Bee community response to multiple stressors along a tropical urban-peri urban gradient

Bee community response to multiple stressors along a tropical urban-peri urban gradient | 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 Bee community response to multiple stressors along a tropical urban-peri urban gradient Aditi Dutta, Indranil Samajpati, Parthiba Basu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4685818/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Sep, 2024 Read the published version in Urban Ecosystems → Version 1 posted 14 You are reading this latest preprint version Abstract Urbanization in tropical landscapes is a complex phenomenon that can lead to community shift rather than simple species extinction in response to multiple stressors in peri-urban and urban settings. We have investigated impacts of different stressors along a tropical urban-peri-urban gradient on the bee community, the health of which is a global conservation concern. Several stressors such as, increased built-up area, pesticide application and air pollution may effectively regulate bee community composition and corresponding functional diversity along urban-peri urban gradients. We investigated the changes in bee community structure in response to associated stressors in 20 locations including parks and gardens along an urban-peri urban gradient surrounding the megacity of Kolkata. Bee community structure differed significantly between urban and peri urban sites with urban sites showing lower value of nestedness. Network analysis also revealed that Apis florea and Lasioglosssum sp. 1 were the most important species in the urban and peri-urban areas respectively. Functional diversity increased with urbanization and decreased with pesticide toxicity. Functional redundancy decreased with urbanization. Individual stressor impacted the bee assemblage differentially along the urbanization gradient. SO 2 and pesticide toxicity negatively influenced bee abundance and diversity. Urban sites sustained more specialized species and therefore are more vulnerable to shocks while peri-urban sites had a more functionally redundant community making it comparatively more resilient. Bee community urbanization stressors functional diversity air pollution pesticide toxicity Figures Figure 1 Figure 2 Introduction The detrimental effect of urbanization on biodiversity has been extensively studied over the last decade (Fortel et al., 2014 , Cardoso and Goncalves, 2018). Urbanization homogenizes the rural landscape, leading to biotic loss and a subsequent reduction in essential ecosystem services (Elmqvist et al., 2013 ; Lokatis and Jeschke, 2022 ).Studying biodiversity loss due to urbanization along rural/peri-urban – urban gradient is a much-used approach, and numerous studies focusing on urbanization gradients have utilized this method to quantify the extent of species loss or changes in the community structure (Castro et al., 2020 ; Birdshire et al., 2020 ; Meyer et al., 2021 ; Marcacci et al., 2022 ). Earlier studies have indicated that a multitude of stressors impact biodiversity along rural - urban gradients (McKinney, 2008 ). Understanding the synergistic interaction between the multiple stressors is crucial, as it may lead to accelerated biodiversity loss (Côté et al., 2016 ; Beaumelle et al., 2021 ). Even more, investigating stressors that affect biodiversity in rural or peri-urban areas is crucial since they differ from those in urban or metropolitan areas (Theodorou et al., 2020 ). The major stressors of rural and peri-urban areas are factors associated with intensive agriculture (Tscharntke et al., 2005 ), unsustainable harvesting practices (Simon, 2008 ), landscape fragmentation, mining and industrialization (Sahoo et al., 2021 ). In contrast, urban areas face different sets of stressors such as habitat destruction, air and water pollution, increases in impervious surface, and introduction of non-native invasive species (Parnell et al., 2013 ). Although good number of studies are available that looked into individual rural/peri-urban or urban stressors, more comprehensive studies that elucidate the differential responses of communities to these stressors together along the urbanization gradient are sparse (McKinney, 2008 ; Beaumelle et al., 2021 ). Additionally, more work is needed to understand how different stressors impact such biodiversity changes along the rural-urban gradient in different socio-ecological setting across various global regions. The scenario could potentially be different in tropical developing countries where the nature of urban-peri-urban gradient may be different from that in a western economically developed country. Studies by Chamberlain et al. ( 2016 ) and Lopez et al. ( 2018 ) emphasized the need to consider biodiversity trends along urban gradients in tropical regions due to high rates of urbanization and existence of significant biodiversity hotspots in developing tropical countries. Sharip & Noor ( 2021 ) highlights the importance of sustainable financing for biodiversity conservation in urban and peri-urban areas, particularly in tropical regions. The rural agricultural landscape in the developing countries are increasingly coming under agricultural intensification (Hernández et al., 2016; Goulart et al., 2023 ) or under industrialization of different scales, generally small and medium-scale industries (Wu et al., 2023 ). In tropical countries there is diverse patterns and degrees of urbanization which unlike more economically developed Western countries generally lack effective urban planning and environmental protection, resulting in a more heterogenous landscape (Gebreselassie et al., 2022 ; Bajaru et al., 2020 ). Therefore, it is important to investigate urban- peri-urban gradient in tropical developing countries and how biodiversity changes along this gradient. Pollinator decline is a global threat to the ecosystem affecting its stability and sustainability (Biesmeijer et al., 2006 ). The pollinators are threatened due to various factors such as loss of natural vegetation, habitat loss, increase in built up areas (Alberti, 2005 ) and environmental pollution. Increased pollution directly affects pollinator health (Reitmayer et al., 2019 ) and foraging ability (Fuentes et al., 2016 ; Girling et al., 2013 ; McFrederick et al., 2008 ). Air pollution has one of the most ubiquitous impacts on global decline of pollinators (Duque and Steffan-Dewenter, 2024 ). As the use of motor vehicles is increasing worldwide and especially in the developing nations where pollution is less monitored, air pollutants such as coal smoke, CO 2 , NO X and SO 2 and particulate matter (PM) are increasing (Amoatey et al., 2018 ). On the other hand, the peri-urban areas in the tropical countries are increasingly affected by air pollution due to unregulated vehicular fuel use and emissions from the expanding medium and small industries (UNEP, 1999 ). These pollutants accumulate in the different body parts and cause distress for the pollinators (Papa et al., 2021 ; Thimmegowda et al., 2020 ). Bees act as the major pollinators of pollination dependent crops and wild plants globally (Rader et al., 2015 ; Klatt et al., 2014 ). Studies have indicated that increased urbanization results in decreased bee abundance, richness and diversity (Fauviau et al., 2022 ; Birdshire et al., 2020 ; Egerer et al., 2019 ). In urban areas habitat loss and air pollution is assumed to be a major cause of bee diversity loss (Schueller et al., 2023 ; Feldhaar and Otti, 2020 ). Urbanization reduces the foraging capacity and nesting opportunity for bees as natural flower patches and non-crop plants decrease while impervious surface area increases (Karnchananiyom et al., 2023 ). Similarly in the rural agricultural systems, bees are often threatened by other stressors such as simplification of landscape (increased monoculture) and intensified pesticide use, which reduce food availability and nesting opportunities for bees (Potts et al., 2010 ; Heard et al., 2017 ). Pesticide acts as a major factor of pollinator loss in peri-urban or rural agricultural systems (Fischer, 2023 ). In addition, air pollution may be a key factor that may negatively influence the bee community in peri-urban areas. Although the impact of industrial activities on air quality has been traditionally associated with urban and industrial regions, studies have shown that peri-urban areas are also affected by air pollution due to long-range transport of pollutants (Callén et al., 2011 ; Tecer et al., 2017 ). This transport of pollutants to rural and peri-urban areas can lead to increased levels of particulate matter and other harmful substances, impacting the health and well-being of pollinators in these regions (Thimmegowda et al., 2020 ). The use of solid fuels for cooking and heating in rural households has also been identified as a significant source of indoor air pollution, further exacerbating the overall pollution levels in rural areas (Du et al., 2021 ; Krishnamoorty et al., 2018). Therefore, these multitudes of stressors along the urban- peri-urban gradient are capable of shaping the communities of bees in these two regions. Despite the growing evidence that urbanization negatively affects bee population, sometimes the scenario can be completely different. Urban areas may act as refuge for the bee community in cases where peri-urban stressors such as pesticide, unregulated industrialization, rapid habitat destruction, landscape homogenization are more detrimental to the bee community (Millard et al., 2021 ; Wenzel et al., 2020 ). Cities often harbor a greater diversity of flowering plants than peri-urban landscapes due to the presence of non-native, cultivated and introduced (e.g. ornamental and exotic) plant species (Lowenstein et al., 2019 ; Seitz et al., 2022 ). The plant communities within such patches depend on pollinators for their reproduction. Despite the harmful effects of urbanization on the habitat and ecosystems, green spaces within urban areas play a significant role in preserving bee diversity (Matteson et al., 2008 ) compared to the agricultural or natural ecosystems (Williams and Kremen, 2007 ) Urbanization in India is rapidly increasing, with an estimated urban population of 40 percent by 2030 (Ali, 2020 ). This rapid urbanization is leading to large scale deforestation (Mondal, 2023 ), increased air pollution (Anwar et al., 2021 ), increased impervious layer (Chen et al., 2021 ) and introduction of non-native invasive species (Jain et al., 2021 ). All these factors can escalate the loss of pollinators in Indian urban areas (Wenzel et al., 2020 ). From a peri-urban perspective, Indian agriculture has undergone rapid intensification throughout the years which creates challenges for the local biodiversity. One of the major threats is unregulated pesticide usage by farmers that may directly kill or impair essential biodiversity components (Basu et al., 2016 ). Atmospheric pollution, although considered an urban issue is also rapidly increasing in rural India. The major sources of air pollution are residential and commercial biomass incineration, mineral aerosol (from mining activities), Coal based electricity production, industrial emissions, straw and Agri waste burning, construction, brick factory and diesel-powered vehicles and generators (Pathak and Kuttippurath, 2024 ). In this paper we investigate how different stressors along a tropical urban- peri-urban gradient in and around Kolkata Megacity in India impact the bee diversity. We ask- How the wild bee community and functional assemblage change along the urbanization gradient in a tropical landscape? What is the role of urban and peri-urban stressors in structuring the wild bee community along the urbanization gradient? Methodology Study area The study was conducted in the metropolitan city of Kolkata and surrounding suburbs in West Bengal, India. Kolkata is one of the oldest metropolitan cities in eastern part of India over a large alluvial plain. It is situated over the moribund delta of the Hooghly River at an elevation of 1.5 to 9 m from mean sea level. Peri-urban areas in the Kolkata metropolitan area incorporate parts of the following districts: North 24 Parganas, South 24 Parganas and Howrah. Site selection We selected 20 sampling sites; mainly parks and gardens extending from urban Kolkata to the neighboring peri-urban area, thereby forming an urban- peri-urban gradient extending north and south of the city. We maintained a minimum distance of 1 km between the sites, so they remain independent of each other. Within the parks and gardens, we mainly focused our sampling effort on the wild plants. Wild plant species within the study sites grew completely naturally, but to some extents were exposed to disturbance by human activities Calculation of urbanization We quantified urbanization intensity on the basis of the percentage of built-up area (also termed as impervious layer: i.e., all sealed surfaces, such as roads, buildings, etc.) (Geslin et al., 2016 ; Marcacci et al., 2022 ). We calculated the percentage of urban area, natural and semi natural vegetation around each site at 1000 m by using LULC maps from sentinel 2 satellite images (ESRI, Inc., Redlands, CA, USA) by supervised classification method in Arc-GIS software version 10.8.1. Following the initial classification, the 20 sampling sites were separated into 10 urban and 10 peri urban sites based on CCA clustering of the sampling sites with different urbanization parameters as explanatory variables (built-up area, pesticide toxicity and plant diversity). The peri urban and urban sites showed significant clustering difference in the CCA biplot (Fig. 1 ). Calculation of pesticide toxicity We obtained the pesticide data by surveying the care takers and the gardeners of the parks and gardens. The records contained the application rate and date of pesticides applied throughout the sampling period, including all herbicides, fungicides, and insecticides. We calculated the pesticide toxicity score for each field by using Environmental Impact Quotient (EIQ) Field Use Rating formula (see below), this included both the pesticide toxicity of bees and the quantity of active ingredient applied in the field (Kovach et al., 1992 ). To calculate the toxicity scores of the active ingredients, we multiplied the application rate (ml/m2) by the percent of the active ingredient for each pesticide and then multiplied this amount by the ‘‘bee-toxicity value’’ for the active ingredient following the 2010 EIQ database of Eshenaur et al. ( 2010 ). The bee-toxicity value for individual pesticide active ingredients was determined by multiplying acute toxicity of the ingredient to bees on a scale of 1–5 [1 represent relatively nontoxic (LD50 > 100 µg/bee), 3 represents moderately toxic (LD50 = 2–10.99 µg/bee), 5 represents highly toxic (LD50 < 2 µg/bee) (Mallinger et al., 2015 ; Kovach et al., 1992 ). To obtain the total toxicity score of each garden/park, the pesticide survey data taken during the whole sampling period was cumulated using the following formula (Mallinger et al.,2015). $$\:\text{T}\text{o}\text{x}\text{i}\text{c}\text{i}\text{t}\text{y}\:\text{S}\text{c}\text{o}\text{r}\text{e}=\:\sum\:\left[\text{R}\text{a}\text{t}\text{e}\:\right(\text{m}\text{l}/{\text{m}}^{2})\:\times\:\:\text{P}\text{e}\text{r}\text{c}\text{e}\text{n}\text{t}\:\text{a}\text{c}\text{t}\text{i}\text{v}\text{e}\:\text{i}\text{n}\text{g}\text{r}\text{e}\text{d}\text{i}\text{e}\text{n}\text{t}\:\times\:\:\text{E}\text{I}\text{Q}\:\text{b}\text{e}\text{e}\:\text{t}\text{o}\text{x}\text{i}\text{c}\text{i}\text{t}\text{y}\:\text{v}\text{a}\text{l}\text{u}\text{e}]$$ Air pollution parameters The air pollution parameters (NO 2 , SO 2 , and PM10) were obtained from West Bengal Pollution Control Board (. The data was collected 3 times during the sampling period and the average value calculated for further analysis. Plant diversity measurement Abundance and diversity of all non-crop plants were recorded from all the 20 sites during the sampling period. 10 quadrates were randomly selected in each of the 20 sites three times during the study period and the number of plants were noted. The data was pooled for the three sampling bouts. Shannon diversity index was calculated. Bee collection Sampling was carried out from April 2019 to February 2020 on three different sampling rounds at each of the 20 sampling locations (One round of sampling in all 20 sites in each sampling bout, a total of 60 days of sampling). Passive sampling and active sampling were used to quantify the bee community at each of our gardens. In each sampling round, a "pan trap station" was set up at all the sampling sites for passive sampling purposes. We followed Cane et al., 2000 and placed the pan traps of different colours at the same height as the floral resources to minimize the sampling bias. Although pan trapping does not capture all bee species during the flowering period, it has been reported as an efficient method that can provide insight into bee diversity that is otherwise unobtainable (Cane et al., 2000 ). A cluster of five traps (each “trap” comprising of three bowls, one each of white, blue, and yellow painted with UV reflective paints) (BOSNY paint) were set up within a randomly chosen 10m ×10m wild floral patch. Traps were filled with water and approximately 5 mg of washing powder was added to reduce the surface tension. Traps were left open for 24 hr. A total of 15 bowls were placed at each of the 20 sampling locations in every sampling bout. For active sampling we established a 10m × 2m transect in each park or garden. Sweep net method was used to capture the bee population along the transect. 10 min of sweeping was done by a single collector every hour at 1 hour interval from 7am to 2pm.The insects collected were at first rinsed with distilled water and then preserved in 70% alcohol in the field. In the laboratory, the bee specimens were dried, pinned and identified to species or morphospecies level using taxonomic keys following Michener ( 2000 ). Data from the three different s was pooled for each site for further analysis. Bee functional trait estimation For all species, we compiled information on their life history traits. We also recorded body size, dietary specialization, nesting location, nest construction behaviour, sociality, hairiness (density of hair because it affects pollen grain deposition on stigmas) (Stavert et al., 2016) and glossa length (associated with a range of floral types that bees can access; mean of pollen uptake and transportation, both related to the versatility of interaction with pollen grains; and specialty in obtaining some resource (Michener, 2000 ; Martins et al., 2015 ). Body size was the quantitative functional attribute measured, and for this, we used the mean inter-tegular distance of the specimens collected (Greenleaf et al., 2007 ). For each species, at least five individuals were measured for the functional traits. For species of which we had fewer than five individuals, we used the number of individuals available in our dataset. Information on the categorical functional attributes was obtained from the extensive survey of the specialized literature. The species for which the trait category was not assigned were excluded from the analysis. The traits used in our analysis are listed in Table .1. Statistical analysis To classify the 20 sampling sites into urban or peri-urban categories, we utilized Canonical Correspondence Analysis (CCA) of the study sites. Network analysis To understand the bee community compositional difference between urban and peri-urban sites we developed “Bee-site networks” of urban and peri -urban cluster separately. The network analysis was done with the help of “bipartite” package in R. Different “Bee-site” network parameters such as NODF, connectance, weighted NODF, specialization index (H’) and modularity was calculated with the help of the command “networklevel “within the “bipartite” package. For identifying key bee species in urban and per-urban cluster “specieslevel” command within the same package was utilized. We calculated Normalized Degree (ND), Betweenness Centrality (BC) and Specialization index for species. Normalized Degree (ND) measures the number of connections a node (species) is connected with normalized by the total possible number of connections. It indicates the connectance of a species within the network. Betweenness Centrality (BC) metric measures the frequency with which a node (species) lies on the shortest path between other nodes. It reflects the species' role in facilitating interactions or connections between other species. Specialization index measures whether the interactions are specialized or generalized within the network. Functional diversity calculation To obtain additional information regarding bee Functional diversity (FD), we used Functional Dispersion indices (FDis) that reflect the important properties of this diversity (Hooper et al., 2002 ). The functional dispersion quantifies the mean distance of each species from its community centroid in a multivariate space defined by all included traits (Laliberté and Legendre, 2010 ). To calculate functional dispersion (FDis), we used the function dbFD in package FD, in R version 4.3.3 (2024) with the Cailliez correction for non- Euclidean distances generated by the inclusion of categorical traits (Laliberté et al., 2014 ). To understand the association between urbanization and functional diversity indices we used GLMs with gaussian error distribution and “log” link function. To see if there is significant difference in FD indices (redundancy, functional dispersion, etc.) between urban and peri-urban sites Mannn-Whitney U test was performed. To measure the effect of urbanization on bee species assemblage (abundance and diversity) we used generalized linear models (GLM). In cases where response variable was total bee abundance, bee family abundance and bee richness we utilized GLM models with negative binomial error distribution and “log” link function. The predictor variables were concentration of SO 2 in air, percentage of agriculture within 1km radius, pesticide toxicity and percentage built-up area within 1km radius of each site. To model the relationship between bee diversity indices and explanatory variables linear models (lm) were used. The linear models were tested for normality of residuals and homogeneity of variance by Shapiro-wilk test and Levene’s test respectively prior to final selection. To elucidate the difference of bee assemblage between the urban and peri urban sites we performed the Mann-Whitney U test or Students “t” test taking different bee family or subfamily abundance data as the focal variables. All statistical analysis was performed in R version 4.3.3 (R Core team, 2024) with the help of packages “car” (Fox and Weisberg, 2019 ), “MASS” (Venable and Ripley ,2002), DHARMa (Hartig, 2018 ), FD (Laliberté, E., Legendre, P. & Shipley, 2014), and “bipartite” (Dormann et al. 2008). Results A total of 742 bee specimens were found in this study out of which 611 bees were collected from passive collection and the rest were collected by active collection. 22 genera and 46 species were observed in the 2019–2020 sampling session across 20 urban and peri-urban sites across Kolkata, West Bengal. All bees were identified up to the morphospecies level. Among the bees collected, the most abundant families were Halictidae (47.57%), followed by Apidae (43.53%) and Megachilidae (8.9%). The Urban – Peri-urban Gradient The CCA analysis revealed that the three selected explanatory variables (Built up area, pesticide toxicity and plant diversity) explained at least 20% of the total variation each. The first two canonical axis explained 92.82% of the total variation. There was two different cluster of sites observed in the CCA scatter plot based on which the peri urban and urban sites could be separated (Fig. 1 ) The urban and peri-urban clusters were significantly different (t = 4.896, p = .00058) based on percentage of built-up area. Site-Bee community network The peri-urban sites and bee community network showed higher degree of nestedness (NODF = 50.514, weighted NODF = 26.716) whereas the urban sites and bee community network showed lower degree of nestedness (NODF = 40.550, weighted NODF = 19.869) (Fig. 2 ) The network parameters of the site-species matrix of urban and peri urban areas showed that the Normalized Degree (ND) is highest in Apis florea , in urban area whereas in peri urban region the ND is more in Lasioglossum sp.1. The specialization index is highest in Amegilla sp. 5 and Amegilla sp. 6 in the urban regions and Amegilla sp. 4 had the highest specialization index in peri urban regions. Betweenness centrality value is different in the two regions. In the urban region Braunsapis puangensis has the highest betweenness centrality (BC) value whereas in peri urban region Lasioglossum sp. 1 has the highest BC values. All the network parameter values are listed in Table 2 . Table 1 Traits used in analyses for measuring functional dispersion of bee community. Trait Categories Source Area of scopa NA Direct measurement Inter-tegular distance NA Direct measurement Mouthpart length (mm) NA Direct measurement Means of pollen transport (1) Corbiculae; (2) Ventral scopa; (3) Scopa (hind trochater- basitursus); (4) Scopa (tibia and basitarsus) Michener 2000 , Viana and Kleinert 2005 Means of pollen uptake (1) First leg; (2) Vibration;(3) First leg and Mandible; or (4) First leg and Vibration Michener 2000 , Viana and Kleinert 2006 Buzz pollination buzz/non buzz Michener 2000 , Viana and Kleinert 2007 Peak activity time (1) within 0–3 hours after sunrise; (2) within 3–6 hours after sunrise Michener 2000 , Viana and Kleinert 2008 Dietary specialization oligolectic/polylectic Michener 2000 , Viana and Kleinert 2009 Sociality solitary/social/parasitic Michener 2000 , Viana and Kleinert 2010 Nesting location nesting above ground/ below ground/mixed Michener 2000 , Viana and Kleinert 2011 Nest construction excavate/rent/cleptoparasitic Michener 2000 , Viana and Kleinert 2012 Hairiness (density of hair) (dense / sparse) Michener 2000 , Viana and Kleinert 2013 Table 2 Parameters of bee-site network matrix in urban peri-urban region. Cluster Species Parameter Value Peri-urban Lassiglousm sp.1 Normalised degree 1 Peri-urban Amegilla sp.4 Specialization index 1.82 Peri-urban Lasioglossum sp.1 Betweenness centrality 0.138 Urban Apis florea Normalised degree 0.9 Urban Amegilla sp.5 Specialization index 1.794118 Urban Amegilla sp.6 Specialization index 1.794118 Urban Braunsapispuangensis Betweenness centrality 0.175 Functional dispersion Functional dispersion measures the average distance between species based on their functional attributes and relative abundance in a multidimensional space. Thus, functional dispersion shows the diversity of role the species play in an ecosystem. Functional dispersion (Fdis) increased significantly with an increase in percentage of built-up area (p = 0.0206) (gaussian log). Functional dispersion (Fdis) differed significantly between urban and peri-urban regions (p = 0.005, u = 13, Z score = 2.759). Functional redundancy decreased with increase in percentage of built-up area (p = 0.015, Table 3 ) and increased with pesticide toxicity (p = 0.034, Table 3 ). The specialist species are dominated by solitary bees because there is a significant difference between of sociality between the urban and peri-urban regions (p = 0.001, Z score = 3.288 and u value = 6). Table 3 Results of generalized linear models performed. Predictor Response Error structure Chi-sqaure Estimate df P value Pesticide toxicity Total bee abundance Negative binomial 4.16 -0.001 18 0.03 Pesticide toxicity Bee richness Negative binomial 4.861 -0.001 18 0.037 Percentage of agriculture Megachilidae Negative binomial 7.01 -0.065 18 0.015 Pesticide toxicity Apidae abundance Negative binomial 4.752 -0.002 18 0.025 SO 2 concentration Halictidae abundance Negative binomial 4.853 0.1 18 0.027 Percentage of built-up Apidae abundance Negative binomial 3.171 0.01 18 0.056 Percentage of built-up Functional dispersion (Fdis) Gaussian 0.012 0.006 18 0.02 Percentage of built-up Functional redundancy Gaussian 0.018 -0.0016 18 0.015 Pesticide toxicity Functional redundancy Gaussian 0.014 2.25E-04 18 0.0348 Abundance and diversity along the urban-peri-urban gradient: The total bee abundance did not differ significantly between urban and peri-urban sites (mean urban = 30.7, SD = 16.6403 and mean peri-urban = 43.5, SD = 31.334). Bee diversity also did not differ between urban and peri-urban regions. However, Mann -Whitney U test showed that subfamily Apinae (z = − 2.45, u = 17, p = 0.013) and genus Apis (z = 2.683, U = 14, p = 0.007) abundance differed significantly between urban and peri-urban sites with Urban (Mean = 8.4, SD = 6.11) areas containing more Apinae than peri urban (Mean = 3.4, SD = 2.63) and Apis abundance peri urban (Mean = 2.4, SD = 1.77) and urban (Mean = 7.6, SD = 6.38). Although plant diversity proved to be an important factor in our CCA scatter plot but it did not yield any significant difference with bee abundance and diversity. Effect of urban and peri-urban stressors on bee community along the urbanization gradient The total bee abundance (p = 0.03, Table 3 ) and bee richness decreased (p = 0.037, Table 3 ) significantly with the increase in pesticide toxicity. Bee diversity (linear model, F = 4.564, df = 18, p = 0.046) and evenness (linear model, F = 4.841, df = 18, p = 0.041) decreased significantly with the increase in SO 2 concentration. NO 2 and PM10 did not show any significant difference with bee abundance and diversity. When we grouped the bee species according to their families, we found that the Megachilidae abundance decreased with the increase in the percentage of agricultural area (p = 0.015, Table 3 ) and Apidae abundance (p = 0.025, Table 3 ) decreased with the increase in pesticide toxicity. Halictidae abundance (p = 0.027, Table 3 ) increased with the increase in SO 2 concentration whereas Apidae abundance (p = 0.056, Table 3 ) increased with the increase in the percentage of built-up area. Discussion Our study focused on the bee species assemblage along an urban - peri-urban gradient around the megacity of Kolkata and highlighted how different stressors (both urban and peri-urban) influenced bee community structure. The site-by-bee network analysis revealed distinct differences between urban and peri-urban clusters, with urban sites containing more specialized bee species and lower nestedness value compared to the peri-urban sites. On the other hand, generalist species were more predominant in the peri-urban sites in our study with a higher value of nestedness. This difference of nestedness suggests that urban environments may support certain species that can adapt well to higher levels of urbanization and associated stressors. Specialist bee species are becoming more prevalent in urban areas possibly due to niche differentiation and resource partitioning (Fortel et al., 2014 ) We observed that different bee species were important in the two separate clusters of urban and peri-urban sites, indicating distinct community composition along the urbanization gradient. For example, Apis florea had the highest normalized degree ( Apis florea interacted with the highest number of different sites compared to other bee species) in urban bee-site network. Apis florea is likely to be present in all the urban sites and hence may act as an ecological generalist capable of foraging and nesting in a wide variety of urban environments. On the other hand, Lassioglossum sp. 1 had the highest normalized degree in the peri-urban-site network. The betweenness centrality (BC) values showed that the Braunsapis puangensis and Apis florea exhibited the highest BC values, indicating these species are crucial for maintaining connectivity and stability of urban sites. Whereas, in the peri-urban areas, Lasioglossum sp. 1 and Ceratina cognata showed the highest (BC) values indicating their importance in maintaining a stable community of bees. These findings further consolidate the varying ecological dynamics of bee communities across different levels of urbanization. Our findings suggest that urban environments might be better suited for certain bee species compared to peri-urban areas. The functional dispersion (Fdis) value of bee community differed significantly between the urban (Mean = 0.19, SD = 0.04) and peri-urban (Mean = 0.13, SD = 0.04) cluster with urban areas showing a greater level of functional dispersion. Additionally, functional dispersion increased with the percentage of built-up area (an indicator of urbanization) in the landscape. Functional dispersion measures the distribution and variety of functional traits within a community. The significant increase of FDis with urbanization implies that cities support bees with a diverse array of functional traits which may be due to heterogeneous nature of urban habitats providing a variety of niches and resources for a diverse range of species. A reduced functional dispersion of bees in the per-urban area shows that they are becoming more functionally similar or fewer species are contributing to certain ecological functions. There may be several factors influencing the unique bee assemblage in urban areas such as diverse habitat patches (Ayers and Rehan, 2021 ), nutritional stability (Bhatta and Kumar, 2021 ), refuge from rural and peri-urban stressors (Prendergast et al., 2022 ), exotic floral resources (Wilson and Jamieson, 2019 ) and nesting opportunity (Banaszak-Cibicka and Żmihorski, 2020 ). Some studies have also indicated that urban landscape may sustain a greater abundance and diversity of bees compared to rural or natural landscape, adding to the favorability of cities as an alternative habitat for pollinators (Hall et al., 2017). A higher amount of habitat diversity in urban areas can sustain different groups of bees with their diverse nesting and feeding requirements (Aguirre- Gutiérrez et al., 2015; Boscolo et al., 2017 ; Nery et al., 2018 ). While the bee community was distinct in the urban cluster, functional redundancy analysis showed that urban bees may be more vulnerable (because of the uniqueness of the community) to environmental and anthropogenic stressors since redundancy decreased with built-up area – an indicator of urbanization. On the contrary, functional redundancy increased with pesticide toxicity. Pesticide toxicity values in our study sites were higher in peri-urban sites with greater degree of agricultural activity. The reduced functional dispersion coupled with increasing redundancy indicates that pesticide may be acting as a major stressor that reduces functional divergence and acts as an environmental filter to the bee assemblage. The selection pressure imposed by pesticide results in a community with functionally redundant species with overlapping traits. The highly specialized bee species may be filtered out which leads to a more homogenized bee community. The prevalence of specialized species in urban areas emphasize the necessity of conserving the bee community along with necessary resources as a sudden alteration in the environment may lead to community collapse. Effect of Individual stressor on bee community A multitude of stressors in our study system may impose limits to the bee community. These environmental stressors such as built -up area (increase in impervious layer), air quality (AQI), SO 2 pollution and pesticide toxicity varied along urban- peri-urban gradient and showed differential impact on bee species or family. Pesticide toxicity being the most notable stressor which negatively affects the bee community by affecting its abundance and richness. A number of studies have already established the role of pesticide in regulating bee assemblage, with most indicating decline of abundance, richness, foraging activity and nesting behavior (Raine and Rundlöf, 2024 ; Nicholson, 2023; Schaad et al., 2023 ; Raine, 2018 ). Bee diversity and evenness decreased with higher SO 2 concentrations in the air. Air pollution has already been established as a critical stressor that may hinder the sustenance of heathy pollinator community and subsequently reduce ecosystem services (Thimmegowda et al., 2020 ). SO 2 pollution is mainly due to vehicular pollution and from coal-fired industries in the peri urban areas (Srirattana and Piaowan, 2020 ; Hu et al., 2020 ). From our result it is evident that SO 2 pollution from unregulated vehicular exhaustion and industrial sources may be a critical factor that regulates the assemblage of bee community along the urbanization gradient and specially affecting peri-urban bees. This higher SO 2 concentration threatens bee health by limiting olfactory signal pathways (Ibrahim and Devi, 2022 ). Thus, SO 2 along with pesticide toxicity plays a detrimental role in structuring bee communities in peri-urban areas. With regards to the taxonomic families of bee, Megachilidae abundance decreased with increase in agricultural area. This again supports our previous results that the peri urban environmental stressors are harmful to certain groups of bees and suggests that agricultural intensification, landscape homogenization, and destruction of suitable nesting habitats makes it difficult for Megachilidae to survive in such areas (McCabe et al., 2023 ; Abudulai et al., 2022 ). On the other hand, Halictidae abundance increased significantly with an increase in SO 2 concentration. This result suggests that although SO 2 concentration is higher in peri-urban areas, the presence of more bear ground, open areas and fallow land maybe suitable for ground nesting bees compared to cities where impervious layer is higher (Brancher et al., 2022). Apidae abundance increased with the percentage of built-up area which further supports that urban environments may provide suitable nesting habitats for certain bee families which are mostly above ground nesting. In urban areas, ground-nesting bees cannot thrive due to the presence of impervious surfaces. However, Above- ground nesting bees may be favored in more heterogeneous cities due to numerous nesting opportunities, such as holes in building walls, vacant lots, garden fences and tree hollows present in impervious and vegetated areas (Cane et al., 2006; Geslin et al., 2016 ; Pardee & Philpott, 2014 ). In conclusion, our study demonstrates that urban and peri-urban bee communities exhibit distinct network parameters and responses to environmental stressors. Urban areas appear to support more specialized and functionally diverse bee communities, although these communities might be more susceptible to environmental changes due to lower functional redundancy. Peri-urban areas, while less specialized, maintain higher functional redundancy, potentially offering greater resilience to certain stressors. These findings emphasize the need for tailored conservation strategies that consider the unique characteristics and challenges of urban and peri-urban environments to support and sustain healthy bee populations. More studies should evaluate the conservation value of urban green spaces such as public parks, gardens, rooftop gardens and undisturbed vegetation fragments as a refuge for native bee species. In addition, the importance of stressors in modulating the bee community assemblage should be kept in mind while planning for tropical megacities. Declarations Conflicts of interest The authors declare that there are no conflicts of interest. Informed consent All authors have reviewed and endorsed the submitted manuscript. Author Contribution AD designed the study, conducted field work, analyzed data and wrote the manuscript; IS analyzed the data, wrote the manuscript; PB conceived and designed the study, contributed in analysis and wrote the manuscript. Acknowledgement This work was supported by University Research Fellowship (URF) to the first author. We convey our heartfelt thanks to the Urban Recreation Forestry Division, Mudialy Fishermen Co Operative Society- Kolkata, MSME tool room, Kolkata, Caretakers of the different parks and gardens for allowing us to work unconditionally. We also thank Anirban Chakraborty for his help in analysis. 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Proceedings of the National Academy of Sciences, 117 (34), 20653-20661. https://doi.org/10.1073/pnas.2001489117 Ticktin, T. (2004). The ecological implications of harvesting non-timber forest products. Journal of Applied Ecology, 41 (1), 11-21. https://doi.org/10.1111/j.1365-2664.2004.00876.x Tilman, D., Fargione, J., Wolff, B., D'Antonio, C., Dobson, A., Howarth, R., ... & Swackhamer, D. (2001). Forecasting agriculturally driven global environmental change. Science, 292 (5515), 281-284. https://doi.org/10.1126/science.1057544 Tscharntke, T., Klein, A. M., Kruess, A., Steffan-Dewenter, I., & Thies, C. (2005). Landscape perspectives on agricultural intensification and biodiversity – Ecosystem service management. Ecology Letters, 8 (8), 857-874. https://doi.org/10.1111/j.1461-0248.2005.00782.x UNEP. (1999). Global Environment Outlook . Earthscan. Venables, W. N., & Ripley, B. D. (2002). Modern Applied Statistics with S . Springer. Wenzel, A., Grass, I., Belavadi, V. V., & Tscharntke, T. (2020). How urbanization is driving pollinator diversity and pollination – A systematic review. Biological Conservation, 241 , 108321. https://doi.org/10.1016/j.biocon.2019.108321 Williams, N. M., & Kremen, C. (2007). Resource distributions among habitats determine solitary bee offspring production in a mosaic landscape. Ecological Applications, 17 (3), 910-921. https://doi.org/10.1890/05-1839 Wilson, C. J., & Jamieson, M. A. (2019). The effects of urbanization on bee communities depends on floral resource availability and bee functional traits. PloS One, 14 (12), e0225852. https://doi.org/10.1371/journal.pone.0225852 Wu, K., Kong, D., & Yang, X. (2023). The impact of rural industrial development on farmers’ livelihoods – Taking fruit-producing area as an example. Land, 12 (8), 1478. https://doi.org/10.3390/land12081478 Zermeño-Hernández, I., Pingarroni, A., & Martínez-Ramos, M. (2016). Agricultural land-use diversity and forest regeneration potential in human-modified tropical landscapes. Agriculture, Ecosystems & Environment, 230 , 210-220. https://doi.org/10.1016/j.agee.2016.05.018 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Sep, 2024 Read the published version in Urban Ecosystems → Version 1 posted Editorial decision: Revision requested 31 Jul, 2024 Reviews received at journal 31 Jul, 2024 Reviews received at journal 31 Jul, 2024 Reviews received at journal 26 Jul, 2024 Reviewers agreed at journal 25 Jul, 2024 Reviewers agreed at journal 25 Jul, 2024 Reviewers agreed at journal 24 Jul, 2024 Reviewers agreed at journal 22 Jul, 2024 Reviewers agreed at journal 18 Jul, 2024 Reviewers agreed at journal 10 Jul, 2024 Reviewers invited by journal 07 Jul, 2024 Editor assigned by journal 05 Jul, 2024 Submission checks completed at journal 05 Jul, 2024 First submitted to journal 04 Jul, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4685818","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":332843516,"identity":"95f3d5fe-6d5f-4667-ad5e-df3dffb4c73c","order_by":0,"name":"Aditi Dutta","email":"","orcid":"","institution":"University of Calcutta","correspondingAuthor":false,"prefix":"","firstName":"Aditi","middleName":"","lastName":"Dutta","suffix":""},{"id":332843517,"identity":"1c5f699f-f0fc-4433-b57e-5223eeb955b7","order_by":1,"name":"Indranil Samajpati","email":"","orcid":"","institution":"University of Calcutta","correspondingAuthor":false,"prefix":"","firstName":"Indranil","middleName":"","lastName":"Samajpati","suffix":""},{"id":332843518,"identity":"fc2fcb3e-8add-49e5-8233-56be88c845ef","order_by":2,"name":"Parthiba Basu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACAxDB2CCRwA9iJBQQq+UgUItkA0iLAfFagIoPwLkEgLl087PPH3dY5BmfX5344YEBgzy/2AH8WiznHDOecfCMRLHZjbebJYAOM5w5O4GAw24kGDMcbJNI3Hbj7AaQlgSD2wS1pH8Ga9k84+zmH0RqyYHYsoG/dxtxtljOOVPMcLZNoljiBu82iwQDCcJ+MZdu38xQ2VaXx99/dvPNHxU28vzSBLQwSMAZCShcYrTwHyBC9SgYBaNgFIxIAABA6klFIZt+1wAAAABJRU5ErkJggg==","orcid":"","institution":"University of Calcutta","correspondingAuthor":true,"prefix":"","firstName":"Parthiba","middleName":"","lastName":"Basu","suffix":""}],"badges":[],"createdAt":"2024-07-04 10:21:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4685818/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4685818/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11252-024-01609-y","type":"published","date":"2024-09-10T15:57:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62126037,"identity":"674961a8-fb6d-467c-8bd0-6136d53c095c","added_by":"auto","created_at":"2024-08-09 14:45:13","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":235338,"visible":true,"origin":"","legend":"\u003cp\u003eCCA biplot showing two separate clusters for urban (Red ellipse) and peri-urban (green ellipse) sites.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4685818/v1/5ab8f3ab348747be51ce4689.jpeg"},{"id":62126953,"identity":"1ec80525-7fc9-469d-9adb-3d4721ce2f74","added_by":"auto","created_at":"2024-08-09 14:53:13","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1154877,"visible":true,"origin":"","legend":"\u003cp\u003eBee-site network of urban (a) and peri-urban (b) clusters. Red boxes indicate the sites and green boxes bee species.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4685818/v1/cdb17d097c962083197b47dd.jpeg"},{"id":64619187,"identity":"b26bf974-ab00-4855-8a25-8260635512d3","added_by":"auto","created_at":"2024-09-16 16:12:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2131802,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4685818/v1/f701763f-99c8-4c7f-a494-d31eba1fed0b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bee community response to multiple stressors along a tropical urban-peri urban gradient","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe detrimental effect of urbanization on biodiversity has been extensively studied over the last decade (Fortel et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Cardoso and Goncalves, 2018). Urbanization homogenizes the rural landscape, leading to biotic loss and a subsequent reduction in essential ecosystem services (Elmqvist et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lokatis and Jeschke, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).Studying biodiversity loss due to urbanization along rural/peri-urban \u0026ndash; urban gradient is a much-used approach, and numerous studies focusing on urbanization gradients have utilized this method to quantify the extent of species loss or changes in the community structure (Castro et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Birdshire et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Meyer et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Marcacci et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEarlier studies have indicated that a multitude of stressors impact biodiversity along rural - urban gradients (McKinney, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Understanding the synergistic interaction between the multiple stressors is crucial, as it may lead to accelerated biodiversity loss (C\u0026ocirc;t\u0026eacute; et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Beaumelle et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Even more, investigating stressors that affect biodiversity in rural or peri-urban areas is crucial since they differ from those in urban or metropolitan areas (Theodorou et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The major stressors of rural and peri-urban areas are factors associated with intensive agriculture (Tscharntke et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), unsustainable harvesting practices (Simon, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), landscape fragmentation, mining and industrialization (Sahoo et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, urban areas face different sets of stressors such as habitat destruction, air and water pollution, increases in impervious surface, and introduction of non-native invasive species (Parnell et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Although good number of studies are available that looked into individual rural/peri-urban or urban stressors, more comprehensive studies that elucidate the differential responses of communities to these stressors together along the urbanization gradient are sparse (McKinney, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Beaumelle et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, more work is needed to understand how different stressors impact such biodiversity changes along the rural-urban gradient in different socio-ecological setting across various global regions.\u003c/p\u003e \u003cp\u003eThe scenario could potentially be different in tropical developing countries where the nature of urban-peri-urban gradient may be different from that in a western economically developed country. Studies by Chamberlain et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Lopez et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) emphasized the need to consider biodiversity trends along urban gradients in tropical regions due to high rates of urbanization and existence of significant biodiversity hotspots in developing tropical countries. Sharip \u0026amp; Noor (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) highlights the importance of sustainable financing for biodiversity conservation in urban and peri-urban areas, particularly in tropical regions.\u003c/p\u003e \u003cp\u003eThe rural agricultural landscape in the developing countries are increasingly coming under agricultural intensification (Hern\u0026aacute;ndez et al., 2016; Goulart et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) or under industrialization of different scales, generally small and medium-scale industries (Wu et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In tropical countries there is diverse patterns and degrees of urbanization which unlike more economically developed Western countries generally lack effective urban planning and environmental protection, resulting in a more heterogenous landscape (Gebreselassie et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bajaru et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, it is important to investigate urban- peri-urban gradient in tropical developing countries and how biodiversity changes along this gradient.\u003c/p\u003e \u003cp\u003ePollinator decline is a global threat to the ecosystem affecting its stability and sustainability (Biesmeijer et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The pollinators are threatened due to various factors such as loss of natural vegetation, habitat loss, increase in built up areas (Alberti, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and environmental pollution. Increased pollution directly affects pollinator health (Reitmayer et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and foraging ability (Fuentes et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Girling et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; McFrederick et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Air pollution has one of the most ubiquitous impacts on global decline of pollinators (Duque and Steffan-Dewenter, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As the use of motor vehicles is increasing worldwide and especially in the developing nations where pollution is less monitored, air pollutants such as coal smoke, CO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003eX\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e and particulate matter (PM) are increasing (Amoatey et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). On the other hand, the peri-urban areas in the tropical countries are increasingly affected by air pollution due to unregulated vehicular fuel use and emissions from the expanding medium and small industries (UNEP, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). These pollutants accumulate in the different body parts and cause distress for the pollinators (Papa et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Thimmegowda et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBees act as the major pollinators of pollination dependent crops and wild plants globally (Rader et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Klatt et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Studies have indicated that increased urbanization results in decreased bee abundance, richness and diversity (Fauviau et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Birdshire et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Egerer et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In urban areas habitat loss and air pollution is assumed to be a major cause of bee diversity loss (Schueller et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Feldhaar and Otti, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Urbanization reduces the foraging capacity and nesting opportunity for bees as natural flower patches and non-crop plants decrease while impervious surface area increases (Karnchananiyom et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similarly in the rural agricultural systems, bees are often threatened by other stressors such as simplification of landscape (increased monoculture) and intensified pesticide use, which reduce food availability and nesting opportunities for bees (Potts et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Heard et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Pesticide acts as a major factor of pollinator loss in peri-urban or rural agricultural systems (Fischer, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, air pollution may be a key factor that may negatively influence the bee community in peri-urban areas. Although the impact of industrial activities on air quality has been traditionally associated with urban and industrial regions, studies have shown that peri-urban areas are also affected by air pollution due to long-range transport of pollutants (Call\u0026eacute;n et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tecer et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This transport of pollutants to rural and peri-urban areas can lead to increased levels of particulate matter and other harmful substances, impacting the health and well-being of pollinators in these regions (Thimmegowda et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The use of solid fuels for cooking and heating in rural households has also been identified as a significant source of indoor air pollution, further exacerbating the overall pollution levels in rural areas (Du et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Krishnamoorty et al., 2018). Therefore, these multitudes of stressors along the urban- peri-urban gradient are capable of shaping the communities of bees in these two regions.\u003c/p\u003e \u003cp\u003eDespite the growing evidence that urbanization negatively affects bee population, sometimes the scenario can be completely different. Urban areas may act as refuge for the bee community in cases where peri-urban stressors such as pesticide, unregulated industrialization, rapid habitat destruction, landscape homogenization are more detrimental to the bee community (Millard et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wenzel et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Cities often harbor a greater diversity of flowering plants than peri-urban landscapes due to the presence of non-native, cultivated and introduced (e.g. ornamental and exotic) plant species (Lowenstein et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Seitz et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The plant communities within such patches depend on pollinators for their reproduction. Despite the harmful effects of urbanization on the habitat and ecosystems, green spaces within urban areas play a significant role in preserving bee diversity (Matteson et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) compared to the agricultural or natural ecosystems (Williams and Kremen, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eUrbanization in India is rapidly increasing, with an estimated urban population of 40 percent by 2030 (Ali, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This rapid urbanization is leading to large scale deforestation (Mondal, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), increased air pollution (Anwar et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), increased impervious layer (Chen et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and introduction of non-native invasive species (Jain et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). All these factors can escalate the loss of pollinators in Indian urban areas (Wenzel et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). From a peri-urban perspective, Indian agriculture has undergone rapid intensification throughout the years which creates challenges for the local biodiversity. One of the major threats is unregulated pesticide usage by farmers that may directly kill or impair essential biodiversity components (Basu et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Atmospheric pollution, although considered an urban issue is also rapidly increasing in rural India. The major sources of air pollution are residential and commercial biomass incineration, mineral aerosol (from mining activities), Coal based electricity production, industrial emissions, straw and Agri waste burning, construction, brick factory and diesel-powered vehicles and generators (Pathak and Kuttippurath, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this paper we investigate how different stressors along a tropical urban- peri-urban gradient in and around Kolkata Megacity in India impact the bee diversity.\u003c/p\u003e \u003cp\u003eWe ask-\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHow the wild bee community and functional assemblage change along the urbanization gradient in a tropical landscape?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat is the role of urban and peri-urban stressors in structuring the wild bee community along the urbanization gradient?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe study was conducted in the metropolitan city of Kolkata and surrounding suburbs in West Bengal, India. Kolkata is one of the oldest metropolitan cities in eastern part of India over a large alluvial plain. It is situated over the moribund delta of the Hooghly River at an elevation of 1.5 to 9 m from mean sea level. Peri-urban areas in the Kolkata metropolitan area incorporate parts of the following districts: North 24 Parganas, South 24 Parganas and Howrah.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSite selection\u003c/h2\u003e \u003cp\u003eWe selected 20 sampling sites; mainly parks and gardens extending from urban Kolkata to the neighboring peri-urban area, thereby forming an urban- peri-urban gradient extending north and south of the city. We maintained a minimum distance of 1 km between the sites, so they remain independent of each other. Within the parks and gardens, we mainly focused our sampling effort on the wild plants. Wild plant species within the study sites grew completely naturally, but to some extents were exposed to disturbance by human activities\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCalculation of urbanization\u003c/h2\u003e \u003cp\u003eWe quantified urbanization intensity on the basis of the percentage of built-up area (also termed as impervious layer: i.e., all sealed surfaces, such as roads, buildings, etc.) (Geslin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Marcacci et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We calculated the percentage of urban area, natural and semi natural vegetation around each site at 1000 m by using LULC maps from sentinel 2 satellite images (ESRI, Inc., Redlands, CA, USA) by supervised classification method in Arc-GIS software version 10.8.1.\u003c/p\u003e \u003cp\u003eFollowing the initial classification, the 20 sampling sites were separated into 10 urban and 10 peri urban sites based on CCA clustering of the sampling sites with different urbanization parameters as explanatory variables (built-up area, pesticide toxicity and plant diversity). The peri urban and urban sites showed significant clustering difference in the CCA biplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCalculation of pesticide toxicity\u003c/h2\u003e \u003cp\u003eWe obtained the pesticide data by surveying the care takers and the gardeners of the parks and gardens. The records contained the application rate and date of pesticides applied throughout the sampling period, including all herbicides, fungicides, and insecticides. We calculated the pesticide toxicity score for each field by using Environmental Impact Quotient (EIQ) Field Use Rating formula (see below), this included both the pesticide toxicity of bees and the quantity of active ingredient applied in the field (Kovach et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). To calculate the toxicity scores of the active ingredients, we multiplied the application rate (ml/m2) by the percent of the active ingredient for each pesticide and then multiplied this amount by the \u0026lsquo;\u0026lsquo;bee-toxicity value\u0026rsquo;\u0026rsquo; for the active ingredient following the 2010 EIQ database of Eshenaur et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The bee-toxicity value for individual pesticide active ingredients was determined by multiplying acute toxicity of the ingredient to bees on a scale of 1\u0026ndash;5 [1 represent relatively nontoxic (LD50\u0026thinsp;\u0026gt;\u0026thinsp;100 \u0026micro;g/bee), 3 represents moderately toxic (LD50\u0026thinsp;=\u0026thinsp;2\u0026ndash;10.99 \u0026micro;g/bee), 5 represents highly toxic (LD50\u0026thinsp;\u0026lt;\u0026thinsp;2 \u0026micro;g/bee) (Mallinger et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kovach et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). To obtain the total toxicity score of each garden/park, the pesticide survey data taken during the whole sampling period was cumulated using the following formula (Mallinger et al.,2015).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{T}\\text{o}\\text{x}\\text{i}\\text{c}\\text{i}\\text{t}\\text{y}\\:\\text{S}\\text{c}\\text{o}\\text{r}\\text{e}=\\:\\sum\\:\\left[\\text{R}\\text{a}\\text{t}\\text{e}\\:\\right(\\text{m}\\text{l}/{\\text{m}}^{2})\\:\\times\\:\\:\\text{P}\\text{e}\\text{r}\\text{c}\\text{e}\\text{n}\\text{t}\\:\\text{a}\\text{c}\\text{t}\\text{i}\\text{v}\\text{e}\\:\\text{i}\\text{n}\\text{g}\\text{r}\\text{e}\\text{d}\\text{i}\\text{e}\\text{n}\\text{t}\\:\\times\\:\\:\\text{E}\\text{I}\\text{Q}\\:\\text{b}\\text{e}\\text{e}\\:\\text{t}\\text{o}\\text{x}\\text{i}\\text{c}\\text{i}\\text{t}\\text{y}\\:\\text{v}\\text{a}\\text{l}\\text{u}\\text{e}]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAir pollution parameters\u003c/h2\u003e \u003cp\u003eThe air pollution parameters (NO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, and PM10) were obtained from West Bengal Pollution Control Board (. The data was collected 3 times during the sampling period and the average value calculated for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePlant diversity measurement\u003c/h2\u003e \u003cp\u003eAbundance and diversity of all non-crop plants were recorded from all the 20 sites during the sampling period. 10 quadrates were randomly selected in each of the 20 sites three times during the study period and the number of plants were noted. The data was pooled for the three sampling bouts. Shannon diversity index was calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBee collection\u003c/h2\u003e \u003cp\u003eSampling was carried out from April 2019 to February 2020 on three different sampling rounds at each of the 20 sampling locations (One round of sampling in all 20 sites in each sampling bout, a total of 60 days of sampling). Passive sampling and active sampling were used to quantify the bee community at each of our gardens.\u003c/p\u003e \u003cp\u003eIn each sampling round, a \"pan trap station\" was set up at all the sampling sites for passive sampling purposes. We followed Cane et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2000\u003c/span\u003e and placed the pan traps of different colours at the same height as the floral resources to minimize the sampling bias. Although pan trapping does not capture all bee species during the flowering period, it has been reported as an efficient method that can provide insight into bee diversity that is otherwise unobtainable (Cane et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). A cluster of five traps (each \u0026ldquo;trap\u0026rdquo; comprising of three bowls, one each of white, blue, and yellow painted with UV reflective paints) (BOSNY paint) were set up within a randomly chosen 10m \u0026times;10m wild floral patch. Traps were filled with water and approximately 5 mg of washing powder was added to reduce the surface tension. Traps were left open for 24 hr. A total of 15 bowls were placed at each of the 20 sampling locations in every sampling bout.\u003c/p\u003e \u003cp\u003eFor active sampling we established a 10m \u0026times; 2m transect in each park or garden. Sweep net method was used to capture the bee population along the transect. 10 min of sweeping was done by a single collector every hour at 1 hour interval from 7am to 2pm.The insects collected were at first rinsed with distilled water and then preserved in 70% alcohol in the field. In the laboratory, the bee specimens were dried, pinned and identified to species or morphospecies level using taxonomic keys following Michener (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Data from the three different s was pooled for each site for further analysis.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eBee functional trait estimation\u003c/h2\u003e \u003cp\u003eFor all species, we compiled information on their life history traits. We also recorded body size, dietary specialization, nesting location, nest construction behaviour, sociality, hairiness (density of hair because it affects pollen grain deposition on stigmas) (Stavert et al., 2016) and glossa length (associated with a range of floral types that bees can access; mean of pollen uptake and transportation, both related to the versatility of interaction with pollen grains; and specialty in obtaining some resource (Michener, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Martins et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Body size was the quantitative functional attribute measured, and for this, we used the mean inter-tegular distance of the specimens collected (Greenleaf et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor each species, at least five individuals were measured for the functional traits. For species of which we had fewer than five individuals, we used the number of individuals available in our dataset. Information on the categorical functional attributes was obtained from the extensive survey of the specialized literature. The species for which the trait category was not assigned were excluded from the analysis. The traits used in our analysis are listed in Table .1.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo classify the 20 sampling sites into urban or peri-urban categories, we utilized Canonical Correspondence Analysis (CCA) of the study sites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNetwork analysis\u003c/h2\u003e \u003cp\u003eTo understand the bee community compositional difference between urban and peri-urban sites we developed \u0026ldquo;Bee-site networks\u0026rdquo; of urban and peri -urban cluster separately. The network analysis was done with the help of \u0026ldquo;bipartite\u0026rdquo; package in R. Different \u0026ldquo;Bee-site\u0026rdquo; network parameters such as NODF, connectance, weighted NODF, specialization index (H\u0026rsquo;) and modularity was calculated with the help of the command \u0026ldquo;networklevel \u0026ldquo;within the \u0026ldquo;bipartite\u0026rdquo; package. For identifying key bee species in urban and per-urban cluster \u0026ldquo;specieslevel\u0026rdquo; command within the same package was utilized. We calculated Normalized Degree (ND), Betweenness Centrality (BC) and Specialization index for species. Normalized Degree (ND) measures the number of connections a node (species) is connected with normalized by the total possible number of connections. It indicates the connectance of a species within the network. Betweenness Centrality (BC) metric measures the frequency with which a node (species) lies on the shortest path between other nodes. It reflects the species' role in facilitating interactions or connections between other species. Specialization index measures whether the interactions are specialized or generalized within the network.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFunctional diversity calculation\u003c/h2\u003e \u003cp\u003eTo obtain additional information regarding bee Functional diversity (FD), we used Functional Dispersion indices (FDis) that reflect the important properties of this diversity (Hooper et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The functional dispersion quantifies the mean distance of each species from its community centroid in a multivariate space defined by all included traits (Lalibert\u0026eacute; and Legendre, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). To calculate functional dispersion (FDis), we used the function dbFD in package FD, in R version 4.3.3 (2024) with the Cailliez correction for non- Euclidean distances generated by the inclusion of categorical traits (Lalibert\u0026eacute; et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). To understand the association between urbanization and functional diversity indices we used GLMs with gaussian error distribution and \u0026ldquo;log\u0026rdquo; link function. To see if there is significant difference in FD indices (redundancy, functional dispersion, etc.) between urban and peri-urban sites Mannn-Whitney U test was performed.\u003c/p\u003e \u003cp\u003eTo measure the effect of urbanization on bee species assemblage (abundance and diversity) we used generalized linear models (GLM). In cases where response variable was total bee abundance, bee family abundance and bee richness we utilized GLM models with negative binomial error distribution and \u0026ldquo;log\u0026rdquo; link function. The predictor variables were concentration of SO\u003csub\u003e2\u003c/sub\u003e in air, percentage of agriculture within 1km radius, pesticide toxicity and percentage built-up area within 1km radius of each site. To model the relationship between bee diversity indices and explanatory variables linear models (lm) were used. The linear models were tested for normality of residuals and homogeneity of variance by Shapiro-wilk test and Levene\u0026rsquo;s test respectively prior to final selection.\u003c/p\u003e \u003cp\u003eTo elucidate the difference of bee assemblage between the urban and peri urban sites we performed the Mann-Whitney U test or Students \u0026ldquo;t\u0026rdquo; test taking different bee family or subfamily abundance data as the focal variables.\u003c/p\u003e \u003cp\u003eAll statistical analysis was performed in R version 4.3.3 (R Core team, 2024) with the help of packages \u0026ldquo;car\u0026rdquo; (Fox and Weisberg, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), \u0026ldquo;MASS\u0026rdquo; (Venable and Ripley ,2002), DHARMa (Hartig, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), FD (Lalibert\u0026eacute;, E., Legendre, P. \u0026amp; Shipley, 2014), and \u0026ldquo;bipartite\u0026rdquo; (Dormann et al. 2008).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 742 bee specimens were found in this study out of which 611 bees were collected from passive collection and the rest were collected by active collection. 22 genera and 46 species were observed in the 2019\u0026ndash;2020 sampling session across 20 urban and peri-urban sites across Kolkata, West Bengal. All bees were identified up to the morphospecies level. Among the bees collected, the most abundant families were Halictidae (47.57%), followed by Apidae (43.53%) and Megachilidae (8.9%).\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eThe Urban \u0026ndash; Peri-urban Gradient\u003c/h2\u003e \u003cp\u003eThe CCA analysis revealed that the three selected explanatory variables (Built up area, pesticide toxicity and plant diversity) explained at least 20% of the total variation each. The first two canonical axis explained 92.82% of the total variation. There was two different cluster of sites observed in the CCA scatter plot based on which the peri urban and urban sites could be separated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe urban and peri-urban clusters were significantly different (t\u0026thinsp;=\u0026thinsp;4.896, p\u0026thinsp;=\u0026thinsp;.00058) based on percentage of built-up area.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSite-Bee community network\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe peri-urban sites and bee community network showed higher degree of nestedness (NODF\u0026thinsp;=\u0026thinsp;50.514, weighted NODF\u0026thinsp;=\u0026thinsp;26.716) whereas the urban sites and bee community network showed lower degree of nestedness (NODF\u0026thinsp;=\u0026thinsp;40.550, weighted NODF\u0026thinsp;=\u0026thinsp;19.869) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe network parameters of the site-species matrix of urban and peri urban areas showed that the Normalized Degree (ND) is highest in \u003cem\u003eApis florea\u003c/em\u003e, in urban area whereas in peri urban region the ND is more in \u003cem\u003eLasioglossum\u003c/em\u003e sp.1. The specialization index is highest in \u003cem\u003eAmegilla\u003c/em\u003e sp. 5 and \u003cem\u003eAmegilla\u003c/em\u003e sp. 6 in the urban regions and \u003cem\u003eAmegilla\u003c/em\u003e sp. 4 had the highest specialization index in peri urban regions. Betweenness centrality value is different in the two regions. In the urban region \u003cem\u003eBraunsapis puangensis\u003c/em\u003e has the highest betweenness centrality (BC) value whereas in peri urban region \u003cem\u003eLasioglossum\u003c/em\u003e sp. 1 has the highest BC values. All the network parameter values are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eTraits used in analyses for measuring functional dispersion of bee community.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea of scopa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirect measurement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInter-tegular distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirect measurement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMouthpart length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirect measurement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeans of pollen transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1) Corbiculae; (2) Ventral scopa; (3) Scopa (hind trochater- basitursus); (4) Scopa (tibia and basitarsus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMichener \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Viana and Kleinert 2005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeans of pollen uptake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1) First leg; (2) Vibration;(3) First leg and Mandible; or (4) First leg and Vibration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMichener \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Viana and Kleinert 2006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuzz pollination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebuzz/non buzz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMichener \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Viana and Kleinert 2007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak activity time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1) within 0\u0026ndash;3 hours after sunrise; (2) within 3\u0026ndash;6 hours after sunrise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMichener \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Viana and Kleinert 2008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary specialization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eoligolectic/polylectic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMichener \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Viana and Kleinert 2009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSociality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esolitary/social/parasitic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMichener \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Viana and Kleinert 2010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNesting location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enesting above ground/ below ground/mixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMichener \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Viana and Kleinert 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNest construction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexcavate/rent/cleptoparasitic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMichener \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Viana and Kleinert 2012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHairiness (density of hair)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(dense / sparse)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMichener \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Viana and Kleinert 2013\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\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters of bee-site network matrix in urban peri-urban region.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeri-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLassiglousm sp.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormalised degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeri-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAmegilla sp.4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecialization index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeri-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLasioglossum sp.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBetweenness centrality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eApis florea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormalised degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAmegilla sp.5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecialization index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.794118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAmegilla sp.6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecialization index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.794118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBraunsapispuangensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBetweenness centrality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFunctional dispersion\u003c/h2\u003e \u003cp\u003eFunctional dispersion measures the average distance between species based on their functional attributes and relative abundance in a multidimensional space. Thus, functional dispersion shows the diversity of role the species play in an ecosystem. Functional dispersion (Fdis) increased significantly with an increase in percentage of built-up area (p\u0026thinsp;=\u0026thinsp;0.0206) (gaussian log). Functional dispersion (Fdis) differed significantly between urban and peri-urban regions (p\u0026thinsp;=\u0026thinsp;0.005, u\u0026thinsp;=\u0026thinsp;13, Z score\u0026thinsp;=\u0026thinsp;2.759). Functional redundancy decreased with increase in percentage of built-up area (p\u0026thinsp;=\u0026thinsp;0.015, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and increased with pesticide toxicity (p\u0026thinsp;=\u0026thinsp;0.034, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The specialist species are dominated by solitary bees because there is a significant difference between of sociality between the urban and peri-urban regions (p\u0026thinsp;=\u0026thinsp;0.001, Z score\u0026thinsp;=\u0026thinsp;3.288 and u value\u0026thinsp;=\u0026thinsp;6).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of generalized linear models performed.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eError structure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChi-sqaure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesticide toxicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal bee abundance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative binomial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesticide toxicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBee richness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative binomial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of agriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMegachilidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative binomial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesticide toxicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApidae abundance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative binomial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e concentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHalictidae abundance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative binomial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of built-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApidae abundance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative binomial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of built-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFunctional dispersion (Fdis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGaussian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of built-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFunctional redundancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGaussian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesticide toxicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFunctional redundancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGaussian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.25E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAbundance and diversity along the urban-peri-urban gradient:\u003c/h2\u003e \u003cp\u003eThe total bee abundance did not differ significantly between urban and peri-urban sites (mean urban\u0026thinsp;=\u0026thinsp;30.7, SD\u0026thinsp;=\u0026thinsp;16.6403 and mean peri-urban\u0026thinsp;=\u0026thinsp;43.5, SD\u0026thinsp;=\u0026thinsp;31.334). Bee diversity also did not differ between urban and peri-urban regions. However, Mann -Whitney U test showed that subfamily Apinae (z\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.45, u\u0026thinsp;=\u0026thinsp;17, p\u0026thinsp;=\u0026thinsp;0.013) and genus Apis (z\u0026thinsp;=\u0026thinsp;2.683, U\u0026thinsp;=\u0026thinsp;14, p\u0026thinsp;=\u0026thinsp;0.007) abundance differed significantly between urban and peri-urban sites with Urban (Mean\u0026thinsp;=\u0026thinsp;8.4, SD\u0026thinsp;=\u0026thinsp;6.11) areas containing more Apinae than peri urban (Mean\u0026thinsp;=\u0026thinsp;3.4, SD\u0026thinsp;=\u0026thinsp;2.63) and Apis abundance peri urban (Mean\u0026thinsp;=\u0026thinsp;2.4, SD\u0026thinsp;=\u0026thinsp;1.77) and urban (Mean\u0026thinsp;=\u0026thinsp;7.6, SD\u0026thinsp;=\u0026thinsp;6.38). Although plant diversity proved to be an important factor in our CCA scatter plot but it did not yield any significant difference with bee abundance and diversity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eEffect of urban and peri-urban stressors on bee community along the urbanization gradient\u003c/h2\u003e \u003cp\u003eThe total bee abundance (p\u0026thinsp;=\u0026thinsp;0.03, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and bee richness decreased (p\u0026thinsp;=\u0026thinsp;0.037, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) significantly with the increase in pesticide toxicity. Bee diversity (linear model, F\u0026thinsp;=\u0026thinsp;4.564, df\u0026thinsp;=\u0026thinsp;18, p\u0026thinsp;=\u0026thinsp;0.046) and evenness (linear model, F\u0026thinsp;=\u0026thinsp;4.841, df\u0026thinsp;=\u0026thinsp;18, p\u0026thinsp;=\u0026thinsp;0.041) decreased significantly with the increase in SO\u003csub\u003e2\u003c/sub\u003e concentration. NO\u003csub\u003e2\u003c/sub\u003e and PM10 did not show any significant difference with bee abundance and diversity.\u003c/p\u003e \u003cp\u003eWhen we grouped the bee species according to their families, we found that the Megachilidae abundance decreased with the increase in the percentage of agricultural area (p\u0026thinsp;=\u0026thinsp;0.015, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and Apidae abundance (p\u0026thinsp;=\u0026thinsp;0.025, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) decreased with the increase in pesticide toxicity. Halictidae abundance (p\u0026thinsp;=\u0026thinsp;0.027, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) increased with the increase in SO\u003csub\u003e2\u003c/sub\u003e concentration whereas Apidae abundance (p\u0026thinsp;=\u0026thinsp;0.056, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) increased with the increase in the percentage of built-up area.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study focused on the bee species assemblage along an urban - peri-urban gradient around the megacity of Kolkata and highlighted how different stressors (both urban and peri-urban) influenced bee community structure. The site-by-bee network analysis revealed distinct differences between urban and peri-urban clusters, with urban sites containing more specialized bee species and lower nestedness value compared to the peri-urban sites. On the other hand, generalist species were more predominant in the peri-urban sites in our study with a higher value of nestedness. This difference of nestedness suggests that urban environments may support certain species that can adapt well to higher levels of urbanization and associated stressors. Specialist bee species are becoming more prevalent in urban areas possibly due to niche differentiation and resource partitioning (Fortel et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWe observed that different bee species were important in the two separate clusters of urban and peri-urban sites, indicating distinct community composition along the urbanization gradient. For example, \u003cem\u003eApis florea\u003c/em\u003e had the highest normalized degree (\u003cem\u003eApis florea\u003c/em\u003e interacted with the highest number of different sites compared to other bee species) in urban bee-site network. \u003cem\u003eApis florea\u003c/em\u003e is likely to be present in all the urban sites and hence may act as an ecological generalist capable of foraging and nesting in a wide variety of urban environments. On the other hand, \u003cem\u003eLassioglossum\u003c/em\u003e sp. 1 had the highest normalized degree in the peri-urban-site network. The betweenness centrality (BC) values showed that the \u003cem\u003eBraunsapis puangensis\u003c/em\u003e and \u003cem\u003eApis florea\u003c/em\u003e exhibited the highest BC values, indicating these species are crucial for maintaining connectivity and stability of urban sites. Whereas, in the peri-urban areas, \u003cem\u003eLasioglossum\u003c/em\u003e sp. 1 and \u003cem\u003eCeratina cognata\u003c/em\u003e showed the highest (BC) values indicating their importance in maintaining a stable community of bees. These findings further consolidate the varying ecological dynamics of bee communities across different levels of urbanization.\u003c/p\u003e \u003cp\u003eOur findings suggest that urban environments might be better suited for certain bee species compared to peri-urban areas. The functional dispersion (Fdis) value of bee community differed significantly between the urban (Mean\u0026thinsp;=\u0026thinsp;0.19, SD\u0026thinsp;=\u0026thinsp;0.04) and peri-urban (Mean\u0026thinsp;=\u0026thinsp;0.13, SD\u0026thinsp;=\u0026thinsp;0.04) cluster with urban areas showing a greater level of functional dispersion. Additionally, functional dispersion increased with the percentage of built-up area (an indicator of urbanization) in the landscape. Functional dispersion measures the distribution and variety of functional traits within a community. The significant increase of FDis with urbanization implies that cities support bees with a diverse array of functional traits which may be due to heterogeneous nature of urban habitats providing a variety of niches and resources for a diverse range of species. A reduced functional dispersion of bees in the per-urban area shows that they are becoming more functionally similar or fewer species are contributing to certain ecological functions. There may be several factors influencing the unique bee assemblage in urban areas such as diverse habitat patches (Ayers and Rehan, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), nutritional stability (Bhatta and Kumar, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), refuge from rural and peri-urban stressors (Prendergast et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), exotic floral resources (Wilson and Jamieson, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and nesting opportunity (Banaszak-Cibicka and Żmihorski, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Some studies have also indicated that urban landscape may sustain a greater abundance and diversity of bees compared to rural or natural landscape, adding to the favorability of cities as an alternative habitat for pollinators (Hall et al., 2017). A higher amount of habitat diversity in urban areas can sustain different groups of bees with their diverse nesting and feeding requirements (Aguirre- Guti\u0026eacute;rrez et al., 2015; Boscolo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nery et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile the bee community was distinct in the urban cluster, functional redundancy analysis showed that urban bees may be more vulnerable (because of the uniqueness of the community) to environmental and anthropogenic stressors since redundancy decreased with built-up area \u0026ndash; an indicator of urbanization. On the contrary, functional redundancy increased with pesticide toxicity. Pesticide toxicity values in our study sites were higher in peri-urban sites with greater degree of agricultural activity. The reduced functional dispersion coupled with increasing redundancy indicates that pesticide may be acting as a major stressor that reduces functional divergence and acts as an environmental filter to the bee assemblage. The selection pressure imposed by pesticide results in a community with functionally redundant species with overlapping traits. The highly specialized bee species may be filtered out which leads to a more homogenized bee community. The prevalence of specialized species in urban areas emphasize the necessity of conserving the bee community along with necessary resources as a sudden alteration in the environment may lead to community collapse.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eEffect of Individual stressor on bee community\u003c/h2\u003e \u003cp\u003eA multitude of stressors in our study system may impose limits to the bee community. These environmental stressors such as built -up area (increase in impervious layer), air quality (AQI), SO\u003csub\u003e2\u003c/sub\u003e pollution and pesticide toxicity varied along urban- peri-urban gradient and showed differential impact on bee species or family. Pesticide toxicity being the most notable stressor which negatively affects the bee community by affecting its abundance and richness. A number of studies have already established the role of pesticide in regulating bee assemblage, with most indicating decline of abundance, richness, foraging activity and nesting behavior (Raine and Rundl\u0026ouml;f, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Nicholson, 2023; Schaad et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Raine, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Bee diversity and evenness decreased with higher SO\u003csub\u003e2\u003c/sub\u003e concentrations in the air. Air pollution has already been established as a critical stressor that may hinder the sustenance of heathy pollinator community and subsequently reduce ecosystem services (Thimmegowda et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). SO\u003csub\u003e2\u003c/sub\u003e pollution is mainly due to vehicular pollution and from coal-fired industries in the peri urban areas (Srirattana and Piaowan, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). From our result it is evident that SO\u003csub\u003e2\u003c/sub\u003e pollution from unregulated vehicular exhaustion and industrial sources may be a critical factor that regulates the assemblage of bee community along the urbanization gradient and specially affecting peri-urban bees. This higher SO\u003csub\u003e2\u003c/sub\u003e concentration threatens bee health by limiting olfactory signal pathways (Ibrahim and Devi, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, SO\u003csub\u003e2\u003c/sub\u003e along with pesticide toxicity plays a detrimental role in structuring bee communities in peri-urban areas.\u003c/p\u003e \u003cp\u003eWith regards to the taxonomic families of bee, Megachilidae abundance decreased with increase in agricultural area. This again supports our previous results that the peri urban environmental stressors are harmful to certain groups of bees and suggests that agricultural intensification, landscape homogenization, and destruction of suitable nesting habitats makes it difficult for Megachilidae to survive in such areas (McCabe et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Abudulai et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). On the other hand, Halictidae abundance increased significantly with an increase in SO\u003csub\u003e2\u003c/sub\u003e concentration. This result suggests that although SO\u003csub\u003e2\u003c/sub\u003e concentration is higher in peri-urban areas, the presence of more bear ground, open areas and fallow land maybe suitable for ground nesting bees compared to cities where impervious layer is higher (Brancher et al., 2022). Apidae abundance increased with the percentage of built-up area which further supports that urban environments may provide suitable nesting habitats for certain bee families which are mostly above ground nesting. In urban areas, ground-nesting bees cannot thrive due to the presence of impervious surfaces. However, Above- ground nesting bees may be favored in more heterogeneous cities due to numerous nesting opportunities, such as holes in building walls, vacant lots, garden fences and tree hollows present in impervious and vegetated areas (Cane et al., 2006; Geslin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pardee \u0026amp; Philpott, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn conclusion, our study demonstrates that urban and peri-urban bee communities exhibit distinct network parameters and responses to environmental stressors. Urban areas appear to support more specialized and functionally diverse bee communities, although these communities might be more susceptible to environmental changes due to lower functional redundancy. Peri-urban areas, while less specialized, maintain higher functional redundancy, potentially offering greater resilience to certain stressors. These findings emphasize the need for tailored conservation strategies that consider the unique characteristics and challenges of urban and peri-urban environments to support and sustain healthy bee populations. More studies should evaluate the conservation value of urban green spaces such as public parks, gardens, rooftop gardens and undisturbed vegetation fragments as a refuge for native bee species. In addition, the importance of stressors in modulating the bee community assemblage should be kept in mind while planning for tropical megacities.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eConflicts of interest\u003c/strong\u003e \u003cp\u003eThe authors declare that there are no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eInformed consent\u003c/h2\u003e \u003cp\u003eAll authors have reviewed and endorsed the submitted manuscript.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAD designed the study, conducted field work, analyzed data and wrote the manuscript; IS analyzed the data, wrote the manuscript; PB conceived and designed the study, contributed in analysis and wrote the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by University Research Fellowship (URF) to the first author. We convey our heartfelt thanks to the Urban Recreation Forestry Division, Mudialy Fishermen Co Operative Society- Kolkata, MSME tool room, Kolkata, Caretakers of the different parks and gardens for allowing us to work unconditionally. We also thank Anirban Chakraborty for his help in analysis.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be available on request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbudulai, M., Nboyine, J. A., Quandahor, P., Seidu, A., \u0026amp; Traore, F. (2022). Agricultural intensification causes decline in insect biodiversity. \u003cem\u003eGlobal Decline of Insects\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eAguirre‐Guti\u0026eacute;rrez, J., Biesmeijer, J. C., van Loon, E. E., Reemer, M., WallisDeVries, M. F., \u0026amp; Carvalheiro, L. G. (2015). 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Agricultural land-use diversity and forest regeneration potential in human-modified tropical landscapes. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment, 230\u003c/em\u003e, 210-220. https://doi.org/10.1016/j.agee.2016.05.018\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Bee community, urbanization, stressors, functional diversity, air pollution, pesticide toxicity","lastPublishedDoi":"10.21203/rs.3.rs-4685818/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4685818/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrbanization in tropical landscapes is a complex phenomenon that can lead to community shift rather than simple species extinction in response to multiple stressors in peri-urban and urban settings. We have investigated impacts of different stressors along a tropical urban-peri-urban gradient on the bee community, the health of which is a global conservation concern. Several stressors such as, increased built-up area, pesticide application and air pollution may effectively regulate bee community composition and corresponding functional diversity along urban-peri urban gradients. We investigated the changes in bee community structure in response to associated stressors in 20 locations including parks and gardens along an urban-peri urban gradient surrounding the megacity of Kolkata. Bee community structure differed significantly between urban and peri urban sites with urban sites showing lower value of nestedness. Network analysis also revealed that \u003cem\u003eApis florea\u003c/em\u003e and \u003cem\u003eLasioglosssum\u003c/em\u003e sp. 1 were the most important species in the urban and peri-urban areas respectively. Functional diversity increased with urbanization and decreased with pesticide toxicity. Functional redundancy decreased with urbanization. Individual stressor impacted the bee assemblage differentially along the urbanization gradient. SO\u003csub\u003e2\u003c/sub\u003e and pesticide toxicity negatively influenced bee abundance and diversity. Urban sites sustained more specialized species and therefore are more vulnerable to shocks while peri-urban sites had a more functionally redundant community making it comparatively more resilient.\u003c/p\u003e","manuscriptTitle":"Bee community response to multiple stressors along a tropical urban-peri urban gradient","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 14:45:09","doi":"10.21203/rs.3.rs-4685818/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-31T14:56:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-31T14:24:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-31T12:15:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-26T15:38:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305793488417694738146387284171814786797","date":"2024-07-25T12:02:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62771192004010977075287151945610848563","date":"2024-07-25T11:28:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161432406930078271905661557362202631613","date":"2024-07-25T01:10:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285576758865262317356094323348781679967","date":"2024-07-22T12:29:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140639608495901719392648899600640615574","date":"2024-07-18T08:24:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168779243067028996509534891390472488824","date":"2024-07-10T10:31:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-07T17:55:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-05T13:54:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-05T13:27:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Urban Ecosystems","date":"2024-07-04T10:20:40+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":"d60ef86d-7cd1-4d7e-9bb0-0f5a43c13b23","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-16T16:03:07+00:00","versionOfRecord":{"articleIdentity":"rs-4685818","link":"https://doi.org/10.1007/s11252-024-01609-y","journal":{"identity":"urban-ecosystems","isVorOnly":false,"title":"Urban Ecosystems"},"publishedOn":"2024-09-10 15:57:49","publishedOnDateReadable":"September 10th, 2024"},"versionCreatedAt":"2024-08-09 14:45:09","video":"","vorDoi":"10.1007/s11252-024-01609-y","vorDoiUrl":"https://doi.org/10.1007/s11252-024-01609-y","workflowStages":[]},"version":"v1","identity":"rs-4685818","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4685818","identity":"rs-4685818","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}