Weisser WW and Tscharntke T (2008) How does plant richness affect pollinator richness and temporal stability of flower visits? Oikos 117(12):1808-1815. https://doi.org/10.1111/j.1600-0706.2008.16819.x Egozcue JJ, Pawlowsky-Glahn V, Mateu-Figueras G and Barceló-Vidal C (2003) Isometric log ratio transformations for compositional data analysis. Math Geol 35(3):279-300. https://doi.org/10.1023/A:1023818214614 Filipiak M (2019) Key pollen host plants provide balanced diets for wild bee larvae: A lesson for planting flower strips and hedgerows J Appl Ecol 56(6):1410-1418. https://doi.org/10.1111/1365-2664.13383 Fletcher Jr RJ, Green RE, Bladon EK, Atkinson PW, Phalan BT, Williams D, Visconti P and Balmford A (2025) Beyond species richness for biological conservation. Conserv Lett 18(4):e13124. https://doi.org/10.1111/conl.13124 Fortel L, Henry M, Guilbaud L, Guirao AL, Kuhlmann M, Mouret H, Rollin O and Vaissière BE (2014) Decreasing abundance, increasing diversity and changing structure of the wild bee community (Hymenoptera: Anthophila) along an urbanization gradient. PloS one 9(8):e104679. https://doi.org/10.1371/journal.pone.0104679 Genini J, Guimaraes Jr PR, Sazima M, Sazima I and Morellato LPC (2021) Temporal organization among pollination systems in a tropical seasonal forest. Sci Nat 108:34 https://doi.org/10.1007/s00114-021-01744-y Geslin B, Le Féon V, Folschweiller M, Flacher F, Carmignac D, Motard E, Perret S and Dajoz I (2016) The proportion of impervious surfaces at the landscape scale structures wild bee assemblages in a densely populated region. Ecol Evol 6(18):6599-6615. https://doi.org/10.1002/ece3.2374 Gómez‐Martínez C, González‐Estévez MA, Cursach J and Lázaro A (2022) Pollinator richness, pollination networks, and diet adjustment along local and landscape gradients of resource diversity. Ecol Appl 32(6): e2634. https://doi.org/10.1002/eap.2634 Graffigna S, González-Vaquero RA, Torretta JP and Marrero HJ (2024) Importance of urban green areas’ connectivity for the conservation of pollinators. Urban Ecosyst 27(2):417-426. https://doi.org/10.1007/s11252-023-01457-2 Harrison T and Winfree R (2015) Urban drivers of plant‐pollinator interactions. Funct Ecol 29(7):879-888. https://doi.org/10.1111/1365-2435.12486 He R, Li L, Wang G, Cao L, Xiong G and Yang F (2024) Plant diversity value of informal green spaces in tropical coastal urban areas: An empirical study of species, functional, and phylogenetic diversity. Sci Total Environ 955:176741. https://doi.org/10.1016/j.scitotenv.2024.176741 Hegland SJ and Boeke L (2006) Relationships between the density and diversity of floral resources and flower visitor activity in a temperate grassland community. Ecol Entomol 31(5):532-538. https://doi.org/10.1111/j.1365-2311.2006.00812.x Hernández-Villa V, Vibrans H, Uscanga-Mortera E and Aguirre-Jaimes A (2020) Floral visitors and pollinator dependence are related to floral display size and plant height in native weeds of central Mexico. Flora 262:151505. https://doi.org/10.1016/j.flora.2019.151505 Hornung-Leoni CT, González-Gómez PL and Troncoso AJ (2013) Morphology, nectar characteristics and avian pollinators in five Andean Puya species (Bromeliaceae). Acta Oecol 51:54-61. https://doi.org/10.1016/j.actao.2013.05.010 Hsieh T, Ma K, Chao A (2025) iNEXT: Interpolation and Extrapolation for Species Diversity. R package version 3.0.2. http://chao.stat.nthu.edu.tw/wordpress/software_download/ INEGI. (2023). Principales resultados del Censo de Población y Vivienda 2020/Yucatán. Instituto Nacional de Estadística y Geografía (INEGI) México,146 p. https://www.inegi.org.mx/app/biblioteca/ficha.html?upc=702825198374 Jackson DA (1993) Stopping rules in principal components analysis: A comparison of heuristical and statistical approaches. Ecology 74(8):2204-2214. https://doi.org/10.2307/1939574 Kantsa A, Raguso RA, Dyer AG, Olesen JM, Tscheulin T and Petanidou T (2018) Disentangling the role of floral sensory stimuli in pollination networks. Nat Commun 9(1):1041. https://doi.org/10.1038/s41467-018-03448-w Kantsa A, Tscheulin T, Junker RR, Petanidou T and Kokkini S (2013) Urban biodiversity hotspots wait to get discovered: The example of the city of Ioannina, NW Greece. Landsc Urban Plann 120:129-137. https://doi.org/10.1016/j.landurbplan.2013.08.013 Klecka J, Hadrava J and Koloušková P (2018) Vertical stratification of plant–pollinator interactions in a temperate grassland. PeerJ 6:e4998. https://doi.org/10.7717/peerj.4998 Knuff AK, Staab M, Frey J, Dormann CF, Asbeck T and Klein AM (2020) Insect abundance in managed forests benefits from multi-layered vegetation. Basic Appl Ecol 48:124-135. https://doi.org/10.1016/j.baae.2020.09.002 Kovács-Hostyánszki A, Szigeti V, Miholcsa Z, Sándor D, Soltész Z, Török E and Fenesi A (2022) Threats and benefits of invasive alien plant species on pollinators. Basic Appl Ecol 64:89-102. https://doi.org/10.1016/j.baae.2022.07.003 Lê, S., Josse, J., & Husson, F. (2008). FactoMineR: An R package for multivariate analysis. J Stat Softw 25(1):1-18. https://doi.org/10.18637/jss.v025.i01 Legendre P and Legendre L (2012). Numerical ecology (3rd ed. Vol 24). Elsevier. Lugo-Hubp J, Aceves-Quesada JF and Espinosa-Pereña R (1992) Rasgos geomorfológicos mayores de la península de Yucatán. Rev Mex Cienc Geol 10(2):143–150. https://rmcg.geociencias.unam.mx/index.php/rmcg/article/view/1236 Lüdecke D, Ben-Shachar MS, Patil I, Waggoner P and Makowski D (2021) performance: An R package for assessment, comparison and testing of statistical models. JOSS 6(60):3139. https://doi.org/10.21105/joss.03139 McGarigal K, Tagil S and Cushman SA (2009) Surface metrics: an alternative to patch metrics for the quantification of landscape structure. Landscape Ecol 24: 433-450. https://doi.org/10.1007/s10980-009-9327-y McKinney ML (2008). Effects of urbanization on species richness: A review of plants and animals. Urban Ecosyst 11:161–176. https://doi.org/10.1007/s11252-007-0045-4 Martins KT, Gonzalez A and Lechowicz MJ (2017) Patterns of pollinator turnover and increasing diversity associated with urban habitats Urban Ecosyst 20:1359-1371. https://doi.org/10.1007/s11252-017-0688-8 Mathis CL, McNeil Jr DJ, Lee MR, Grozinger CM, King DI, Otto CR and Larkin JL (2021) Pollinator communities vary with vegetation structure and time since management within regenerating timber harvests of the Central Appalachian Mountains. For Ecol Manag 496:119373. https://doi.org/10.1016/j.foreco.2021.119373 Mitchell K (2010) Quantitative analysis by the point-centered quarter method. https://doi.org/10.48550/arXiv.1010.3303 Moroń D, Lenda M, Skórka P, Szentgyörgyi H, Settele J and Woyciechowski M (2009) Wild pollinator communities are negatively affected by invasion of alien goldenrods in grassland landscapes. Biol Conserv 142 (7):1322–1332. https://doi.org/10.1016/j.biocon.2008.12.036 Neame LA, Griswold T and Elle E (2013) Pollinator nesting guilds respond differently to urban habitat fragmentation in an oak‐savannah ecosystem. Insect Conserv Divers 6(1):57-66. https://doi.org/10.1111/j.1752-4598.2012.00187.x Orford KA, Murray PJ, Vaughan IP and Memmott J (2016) Modest enhancements to conventional grassland diversity improve the provision of pollination services. J Appl Ecol 53(3):906-915. https://doi.org/10.1111/1365-2664.12608 Ornai A and Keasar T (2020) Floral complexity traits as predictors of plant-bee interactions in a mediterranean pollination web. Plants, 9(11):1432. https://doi.org/10.3390/plants9111432 Palarea-Albaladejo J, Martín-Fernández JA (2015) “zCompositions – R package for multivariate imputation of left-censored data under a compositional approach.” Chemometr Intell Lab Syst 143:85–96. https://doi.org/10.1016/j.chemolab.2015.02.019 Planchuelo G, von Der Lippe M and Kowarik I (2019) Untangling the role of urban ecosystems as habitats for endangered plant species. Landsc Urban Plann 189:320-334. https://doi.org/10.1016/j.landurbplan.2019.05.007 Proctor E, Nol E, Burke D and Crins WJ (2012) Responses of insect pollinators and understory plants to silviculture in northern hardwood forests. Biodivers Conserv 21:1703-1740. https://doi.org/10.1007/s10531-012-0272-8 R Core Team (2024) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. Radhamoni HVN, Queenborough SA, Arietta AZA, Suresh HS, Dattaraja HS, Kumar SS, Sukumar, R and Comita LS (2023). Local‐and landscape‐scale drivers of terrestrial herbaceous plant diversity along a tropical rainfall gradient in Western Ghats, India. J Ecol 111(5):1021-1036. https://doi.org/10.1111/1365-2745.14075 Rebele, F. (1994). Urban ecology and special features of urban ecosystems. Glob Ecol Biogeogr Let 4(6):173–187. https://doi.org/10.2307/2997649 Riis T, Kelly-Quinn M, Aguiar FC, Manolaki P, Bruno D, Bejarano MD, Clerici N, Fernandes MR, Franco JC, Pettit N, Portela AP, Tammeorg O, Tammeorg P, Rodríguez-González PM and Dufour S (2020) Global overview of ecosystem services provided by riparian vegetation. BioScience 70(6):501-514. https://doi.org/10.1093/biosci/biaa041 Roswell M, Dushoff J and Winfree R (2021) A conceptual guide to measuring species diversity. Oikos 130(3):321-338. https://doi.org/10.1111/oik.07202 Servicio Meteorológico Nacional (SMN) (2021) Normales climatológicas 1991–2020. Estación 31036, Mérida, Yucatán . Comisión Nacional del Agua. Recovered from https://smn.conagua.gob.mx/tools/RESOURCES/Normales_Climatologicas/Normales9120/yuc/nor9120_31036.txt Silva VHD, Gomes IN, Cardoso JCF, Bosenbecker C, Silva JLS, Cruz-Neto O, Oliveira W, Stewart AB, Lopes AV and Maruyama PK (2023) Diverse urban pollinators and where to find them. Biol. Conserv 281: 110036. https://doi.org/10.1016/j.biocon.2023.110036 Skaldina O and Blande JD (2025) Global biases in ecology and conservation research: insight from pollinator studies. Ecol. Lett 28(1):e70050. https://doi.org/10.1111/ele.70050 Smith AC, Dahlin KM, Record S, Costanza JK, Wilson AM and Zarnetske PL (2021) The geodiv r package: Tools for calculating gradient surface metrics. Methods Ecol Evol 12(11):2094-2100. https://doi.org/10.1111/2041-210X.13677 Sponsler D, Iverson A and Steffan‐Dewenter I (2023) Pollinator competition and the structure of floral resources. Ecography 2023(9):e06651. https://doi.org/10.1111/ecog.06651 Steffan-Dewenter I and Tscharntke T (2001) Succession of bee communities on fallows. Ecography 24(1):83–93. https://doi.org/10.1034/j.1600-0587.2001.240110.x Stephen KW, Chau KD and Rehan SM (2024) Dietary foundations for pollinators: Nutritional profiling of plants for bee health. Front. Sustain Food Syst 8:1411410. https://doi.org/10.3389/fsufs.2024.1411410 Tavares Brancher KP, Graf LV, Heringer G and Zenni RD (2024) Urbanization and abundance of floral resources affect bee communities in medium‐sized tropical cities. Austral Ecol 49(1):e13299. https://doi.org/10.1111/aec.13299 Templ M, Hron K and Filzmoser P (2011) robCompositions: An R-package for robust statistical analysis of compositional data. In: Pawlowsky-Glahn V and Buccianti A (eds) Compositional Data Analysis: Theory and Applications 341–355 https://doi.org/10.1002/9781119976462.ch25 Tews J, Brose U, Grimm V, Tielbörger K, Wichmann M C, Schwager M and Jeltsch F (2004) Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures. J Biogeogr 31(1) 79-92. https://doi.org/10.1046/j.0305-0270.2003.00994.x Theodorou P (2022) The effects of urbanisation on ecological interactions. Curr Opin Insect Sci 52:100922. https://doi.org/10.1016/j.cois.2022.100922 Theodorou P, Herbst SC, Kahnt B, Landaverde-González P, Baltz LM, Osterman J and Paxton RJ (2020) Urban fragmentation leads to lower floral diversity, with knock-on impacts on bee biodiversity. Sci Rep 10 (1):21756. https://doi.org/10.1038/s41598-020-78736-x Torresani M, Rocchini D, Ceola G, de Vries JPR, Feilhauer H, Moudrý V, Bartholomeus H, Perrone M, Anderle M, Gamper HA, Chieffallo L, Guatelli E, Gatti RC and Kleijn D (2024) Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach. Sci Rep 14(1):809. https://doi.org/10.1038/s41598-023-50308-9 Vähätalo AV, Pulli A, Kulmala T, Marin R and Haimi J (2024) Urbanization related changes in lepidopteran community. Urban Ecosyst 27(2):377-386. https://doi.org/10.1007/s11252-023-01456-3 Valdiviezo ZI (2014) Merida the golden expansion. A metropolitan reinterpretation and the proposal of a design regeneration strategy. Master Dissertation, Politecnico di Milano. Venjakob C, Klein AM, Ebeling A, Tscharntke T and Scherber C (2016) Plant diversity increases spatio‐temporal niche complementarity in plant‐pollinator interactions. Ecol Evol 6(8):2249-2261. https://doi.org/10.1002/ece3.2026 Venter ZS, Barton DN, Chakraborty T, Simensen T and Singh G (2022) Global 10 m land use land cover datasets: A comparison of Dynamic World, WorldCover, and Esri Land Cover. Remote Sens 14(16):4101. https://doi.org/10.3390/rs14164101 Wäckers FL, Romeis J and van Rijn P (2007) Nectar and pollen feeding by insect herbivores and implications for multitrophic interactions. Annu Rev Entomol 52(1):301-323. https://doi.org/10.1146/annurev.ento.52.110405.091352 Xiao Y, Li X, Cao Y and Dong M (2016) The diverse effects of habitat fragmentation on plant–pollinator interactions. Plant Ecol 217(7):857-868. https://doi.org/10.1007/s11258-016-0608-7 Xing, S., Leahy, L., Ashton, L. A., Kitching, R. L., Bonebrake, T. C., & Scheffers, B. R. (2023). Ecological patterns and processes in the vertical dimension of terrestrial ecosystems. J Anim Ecol 92(3):538-551. https://doi.org/10.1111/1365-2656.13881 Xu P, Tsendbazar NE, Herold M, de Bruin S, Koopmans M, Birch T, Carter S, Fritz S, Lesiv M, Mazur E, Pickens A, Potapov P, Stolle F, Tyukavina A, Van De Kerchove R and Zanaga D (2024) Comparative validation of recent 10 m-resolution global land cover maps. Remote Sens Environ 311:114316. https://doi.org/10.1016/j.rse.2024.114316 Zanaga D, Van De Kerchove R, De Keersmaecker W, Souverijns N, Brockmann C, Quast R, Wevers J, Grosu A, Paccini A, Vergnaud S, Cartus O, Santoro M, Fritz S, Georgieva I, Lesiv M, Carter S, Herold M, Li L, Tsendbazar NE, Ramoino F and Arino, O (2021) ESA WorldCover 10 m 2020 v100. European Space Agency. https://doi.org/10.5281/zenodo.5571936 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 31 Mar, 2026 Reviewers agreed at journal 03 Feb, 2026 Reviewers agreed at journal 03 Feb, 2026 Reviewers invited by journal 17 Dec, 2025 Editor assigned by journal 16 Dec, 2025 Submission checks completed at journal 16 Dec, 2025 First submitted to journal 10 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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10:06:15","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":221745,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8329681/v1/97262bd7084eecf8bd8d9b6a.html"},{"id":98763805,"identity":"4e713129-65dd-4bd5-ba76-a0453a58ab9f","added_by":"auto","created_at":"2025-12-22 10:06:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":765941,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the urban natural areas (UNAs) sampled throughout the city of Merida. UNAs (green circles) are numbered in order of increasing impervious surface cover. Circle sizes indicate the 1,000 m characterization area around each study site centroid. Map image is the intellectual property of Esri and is used herein under license. Copyright © 2025 Esri and its licensors.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8329681/v1/b75d8143d809fe5d03cca0f7.png"},{"id":98763802,"identity":"2d80642d-94ef-47ba-956a-a5a550c96aeb","added_by":"auto","created_at":"2025-12-22 10:06:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":388092,"visible":true,"origin":"","legend":"\u003cp\u003eLand cover composition of 14 urban natural areas in the city of Merida, located in the northwest part of the Yucatan state on the Yucatan Peninsula, Mexico. In the top panel, the spatial distribution of different land cover types is shown within a 1,000 m radius from the centroid of each site. The numbers above each bubble correspond to the names of the sites. The bottom panel features a stacked bar chart that displays the percentage of each land cover type at each site, with the sites numbered to match the top panel. The bar sites are arranged from lowest to highest impervious surface cover. Land cover data derived from the © ESA WorldCover project 2021 / Contains modified Copernicus Sentinel data (2021) processed by the ESA WorldCover consortium.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8329681/v1/a0827eada268a345b496d890.png"},{"id":98778676,"identity":"31209464-4680-45bf-94df-59db0725ac36","added_by":"auto","created_at":"2025-12-22 12:29:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":266921,"visible":true,"origin":"","legend":"\u003cp\u003ePCA biplots showing relationships among land cover types (a \u0026amp; b), vegetation structure complexity (c), and\u003c/p\u003e\n\u003cp\u003eground cover complexity (d).\u003cstrong\u003e \u003c/strong\u003eVariable contributions are indicated by cos² values, color-coded with orange representing variables with heavier contributions. Vectors show the direction and strength of each variable within the ordination space, and cos² values are summed across the two displayed components.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8329681/v1/9ccb4e1bd7622befb290606b.png"},{"id":98780591,"identity":"92a967e8-5805-4145-af69-a081b7f20d83","added_by":"auto","created_at":"2025-12-22 12:31:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":206702,"visible":true,"origin":"","legend":"\u003cp\u003ePollinator activity and community composition across 14 urban natural areas (UNAs): (a) Total number of pollinator visits per site, with bars showing contributions from primary and incidental pollinators. Numbers indicate the total visits recorded for each site; (b) Species richness of floral visitors per site, grouped by pollinator order. Bars represent the number of species within each order, stacked to show community composition. Numbers above bars indicate total species richness recorded per site. Urban natural areas are arranged from top (higher impervious surface cover) to bottom (lower impervious surface cover).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8329681/v1/06b8d3e14207107a96070a93.png"},{"id":98763811,"identity":"36c2bf25-1646-463e-82b6-5ab0e7cf3b23","added_by":"auto","created_at":"2025-12-22 10:06:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":226511,"visible":true,"origin":"","legend":"\u003cp\u003eSample coverage and observed Hill numbers of pollinator communities across 14 urban natural areas (UNAs). Panel (a) shows the proportion of each UNA’s pollinator community represented by the observed species, calculated up to 1,000 individuals. Panel (b) similarly shows the proportion of the plant community covered by observed species, calculated up to 150 individuals. Dotted lines indicate extrapolated values. For pollinators, all sites reached an average sample coverage of 96%, while plant sampling reached an average of 84% coverage across sites. Overall, sample completeness was high for both groups across sites, with pollinator communities showing moderately higher coverage than plant communities.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8329681/v1/65865ad62dd57c2a588ada60.png"},{"id":98763818,"identity":"795654ae-2de1-4fa9-a8f0-df79b056de8f","added_by":"auto","created_at":"2025-12-22 10:06:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":151166,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of plant species in urban natural areas (UNAs), grouped by plant family. Bars represent the number of species within the 10 most common plant families across the study, stacked to show community composition at each site. All remaining families were grouped as “others” for each site. Numbers above the bars indicate the total species richness recorded per UNA over the entire sampling period. UNAS are ordered according to species richness.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8329681/v1/9b31e43fe91cc5839117cd51.png"},{"id":98763814,"identity":"c62c937f-b4fd-4e8a-9690-88713cef41d4","added_by":"auto","created_at":"2025-12-22 10:06:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":113922,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of plant species in urban natural areas (UNAs), grouped by plant family. Bars represent the number of species within the 10 most common plant families across the study, stacked to show community composition at each site. All remaining families were grouped as “others” for each site. Numbers above the bars indicate the total species richness recorded per UNA over the entire sampling period. UNAS are ordered according to species richness.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8329681/v1/d6f722fde0694fcc299714cb.png"},{"id":98797628,"identity":"d29db926-5ffe-4299-8be7-a05824591418","added_by":"auto","created_at":"2025-12-22 13:36:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2649462,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8329681/v1/1dac2ea3-4035-4add-83d4-d2687b14a79c.pdf"},{"id":98778725,"identity":"50fa3525-d0ef-4c7d-b503-960fee265c38","added_by":"auto","created_at":"2025-12-22 12:29:34","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":66879,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8329681/v1/46c790c7865856edaed4f1ee.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diversity of pollinator communities along urban environmental gradients in Merida, Yucatan, a tropical city in Mexico","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUrbanization transforms the original natural vegetation of an area, increases the coverage of impervious surfaces, introduces non-native species, and changes the availability of resources and the abundance of consumers, reshaping ecological communities (Aronson et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; de Barros Ruas et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Harrison \u0026amp; Winfree, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; McKinney, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Rebele, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Theodorou, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Theodorou et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, urban systems like cities are not without ecological function. These systems can support communities of urban-tolerant plants and animals and, in some cases, even serve as biodiversity hotspots (Adams et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kantsa et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) or refuges for endangered species (Planchuelo et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVegetation in urban systems is crucial for supporting animal diversity, as it provides essential food resources (Campos-Silva \u0026amp; Piratelli, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Christie et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Of vegetation food resources, floral nectar and pollen drive the foraging behaviors and interactions of pollinators. Flowers also provide resources, such as shelter and hunting grounds, which further influence the structure and activity of pollinator communities (Chien, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Sponsler et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In particular, the introduction of non-native plant species in urban areas in some cases can partially compensate for vegetation loss and provide new floral rewards for native pollinators, but often fails to support the original diversity of pollinators (Kov\u0026aacute;cs-Hosty\u0026aacute;nszki et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Moroń et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), functionally simplifying plant-pollinator communities.\u003c/p\u003e \u003cp\u003eIn addition to vegetation loss and the introduction of non-native plants, urbanization reduces ecological connectivity by restricting the movement of pollinators, thereby constraining pollinator diversity among urban vegetation patches (Xiao et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, it is possible that enhancing and preserving certain components of habitat quality may mitigate pollinator diversity loss. Enhancing native plant diversity and structural complexity within urban green spaces creates a mosaic of microhabitats, expanding niche availability and supporting diverse pollinator communities. Varying canopy layers, understory vegetation types, and natural ground cover can provide additional nesting substrates, feeding grounds, and other valuable resources (de Lima et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dusza et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Knuff et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These finer-scale improvements may enable pollinator persistence by buffering against the broader negative impacts of urban development and vegetation loss, promoting the coexistence of diverse species within urban ecosystems.\u003c/p\u003e \u003cp\u003eResearch on the effects of urbanization on plant and animal diversity has largely focused on temperate areas, overlooking tropical and subtropical urban areas where biodiversity is greater, and plant resources are more finely partitioned (Harrison \u0026amp; Winfree, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Silva et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In tropical and subtropical climates, consistent warmth and humidity with minimal seasonality, enable year-round pollinator activity, unlike temperate regions (Abrahamczyk et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Genini et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). There are also notable taxonomic biases in the knowledge of pollinator diversity in urban systems. Much of the literature has focused on specific groups, such as bees, which are often considered emblematic pollinators due to their ecological importance and ease of study (Silva et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Skaldina \u0026amp; Blande, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This narrow focus overlooks the broader pollinator community, which includes diverse taxa such as beetles, flies, ants, birds, and even specialized predators like spiders that may occasionally transport pollen among flowers, all of which play integral roles in urban ecosystems.\u003c/p\u003e \u003cp\u003eWith these ideas in mind, we focus on Merida, a rapidly growing subtropical city in the political state of Yucatan, southeastern Mexico. The urban landscape of this city has undergone significant transformation over the last 30 years, marked by rapidly increasing impervious surface cover, native vegetation loss and fragmentation (Valdiviezo, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As urban expansion in Merida is expected to accelerate over the next decade, it is crucial to document the diversity of plant and animal communities. We also need to understand how environmental gradients in the urban area affect these communities, which are vital for human well-being.\u003c/p\u003e \u003cp\u003eThe objective of our study is to evaluate species diversity of the broader plant\u0026ndash;pollinator communities responding to urban environmental gradients in Merida. Specifically, we aim to disentangle the effects on pollinator communities of land cover gradients associated with urbanization from other local habitat attributes that include variables representing plant diversity, vegetation structure complexity, and ground cover complexity on pollinator communities. We hypothesize a decline in pollinator diversity as urbanization-related gradients increase, as the pollinator communities are likely to become increasingly constrained to a subset of species that can tolerate reduced vital resources and challenging abiotic conditions in urban environments. However, we also expect that increases in plant diversity, together with greater vegetation structure complexity and more complex ground cover, will act as buffering factors, promoting higher pollinator diversity despite urban pressures.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eUrban natural areas selection\u003c/h2\u003e \u003cp\u003eThe city of Merida (20.58\u0026deg; N, 89.37\u0026deg; W) is the capital of the state of Yucatan. Located in the northwest of the Yucatan Peninsula, Mexico, Merida is the largest urban center on the Peninsula, with a population of nearly one million inhabitants (INEGI, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Due to real estate speculation and its status as one of the safest cities in the country, this city is undergoing rapid growth, with urban development replacing former agricultural and natural areas (Aguilar et al., 2025). Merida was founded in 1542 on the ruins of the important pre-Hispanic Mayan city of T\u0026rsquo;h\u0026oacute; (Barteet, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), surrounded by a dry forest landscape. The area is located at an elevation of 10 masl and is characterized by a predominantly flat limestone platform formed by karstic processes. There are no above-ground rivers in this region, and the soils are shallow, rocky, and have a fine to medium-textured soil, making them easily erodible when vegetation cover is lost (Lugo-Hubp et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). The area has a typical Tropical climate, marked by warm, humid summers and mild winters. The mean annual temperature is 26.3\u0026deg;C, with average maximum values of 32.8\u0026deg;C and minimum values of 21.0\u0026deg;C. Rainfall is strongly seasonal, with an annual total of 1,079 mm; monthly values range from 9.6 mm in March to 187.2 mm in September (Servicio Meteorol\u0026oacute;gico Nacional, 2021). To effectively capture the diversity of urban environmental gradients in Merida, we focused on areas identified by local authorities and the community as natural spaces. These regions include remnants of native vegetation, parks, and public gardens, thus reflecting the variety of land cover types and vegetation management practices in the area. Then we selected sites that were accessible, and were spaced at least 1 km apart to avoid spatial overlap and better capture the range of urbanization processes across the city. Using these criteria, we identified 14 urban natural areas (UNAs) distributed across the four cardinal points of the city (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLand cover composition gradient\u003c/h3\u003e\n\u003cp\u003eEach UNA was characterized by land cover within a 1000 m radius using satellite images from ESA WorldCover project 2021 datasets (Zanaga et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The data layers were obtained from Sentinel-1 and Sentinel-2 satellite imagery with a spatial resolution of 10 meters (Copernicus Sentinel data 2021 processed by the ESA WorldCover consortium). Produced by the European Space Agency, WorldCover is recognized for its high reliability and sensitivity to capturing heterogeneous land cover types, making it a robust tool for landscape analysis and particularly suited for rapidly changing environments such as tropical cities (Venter et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For each UNA, we counted the number of pixels of each land cover type: impervious surface, tree cover, grassland, shrubland, cropland, bare ground, water bodies, and wetlands. To ensure that land cover types with larger pixel counts did not disproportionately influence the analysis, we standardized the values. We then applied a Principal Component Analysis (PCA) using \u003cem\u003eR\u003c/em\u003e v4.5 (R Core Team, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and the \u003cem\u003eFactoMineR\u003c/em\u003e package (L\u0026ecirc;, Josse, \u0026amp; Husson, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) to reduce the dimensionality of the multiple land cover types and identify the land cover gradients. This approach increased interpretability while minimizing information loss regarding the distribution of land covers across sites. Using the same land cover pixel counts, we calculated proportional values in percentage for each cover type to visualize the composition of each site as stacked bars. To determine the number of principal components to retain for subsequent analysis of environmental urban gradients, we applied a broken-stick model criterion (Jackson, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), which compares observed eigenvalues to those expected under random distributions (Legendre \u0026amp; Legendre, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eVegetation structure and ground cover gradients\u003c/h3\u003e\n\u003cp\u003eTo estimate the complexities of ground cover and vegetation structure, we utilize the point-centered quarter method (Mitchell, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) by setting up a 50 m long transect in each urban natural area. We sampled vegetation along these transects at randomly established points separated by at least 5 m. At each point, we ran an imaginary line perpendicular to the transect dividing the space into four quarters. In each quarter, we identified the tree nearest to the central point and registered its distance from the central point, treetop cover, and trunk diameter. For the ground cover, we recorded the percentage of ground types within a 1m x 1m square in each quarter at each point along the transects. Ground types registered were classified as sand-dominated ground, clay-dominated ground, rocky ground, vegetation, leaf litter, logs, impervious and debris surfaces.\u003c/p\u003e \u003cp\u003eTo analyze vegetation and reduce multiple variable measurements into a few key variables that reflect a gradient of vegetation structure, we conducted a PCA, and then to obtain a single complexity metric per urban natural area, we used a surface roughness metric that measures structural heterogeneity by assessing the degree of deviation of individual observations from the mean (McGarigal et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). We calculated surface roughness as the sum of the differences between each tree\u0026rsquo;s PC1 score and the mean PC1 score for the urban natural area, providing an index of within-site variability in the complexity of vegetation structure. We calculated surface roughness using the \u003cem\u003egeodiv\u003c/em\u003e package (Smith et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in \u003cem\u003eR\u003c/em\u003e v 4.5 (R Core Team, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo analyze ground cover types, we also applied a PCA to summarize variation in the relative proportion of each ground type recorded for each urban natural area. Because proportional data are constrained to a constant sum, they do not meet the assumptions of PCA, which requires variables to be unconstrained and vary independently in Euclidean space (Aitchison, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Egozcue et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Hence, we used a centered log-ratio (CLR) transformation that projects compositional data from a space constrained by constant totals (i.e., proportions that must sum to one) into regular, unconstrained space where standard statistical techniques can be validly used (like PCA). The CLR transformation achieves this by taking the logarithm of the ratio between each component and the geometric mean of all components in a given composition, effectively removing the unit-sum constraint while preserving relative information among components. Before applying the transformation, we replaced zero values using the count zero multiplicative (CZM) method from the \u003cem\u003ezCompositions\u003c/em\u003e package (Palarea-Albaladejo \u0026amp; Mart\u0026iacute;n-Fern\u0026aacute;ndez, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This step is necessary because the CLR transformation involves logarithmic operations, which cannot be performed on zeros. The CZM method estimates small positive values for zero components while preserving the internal structure of the proportions, allowing the dataset to be safely log-transformed. We then used the \u003cem\u003epcaCoDa()\u003c/em\u003e function from the \u003cem\u003erobCompositions\u003c/em\u003e package (Templ et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which applies the CLR transformation and performs PCA in the resulting euclidean space. As with the vegetation structure complexity data, we used the scores of the first PC for each urban natural area and calculated surface roughness as a complexity metric using the \u003cem\u003egeodiv\u003c/em\u003e package.\u003c/p\u003e\n\u003ch3\u003ePlant and pollinator sampling\u003c/h3\u003e\n\u003cp\u003eWe conducted sampling from January 23, 2023, to October 23, 2024, with each urban natural area visited eight times at intervals of two to three months. Our sampling encompassed the typical weather patterns in Yucatan, covering both the dry months (November \u0026ndash; May) and wet months (June \u0026ndash; October). This approach allowed us to obtain a comprehensive snapshot of the plant-pollinator communities across the year.\u003c/p\u003e \u003cp\u003eIn each UNA, we recorded the pollinators visiting flowers by setting up a starting point to walk, usually near a trail or vegetation clearing. We walked along the trail or clearing, stopping at each of the first 17 plants or plant clusters in bloom we encountered. We observed individual plants only if they had five or more open flowers or inflorescences at the time of sampling. We observed clusters of the same plant species, each measuring 50 x 50 cm, specifically for those species that typically produce one or a few flowers per individual. We excluded any plant or clusters that did not meet these criteria and ensured there was at least 2.5 m between points to approach independence among recordings. We identified the plant species at each sampling point for estimating total plant diversity at the urban natural area level. Although the flowering periods of the plant species varied across sampling visits, compiling records from all visits allowed us to create a more complete representation of the local blooming plant community within each UNA. At each individual floral cluster, we registered pollinators at flowers for five minutes, starting observations between 9:00 AM and 9:30 AM and ending around 12:00 PM every sampling day. Visits were conducted under sunny or slightly cloudy conditions to maximize the likelihood of observing the typical pollinator activity. Three field technicians trained in entomological and botanical identification assisted in identifying both plant and pollinator species. The team worked either directly in the field or by using photographs taken during sampling, as well as by comparing samples with herbarium specimens. To support taxonomic identification, we uploaded photographs to \u003cem\u003eiNaturalist\u003c/em\u003e. In instances where we could not confidently identify the species, individuals were collected for later identification. All specimens were identified to the finest taxonomic level possible. For samples that could not be reliably assigned to a species level, we used the next confident taxonomic rank, which was typically genus, and classified them as morphospecies.\u003c/p\u003e \u003cp\u003eA record was deemed valid for this study when a flower visitor, referred to as a pollinator, made contact with any part of the inner side of the flower, whether petals, anthers, or pistil, thereby potentially transporting pollen. If a pollinator visited the flower multiple times, it was recorded only once, provided it remained in view of the observer.\u003c/p\u003e\n\u003ch3\u003eDiversity analysis\u003c/h3\u003e\n\u003cp\u003eThe diversity of plants and pollinators across the 14 UNAs was evaluated using Hill numbers (\u003cem\u003eq\u003c/em\u003e) implemented in the iNEXT package (Hsieh et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which computes species accumulation curves based on the rarefaction and extrapolation framework. First, we calculated sample completeness to an endpoint slightly larger than the most abundant site. Sample completeness represents the proportion of individuals expected to belong to species already observed, providing a measure of sampling effort effectiveness. Using a common endpoint allowed us to assess whether sampling effort was sufficient and to compare sites on equal terms. We also calculated diversity at three values of \u003cem\u003eq\u003c/em\u003e for each urban natural area: the first, species richness (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0), represents the total number of species in a community; the second, the exponential of Shannon entropy (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1), also referred to as Shannon diversity, reflects the effective number of common species in a community; and the third, the inverse of Simpson concentration, is referred to as Simpson diversity (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2), and indicates the effective number of dominant species (Chao et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Sample completeness was calculated to estimate the proportion of individuals in the community that are expected to belong to species already observed in the sample (Chao \u0026amp; Jost, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and allow comparability across sites.\u003c/p\u003e \u003cp\u003eFor plants, we focused specifically on Shannon diversity (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1) as a predictor variable, as it reflects the effective number of common species in a community and provides a balanced measure between rare and dominant species. Species richness (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0), although widely used, can obscure important ecological patterns because it treats all species equally, overemphasizing the contribution of rare taxa that may have little influence on community functioning or interaction dynamics (Fletcher et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This limitation is particularly relevant in urban contexts, where variation in sampling completeness and habitat heterogeneity can amplify such biases. In contrast, Simpson diversity (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2) disproportionately weights the most abundant species, potentially masking variation among moderately abundant and ecologically meaningful taxa. Shannon diversity (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1), calculated as the exponential of Shannon entropy, integrates both species presence and relative abundance, yielding a metric that is sensitive to changes in community composition while remaining robust to extremes in species Simpson diversity (Chao \u0026amp; Jost, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Chao et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Roswell et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEffect of urban environmental gradients on pollinator diversity\u003c/h2\u003e \u003cp\u003eTo assess the relationship between pollinator diversity and urban environmental gradients across urban natural areas and obtain effect coefficients, we fitted a generalized linear mixed model (GLMM) to the data using the \u003cem\u003eglmmTMB\u003c/em\u003e package (Brooks et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in R v.4.5 (R Core Team, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We built separate models for pollinator species richness (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0), Shannon diversity (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1), and Simpson diversity (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2) as response variables. As predictor variables, we utilized the urban environmental gradients identified through surface image analysis of land covers in Merida, and gradients of ground cover and vegetation structure complexity, as well as plant diversity (Shannon diversity) at each site. We modeled the visits as a random effect denoting UNAs intrinsic variation in pollinator diversity due to repeated sampling of plants and pollinators. Diversity data typically has a right-skewed distribution; therefore, we set a negative binomial error distribution and a logarithmic link function in our model. Predictor variables were standardized using z-transformation, allowing the obtaining of comparable effect coefficients. We tested the assumptions of normality, overdispersion, zero-inflation, and collinearity of model residuals with the \u003cem\u003eperformance\u003c/em\u003e package (L\u0026uuml;decke et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To compare the impact of predictor variables on parameters of pollinator diversity, we extracted the coefficients and their confidence intervals and visualized them as forest plots using the \u003cem\u003ebroom.mixed\u003c/em\u003e package (Bolker and Robinson, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLand cover composition gradients\u003c/h2\u003e \u003cp\u003eThe 14 urban natural areas in Merida varied in land cover composition. Most sites were dominated by a mix of impervious surfaces, tree cover, and grassland, with higher impervious cover generally corresponding to lower vegetation and vice versa. Minor land cover types, including shrubland, cropland, water bodies, and wetlands, contributed variably across sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) Sites like Parque Arqueoecol\u0026oacute;gico (ARQ) and Kai Luum (KLM) exhibited small fractions of cropland cover. Grasslands were present and relatively abundant across all sites, particularly in mid-disturbance peripheral areas such as MAPSA, KLM, and Las Am\u0026eacute;ricas (LAM). Shrubland cover varied among sites, with the highest fractions found in Dzoyaxch\u0026eacute; (DYX), Tixcuyt\u0026uacute;n (TIX), and PCTY, which also had the greatest tree cover. Bare ground was generally rare, most noticeable in KLM, MAPSA, and LAM. Water bodies were limited, occurring mainly in Acuaparque (AQP), ARQ, and MAPSA, all of which also contained some wetland cover. Overall, the sites with the most extensive vegetation cover were DYX, TIX, and PCTY, contrasting with those dominated by impervious surfaces, particularly Itzimn\u0026aacute; (ITZ), La Iberica (LIB), and the Roger Orellana Botanical Garden (JBRRO), with the first grouping located on the outskirts, far from the city core, and the second grouping embedded within the main urban mass. Land cover pixel counts by UNA are in the Supplementary Material.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe first three principal components from the PCA explained 89.9% of the variation in land cover types, representing the most informative environmental urban gradients for the city of Merida. PC1 explained 35.5% of environmental variation, indicating a gradient of impervious surface, ranging from fully built-up areas on one end to extensive vegetation cover, including tree cover, grassland, and shrubland, on the other. In Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, we flipped the axis for better interpretation so that higher PC1 values reflect more urbanized landscapes. PC2 accounted for 31.1% of the variation and represents a vegetation type gradient, separating tall and woody areas with trees and shrubs from areas with low height vegetation or open ground, either with cropland, grassland, or bare ground. Areas with concrete, asphalt, and other artificial structures were near the center of this PC axis, reflecting an urban landscape of scattered greenery between buildings. PC3 accounted for 23.3%, featuring a gradient of water cover that ranged from small wetlands and water bodies at one end to drier upland cover, including cropland, and shrubland, at the other end (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea \u0026amp; \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eVegetation structure and ground covers gradients\u003c/h2\u003e \u003cp\u003eVegetation structure and ground cover complexity followed similar unidimensional gradients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec \u0026amp; \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Summary values for each site are presented in the Supplementary Material. For vegetation structure, PC1 explained 83.7% of the total variance, with higher values corresponding to greater tree height, trunk diameter, and canopy cover in both north-to-south and east-to-west orientations. PC1 for the ground cover complexity gradient explained 44.7% of the variance, ranging from impervious surfaces at higher values to sites with more diverse ground covers, including vegetation, rocks, leaf litter, and, to a lesser extent, logs and debris, at lower values. In both cases, most of the variation was concentrated along PC1, which was used as the main complexity axis in subsequent analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePollinator and Plant Diversity\u003c/h2\u003e \u003cp\u003eTotal pollinator visits to flowers differed among urban natural areas. Primary pollinators, defined based on previous reports as taxa that regularly visit flowers and actively contribute to pollen transfer, included Apodiformes (hummingbirds), Coleoptera, Hymenoptera, Lepidoptera, and Diptera, and contributed the majority of visits. Incidental visitors included Araneae, Orthoptera, Hemiptera, and Passeriformes (orioles), which are groups for which pollination has been less consistently or only rarely reported. In general, visit number seems to fluctuate across sites rather than increase with urbanization. Incidental visitors were slightly more common in less populated urban natural areas, though their contribution to total visits remained low overall (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eWe registered a total of 302 pollinator species and morphospecies, spanning 9 taxonomic orders and 79 families. Of these, 93 were classified as morphospecies, with most identified at the genus level. In this study, we refer to both identified species and morphospecies collectively as species to facilitate the interpretation of results. Invertebrates accounted for the vast majority of pollinator diversity, with 289 species distributed among Hymenoptera (82), Lepidoptera (97), Diptera (48), Coleoptera (31), Hemiptera (18), Araneae (8), and Orthoptera (5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Vertebrate visitors were less diverse, comprising only 8 species: 4 Apodiformes and 4 Passeriformes (see Supplementary Material for detailed list of species and morphoespecies registered).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSample completeness at the site level was consistently high for pollinators, with an average sample coverage of 0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005 across sites. This indicates that, on average, 96% of pollinator individuals present in the urban natural areas likely belonged to species detected during sampling, suggesting that most species richness was well represented (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Sample completeness was slightly lower for plants, averaging 0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 across sites, with the lowest site-level coverage at 0.71 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). These values suggest that, while not as thoroughly captured as pollinators, the most common plant species were reasonably well represented at each site.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDiversity metrics for pollinators and plants varied across urban natural areas (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Pollinator richness (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0) ranged from 55 species at ITZ to 118 at DYX, while the effective number of common species (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1) varied between 18.17 at ITZ and 54.80 at KLM. When dominant species were weighted more heavily (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2), diversity ranged from 6.75 at LAM to 32.81 at KLM. Plant richness (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0) ranged from 26 species at ITZ to 65 at DYX, with effective diversity (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1) spanning from 16.36 at ITZ to 49.58 at DYX, and dominant-weighted diversity (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2) ranging from 12.01 at LAM to 38.53 at DYX.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHill numbers calculated for each urban natural area (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0, 1, 2), summarizing both pollinator and plant species richness, Shannon diversity, and Simpson diversity across the entire sampling period.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUrban natural area\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePollinators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003ePlants\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDYX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDZB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJBRRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAPSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCTY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the case of plants, we recorded an assemblage of 202 flowering plant species, spanning 29 taxonomic orders and 55 families (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). A total of 1,822 individual plants were observed across all sites, representing both native (172) and non-native species (30). The most species-rich orders were Fabales (34 species), Lamiales (25), and Asterales (22). At the family level, Fabaceae (34 species), Asteraceae (22), and Euphorbiaceae (14) contributed substantially to overall richness. Regarding growth forms, the most abundant were herbs (924 individuals), followed by trees (423), shrubs (370), vines (81), and aquatic plants (23) (see Supplementary Material for detailed list of species and morphoespecies registered).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEffects of urban environmental gradients on pollinator diversity\u003c/h2\u003e \u003cp\u003eWe found similar trends in the effects of the urban environmental gradients on pollinators for all three diversity indices (species richness, Shannon diversity and Simpson diversity; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Complete model summaries are included in the Supplementary Material. The gradient of impervious surface (PC1) had a weak effect, with overlapping confidence intervals across all indices. Additionally, the gradients of water cover (PC3) and ground cover complexity did not show any significant effect in any of the models. Although, there was a slight positive trend in the effect of vegetation complexity, it was not statistically significant. In contrast, the gradient of vegetation cover type (PC2) had a negative effect on all diversity metrics (Positive PC2 values indicate UNAs with more trees and shrubs, and less low vegetation and open ground). This negative trend was particularly pronounced for pollinator species richness, followed by the Shannon diversity and Simpson diversity. Plant diversity had a positive effect on pollinators, resulting in a significant increase in species richness. Specifically, there was a 16% increase in pollinator species richness for each unit increase in plant diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study assessed the influence of different land cover and ecological complexity properties as drivers of pollinator diversity across urban green spaces in a tropical city. Among the six variables we assessed, local plant diversity and landscape-scale vegetation type were the most consistent predictors of three diversity indices: species richness, Shannon diversity, and Simpson diversity. At the same time, the effects of other habitat characteristics such as the impervious surface gradient, water cover gradient and both vegetation cover and ground cover complexities were weak or inconclusive.\u003c/p\u003e \u003cp\u003eDespite expectations regarding the effect of impervious surfaces on pollinator diversity, we did not detect a strong influence of built infrastructure. While high impervious surface cover has been documented to negatively affect pollinator diversity (Bennett \u0026amp; Lovell, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Geslin et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003eh\u0026auml;talo et al., 2024), other studies have found that factors such as the quality and connectivity of vegetation areas may outweigh the broader urban landscape context (Bates et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Graffigna et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), especially when urbanization allows the existence of vegetation areas relatively interconnected at moderate levels (Fortel et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Martins et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In the case of tropical systems, the climatic and biotic conditions in urban areas might encourage vegetation to grow spontaneously and continuously, and form structurally and compositionally rich habitats in the matrix surrounding urban natural areas. Further, lack of maintenance of urban infrastructure frequently allows native flowering plants to grow along sidewalks, vacant lots, or roadsides. These informal green spaces may enhance connectivity by offering critical foraging resources and maintaining spatiotemporal resource continuity across otherwise heavily urbanized areas (Cheng et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; He et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSites characterized by higher values along the gradient of vegetation types, represented by PC2 axis values from the PCA of land cover types, were associated with lower pollinator diversity parameters (species richness, Shannon diversity, Simpson diversity). This gradient reflects a shift from open areas to sites dominated by trees and shrubs. Although increased vertical vegetation structure with layers, such as tree canopy, understory shrubs, and herbs, is assumed to enhance pollinator diversity by increasing habitat heterogeneity, resource availability, and buffering against microclimate extremes (da Silva et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xing et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), these benefits may not extend to pollinator groups in urban environments. In our study, urban areas with denser woody cover limited light penetration, thereby reducing herb and floral availability and foraging opportunities for pollinators that depend on clearing areas (Bozek et al. 2023). This result is consistent with findings from other tropical ecosystems, where increased tree canopy has been linked to a decline in understory floral diversity due to reduced light and space (Chazdon et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Radhamoni et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). At the other end of the vegetation type gradient (shorter vegetation), the positive effect of light in cleared areas on the availability of flowers at the herb strata may help explain the higher pollinator diversity. This pattern has been observed in canopy gaps and forest edges, where floral resources can be more accessible and diverse (Ammann et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Coulin et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Proctor et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mathis et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWater features, such as ponds, wetlands, or riparian zones, can influence pollinator communities by moderating local microclimates and enhancing habitat heterogeneity, particularly through increased humidity and the support of moisture-adapted plant species (Riis et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Some studies have found positive associations between proximity to water and pollinator diversity, particularly for bees and hoverflies (Dylewski et. al, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), likely due to indirect benefits such as greater floral availability in surrounding vegetation. However, aquatic elements do not provide direct foraging or nesting resources for most pollinators, making it difficult to gauge their overall contribution to pollinator communities. In our study, the water cover gradient did not emerge as a strong predictor of pollinator diversity, likely due to the limited variation in this feature. Only a few sites contained permanent or semi-permanent water bodies, reducing our ability to detect potential ecological effects.\u003c/p\u003e \u003cp\u003eIn this study, the gradient of vegetation structure complexity, measured from transect estimates of tree and shrub height, diameter, and canopy cover, did not show a strong relationship with pollinator diversity. This result contradicts the expectation that vertical vegetation complexity is a promoting factor for species richness by increasing habitat and microenvironmental heterogeneity (Tews et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Torresani et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). One possible explanation is that, despite the varying levels of urbanization traits we measured, all our study sites maintained a baseline level of structural complexity in the vegetation sufficient to support a moderate diversity of pollinators, thereby limiting detectable variation in our models. Consistent with this interpretation, Tavares Brancher et al. (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported that medium-sized urban areas can sustain relatively high pollinator diversity because vegetation characteristics reach levels adequate to support diverse communities.\u003c/p\u003e \u003cp\u003eAlternatively, our measure of vegetation structure complexity might not fully reflect the various layers of vegetation that are most relevant to pollinators. For example, we did not consider the variation in herbaceous vegetation height, which may play a larger role in shaping resource accessibility and, consequently, foraging frequency at the lower strata of vegetation (Klecka et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Finer-scale measures of vegetation layering, such as flower stratification or patchiness in canopy gaps, could also produce a more subtle characterization of vegetation complexity and its impact on pollinator diversity. Vertical distribution of floral resources can influence pollinator foraging behavior, as different taxa exhibit preferences based on floral height (Diniz et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hern\u0026aacute;ndez-Villa et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Klecka et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Closely linked to this is the concept of three-dimensional microhabitat availability, where structural complexity creates vertical layers with varying light, temperature, and humidity conditions (Tews et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; De Smedt et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These vertical gradients generate ecological niches that may be differentially exploited depending on species traits such as flight ability, thermal tolerance, or foraging height preferences (Xing et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Without metrics capturing this kind of fine-scale habitat variation, structurally mediated ecological filtering may have gone undetected.\u003c/p\u003e \u003cp\u003eThe gradient of ground cover complexity also failed to show a significant effect on pollinator diversity in our study. This may be partly due to the limitations of our measurements. The first component of PCA of ground cover types captured only 44.7% of the total variation. This suggests that much of the heterogeneity in ground cover types, which pollinators can respond to, occurs at a broader scale than we measured. Furthermore, pollinator responses to different ground cover types should depend on the pollinator nesting guilds (Cane et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Neame et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); however, we did not differentiate between ground-nesting pollinators and other guilds.\u003c/p\u003e \u003cp\u003eIn contrast to the vegetation structure complexity, plant diversity showed a robust and positive relationship with the three parameters of pollinator diversity. This relationship was strongest with species richness, followed by Shannon diversity and Simpson diversity, in that order. This result aligns with a broad body of work emphasizing the foundational role of floral diversity in structuring pollination networks (Kantsa et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; G\u0026oacute;mez-Mart\u0026iacute;nez et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although this relationship appears to be dependent on the density and diversity of floral resources at a small spatial scale (Hegland \u0026amp; Boeke, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), overall evidence points out that increasing plant diversity tends to promote pollinator taxonomic and functional diversity (Orford et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Steffan-Dewenter \u0026amp; Tscharntke, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Venjakob et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The mechanisms proposed include both a direct increase in the number of foraging niches (Ebeling et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Steffan-Dewenter \u0026amp; Tscharntke, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and an expansion of spatio-temporal niche opportunities that facilitate species coexistence (Venjakob et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The stronger effect observed under richness (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0), where all species are weighted equally regardless of abundance, suggests that more diverse plant assemblages increase the likelihood of supporting rare pollinators, probably benefiting specialists with narrow floral requirements.\u003c/p\u003e \u003cp\u003eIn addition to expanding niche space, diverse floral assemblages may enhance the nutritional quality and attractiveness of foraging sites, with pollinators relying on a mixture of floral resources to meet complex physiological demands (Filipiak, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Stephen et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; W\u0026auml;ckers et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Variation in floral morphology, phenology, scent, and color may further improve resource appeal and detectability across pollinator taxa, promoting both generalist and specialist species (Burkle \u0026amp; Runyon, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hornung-Leoni et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ornai \u0026amp; Keasar, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and therefore influencing species richness, Shannon diversity, and Simpson diversity of the species abundances within the pollinator communities. These factors likely contribute to the positive relationship observed between plant diversity and pollinator diversity (Kantsa et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe positive association between plant diversity and pollinator diversity strongly suggests that promoting and enhancing the herbaceous layer within urban green spaces should be a conservation priority. Increasing the species richness, abundance, and presence of herbaceous flowering plants can promote overlapping blooming periods within and across urban green areas. This will help sustain pollinator diversity as urbanization continues to advance. Nevertheless, the contributions of trees and shrubs must also be recognized, as they provide critical resources for pollinators that do not forage at the ground level. Urban habitat design should thus aim for a heterogeneous mosaic, balancing open herbaceous patches with strategically placed woody vegetation, while avoiding excessive vertical closure that could suppress understory floral diversity and abundance.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings offer practical guidance for urban planning and conservation of pollinator diversity in urban environments. Both plant diversity and vegetation type emerged as key drivers, with plant diversity exerting the strongest influence on species richness, and vegetation type shaping all diversity parameters. Sites dominated by tall woody cover supported lower pollinator diversity, underscoring the importance of maintaining open herbaceous layers within urban green spaces. By contrast, impervious surface cover, which we expected to shape community composition, showed only limited effects, likely mitigated by the presence of spontaneous and unmanaged vegetation that sustains floral resources even in heavily built-up areas. Together, these results highlight that conservation strategies in tropical cities must prioritize diverse and heterogeneous vegetation assemblages, balancing herbaceous and woody components, to support the full spectrum of pollinator taxa to safeguard the ecological functions they provide.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSGE was supported by a graduate scholarship from the Secretaría de Ciencia, Humanidades, Tecnología e Innovación (SECIHTI).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to study conception and design. SGE led the study, including field data collection, species identification, data analysis, and writing of the original draft. RRR contributed to field data collection and species identification. AC and REF advised on analytical approaches and provided critical feedback during manuscript revisions. All authors reviewed and commented on previous versions of the manuscript and approved the final version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbrahamczyk S, Kluge J, Gareca Y, Reichle S and Kessler M (2011) The influence of climatic seasonality on the diversity of different tropical pollinator groups. PLoS One 6(11):e27115. https://doi.org/10.1371/journal.pone.0027115\u003c/li\u003e\n \u003cli\u003eAdams BJ, Li E, Bahlai CA, Meineke EK, McGlynn TP and Brown BV (2020). Local‐and landscape‐scale variables shape insect diversity in an urban biodiversity hot spot. Ecol Appl 30(4):e02089. https://doi.org/10.1002/eap.2089Aguilar AG, Flores-Espinosa M and Hern\u0026aacute;ndez J (2025). Metropolizaci\u0026oacute;n, din\u0026aacute;mica inmobiliaria y segregaci\u0026oacute;n socio-territorial. El caso de M\u0026eacute;rida, Yucat\u0026aacute;n. EURE 51(153):1-27. https://doi.org/10.7764/EURE.51.153.03\u003c/li\u003e\n \u003cli\u003eAitchison J (1982). The statistical analysis of compositional data. J R Stat Soc Series B Stat Methodol 44(2):139-160. https://doi.org/10.1111/j.2517-6161.1982.tb01195.x\u003c/li\u003e\n \u003cli\u003eAmmann L, Bosem-Baillod A, Herzog F, Frey D, Entling MH and Albrecht M (2024) Spatio-temporal complementarity of floral resources sustains wild bee pollinators in agricultural landscapes. Agric Ecosyst Environ 359:108754. https://doi.org/10.1016/j.agee.2023.108754\u003c/li\u003e\n \u003cli\u003eAronson MFJ, Handel SN, La Puma IP, and Clemants SE (2015) Urbanization promotes non-native woody species and diverse plant assemblages in the New York metropolitan region. Urban Ecosyst 18:31-45. https://doi.org/10.1007/s11252-014-0382-z\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBarteet C (2015). Representing Tih\u0026oacute;-M\u0026eacute;rida in colonial Yucat\u0026aacute;n: maya and spanish considerations of a colonial capital. South Central Review 32(2):21-47. http://www.jstor.org/stable/44016890\u003c/li\u003e\n \u003cli\u003eBates AJ, Sadler JP, Fairbrass, AJ, Falk SJ, Hale JD and Matthews TJ (2011) Changing bee and hoverfly pollinator assemblages along an urban-rural gradient. PLoS One 6(8):e23459. https://doi.org/10.1371/journal.pone.0023459\u003c/li\u003e\n \u003cli\u003eBennett AB and Lovell S (2019). Landscape and local site variables differentially influence pollinators and pollination services in urban agricultural sites. PLoS One 14(2):e0212034. https://doi.org/10.1371/journal.pone.0212034\u003c/li\u003e\n \u003cli\u003eBolker B, Robinson D (2025) broom.mixed: Tidying methods for mixed models (Version 0.2.9.7) https://github.com/bbolker/broom.mixed\u003c/li\u003e\n \u003cli\u003eBrooks ME, Kristensen K, van Benthem KJ, Magnusson A, Berg CW, Nielsen A, Skaug HJ, Maechler M, Bolker BM (2017) glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. RJ \u003cstrong\u003e9\u003c/strong\u003e(2):378-400. https://doi:10.32614/RJ-2017-066\u003c/li\u003e\n \u003cli\u003eBożek M, Denisow B, Strzałkowska-Abramek M, Chrzanowska E and Winiarczyk K (2023) Non-forest woody vegetation: A critical resource for pollinators in agricultural landscapes: a review. Sustainability 15(11): 8751. https://doi.org/10.3390/su15118751\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBurkle LA and Runyon JB (2019) Floral volatiles structure plant\u0026ndash;pollinator interactions in a diverse community across the growing season. Funct Ecol 33(11): 2116-2129. https://doi.org/10.1111/1365-2435.13424\u003c/li\u003e\n \u003cli\u003eCampos-Silva LA and Piratelli AJ (2021) Vegetation structure drives taxonomic diversity and functional traits of birds in urban private native forest fragments. Urban Ecosyst 24(2):375-390. https://doi.org/10.1007/s11252-020-01045-8\u003c/li\u003e\n \u003cli\u003eCane JH, Minckley RL, Kervin LJ, Roulston TAH and Williams NM (2006) Complex responses within a desert bee guild (Hymenoptera: Apiformes) to urban habitat fragmentation. Ecol Appl 16(2):632-644. https://doi.org/10.1890/1051-0761(2006)016[0632:CRWADB]2.0.CO;2\u003c/li\u003e\n \u003cli\u003eChao A, Gotelli NJ, Hsieh TC, Sander EL, Ma KH, Colwell RK and Ellison AM (2014) Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol Monogr 84(1):45-67. https://doi.org/10.1890/13-0133.1\u003c/li\u003e\n \u003cli\u003eChao A and Jost L. (2012). Coverage‐based rarefaction and extrapolation: standardizing samples by completeness rather than size.Ecol 93(12): 2533-2547. https://doi.org/10.1890/11-1952.1\u003c/li\u003e\n \u003cli\u003eChazdon RL, Pearcy RW, Lee DW and Fetcher NED (1996). Photosynthetic responses of tropical forest plants to contrasting light environments. In: Mulkey SS, Chazdon RL, Smith AP (eds) Tropical forest plant ecophysiology. Springer Boston, MA pp. 5-55 https://doi.org/10.1007/978-1-4613-1163-8_1\u003c/li\u003e\n \u003cli\u003eCheng XL, Cubino JP, Balfour K, Zhu ZX and Wang HF (2022) Drivers of spontaneous and cultivated species diversity in the tropical city of Zhanjiang, China. Urban For Urban Green 67: 127428. https://doi.org/10.1016/j.ufug.2021.127428\u003c/li\u003e\n \u003cli\u003eChien SA and Morse DH (1998) The roles of prey and flower quality in the choice of hunting sites by adult male crab spiders \u003cem\u003eMisumena vatia\u003c/em\u003e (Araneae, Thomisidae). J Arachnol 26:238-243. https://doi.org/10.5281/zenodo.16240103\u003c/li\u003e\n \u003cli\u003eChristie FJ, Cassis G and Hochuli DF (2010) Urbanization affects the trophic structure of arboreal arthropod communities.\u0026nbsp;Urban Ecosyst 13: 169-180. https://doi.org/10.1007/s11252-009-0115-x\u003c/li\u003e\n \u003cli\u003eCoulin C, Aizen MA and Garibaldi LA (2019) Contrasting responses of plants and pollinators to woodland disturbance. Austral Ecol 44(6):1040-1051. https://doi.org/10.1111/aec.12771\u003c/li\u003e\n \u003cli\u003eda Silva CI, de Souza Pacheco Filho AJ, Toppa RH, Arena MVN and de Oliveira PE (2021) Seasonal and vertical distribution of floral resources and its implications for the conservation of pollinators. Flora 278:151787. https://doi.org/10.1016/j.flora.2021.151787\u003c/li\u003e\n \u003cli\u003ede Barros Ruas R, Costa LMS and Bered F (2022) Urbanization driving changes in plant species and communities: A global view. Glob Ecol Conserv 38: e02243. https://doi.org/10.1016/j.gecco.2022.e02243\u003c/li\u003e\n \u003cli\u003ede Lima KB, Ferreira PA, Groppo M, Goldenberg R, Pansarin ER, Barreto RC, Coelho GP, Barros-Souza Y and Boscolo D (2020) Does landscape context affect pollination-related functional diversity and richness of understory flowers in forest fragments of Atlantic Rainforest in southeastern Brazil? Ecol Process 9:1-12. https://doi.org/10.1186/s13717-020-00261-6\u003c/li\u003e\n \u003cli\u003eDe Smedt P, Vangansbeke P, Bracke R, Schauwvliege W, Willems L, Mertens J and Verheyen K (2019) Vertical stratification of moth communities in a deciduous forest in Belgium. Insect Conserv Divers 12(2):121-130. https://doi.org/10.1111/icad.12320\u003c/li\u003e\n \u003cli\u003eDiniz UM, Domingos-Melo A, \u0026amp; Machado, I. C. (2019). Flowers up! The effect of floral height along the shoot axis on the fitness of bat-pollinated species. Ann Bot 124(5):809-818 https://doi.org/10.1093/aob/mcz116\u003c/li\u003e\n \u003cli\u003eDusza Y, Kraepiel Y, Abbadie L, Barot S, Carmignac D, Dajoz I, Gendreau E, Lata JC, Meriguet J, Motard E and Raynaud X (2020) Plant-pollinator interactions on green roofs are mediated by substrate characteristics and plant community composition. Acta Oecol 105:103559. https://doi.org/10.1016/j.actao.2020.103559\u003c/li\u003e\n \u003cli\u003eDylewski Ł, Białas JT, Szymysł A and Banaszak-Cibicka W (2024) Pollinator assemblages in grasslands along river valleys depend on the urban matrix and local habitat scale variables. Ecol Indic 159:111687. https://doi.org/10.1016/j.ecolind.2024.111687\u003c/li\u003e\n \u003cli\u003eEbeling A, Klein AM, Schumacher J, Weisser WW and Tscharntke T (2008) How does plant richness affect pollinator richness and temporal stability of flower visits? Oikos 117(12):1808-1815. https://doi.org/10.1111/j.1600-0706.2008.16819.x\u003c/li\u003e\n \u003cli\u003eEgozcue JJ, Pawlowsky-Glahn V, Mateu-Figueras G and Barcel\u0026oacute;-Vidal C (2003) Isometric log ratio transformations for compositional data analysis. Math Geol\u0026nbsp;35(3):279-300. https://doi.org/10.1023/A:1023818214614\u003c/li\u003e\n \u003cli\u003eFilipiak M (2019) Key pollen host plants provide balanced diets for wild bee larvae: A lesson for planting flower strips and hedgerows J Appl Ecol 56(6):1410-1418. https://doi.org/10.1111/1365-2664.13383\u003c/li\u003e\n \u003cli\u003eFletcher Jr RJ, Green RE, Bladon EK, Atkinson PW, Phalan BT, Williams D, Visconti P and Balmford A (2025) Beyond species richness for biological conservation. Conserv Lett 18(4):e13124. https://doi.org/10.1111/conl.13124\u003c/li\u003e\n \u003cli\u003eFortel L, Henry M, Guilbaud L, Guirao AL, Kuhlmann M, Mouret H, Rollin O and Vaissi\u0026egrave;re BE (2014) Decreasing abundance, increasing diversity and changing structure of the wild bee community (Hymenoptera: Anthophila) along an urbanization gradient. PloS one 9(8):e104679. https://doi.org/10.1371/journal.pone.0104679\u003c/li\u003e\n \u003cli\u003eGenini J, Guimaraes Jr PR, Sazima M, Sazima I and Morellato LPC (2021) Temporal organization among pollination systems in a tropical seasonal forest. Sci Nat 108:34 https://doi.org/10.1007/s00114-021-01744-y\u003c/li\u003e\n \u003cli\u003eGeslin B, Le F\u0026eacute;on V, Folschweiller M, Flacher F, Carmignac D, Motard E, Perret S and Dajoz I (2016) The proportion of impervious surfaces at the landscape scale structures wild bee assemblages in a densely populated region. Ecol Evol 6(18):6599-6615. https://doi.org/10.1002/ece3.2374\u003c/li\u003e\n \u003cli\u003eG\u0026oacute;mez‐Mart\u0026iacute;nez C, Gonz\u0026aacute;lez‐Est\u0026eacute;vez MA, Cursach J and L\u0026aacute;zaro A (2022) Pollinator richness, pollination networks, and diet adjustment along local and landscape gradients of resource diversity. Ecol Appl 32(6): e2634. https://doi.org/10.1002/eap.2634\u003c/li\u003e\n \u003cli\u003eGraffigna S, Gonz\u0026aacute;lez-Vaquero RA, Torretta JP and Marrero HJ (2024) Importance of urban green areas\u0026rsquo; connectivity for the conservation of pollinators. Urban Ecosyst 27(2):417-426. https://doi.org/10.1007/s11252-023-01457-2\u003c/li\u003e\n \u003cli\u003eHarrison T and Winfree R (2015) Urban drivers of plant‐pollinator interactions. Funct Ecol 29(7):879-888. https://doi.org/10.1111/1365-2435.12486\u003c/li\u003e\n \u003cli\u003eHe R, Li L, Wang G, Cao L, Xiong G and Yang F (2024) Plant diversity value of informal green spaces in tropical coastal urban areas: An empirical study of species, functional, and phylogenetic diversity. Sci Total Environ 955:176741. https://doi.org/10.1016/j.scitotenv.2024.176741\u003c/li\u003e\n \u003cli\u003eHegland SJ and Boeke L (2006) Relationships between the density and diversity of floral resources and flower visitor activity in a temperate grassland community. Ecol Entomol 31(5):532-538. https://doi.org/10.1111/j.1365-2311.2006.00812.x\u003c/li\u003e\n \u003cli\u003eHern\u0026aacute;ndez-Villa V, Vibrans H, Uscanga-Mortera E and Aguirre-Jaimes A (2020) Floral visitors and pollinator dependence are related to floral display size and plant height in native weeds of central Mexico. Flora 262:151505. https://doi.org/10.1016/j.flora.2019.151505\u003c/li\u003e\n \u003cli\u003eHornung-Leoni CT, Gonz\u0026aacute;lez-G\u0026oacute;mez PL and Troncoso AJ (2013) Morphology, nectar characteristics and avian pollinators in five Andean \u003cem\u003ePuya\u003c/em\u003e species (Bromeliaceae). Acta Oecol 51:54-61. https://doi.org/10.1016/j.actao.2013.05.010\u003c/li\u003e\n \u003cli\u003eHsieh T, Ma K, Chao A (2025) iNEXT: Interpolation and Extrapolation for Species Diversity. R package version 3.0.2. http://chao.stat.nthu.edu.tw/wordpress/software_download/\u003c/li\u003e\n \u003cli\u003eINEGI. (2023). Principales resultados del Censo de Poblaci\u0026oacute;n y Vivienda 2020/Yucat\u0026aacute;n. Instituto Nacional de Estad\u0026iacute;stica y Geograf\u0026iacute;a (INEGI) M\u0026eacute;xico,146 p. https://www.inegi.org.mx/app/biblioteca/ficha.html?upc=702825198374\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eJackson DA (1993) Stopping rules in principal components analysis: A comparison of heuristical and statistical approaches. Ecology 74(8):2204-2214. https://doi.org/10.2307/1939574\u003c/li\u003e\n \u003cli\u003eKantsa A, Raguso RA, Dyer AG, Olesen JM, Tscheulin T and Petanidou T (2018) Disentangling the role of floral sensory stimuli in pollination networks. Nat Commun 9(1):1041. https://doi.org/10.1038/s41467-018-03448-w\u003c/li\u003e\n \u003cli\u003eKantsa A, Tscheulin T, Junker RR, Petanidou T and Kokkini S (2013) Urban biodiversity hotspots wait to get discovered: The example of the city of Ioannina, NW Greece. Landsc Urban Plann 120:129-137. https://doi.org/10.1016/j.landurbplan.2013.08.013\u003c/li\u003e\n \u003cli\u003eKlecka J, Hadrava J and Kolou\u0026scaron;kov\u0026aacute; P (2018) Vertical stratification of plant\u0026ndash;pollinator interactions in a temperate grassland. PeerJ 6:e4998. https://doi.org/10.7717/peerj.4998\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKnuff AK, Staab M, Frey J, Dormann CF, Asbeck T and Klein AM (2020) Insect abundance in managed forests benefits from multi-layered vegetation. Basic Appl Ecol 48:124-135. https://doi.org/10.1016/j.baae.2020.09.002\u003c/li\u003e\n \u003cli\u003eKov\u0026aacute;cs-Hosty\u0026aacute;nszki A, Szigeti V, Miholcsa Z, S\u0026aacute;ndor D, Solt\u0026eacute;sz Z, T\u0026ouml;r\u0026ouml;k E and Fenesi A (2022) Threats and benefits of invasive alien plant species on pollinators. Basic Appl Ecol 64:89-102. https://doi.org/10.1016/j.baae.2022.07.003\u003c/li\u003e\n \u003cli\u003eL\u0026ecirc;, S., Josse, J., \u0026amp; Husson, F. (2008). FactoMineR: An R package for multivariate analysis. J Stat Softw 25(1):1-18. https://doi.org/10.18637/jss.v025.i01\u003c/li\u003e\n \u003cli\u003eLegendre P and Legendre L (2012). Numerical ecology (3rd ed. Vol 24). Elsevier.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLugo-Hubp J, Aceves-Quesada JF and Espinosa-Pere\u0026ntilde;a R (1992) Rasgos geomorfol\u0026oacute;gicos mayores de la pen\u0026iacute;nsula de Yucat\u0026aacute;n. Rev Mex Cienc Geol 10(2):143\u0026ndash;150. https://rmcg.geociencias.unam.mx/index.php/rmcg/article/view/1236\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eL\u0026uuml;decke D, Ben-Shachar MS, Patil I, Waggoner P and Makowski D (2021) performance: An R package for assessment, comparison and testing of statistical models. JOSS 6(60):3139. https://doi.org/10.21105/joss.03139\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMcGarigal K, Tagil S and Cushman SA (2009) Surface metrics: an alternative to patch metrics for the quantification of landscape structure. Landscape Ecol 24: 433-450. https://doi.org/10.1007/s10980-009-9327-y\u003c/li\u003e\n \u003cli\u003eMcKinney ML (2008). Effects of urbanization on species richness: A review of plants and animals. Urban Ecosyst 11:161\u0026ndash;176. https://doi.org/10.1007/s11252-007-0045-4\u003c/li\u003e\n \u003cli\u003eMartins KT, Gonzalez A and Lechowicz MJ (2017) Patterns of pollinator turnover and increasing diversity associated with urban habitats Urban Ecosyst 20:1359-1371. https://doi.org/10.1007/s11252-017-0688-8\u003c/li\u003e\n \u003cli\u003eMathis CL, McNeil Jr DJ, Lee MR, Grozinger CM, King DI, Otto CR and Larkin JL (2021) Pollinator communities vary with vegetation structure and time since management within regenerating timber harvests of the Central Appalachian Mountains. For Ecol Manag 496:119373. https://doi.org/10.1016/j.foreco.2021.119373\u003c/li\u003e\n \u003cli\u003eMitchell K (2010) Quantitative analysis by the point-centered quarter method. https://doi.org/10.48550/arXiv.1010.3303\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMoroń D, Lenda M, Sk\u0026oacute;rka P, Szentgy\u0026ouml;rgyi H, Settele J and Woyciechowski M (2009) Wild pollinator communities are negatively affected by invasion of alien goldenrods in grassland landscapes. Biol Conserv 142 (7):1322\u0026ndash;1332. https://doi.org/10.1016/j.biocon.2008.12.036\u003c/li\u003e\n \u003cli\u003eNeame LA, Griswold T and Elle E (2013) Pollinator nesting guilds respond differently to urban habitat fragmentation in an oak‐savannah ecosystem. Insect Conserv Divers 6(1):57-66. https://doi.org/10.1111/j.1752-4598.2012.00187.x\u003c/li\u003e\n \u003cli\u003eOrford KA, Murray PJ, Vaughan IP and Memmott J (2016) Modest enhancements to conventional grassland diversity improve the provision of pollination services. J Appl Ecol 53(3):906-915. https://doi.org/10.1111/1365-2664.12608\u003c/li\u003e\n \u003cli\u003eOrnai A and Keasar T (2020) Floral complexity traits as predictors of plant-bee interactions in a mediterranean pollination web. Plants, 9(11):1432. https://doi.org/10.3390/plants9111432\u003c/li\u003e\n \u003cli\u003ePalarea-Albaladejo J, Mart\u0026iacute;n-Fern\u0026aacute;ndez JA (2015) \u0026ldquo;zCompositions \u0026ndash; R package for multivariate imputation of left-censored data under a compositional approach.\u0026rdquo; Chemometr Intell Lab Syst 143:85\u0026ndash;96. https://doi.org/10.1016/j.chemolab.2015.02.019\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePlanchuelo G, von Der Lippe M and Kowarik I (2019) Untangling the role of urban ecosystems as habitats for endangered plant species. Landsc Urban Plann 189:320-334. https://doi.org/10.1016/j.landurbplan.2019.05.007\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eProctor E, Nol E, Burke D and Crins WJ (2012) Responses of insect pollinators and understory plants to silviculture in northern hardwood forests. Biodivers Conserv 21:1703-1740. https://doi.org/10.1007/s10531-012-0272-8\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eR Core Team (2024) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRadhamoni HVN, Queenborough SA, Arietta AZA, Suresh HS, Dattaraja HS, Kumar SS, Sukumar, R and Comita LS (2023). Local‐and landscape‐scale drivers of terrestrial herbaceous plant diversity along a tropical rainfall gradient in Western Ghats, India. J\u0026nbsp;Ecol 111(5):1021-1036. https://doi.org/10.1111/1365-2745.14075\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRebele, F. (1994). Urban ecology and special features of urban ecosystems. Glob Ecol Biogeogr Let 4(6):173\u0026ndash;187. https://doi.org/10.2307/2997649\u003c/li\u003e\n \u003cli\u003eRiis T, Kelly-Quinn M, Aguiar FC, Manolaki P, Bruno D, Bejarano MD, Clerici N, Fernandes MR, Franco JC, Pettit N, Portela AP, Tammeorg O, Tammeorg P, Rodr\u0026iacute;guez-Gonz\u0026aacute;lez PM and Dufour S (2020) Global overview of ecosystem services provided by riparian vegetation. BioScience 70(6):501-514. https://doi.org/10.1093/biosci/biaa041\u003c/li\u003e\n \u003cli\u003eRoswell M, Dushoff J and Winfree R (2021) A conceptual guide to measuring species diversity. Oikos 130(3):321-338. https://doi.org/10.1111/oik.07202\u003c/li\u003e\n \u003cli\u003eServicio Meteorol\u0026oacute;gico Nacional (SMN) (2021) \u003cem\u003eNormales climatol\u0026oacute;gicas 1991\u0026ndash;2020. Estaci\u0026oacute;n 31036, M\u0026eacute;rida, Yucat\u0026aacute;n\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e Comisi\u0026oacute;n Nacional del Agua. Recovered from https://smn.conagua.gob.mx/tools/RESOURCES/Normales_Climatologicas/Normales9120/yuc/nor9120_31036.txt\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSilva VHD, Gomes IN, Cardoso JCF, Bosenbecker C, Silva JLS, Cruz-Neto O, Oliveira W, Stewart AB, Lopes AV and Maruyama PK (2023) Diverse urban pollinators and where to find them. Biol. Conserv 281: 110036. https://doi.org/10.1016/j.biocon.2023.110036\u003c/li\u003e\n \u003cli\u003eSkaldina O and Blande JD (2025) Global biases in ecology and conservation research: insight from pollinator studies. Ecol. Lett 28(1):e70050. https://doi.org/10.1111/ele.70050\u003c/li\u003e\n \u003cli\u003eSmith AC, Dahlin KM, Record S, Costanza JK, Wilson AM and Zarnetske PL (2021) The geodiv r package: Tools for calculating gradient surface metrics. Methods Ecol Evol 12(11):2094-2100.\u0026nbsp;https://doi.org/10.1111/2041-210X.13677\u003c/li\u003e\n \u003cli\u003eSponsler D, Iverson A and Steffan‐Dewenter I (2023) Pollinator competition and the structure of floral resources. Ecography 2023(9):e06651. https://doi.org/10.1111/ecog.06651\u003c/li\u003e\n \u003cli\u003eSteffan-Dewenter I and Tscharntke T (2001) Succession of bee communities on fallows. Ecography 24(1):83\u0026ndash;93. https://doi.org/10.1034/j.1600-0587.2001.240110.x\u003c/li\u003e\n \u003cli\u003eStephen KW, Chau KD and Rehan SM (2024) Dietary foundations for pollinators: Nutritional profiling of plants for bee health. Front. Sustain Food Syst 8:1411410. https://doi.org/10.3389/fsufs.2024.1411410\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTavares Brancher KP, Graf LV, Heringer G and Zenni RD (2024) Urbanization and abundance of floral resources affect bee communities in medium‐sized tropical cities. Austral Ecol 49(1):e13299. https://doi.org/10.1111/aec.13299\u003c/li\u003e\n \u003cli\u003eTempl M, Hron K and Filzmoser P (2011) robCompositions: An R-package for robust statistical analysis of compositional data. In: Pawlowsky-Glahn V and Buccianti A (eds) Compositional Data Analysis: Theory and Applications 341\u0026ndash;355 https://doi.org/10.1002/9781119976462.ch25\u003c/li\u003e\n \u003cli\u003eTews J, Brose U, Grimm V, Tielb\u0026ouml;rger K, Wichmann M C, Schwager M and Jeltsch F (2004) Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures. J Biogeogr 31(1) 79-92. https://doi.org/10.1046/j.0305-0270.2003.00994.x\u003c/li\u003e\n \u003cli\u003eTheodorou P (2022) The effects of urbanisation on ecological interactions. Curr Opin Insect Sci 52:100922. https://doi.org/10.1016/j.cois.2022.100922\u003c/li\u003e\n \u003cli\u003eTheodorou P, Herbst SC, Kahnt B, Landaverde-Gonz\u0026aacute;lez P, Baltz LM, Osterman J and Paxton RJ (2020) Urban fragmentation leads to lower floral diversity, with knock-on impacts on bee biodiversity. Sci Rep 10 (1):21756. https://doi.org/10.1038/s41598-020-78736-x\u003c/li\u003e\n \u003cli\u003eTorresani M, Rocchini D, Ceola G, de Vries JPR, Feilhauer H, Moudr\u0026yacute; V, Bartholomeus H, Perrone M, Anderle M, Gamper HA, Chieffallo L, Guatelli E, Gatti RC and Kleijn D (2024) Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach. Sci Rep 14(1):809. https://doi.org/10.1038/s41598-023-50308-9\u003c/li\u003e\n \u003cli\u003eV\u0026auml;h\u0026auml;talo AV, Pulli A, Kulmala T, Marin R and Haimi J (2024) Urbanization related changes in lepidopteran community. Urban Ecosyst 27(2):377-386. https://doi.org/10.1007/s11252-023-01456-3\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eValdiviezo ZI (2014) Merida the golden expansion. A metropolitan reinterpretation and the proposal of a design regeneration strategy. Master Dissertation, Politecnico di Milano.\u003c/li\u003e\n \u003cli\u003eVenjakob C, Klein AM, Ebeling A, Tscharntke T and Scherber C (2016) Plant diversity increases spatio‐temporal niche complementarity in plant‐pollinator interactions. Ecol Evol 6(8):2249-2261. https://doi.org/10.1002/ece3.2026\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eVenter ZS, Barton DN, Chakraborty T, Simensen T and Singh G (2022) Global 10 m land use land cover datasets: A comparison of Dynamic World, WorldCover, and Esri Land Cover. Remote Sens 14(16):4101. https://doi.org/10.3390/rs14164101\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eW\u0026auml;ckers FL, Romeis J and van Rijn P (2007) Nectar and pollen feeding by insect herbivores and implications for multitrophic interactions. Annu Rev Entomol 52(1):301-323. https://doi.org/10.1146/annurev.ento.52.110405.091352\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eXiao Y, Li X, Cao Y and Dong M (2016) The diverse effects of habitat fragmentation on plant\u0026ndash;pollinator interactions. Plant Ecol 217(7):857-868. https://doi.org/10.1007/s11258-016-0608-7\u003c/li\u003e\n \u003cli\u003eXing, S., Leahy, L., Ashton, L. A., Kitching, R. L., Bonebrake, T. C., \u0026amp; Scheffers, B. R. (2023). Ecological patterns and processes in the vertical dimension of terrestrial ecosystems. J Anim Ecol 92(3):538-551. https://doi.org/10.1111/1365-2656.13881\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eXu P, Tsendbazar NE, Herold M, de Bruin S, Koopmans M, Birch T, Carter S, Fritz S, Lesiv M, Mazur E, Pickens A, Potapov P, Stolle F, Tyukavina A, Van De Kerchove R and Zanaga D (2024) Comparative validation of recent 10 m-resolution global land cover maps. Remote Sens Environ 311:114316. https://doi.org/10.1016/j.rse.2024.114316\u003c/li\u003e\n \u003cli\u003eZanaga D, Van De Kerchove R, De Keersmaecker W, Souverijns N, Brockmann C, Quast R, Wevers J, Grosu A, Paccini A, Vergnaud S, Cartus O, Santoro M, Fritz S, Georgieva I, Lesiv M, Carter S, Herold M, Li L, Tsendbazar NE, Ramoino F and Arino, O (2021) ESA WorldCover 10 m 2020 v100. European Space Agency. https://doi.org/10.5281/zenodo.5571936 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"urban-ecosystems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ueco","sideBox":"Learn more about [Urban Ecosystems](https://www.springer.com/journal/11252)","snPcode":"11252","submissionUrl":"https://submission.nature.com/new-submission/11252/3","title":"Urban Ecosystems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"biodiversity, birds, flower visitors, ground cover complexity, impervious surfaces, insects, PCA, vegetation complexity","lastPublishedDoi":"10.21203/rs.3.rs-8329681/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8329681/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrbanization transforms landscapes and alters plant and pollinator biodiversity and their interactions. In tropical cities, where species richness is high and interspecific interactions are numerous, how urban development reshapes plant\u0026ndash;pollinator communities remains not well understood. We studied diurnal pollinator diversity along urban environmental gradients in Merida, Yucatan, a rapidly growing tropical city. Across 14 urban and peri-urban natural areas, we characterized land cover using satellite images and measured vegetation structure complexity, ground cover complexity, and plant diversity through field surveys. Overall, we recorded 302 pollinator species and morphospecies across nine taxonomic orders, observed on 202 flowering plant species, allowing for a comprehensive community composition assessment. Using generalized linear mixed models, we examined the effects of three land cover gradients (impervious surface, vegetation type, water cover) as well as local habitat features (vegetation structure complexity, ground cover complexity, plant diversity) on pollinator diversity measured with Hill numbers (\u003cem\u003eq\u003c/em\u003e). Plant diversity had a strong positive impact on pollinator species richness (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0) and moderate but consistently positive effects on Shannon diversity (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1) and Simpson diversity (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2). Sites with tall woody vegetation showed reduced pollinator diversity, while the impervious surface and water cover gradients, along with vegetation and ground cover complexity, had limited explanatory power. These findings highlight that promoting herbaceous floral diversity, rather than dense woody cover, better supports pollinator communities. Overall, our results emphasize the role of vegetation composition and vertical structure in shaping pollinator diversity in tropical cities.\u003c/p\u003e","manuscriptTitle":"Diversity of pollinator communities along urban environmental gradients in Merida, Yucatan, a tropical city in Mexico","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 10:06:09","doi":"10.21203/rs.3.rs-8329681/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-31T21:09:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154537021687214349423261786399254380956","date":"2026-02-03T15:05:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160637626717733335109982301885128952189","date":"2026-02-03T10:57:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-17T11:51:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-17T04:02:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-17T01:18:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Urban Ecosystems","date":"2025-12-10T16:42:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"urban-ecosystems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ueco","sideBox":"Learn more about [Urban Ecosystems](https://www.springer.com/journal/11252)","snPcode":"11252","submissionUrl":"https://submission.nature.com/new-submission/11252/3","title":"Urban Ecosystems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f6af21ad-f4eb-4783-bf2c-46eacc42e730","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T10:06:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 10:06:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8329681","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8329681","identity":"rs-8329681","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}