The Cost of Urban Expansion: Habitat Loss and Shifting Distribution of Long-Legged Wading Birds in a Peri-Urban landscape gradient

The Cost of Urban Expansion: Habitat Loss and Shifting Distribution of Long-Legged Wading Birds in a Peri-Urban landscape 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 The Cost of Urban Expansion: Habitat Loss and Shifting Distribution of Long-Legged Wading Birds in a Peri-Urban landscape gradient Anindya Naskar, Gopinathan Maheswaran This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7300140/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Nov, 2025 Read the published version in Urban Ecosystems → Version 1 posted 14 You are reading this latest preprint version Abstract Urbanisation, a key indicator of socioeconomic development, often comes at the cost of natural habitats, particularly in peri-urban wetlands. Human-influenced wetlands may refuge diverse avian species, but the extent of their effectiveness remains uncertain. Ardeidae species are often considered effective bioindicators of wetland health, due to their high mobility and dependence on wetlands for foraging. This study assessed the influence of land use patterns on the Ardeidae community structure across four peri-urban regions of Kolkata, India. A total of 20,537 individuals belonging to six commonly found Ardeidae species were recorded. The aquaculture farms had the highest abundance (75.18% of observations) of Ardeidae species, indicating their importance as foraging habitats. Land Use and Land Cover changes over two decades from Kolkata and its surrounding landscapes revealed rapid urban expansion, increased waterbodies (primarily aquaculture farms), and substantial loss of tree cover. The generalist species comprised 65.26% of overall observations, suggesting higher resilience to urbanised habitats. Whereas marshland specialists showed vulnerability to urban-driven habitat changes. Conversely, open-water foragers were scarce in urban-fringed areas, but abundant in fish farming, which further heightens the conflict between aquaculture farms and the species. The Generalised Linear Mixed Models highlight the importance of habitat heterogeneity to support a wide range of species assemblages. This study emphasised that urban sprawl has negative impacts on Ardeidae community structure. Effective conservation in urbanising areas requires the protection of multifunctional wetlands, establishment of buffer zones, promotion of sustainable aquaculture, and involvement of local communities in conflict mitigation. Urbanisation Peri-urban wetlands Waders (Ardeidae) Google Earth Engine (GEE) Aquaculture farms Generalized Linear Mixed Models (GLMMs) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Wetlands are among the most productive (Ghermandi et al. 2008 ) yet fragile ecosystems on earth (Fraser and Keddy 2005 ), providing a wide array of essential and irreplaceable ecological services (Leibowitz 2003 ). Additionally, human society to some extent significantly depend on wetlands for its various ecological and economic benefit (Russi et al. 2013 ; Sulphey and Safeer 2017 ). Due to their high productivity and biological diversity, wetlands support a complex network of interacting organisms including globally threatened avian species (Paracuellos 2006 ; Ghermandi et al. 2008 ). In Aisa 20% of the threatened bird species are found in wetland habitats (Kumar 2005 ). Over the years, various activities of humans have unfavourably influenced the hydro framework, plant development, and reduced the overall stability of the wetland ecosystem, resulting in the loss of more than 50% of natural wetlands worldwide (Zakaria and Rajpar 2014 ; Davidson 2014 ; Bassi et al. 2014 ; Giosa et al. 2018 ). Despite decades of global conservation efforts, wetlands continue to decline both in quality and quantity (Dahl 2011 ; Hu et al. 2017 ). This rapid decline poses a severe threat to avian communities that depend on these habitats for foraging, breeding, and roosting (Dri et al. 2021 ). Though in India the numbers of Ramsar site has more than doubled in past five years, increased from 42 to 89 (Ramsar, 2020; Ramsar, 2025), but several ecologically valuable wetlands remain unrecognized in this policymaking process, limiting their legal protection and conservation (Turner et al. 2000 ; Ghermandi et al. 2008 ). Many compelling issues such as pressure of population growth, urbanization, and increasing economic activities have further degraded numerous freshwater ecosystems in India (Arora et al. 2024 ). Assessment of the natural wetlands and the human-altered wetlands using hydrology, water chemistry and an array of plants and animals shows colossal contrasts (Campbell et al. 2002 ; Hartzell et al. 2007 ; Hossler and Bouchard 2010 ; Moreno-Mateos et al. 2012 ; Fidorra et al. 2016 ). Alternatively, rapid loss and degradation of natural wetlands make human-influenced marshes and lake areas ecologically significant habitats for a plenty of avian species and to some extent may reduce the detrimental effects of wetland loss (White and Main 2005 ; Sim et al. 2008 ; Ma et al. 2010 ; Ramírez et al. 2012 ; Zakaria and Rajpar 2013 ).Yet, it is unclear and significant to know that at what extent the human-influenced wetlands can serve as an option, in contrast to natural wetland (Fidorra et al. 2016 ). Wading birds are highly mobile and possess the top position in the aquatic food web and considered as suitable bioindicators of aquatic habitats (Noble et al. 2008 ; Frederick et al. 2009 ), especially for the human-influenced wetlands (Zakaria and Rajpar 2014 ; Fidorra et al. 2016 ). Their abundance and richness over a particular wetland indicate the characteristics (Kushlan 1986 ; Noble et al. 2008 ), accessibility of the fish (Frederick et al. 2009 ) and invertebrate prey population of that habitat (Fidorra et al. 2016 ). Heronry (heron, egrets, ibises and storks) birds (Kushlan and Hancock 2005 ) are inseparable from the wetlands as their primary food sources are aquatic invertebrates, fishes, amphibians, and sometimes small reptiles to lower mammals (Ali and Ripley 1978 ). However, rapid colonization of heronry species like egrets, herons, bitterns, storks, spoonbills, and ibises may also happen in the human-altered habitats (Brown and Smith 1998 ; Choi et al. 2007 ). Herons and egrets may play a critical part as bio-indicators to monitor changes in local and regional conditions (Choi and Yoo 2011 ). Urbanization-induced land modifications result in habitat loss, fragmentation, and local extinction of most native species, recent trends indicate more severe impacts in near future (Li et al. 2022 ). Small wetlands may be more vulnerable to the alteration than the larger wetlands, which requires further administration challenges (Custer and Galli 2002 ; McKinney 2002 ; Marzluff and Ewing 2008; Isaksson 2018 ). In India, very few attempts are made to assess the impact of urbanization on wading birds or their adaptation to environmental change (Urfi 2010 ; Charutha et al. 2021 ). Given the ecological importance of both natural and human-modified wetlands, a comprehensive understanding of their role in supporting avian communities is essential informing for wetland conservation and management policies. This study investigates the urbanization driven land-use changes on the community structure of Ardeidae species within the peri-urban landscape of the Kolkata Metropolitan Region, in eastern India. Materials and Methods Study area The study was carried out at four wetland sites located around the Kolkata city (Figure: 1); Kamduni (22.63°N, 88.52°E), Rajarhat (22.61°N, 88.46°E), East Kolkata Wetlands (EKW) (22.56°N, 88.42°E), and Baruipur (22.36°N, 88.38°E). Although these wetlands differ in structure and management, but act as critical habitats for a diverse assemblage of bird species. Kamduni and EKW are dominated by aquaculture ponds, whereas Baruipur and Rajarhat are largely composed of natural marshes, agricultural and fallow land. According to data from the citizen science platform eBird (http://www.ebird.org), each of the four wetlands supports over 150 bird species, underscoring their importance as critical habitats for waterfowl, waders, grassland birds, and raptors, including several returning migrants and threatened species. Land acquisition (KMDA 2011; Roy 2014) and ongoing developmental activities, particularly in Rajarhat (Dhar et al. 2019) and Baruipur (Naskar et al. 2021), pose significant threats to local bird populations. Historically, the EKW, a Ramsar site alone refuge 271 bird species and this numbers have decreased to 162 in 1990s (Mookherjee and Chatterjee 1999), and by 2021 the number further reduced to 92 (Barik et al. 2022). The recent re-discovery of the Blue-breasted Quail ( Synoicus chinensis ) at Baruipur (Bhattacharjee et al. 2020), once presumed locally extinct from EKW and its surroundings wetlands (Mookherjee and Chatterjee 1999), led to a study (Naskar et al. 2021) highlighting major conservation issues. Bird Survey Commonly found Ardeidae species such as Grey Heron ( Ardea cinerea ), Great Egret ( Ardea alba ), Purple Heron ( Ardea purpurea ), Intermediate Egret ( Ardea intermedia ), Little Egret ( Egretta garzetta ) and Pond Heron ( Ardeola grayii ), were selected for the study. The point count method was employed to record the abundance data of selected Ardeidae species at each study site. Data were collected weekly at each area between 0600h and 1800h under standardized weather conditions, avoiding surveys during strong winds or rainfall (Cezilly et al. 1990). Each site comprised of eight fixed point count stations, at least 400 meters apart from each other’s. Surveys were conducted at one site per day, covering all eight points. At each point, observations were made for 10-minutes to record the individual species abundance. Birds flying overhead during the surveys were not recorded. Observations were made using a Nikon 16x50 binocular and a Nikon spotting scope (Prostaff 5 with an 81-A magnification eyepiece). Abundance data was collected from January to May and October to December during 2021 and 2022. LULC and change detection Instead of selecting only the four study sites for landcover map, we rather preferred to select the landscape ( Figure 2 ) to understand the changes in land use pattern of the Kolkata and its surroundings. While doing so, study site EKW was taken as centroid and a radius of 60 km 2 area was selected for final land use land cover (LULC) map in Google Earth Engine (GEE) (Gorelick et al. 2017). To check the change in land use pattern in two-decade (2000-2022), Landsat 5 Thematic Mapper (TM) images (30m spatial resolution) and Landsat 8 OLI and TIRS images (30m spatial resolution) were obtained from United States Geological Survey (USGS) and processed in GEE. Landsat images were selected for the period of January to December each year with less than 10% cloud cover. A median composite was generated from the selected images to reduce seasonal variations (Phan et al. 2020). Total of six classes were taken; water, buildup, bare land, agricultural land, flooded vegetation, and tree cover based on satellite image and field observations ( Table:1 ). A total of 600 samples, 100 samples for each land use class were generated using the high-resolution imagery. To improve the classification accuracy, spectral indices ( S1 table ), along with NASA SRTM 30m Digital Elevation (Farr et al. 2007) were used in GEE as additional input variables (Phan et al. 2020). The Random Forest (RF) classifier was used in GEE for LULC classification (Dubertret et al. 2022). The number of the decision tree was selected using hyperparameter tuning to increase the computational efficiency. The model was trained using a 70:30 training-testing split, and overall accuracy, user’s accuracy, producer’s accuracy, and kappa coefficient were calculated to assess classification reliability. LULC transitions between 2000 and 2022 were analysed using post classification comparison approach. Table 1. Description of different LULC classes. LULC Class type Description Waterbody Area with standing water like lakes, ponds, aquaculture farms etc. Buildup Area with human settlements like urban/rural areas, commercial areas, industrials area, roads, etc. Bare land Lands with sparse or no vegetation. Agricultural lands Human planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land. Tree cover Area with plantation, visible tree canopy, etc. Flooded Vegetation Areas of any type of vegetation with obvious intermixing of water throughout most of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground. Statistical analysis The abundance data followed non-normal distribution (Kolmogorov-Smirnov test: D=0.3182, p<0.001). Hence, non-parametric Kruskal–Wallis tests were applied to compare species abundance between the study areas. Furthermore, (dis)similarity patterns of heron and egret across the study areas were assessed, using non-metric multidimensional scaling (NMDS) (Clarke 1993), based on the Hellinger transformed data (Legendre and Gallagher 2001), using ‘Vegan’ package (Oksanen et al. 2025) in R. In addition, a nonparametric permutation procedure of analysis of similarities (ANOSIM) was used to test for differences in the compositions of the groups formed by the NMDS analysis (9999 permutations) (Clarke 1993). The abundance of each of the six Ardeidae species showed non-normal distribution. Therefore, non-parametric Kruskal-Wallis tests were applied to compare their abundance across the study areas. The abundance data of all six species converted into presence and absence (hence occurrence), for each point counting stations. The occurrence data categorised into four phases ( S2 table ) as per the habitat structure, seasonal change, water level and cultivation regimes. The “landscapemetrics” package (Hesselbarth et al. 2019) was used to quantify the percentage of the land cover types within the 200m buffer for each point count stations for the year 2022. The mean Enhanced vegetation index (EVI) (Liu and Huete 1995), Land surface Temperature (LST ) (Sobrino et al. 1994) values were calculate ( S3 table ) for each four phases within the 200m buffer for the year 2021 to 2022 using GEE. Most of the point count stations show dynamic change in water level, hence normalized difference water index (NDWI) (McFEETERS 1996) was generated at first in GEE for each four phases between 2021 and 2022. Therefore, distance from water (disw) from each counting station was calculated from the NDWI using proximity tool in QGIS (version 3.40). As the dataset had a nested structure, Generalised Linear Mixed-Effects Model (GLMM) with a binomial error distribution was employed using the ‘glmmTMB’ package (Brooks et al. 2017). The predictor variables included distance from water, mean EVI and LST; percentage of built-up area (b), bare land (bl), agricultural land (al), tree cover (tc), and flooded vegetation (fv) within each buffer. All continuous predictors were standardised on the same scale, while ‘year’ (2021 and 2022) was included as a categorical fixed effect. Specifically, for combined species model, we included phase, study sites, point counting station, and species as nested random intercepts. Whereas the species-specific models incorporated phase, study sites, point counting station as nested random intercepts. This allowed us to control for unobserved heterogeneity and potential non-independence of repeated observations (Zuur et al. 2009). The residuals of the fitted model were checked using ‘DHARMa’ (Hartig 2024) (Figure S1-7) and ‘Performance’ (Lüdecke et al. 2021) packages. Multi-model averaging was conducted using the ‘MuMin’ package (Bartoń 2025) in R, with model selection based on AICc weights. Models were ranked by their AICc values, and the best models were identified using ΔAICc and Akaike weights (Wi) (Burnham and Anderson 2004). Multicollinearity among predictor variables for each models was assessed using the generalised variance inflation factor (GVIF), where values between 1 and 5 indicate moderate correlation, and values above 5 suggest high correlation (James et al. 2013). Only the models with predictor variables showing less than 5 GVIF values were retained. GVIF were calculated using the ‘performance’ package (Lüdecke et al. 2021), and all analyses were performed in RStudio (R Core Team 2024). Results Species abundance and composition across study areas A total of 20,537 birds of six species belonging to the family Ardeidae was recorded from four different study sites; of which 56.8% was from Kamduni, 18.3% from EKW, 16.6% from Baruipur and 8.18% from Rajarhat. Species abundance varied significantly across the study sites (Kruskal-Wallis test: H=1639.8, df = 3, p <0.001), pair-wise post-hoc Dunn test with Bonferroni adjustments showed significant difference between all four locations (p<0.05). The NMDS analysis identifies distinct patterns of species composition across the study sites, indicating varying degree of which study sites were (dis)similar. Overall, separations among all study sites were significantly expressed, as shown in Fig.3. In addition, the species composition also varied substantially between the study sites (ANOSIM: Stress = 0.16; R 2 = 0.29, p< 0.001, 9999 permutations). Kamduni exhibited the highest abundance for Ardea cinerea , Ardea alba , and Egretta garzetta , while Ardea intermedia and Ardea purpurea were most abundant in Baruipur (Table 2). Ardeola grayii was relatively evenly distributed across the study sites, with the highest numbers of observed in Kamduni. Non-parametric Kruskal-Wallis tests revealed significant (p<0.001) differences in abundance for each individual species across the study sites (Table 2). The post-hoc Dunn test with Bonferroni adjustment identified not significant pairwise differences only between Baruipur and Rajarhat for Ardea cinerea and Ardea alba , and between EKW and Rajarhat for Ardea intermedia and Ardea purpurea . For the Egretta garzetta and Ardeola grayii all pairwise comparisons found to be statistically significant (p<0.05). Table:2 Species-wise Abundance and Statistical Summary. Species Scientific name Code Total Count % Distribution across the locations Kruskal-Wallis Test (H) P Kamduni EKW Baruipur Rajarhat Grey Heron Ardea cinerea GH 3136 94.3 4.62 0.73 0.38 1141.4 0.001 Great Egret Ardea alba GE 2217 90.3 7.13 1.67 0.90 1055.8 0.001 Intermediate Egret Ardea intermedia IE 1331 28.9 11.3 49.4 10.3 272.95 0.001 Purple Heron Ardea purpurea PH 450 0.44 15.6 69.6 14.4 312.09 0.001 Little Egret Egretta garzetta LE 3416 71.1 8.23 15.0 5.59 771.35 0.001 Pond Heron Ardeola grayii POH 9987 39.0 29.7 18.7 12.6 519.12 0.001 Land use and land cover (LULC) analysis The classification accuracy level for the year 2000 was 98.25%, while for 2022, it was 94.59%, with corresponding kappa coefficient of 0.979 and 0.935, respectively, indicating high classification reliability (Landis and Koch 1977). The land use class transitions between the two decades (2000–2022) are given in S4 table and Figure 4 . The total area of land use changes that have happened over the two period i.e., 2000 and 2022 for each selected LULC parameters are given in Table 3 ; highlights the major significant changes such as there was a substantial increase in water bodies by 65.55 km² (49%), built-up areas by 158.49 km² (51%), whereas there was a sharp decline in tree cover areas by 427.86 km² (54%) in 2022 compared to 2000. Table 3. Area statistics between the year of 2000 to 2022. LULC classes 2000 OA:98.25%; K:0.979 2022 OA:94.59%; K:0.935 Change 2000 to 2022 Area/km 2 % UA PA Area/km 2 % UA PA Area/km 2 % WB 133.51 3.71 1 1.00 199.06 5.53 1.00 0.94 65.55 49 B 311.43 8.65 1 1.00 469.92 13.05 0.97 1.00 158.49 51 BL 648.02 18.00 1 0.96 449.87 12.49 0.96 0.96 -198.14 -31 AL 1441.65 40.04 0.90 1.00 1776.30 49.33 0.88 0.91 334.66 23 TC 788.99 21.91 1 0.97 361.13 10.03 1.00 0.96 -427.86 -54 FV 277.31 7.70 1 0.97 344.61 9.57 0.88 0.91 67.31 24 WB waterbody, B built-up, BL bare land, AL agricultural land, TC tree cover, FV flooded vegetation. OA overall accuracy, K kappa coefficient. UA user accuracy, PA producer accuracy . Ardeidae occurrence patterns using mixed effects models Based on the top model of GLMM the overall occurrence of all six species shows that distance from water (-0.412± 0.090), EVI (-0.388± 0.111), buildup (-0.4760.743±0.187), bare land (-1.083±0.205), flooded vegetation (-0.964±0.189) were significant and negatively influenced the occurrence, and the overall occurrence shows significant increase in 2022 (0.211±0.072) as compared to 2021(S5 Table.). The top model for Ardea cinerea showed that the occurrence probability negatively influenced by increasing distance from water (-2.782± 0296), EVI (-1.049±0.301), bare land (-1.610± 0.375), flooded vegetation (-0.895±0.234), whereas tree cover (0.540±0.203) influence positively. The categorical variable ‘year’ positively influenced occurrence for 2022 (0.511±0.183) as compared to 2021.The top model for the Ardea alba showed that distance from water (-0.691±0.304), EVI (-0.962±0.263), bare land (-1.834±0.327), flooded vegetation (-1.107±0.233) had negatively influenced on occurrence. Whereas the categorical variable ‘year’ positively influenced the occurrence for 2022 (0.665± 0.174) as compared to 2021. The top model for Ardea intermedia shows EVI (-0.414±155), distance from water (-0.355±0.154), had negative influence, and agricultural land (1.219± 0.194) influence positively on the occurrence. The top model for Ardea purpurea shows that tree cover (-1.067± 0.406), buildup (-0.594± 0.280) influence negatively, and agricultural land (0.967± 0.24) influenced positively the occurrence of the species. The top model for the Egretta garzetta occurrence negatively influenced by distance from water (-0.765± 0.205), EVI (-0.583± 0.233), bare land (-1.217± 0.327), flooded vegetation (-1.282± 0.310), and positively influenced by tree cover (0.525± 0.250), agricultural land (0.869± 0.320). The occurrence of Egretta garzetta shows significant increase in the year of 2022 (0.685± 0.160) as compared to 2021. The top model for Ardeola grayii shows that distance from water (-0.469± 0.200), tree cover (-0.932± 0.450), LST (-0.416± 0.190), buildup (-0.910± 0.439), flooded vegetation (-1.226± 0.462) influence the occurrence probability negatively. Whereas the categorical variable year also negatively influence the occurrence for 2022 (-0.975± 0.318) as compared to 2021. Discussion Community Composition and Habitat Association of Ardeidae Our study focused on the Ardeidae species assemblage along the peri-urban landscape of Kolkata, highlighted how urbanization, and habitat modification shaping the Ardeidae community structure. The significant spatial variation in Ardeidae abundance, and composition across the study sites, indicates the differences in habitat quality, land-use pattern, and the anthropogenic pressure. The relatively high abundance of Ardeidae species at Kamduni likely due to the extensive aquaculture farms with periodic hydrological regimes, offering dynamic foraging habitats. While EKW being a Ramsar site and aquaculture farm showed significantly less Ardeidae species assemblage than Kamduni, which was likely due to reduction in wetland cover , increasing development at the periphery of the wetland, and comparatively less dynamic hydrological regimes (Ghosh et al. 2018; Barik et al. 2022). In contrast, lower abundance at Rajarhat study site highlights the consequence of intensive urbanization, and reduction of suitable foraging habitat. The NMDS and ANOSIM showed significant dissimilarities in species composition suggesting that habitat structure, and conditions vary considerably across the study area, influencing species-specific assemblages. Species-specific abundance pattern further reinforces the spatial variation. Ardea cinerea and Ardea alba generally use wide range of habitats (Kushlan and Hancock 2005), often known to adjust their foraging location based on short term changes in prey availability (Gawlik 2002; Beerens et al. 2015). We found Ardea cinerea and Ardea alba as predominantly open water foragers, were least abundant at agriculture mixed landscape of Baruipur and highly urbanized landscape of Rajarhat, as compare to the aquaculture dominant Kamduni and EKW. However, significant abundance differences were also found between Kamduni and EKW, suggesting even localize variation among the sites with high water coverage, can affect the species assemblages. Ardea purpurea typically prefers reedbeds but also forages in rice fields, lakeshores, man-made ditches, canals, etc. (Kushlan and Hancock 2005). The agriculture mixed landscape of Baruipur had the highest abundance of Ardea purpurea , showing significant difference compared to rest of the study areas, indicating limited availability of foraging habitat. In case of Ardea intermedia , the habitat preference varied; mainly inland habitats with abundant emergent aquatic vegetation, including freshwater swamps, pools, floodplains, rice fields, open water, sewage ponds (Kushlan and Hancock 2005). The significant difference between Baruipur and rest of the study sites indicates that the Ardea intermedia prefers the marshland mixed agricultural landscape of Baruipur. However, unlike the Ardea purpurea , it was also moderately abundant in aquaculture dominated landscapes, showing a degree of habitat flexibility. Ardeola grayii and Egretta garzetta are the habitat generalist (Choi et al. 2007; Roshnath and Sinu 2017), and in this study we found Ardeola grayii as the most widespread and abundant species across the study sites. Whereas Egretta garzetta was predominantly found in aquaculture farms, particularly at Kamduni, but also use a wide range of habitats. Notably, both species were frequently observed foraging in seasonal waterlogged patches near human settlement. Highlighting the fact that the high abundance (65. 26%) and flexible habitat use of both these generalist species may indicate that the generalist species are to some extent, resilient in the urbanized landscape. Urban Expansion and Land Use Dynamics The results from the LULC change detection between two decades, provides intensified insight about the ongoing changes in and around the landscape of Kolkata, and its shows extensive increase of urbanization, and associated land-use shifts. A striking increase in built-up areas highlights direct urban expansion (Mahata et al. 2024b). While most of the increased waterbodies are the aquaculture farms, may not be the natural wetlands (Halder et al. 2022). The Agricultural lands are also expanded at the expense of tree cover, indicating large-scale reduction of vegetation. Whereas the increase of flooded vegetation may associate to the shift of local hydrology or land conversion which promote seasonal water retention. The decrease in bare lands suggests previously unused or marginal lands are converted for further use. This trend suggests the expansion of urban growth beyond the buildup settlement, which involves transformation of the green space into managed or partially managed system. Environmental Drivers and Species Occurrence The GLMM results provide compelling evidence for the influence of specific landscape-level variables on the occurrence of Ardeidae species. Distance from water (Figure.5a) emerged as significant negative predictor for all species, indicating the important role of aquatic proximity for foraging, roosting, and nesting (Kushlan 1986; Choi et al. 2007; Kelly et al. 2008; Choi and Yoo 2011). Urban expansion is known to adversely affect the bird communities adjacent to natural habitat, as reflected by the negative effects of built-up, bare land, and LST, which likely reduces habitat suitability, through disturbance and prey dynamics (McKinney 2006; Marzluff and Ewing 2008; Ma et al. 2010). The negative response to flooded vegetation and EVI which often associated with wetland health, suggested that overly grown vegetation may impede the efficiency of visual foragers. While interannual comparison between 2021 and 2022 shows a significant increase in Ardeidae’s occurrence, which most likely due to local management practices and fluctuating water availability, especially in aquaculture farms. Species specific models further emphasize distinct ecological strategies. Ardea cinerea and Ardea alba (Figure.5b & c) show strong negative response with distance from water, bare land, flooded vegetation, and EVI, indicating a preference for open water condition over densely vegetated areas. Tree cover had a positive effect on Ardea cinerea occurrence, likely due to its role in providing roosting substrates as no nesting was observed. An increase in occurrence from 2021 to 2022 for both the species may reflect to the local management practices. Egretta garzetta (Figure.5d) exhibit a strong preference for areas near waterbodies, with a sharp decline in occurrence as distance increased. The species showed avoidance of dense vegetation and bare ground, likely due to reduced prey visibility or unsuitable habitat structure. Positive association with agricultural land and tree cover indicating its use of paddy field for foraging and nearby trees for roosting. The increase in occurrence in 2022 further indicates sensitivity to short-term habitat changes. Despite significant negative effects from several predictors, Ardeola grayii (Figure.5e) maintained consistently high occurrence probabilities (>0.9), emphasizing its ecological plasticity and high adaptability in modified urban environments. The weak negative association with flooded vegetation may reflect a preference for open edges, but this did not substantially limit it presence. Ardea intermedia (Figure.5f) showed a clear preference for habitat close to waterbodies, and avoided high EVI zones, indicating avoidance of densely vegetated wetlands. Its positive association with agricultural land highlights its use of semi modified habitats such as rice field, as an important foraging habitat (Sundar and Kittur 2013). Ardea purpurea (Figure.5g) showed relatively low occurrence across the study sites, with weak positive association with agricultural land and negative response to tree cover, and built-up areas. This indicates rice field may serve as critical refugia amid habitat loss. Conflict with humans and Conservation Implications Piscivorous species are reported to damage local fish stocks (Harris et al. 2008) and we found herons and egrets were the most abundant (75.18%) species at Kamduni and EKW. Fishermen often cover the commercial fishponds with fine fishing lines or nets (Barik et al. 2022). The same tendency was also observed at paddy cultivation lands during seed sowing and early vegetative phase at Baruipur. These activities of locals restrict the entry of the larger fish-eating birds for foraging. Such restricted access to preferred foraging habitats may adversely affects bird species belong to a specialized foraging group (Burin et al. 2016). In the study area, locals even hunt herons and egrets for meat consumption and often as hobby. Fish farm owners frequently engage people to scare away fish-eating birds either by using slings or by beating steel plates or combination of both. Such strategies were employed only to minimize the fish loss, and maximize the financial gain. Over the years reduction in natural wetlands, and increase in aquaculture farms may led to more conflict between fish-eating birds and humans. The distribution, and habitat use assessment information is often required for making conservation, and management decision. Unfortunately, these herons and egrets are least studied species in India. Here we attempted to understand how the occurrence probability and abundance of these species varied in an urban landscape. Although all six species are listed as the least concern species, Ardea purpurea (BirdLife International 2019) and Ardea intermedia (BirdLife International 2020) are projected to show global population decreasing trend. A similar trend was observed in our study, where these two species were the least abundant across the study sites. Furthermore, emergent vegetation patches are disappearing as the urban encroachment increases in the study areas. On the other hand, built-up areas negatively impact the bird population, yet the generalist species show a moderate to high level of tolerance towards such alternative environment (Sultana et al. 2021). These condition leads to biotic homogenization (McKinney 2002) wherein specialist species are less adaptive and urban exploiters are thriving (Palomino and Carrascal 2006). We suggest that maintaining multifunctional wetlands and setting urban sprawl limits requires immediate attention. Future conservation efforts should focus on conserving the remaining wetlands, promoting coexistence through community-based conflict mitigation strategies, and including wetland buffers into urban planning. Promoting non-lethal deterrents and long-term monitoring, and awareness campaigns might help to reduce the conflict. Policies on urban biodiversity should support the sustainable aquaculture and preservation of remaining wetlands. More studies on foraging ecology and habitat use of common Ardeidae are needed to support adaptive management and protect bird populations in rapidly urbanizing areas. Limitations and Future Research Directions This study provides valuable insights into the community structure of Ardeidae species in urban and peri urban wetlands; certain areas should be identified for future research. The study covered four representative wetlands and two years of data, providing insightful interannual comparisons but limited spatial and temporal scope. Incorporating more sites across varied urban gradient and extended period of monitoring would reveal the long-term trends. Additionally, sampling did not cover during the monsoon, a critical period of Ardeidae breeding (Ali and Ripley 1978), although only two confirmed nesting of least abundant Ardea purpurea was recorded. The models focused on broad habitat attributes; species specific habitat utilization and foraging studies may provide further critical insight. Based on these findings, future studies can provide more significant ecological insights and support adaptive wetland management in rapidly urbanizing landscapes. Conclusion This study demonstrates that urban expansion and aquaculture intensification in peri-urban landscape of Kolkata have altered the availability and structure of wetland habitat, leading to species-specific responses within the Ardeidae community. Commercial fish ponds with proper banks and dikes produce suitable feeding habitats, whereas a mix of agricultural and fallow lowlands can also increase the bird assemblage (Barik et al. 2022 ). The aquaculture farms at Kamduni have the highest abundance of fish-eating birds. The submerged banks and biannual recycling process resulted in great variation in water levels, which offer suitable foraging habitats. In contrast, despite being a Ramsar Site with an aquaculture-dominated landscape, the EKW study areas showed comparatively lower abundance of Ardeidae species than Kamduni. This may be due to limited vegetation being allowed to grow except for the water hyacinth at the fringes of fish ponds and the maintenance of relatively high-water levels (Mookherjee and Chatterjee 1999 ), which may reduce the foraging efficiency. Prior to land acquisition, the habitats of Baruipur and Rajarhat were characterised by agricultural and fallow land (Roy 2014 ), which became suitable for wetland-dependent birds during the transitional phase. However, with the initiation of large-scale urban development (Naskar et al. 2021 ; Mahata et al. 2024a ), bird species assemblage declined sharply. The ongoing rapid loss of natural wetlands and presence of high density of fish in aquaculture farms likely increase the pressure of fish-eating birds on commercial ponds (Fasola and Brangi 2010; Feaga et al. 2015; Burr et al. 2020). Generalist species such as the Ardeola grayii and Egretta garzetta show some adaptability to these modified environments. In contrast, specialist species such as the Ardea purpurea and Ardea intermedia , which depend largely on marshlands and, to some extent, agricultural fields may become more vulnerable. While species like Ardea cinerea and Ardea alba were found as predominantly open water foragers, and preferred aquaculture farms with dynamic water regimes, potentially heightening conflicts with fish farmers. Species-specific models revealed varied ecological responses, emphasising the need for habitat mosaics to support diverse assemblages. Urgent attention is needed to set the limit of urban sprawl and prioritise the conservation of multifunctional wetlands. Conservation and management strategies should focus on protecting the remaining wetlands, integrating ecological buffers into urban planning, enhancing public awareness, promoting community-based conflict mitigation, encouraging non-lethal deterrents, and implementation of continuous monitoring. Declarations Acknowledgements We thank the Director, Zoological Survey of India for support and encouragement. We are also thankful to the University Grants Commission, India, for fellowship support to the first author. Authors’ contributions GM and AN designed the study. AN carried-out field survey and did the data analyses. AN and GM wrote the draft manuscript. All authors read and approved the final manuscript. Funding This work was financially supported by a fellowship to the first author by University Grants Commission (UGC), Government of India. Data availability statement Raster data related to land use and spectral index variables were used for model building using open-source datasets. The abundance and occurrence data of the species can be made available upon reasonable request. Consent for publication The article submitted herewith contains the findings of our original research, is not under consideration for publication elsewhere, and is approved by all authors of this manuscript. Competing interests The authors declare that they have no conflict of interest in publishing the manuscript. References Ali and Ripley (1978) Handbook of Birds of India and Pakistan Vol 1 Arora R, Balachander T, Agrawal I, et al (2024) Conserving Freshwater Ecosystems in India: A call to action. Aquatic Conservation 34:e4165. https://doi.org/10.1002/aqc.4165 Barik S, Saha GK, Mazumdar S (2022) Influence of land cover features on avian community and potential conservation priority areas for biodiversity at a Ramsar site in India. 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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-7300140","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502589543,"identity":"c1381353-c22e-46ef-8eba-6b933e462921","order_by":0,"name":"Anindya Naskar","email":"","orcid":"","institution":"Zoological Survey of India, M-Block","correspondingAuthor":false,"prefix":"","firstName":"Anindya","middleName":"","lastName":"Naskar","suffix":""},{"id":502589544,"identity":"c6f068d3-62d5-4a09-bf6f-0b96472093a6","order_by":1,"name":"Gopinathan Maheswaran","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYFACHhiD+QADgwGEeYBILWwJJGvhMSDOWbrtZw8+/Nlml88/u+fzxx8FDHb9EgmMhwvwaDE7k5dszNuWbDnjztkNxkCLkmfOSGA4PAOflgM5ZtKM25gNGG7kbkgG+iXZ4MwBhsM8+LScf2P+8+e2egP5GzkPDv4gSsuNHDMG3m2HDQxu5DA2AB1mZ3C8gZCWN8bSvP+OGxjeSDNm5jGQSJBsb2wg4LAcw48/zlQbyN1Ifvzxxx8be35m5sOf8WlBBxKJDQyMDSRoAAJ70pSPglEwCkbBSAAA/TpNoJpa/n0AAAAASUVORK5CYII=","orcid":"","institution":"Zoological Survey of India, M-Block","correspondingAuthor":true,"prefix":"","firstName":"Gopinathan","middleName":"","lastName":"Maheswaran","suffix":""}],"badges":[],"createdAt":"2025-08-05 11:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7300140/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7300140/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11252-025-01845-w","type":"published","date":"2025-11-02T15:58:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89549556,"identity":"bde7d9e8-e1a1-487b-bb63-696580729b5b","added_by":"auto","created_at":"2025-08-21 08:03:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1500684,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study areas, showing four selected wetlands i.e., 1. Baruipur 2. East Kolkata wetlands (EKW), 3. Kamduni, and 4. Rajarhat.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7300140/v1/d3712afdab3a54eb4586ecab.png"},{"id":89549219,"identity":"edb081c6-550a-4a39-936a-817bb7e25b70","added_by":"auto","created_at":"2025-08-21 07:55:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1470145,"visible":true,"origin":"","legend":"\u003cp\u003eShowing land use land cover map of 2000 and 2022.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7300140/v1/fa7849a1f5f7fad338fb21ab.png"},{"id":89549218,"identity":"86b7950b-917f-4e4f-893d-8e0097a54119","added_by":"auto","created_at":"2025-08-21 07:55:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":399898,"visible":true,"origin":"","legend":"\u003cp\u003eNMDS plots for Hellinger transformed abundance data of six heron and egret species at four different study area.\u003cem\u003e Ardea cinerea (GH), Ardea alba (GE), Ardea intermedia(IE), Ardea purpurea (PH), Egretta garzetta (LE), Ardeola grayii (POH).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7300140/v1/e1a3929d3354506f0bd2b25c.png"},{"id":89549223,"identity":"51a83251-3d80-44a7-a725-bdb4cea6b18c","added_by":"auto","created_at":"2025-08-21 07:55:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":702760,"visible":true,"origin":"","legend":"\u003cp\u003eSankey diagram showing the Land Use pattern change between the year of 2000 and 2022.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7300140/v1/2c76b3a05545dd37c64aaec7.png"},{"id":89549559,"identity":"3d08cc11-ff55-4a6a-9e1e-20d95247921d","added_by":"auto","created_at":"2025-08-21 08:03:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":430475,"visible":true,"origin":"","legend":"\u003cp\u003ePredictions from GLMM of the occurrence of heron and egrets in response to the predictor variables.\u003cstrong\u003e a) \u003c/strong\u003eEffects of standardized predictor variables on the occurrence probability of all six species. \u003cstrong\u003eb-g)\u003c/strong\u003e showing effects of predictor variables on the occurrence probability of each selected species. \u003cem\u003eArdea cinerea (GH), Ardea alba (GE), Ardea intermedia(IE), Ardea purpurea (PH), Egretta garzetta (LE), Ardeola grayii (POH).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7300140/v1/66205f8f76e2e105b131757f.png"},{"id":95041518,"identity":"984b1f9d-c6c3-497e-9b3f-adbebcd5a553","added_by":"auto","created_at":"2025-11-03 16:11:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5460059,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7300140/v1/a43327f0-5536-49f2-8f14-767f1990fdd7.pdf"},{"id":89549225,"identity":"0a12e8e3-601c-4852-a018-20de321fdbdd","added_by":"auto","created_at":"2025-08-21 07:55:49","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":283617,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-7300140/v1/c27d9b340925b00ffce22662.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Cost of Urban Expansion: Habitat Loss and Shifting Distribution of Long-Legged Wading Birds in a Peri-Urban landscape gradient","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWetlands are among the most productive (Ghermandi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) yet fragile ecosystems on earth (Fraser and Keddy \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), providing a wide array of essential and irreplaceable ecological services (Leibowitz \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Additionally, human society to some extent significantly depend on wetlands for its various ecological and economic benefit (Russi et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sulphey and Safeer \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Due to their high productivity and biological diversity, wetlands support a complex network of interacting organisms including globally threatened avian species (Paracuellos \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ghermandi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In Aisa 20% of the threatened bird species are found in wetland habitats (Kumar \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOver the years, various activities of humans have unfavourably influenced the hydro framework, plant development, and reduced the overall stability of the wetland ecosystem, resulting in the loss of more than 50% of natural wetlands worldwide (Zakaria and Rajpar \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Davidson \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bassi et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Giosa et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Despite decades of global conservation efforts, wetlands continue to decline both in quality and quantity (Dahl \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This rapid decline poses a severe threat to avian communities that depend on these habitats for foraging, breeding, and roosting (Dri et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Though in India the numbers of Ramsar site has more than doubled in past five years, increased from 42 to 89 (Ramsar, 2020; Ramsar, 2025), but several ecologically valuable wetlands remain unrecognized in this policymaking process, limiting their legal protection and conservation (Turner et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Ghermandi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMany compelling issues such as pressure of population growth, urbanization, and increasing economic activities have further degraded numerous freshwater ecosystems in India (Arora et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Assessment of the natural wetlands and the human-altered wetlands using hydrology, water chemistry and an array of plants and animals shows colossal contrasts (Campbell et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Hartzell et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Hossler and Bouchard \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Moreno-Mateos et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fidorra et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Alternatively, rapid loss and degradation of natural wetlands make human-influenced marshes and lake areas ecologically significant habitats for a plenty of avian species and to some extent may reduce the detrimental effects of wetland loss (White and Main \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Sim et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ram\u0026iacute;rez et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zakaria and Rajpar \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).Yet, it is unclear and significant to know that at what extent the human-influenced wetlands can serve as an option, in contrast to natural wetland (Fidorra et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWading birds are highly mobile and possess the top position in the aquatic food web and considered as suitable bioindicators of aquatic habitats (Noble et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Frederick et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), especially for the human-influenced wetlands (Zakaria and Rajpar \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Fidorra et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Their abundance and richness over a particular wetland indicate the characteristics (Kushlan \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Noble et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), accessibility of the fish (Frederick et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and invertebrate prey population of that habitat (Fidorra et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Heronry (heron, egrets, ibises and storks) birds (Kushlan and Hancock \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) are inseparable from the wetlands as their primary food sources are aquatic invertebrates, fishes, amphibians, and sometimes small reptiles to lower mammals (Ali and Ripley \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). However, rapid colonization of heronry species like egrets, herons, bitterns, storks, spoonbills, and ibises may also happen in the human-altered habitats (Brown and Smith \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Choi et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Herons and egrets may play a critical part as bio-indicators to monitor changes in local and regional conditions (Choi and Yoo \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUrbanization-induced land modifications result in habitat loss, fragmentation, and local extinction of most native species, recent trends indicate more severe impacts in near future (Li et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Small wetlands may be more vulnerable to the alteration than the larger wetlands, which requires further administration challenges (Custer and Galli \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; McKinney \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Marzluff and Ewing 2008; Isaksson \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In India, very few attempts are made to assess the impact of urbanization on wading birds or their adaptation to environmental change (Urfi \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Charutha et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Given the ecological importance of both natural and human-modified wetlands, a comprehensive understanding of their role in supporting avian communities is essential informing for wetland conservation and management policies. This study investigates the urbanization driven land-use changes on the community structure of Ardeidae species within the peri-urban landscape of the Kolkata Metropolitan Region, in eastern India.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy area\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was carried out at four wetland sites located around the Kolkata city (Figure: 1); Kamduni (22.63\u0026deg;N, 88.52\u0026deg;E), Rajarhat (22.61\u0026deg;N, 88.46\u0026deg;E), East Kolkata Wetlands (EKW) (22.56\u0026deg;N, 88.42\u0026deg;E), and Baruipur (22.36\u0026deg;N, 88.38\u0026deg;E). Although these wetlands differ in structure and management, but act as critical habitats for a diverse assemblage of bird species. Kamduni and EKW are dominated by aquaculture ponds, whereas Baruipur and Rajarhat are largely composed of natural marshes, agricultural and fallow land. According to data from the citizen science platform eBird (http://www.ebird.org), each of the four wetlands supports over 150 bird species, underscoring their importance as critical habitats for waterfowl, waders, grassland birds, and raptors, including several returning migrants and threatened species. Land acquisition (KMDA 2011; Roy 2014) and ongoing developmental activities, particularly in Rajarhat (Dhar et al. 2019) and Baruipur (Naskar et al. 2021), pose significant threats to local bird populations. Historically, the EKW, a Ramsar site alone refuge 271 bird species and this numbers have decreased to 162 in 1990s (Mookherjee and Chatterjee 1999), and by 2021 the number further reduced to 92 (Barik et al. 2022). The recent re-discovery of the Blue-breasted Quail (\u003cem\u003eSynoicus chinensis\u003c/em\u003e) at Baruipur (Bhattacharjee et al. 2020), once presumed locally extinct from EKW and its surroundings wetlands (Mookherjee and Chatterjee 1999), led to a study \u0026nbsp;(Naskar et al. 2021) highlighting major conservation issues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBird Survey\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCommonly found Ardeidae species such as Grey Heron (\u003cem\u003eArdea cinerea\u003c/em\u003e), Great Egret (\u003cem\u003eArdea alba\u003c/em\u003e), Purple Heron (\u003cem\u003eArdea purpurea\u003c/em\u003e), Intermediate Egret (\u003cem\u003eArdea intermedia\u003c/em\u003e), Little Egret (\u003cem\u003eEgretta garzetta\u003c/em\u003e) and Pond Heron (\u003cem\u003eArdeola grayii\u003c/em\u003e), were selected for the study. The point count method was employed to record the abundance data of selected Ardeidae species at each study site. Data were collected weekly at each area between 0600h and 1800h under standardized weather conditions, avoiding surveys during strong winds or rainfall (Cezilly et al. 1990). Each site comprised of eight fixed point count stations, at least 400 meters apart from each other\u0026rsquo;s. Surveys were conducted at one site per day, covering all eight points. At each point, observations were made for 10-minutes to record the individual species abundance. Birds flying overhead during the surveys were not recorded. Observations were made using a Nikon 16x50 binocular and a Nikon spotting scope (Prostaff 5 with an 81-A magnification eyepiece). Abundance data was collected from January to May and October to December during 2021 and 2022.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLULC and change detection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInstead of selecting only the four study sites for landcover map, we rather preferred to select the landscape (\u003cstrong\u003eFigure 2\u003c/strong\u003e) to understand the changes in land use pattern of the Kolkata and its surroundings. While doing so, study site EKW was taken as centroid and a radius of 60 km\u003csup\u003e2\u003c/sup\u003e area was selected for final land use land cover (LULC) map in Google Earth Engine (GEE) (Gorelick et al. 2017). To check the change in land use pattern in two-decade (2000-2022), Landsat 5 Thematic Mapper (TM) images (30m spatial resolution) and Landsat 8 OLI and TIRS images (30m spatial resolution) were obtained from United States Geological Survey (USGS) and processed in GEE. Landsat images were selected for the period of January to December each year with less than 10% cloud cover. A median composite was generated from the selected images to reduce seasonal variations (Phan et al. 2020). Total of six classes were taken; water, buildup, bare land, agricultural land, flooded vegetation, and tree cover based on satellite image and field observations (\u003cstrong\u003eTable:1\u003c/strong\u003e). A total of 600 samples, 100 samples for each land use class were generated using the high-resolution imagery. To improve the classification accuracy, spectral indices (\u003cstrong\u003eS1 table\u003c/strong\u003e), along with NASA SRTM 30m Digital Elevation (Farr et al. 2007) were used in GEE as additional input variables (Phan et al. 2020).\u003c/p\u003e\n\u003cp\u003eThe Random Forest (RF) classifier was used in GEE for LULC classification (Dubertret et al. 2022). The number of the decision tree was selected using hyperparameter tuning to increase the computational efficiency. The model was trained using a 70:30 training-testing split, and overall accuracy, user\u0026rsquo;s accuracy, producer\u0026rsquo;s accuracy, and kappa coefficient were calculated to assess classification reliability. LULC transitions between 2000 and 2022 were analysed using post classification comparison approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDescription of different LULC classes.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"668\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eLULC Class type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 498px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eWaterbody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 498px;\"\u003e\n \u003cp\u003eArea with standing water like lakes, ponds, aquaculture farms etc.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eBuildup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 498px;\"\u003e\n \u003cp\u003eArea with human settlements like urban/rural areas, commercial areas, industrials area, roads, etc.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eBare land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 498px;\"\u003e\n \u003cp\u003eLands with sparse or no vegetation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eAgricultural lands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 498px;\"\u003e\n \u003cp\u003eHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eTree cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 498px;\"\u003e\n \u003cp\u003eArea with plantation, visible tree canopy, etc.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eFlooded Vegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 498px;\"\u003e\n \u003cp\u003eAreas of any type of vegetation with obvious intermixing of water throughout most of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe abundance data followed non-normal distribution (Kolmogorov-Smirnov test: D=0.3182, p\u0026lt;0.001). Hence, non-parametric Kruskal\u0026ndash;Wallis tests were applied to compare species abundance between the study areas. Furthermore, (dis)similarity patterns of heron and egret across the study areas were assessed, using non-metric multidimensional scaling (NMDS) (Clarke 1993), based on the Hellinger transformed data (Legendre and Gallagher 2001), using \u0026lsquo;Vegan\u0026rsquo; package (Oksanen et al. 2025) in R. In addition, a nonparametric permutation procedure of analysis of similarities (ANOSIM) was used to test for differences in the compositions of the groups formed by the NMDS analysis (9999 permutations) (Clarke 1993). The abundance of each of the six Ardeidae species showed non-normal distribution. Therefore, non-parametric Kruskal-Wallis tests were applied to compare their abundance across the study areas.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe abundance data of all six species converted into presence and absence (hence occurrence), for each point counting stations. The occurrence data categorised into four phases (\u003cstrong\u003eS2 table\u003c/strong\u003e) as per the habitat structure, seasonal change, water level and cultivation regimes. The \u0026ldquo;landscapemetrics\u0026rdquo; package (Hesselbarth et al. 2019) was used to quantify the percentage of the land cover types within the 200m buffer for each point count stations for the year 2022. The mean Enhanced vegetation index (EVI) (Liu and Huete 1995), Land surface Temperature (LST ) (Sobrino et al. 1994) values were calculate (\u003cstrong\u003eS3 table\u003c/strong\u003e) for each four phases within the 200m buffer for the year 2021 to 2022 using GEE. Most of the point count stations show dynamic change in water level, hence normalized difference water index (NDWI) (McFEETERS 1996) was generated at first in GEE for each four phases between 2021 and 2022. Therefore, distance from water (disw) from each counting station was calculated from the NDWI using proximity tool in QGIS (version 3.40).\u003c/p\u003e\n\u003cp\u003eAs the dataset had a nested structure, \u0026nbsp;Generalised Linear Mixed-Effects Model (GLMM) with a binomial error distribution was employed using the \u0026lsquo;glmmTMB\u0026rsquo; package (Brooks et al. 2017). The predictor variables included distance from water, mean EVI and LST; percentage of built-up area (b), bare land (bl), agricultural land (al), tree cover (tc), and flooded vegetation (fv) within each buffer. All continuous predictors were standardised on the same scale, while \u0026lsquo;year\u0026rsquo; (2021 and 2022) was included as a categorical fixed effect. Specifically, for combined species model, we included phase, study sites, point counting station, and species as nested random intercepts. Whereas the species-specific models incorporated phase, study sites, point counting station as nested random intercepts. This allowed us to control for unobserved heterogeneity and potential non-independence of repeated observations (Zuur et al. 2009). The residuals of the fitted model were checked using \u0026lsquo;DHARMa\u0026rsquo; (Hartig 2024) (Figure S1-7) and \u0026lsquo;Performance\u0026rsquo; (L\u0026uuml;decke et al. 2021) packages.\u003c/p\u003e\n\u003cp\u003eMulti-model averaging was conducted using the \u0026lsquo;MuMin\u0026rsquo; package (Bartoń 2025) in R, with model selection based on AICc weights. Models were ranked by their AICc values, and the best models were identified using \u0026Delta;AICc and Akaike weights (Wi) (Burnham and Anderson 2004). Multicollinearity among predictor variables for each models was assessed using the generalised variance inflation factor (GVIF), where values between 1 and 5 indicate moderate correlation, and values above 5 suggest high correlation (James et al. 2013). Only the models with predictor variables showing less than 5 GVIF values were retained. GVIF were calculated using the \u0026lsquo;performance\u0026rsquo; package (L\u0026uuml;decke et al. 2021), and all analyses were performed in RStudio (R Core Team 2024).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSpecies abundance and composition across study areas\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 20,537 birds of six species belonging to the family Ardeidae was recorded from four different study sites; of which 56.8% was from Kamduni, 18.3% from EKW, 16.6% from Baruipur and 8.18% from Rajarhat. Species abundance varied significantly across the study sites (Kruskal-Wallis test: H=1639.8, df = 3, p \u0026lt;0.001), pair-wise post-hoc Dunn test with Bonferroni adjustments showed significant difference between all four locations (p\u0026lt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe NMDS analysis identifies distinct patterns of species composition across the study sites, indicating varying degree of which study sites were (dis)similar. Overall, separations among all study sites were significantly expressed, as shown in Fig.3. In addition, the species composition also varied substantially between the study sites (ANOSIM: Stress = 0.16; R\u003csup\u003e2\u003c/sup\u003e= 0.29, p\u0026lt; 0.001, 9999 permutations).\u003c/p\u003e\n\u003cp\u003eKamduni exhibited the highest abundance for \u003cem\u003eArdea cinerea\u003c/em\u003e , \u003cem\u003eArdea alba\u003c/em\u003e, and \u003cem\u003eEgretta garzetta\u003c/em\u003e , while \u003cem\u003eArdea intermedia\u003c/em\u003e and \u003cem\u003eArdea purpurea\u003c/em\u003e were most abundant in Baruipur (Table 2). \u003cem\u003eArdeola grayii\u003c/em\u003e was relatively evenly distributed across the study sites, with the highest numbers of observed in Kamduni. Non-parametric Kruskal-Wallis tests revealed significant (p\u0026lt;0.001) differences in abundance for each individual species across the study sites (Table 2). The post-hoc Dunn test with Bonferroni adjustment identified not significant pairwise differences only between Baruipur and Rajarhat for \u003cem\u003eArdea cinerea\u003c/em\u003e and \u003cem\u003eArdea alba\u003c/em\u003e, and between EKW and Rajarhat for \u003cem\u003eArdea intermedia\u003c/em\u003e and \u003cem\u003eArdea purpurea\u003c/em\u003e. For the \u003cem\u003eEgretta garzetta\u003c/em\u003e and \u003cem\u003eArdeola grayii\u003c/em\u003e all pairwise comparisons found to be statistically significant (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable:2\u003c/strong\u003e Species-wise Abundance and Statistical Summary.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"738\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eScientific name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eCode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eTotal Count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e% Distribution across the locations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eKruskal-Wallis Test (H)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eKamduni\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eEKW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eBaruipur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eRajarhat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eGrey Heron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cem\u003eArdea cinerea\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eGH\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e94.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e4.62\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.73\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1141.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eGreat Egret\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cem\u003eArdea alba\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eGE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e90.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e7.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.67\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1055.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eIntermediate Egret\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cem\u003eArdea intermedia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eIE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e28.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e11.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e49.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e272.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003ePurple Heron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cem\u003eArdea purpurea\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.44\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e15.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e69.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e312.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eLittle Egret\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cem\u003eEgretta garzetta\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e71.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e8.23\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e15.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e5.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e771.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003ePond Heron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cem\u003eArdeola grayii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003ePOH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e9987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e39.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e29.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e18.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e519.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLand use and land cover (LULC) analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe classification accuracy level for the year 2000 was 98.25%, while for 2022, it was 94.59%, with corresponding kappa coefficient of 0.979 and 0.935, respectively, indicating high classification reliability (Landis and Koch 1977). The land use class transitions between the two decades (2000\u0026ndash;2022) are given in \u003cstrong\u003eS4 table\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eFigure 4\u003c/strong\u003e. The total area of land use changes that have happened over the two period i.e., 2000 and 2022 for each selected LULC parameters are given in \u003cstrong\u003eTable 3\u003c/strong\u003e;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e highlights the major significant changes such as there was a substantial increase in water bodies by 65.55 km\u0026sup2; (49%), built-up areas by 158.49 km\u0026sup2; (51%), whereas there was a sharp decline in tree cover areas by 427.86 km\u0026sup2; (54%) in 2022 compared to 2000.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eArea statistics between the year of 2000 to 2022.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"699\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLULC classes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003cp\u003eOA:98.25%; K:0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003cp\u003eOA:94.59%; K:0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eChange 2000 to 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eArea/km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003ePA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eArea/km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003ePA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eArea/km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eWB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e133.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e199.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e65.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e311.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e8.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e469.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e13.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e158.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eBL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e648.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e18.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e449.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e12.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-198.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1441.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e40.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1776.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e49.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e334.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e788.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e361.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e10.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-427.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eFV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e277.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e7.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e344.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e9.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e67.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eWB waterbody, B built-up, BL bare land, AL agricultural land, TC tree cover, FV flooded vegetation. OA overall accuracy, K kappa coefficient. UA user accuracy, PA producer accuracy\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArdeidae occurrence patterns using mixed effects models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the top model of GLMM the overall occurrence of all six species shows that distance from water (-0.412\u0026plusmn; 0.090), EVI (-0.388\u0026plusmn; 0.111), buildup (-0.4760.743\u0026plusmn;0.187), bare land (-1.083\u0026plusmn;0.205), flooded vegetation (-0.964\u0026plusmn;0.189) were significant and negatively influenced the occurrence, and the overall occurrence shows significant increase in 2022 (0.211\u0026plusmn;0.072) as compared to 2021(S5 Table.).\u003c/p\u003e\n\u003cp\u003eThe top model for \u003cem\u003eArdea cinerea\u003c/em\u003e showed that the occurrence probability negatively influenced by increasing distance from water (-2.782\u0026plusmn; 0296), EVI (-1.049\u0026plusmn;0.301), bare land (-1.610\u0026plusmn; 0.375), flooded vegetation (-0.895\u0026plusmn;0.234), whereas tree cover (0.540\u0026plusmn;0.203) influence positively. The categorical variable \u0026lsquo;year\u0026rsquo; positively influenced occurrence for 2022 (0.511\u0026plusmn;0.183) as compared to 2021.The top model for the \u003cem\u003eArdea alba\u003c/em\u003e showed that distance from water (-0.691\u0026plusmn;0.304), EVI (-0.962\u0026plusmn;0.263), bare land (-1.834\u0026plusmn;0.327), flooded vegetation (-1.107\u0026plusmn;0.233) had negatively influenced on occurrence. Whereas the categorical variable \u0026lsquo;year\u0026rsquo; positively influenced the occurrence for 2022 (0.665\u0026plusmn; 0.174) as compared to 2021. The top model for \u003cem\u003eArdea intermedia\u003c/em\u003e shows EVI (-0.414\u0026plusmn;155), distance from water (-0.355\u0026plusmn;0.154), had negative influence, and agricultural land (1.219\u0026plusmn; 0.194) influence positively on the occurrence. The top model for \u003cem\u003eArdea purpurea\u003c/em\u003e shows that tree cover (-1.067\u0026plusmn; 0.406), buildup (-0.594\u0026plusmn; 0.280) influence negatively, and agricultural land (0.967\u0026plusmn; 0.24) influenced positively the occurrence of the species. The top model for the \u003cem\u003eEgretta garzetta\u003c/em\u003e occurrence negatively influenced by distance from water (-0.765\u0026plusmn; 0.205), EVI (-0.583\u0026plusmn; 0.233), bare land (-1.217\u0026plusmn; 0.327), flooded vegetation (-1.282\u0026plusmn; 0.310), and positively influenced by tree cover (0.525\u0026plusmn; 0.250), agricultural land (0.869\u0026plusmn; 0.320). The occurrence of \u003cem\u003eEgretta garzetta\u003c/em\u003e shows significant increase in the year of 2022 (0.685\u0026plusmn; 0.160) as compared to 2021. The top model for \u003cem\u003eArdeola grayii\u003c/em\u003e shows that distance from water (-0.469\u0026plusmn; 0.200), tree cover (-0.932\u0026plusmn; 0.450), LST (-0.416\u0026plusmn; 0.190), buildup (-0.910\u0026plusmn; 0.439), flooded vegetation (-1.226\u0026plusmn; 0.462) influence the occurrence probability negatively. Whereas the categorical variable year also negatively influence the occurrence for 2022 (-0.975\u0026plusmn; 0.318) as compared to 2021.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eCommunity Composition and Habitat Association of Ardeidae\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study focused on the Ardeidae species assemblage along the peri-urban landscape of Kolkata, highlighted how urbanization, and habitat modification shaping the Ardeidae community structure. The significant spatial variation in Ardeidae abundance, and composition across the study sites, indicates the differences in habitat quality, land-use pattern, and the anthropogenic pressure. The relatively high abundance of Ardeidae species at Kamduni likely due to the extensive aquaculture farms with periodic hydrological regimes, offering dynamic foraging habitats. While EKW being a Ramsar site and aquaculture farm showed significantly less Ardeidae species assemblage than Kamduni, which was likely due to reduction in wetland cover , increasing development at the periphery of the wetland, and comparatively less dynamic hydrological regimes\u0026nbsp;(Ghosh et al. 2018; Barik et al. 2022). In contrast, lower abundance at Rajarhat study site highlights the consequence of intensive urbanization, and reduction of suitable foraging habitat.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe NMDS and ANOSIM showed significant dissimilarities in species composition suggesting that habitat structure, and conditions vary considerably across the study area, influencing species-specific assemblages. Species-specific abundance pattern further reinforces the spatial variation.\u0026nbsp;\u003cem\u003eArdea cinerea\u003c/em\u003e and\u0026nbsp;\u003cem\u003eArdea alba\u003c/em\u003e generally use wide range of habitats\u0026nbsp;(Kushlan and Hancock 2005), often known to adjust their foraging location based on short term changes in prey availability\u0026nbsp;(Gawlik 2002; Beerens et al. 2015). \u0026nbsp;We found\u0026nbsp;\u003cem\u003eArdea cinerea\u003c/em\u003e and\u0026nbsp;\u003cem\u003eArdea alba\u003c/em\u003e as predominantly open water foragers, were least abundant at agriculture mixed landscape of Baruipur and highly urbanized landscape of Rajarhat, as compare to the aquaculture dominant Kamduni and EKW. However, significant abundance differences were also found between Kamduni and EKW, suggesting even localize variation among the sites with high water coverage, can affect the species assemblages.\u0026nbsp;\u003cem\u003eArdea purpurea\u003c/em\u003e typically prefers reedbeds but also forages in rice fields, lakeshores, man-made ditches, canals, etc.\u0026nbsp;(Kushlan and Hancock 2005). The agriculture mixed landscape of Baruipur had the highest abundance of\u0026nbsp;\u003cem\u003eArdea purpurea\u003c/em\u003e, showing significant difference compared to rest of the study areas, indicating limited availability of foraging habitat. \u0026nbsp;In case of\u0026nbsp;\u003cem\u003eArdea intermedia\u003c/em\u003e, the habitat preference varied; mainly inland habitats with abundant emergent aquatic vegetation, including freshwater swamps, pools, floodplains, rice fields, open water, sewage ponds\u0026nbsp;(Kushlan and Hancock 2005). The significant difference between Baruipur and rest of the study sites indicates that the\u0026nbsp;\u003cem\u003eArdea intermedia\u003c/em\u003e prefers the marshland mixed agricultural landscape of Baruipur. However, unlike the\u0026nbsp;\u003cem\u003eArdea purpurea\u003c/em\u003e, it was also moderately abundant in aquaculture dominated landscapes, showing a degree of habitat flexibility.\u0026nbsp;\u003cem\u003eArdeola grayii\u003c/em\u003e and \u003cem\u003eEgretta garzetta\u003c/em\u003e are the habitat generalist (Choi et al. 2007; Roshnath and Sinu 2017), and in this study we found \u003cem\u003eArdeola grayii\u003c/em\u003e as the most widespread and abundant species across the study sites. Whereas \u003cem\u003eEgretta garzetta\u003c/em\u003e was predominantly found in aquaculture farms, particularly at Kamduni, but also use a wide range of habitats. Notably, both species were frequently observed foraging in seasonal waterlogged patches near human settlement. Highlighting the fact that the high abundance (65. 26%) and flexible habitat use of both these generalist species may indicate that the generalist species are to some extent, resilient in the urbanized landscape.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUrban Expansion and Land Use Dynamics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results from the LULC change detection between two decades, provides intensified insight about the ongoing changes in and around the landscape of Kolkata, and its shows extensive increase of urbanization, and associated land-use shifts. A striking increase in built-up areas highlights direct urban expansion\u0026nbsp;(Mahata et al. 2024b). While most of the increased waterbodies are the aquaculture farms, may not be the natural wetlands\u0026nbsp;(Halder et al. 2022). The Agricultural lands are also expanded at the expense of tree cover, indicating large-scale reduction of vegetation. Whereas the increase of flooded vegetation may associate to the shift of local hydrology or land conversion which promote seasonal water retention. The decrease in bare lands suggests previously unused or marginal lands are converted for further use. This trend suggests the expansion of urban growth beyond the buildup settlement, which involves transformation of the green space into managed or partially managed system. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnvironmental Drivers and Species Occurrence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GLMM results provide compelling evidence for the influence of specific landscape-level variables on the occurrence of Ardeidae species. Distance from water (Figure.5a) emerged as significant negative predictor for all species, indicating the important role of aquatic proximity for foraging, roosting, and nesting\u0026nbsp;(Kushlan 1986; Choi et al. 2007; Kelly et al. 2008; Choi and Yoo 2011). Urban expansion is known to adversely affect the bird communities adjacent to natural habitat, as reflected by the negative effects of built-up, bare land, and LST, which likely reduces habitat suitability, through disturbance and prey dynamics\u0026nbsp;(McKinney 2006; Marzluff and Ewing 2008; Ma et al. 2010). The negative response to flooded vegetation and EVI which often associated with wetland health, suggested that overly grown vegetation may impede the efficiency of visual foragers. While interannual comparison between 2021 and 2022 shows a significant increase in Ardeidae\u0026rsquo;s occurrence, which most likely due to local management practices and fluctuating water availability, especially in aquaculture farms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpecies specific models further emphasize distinct ecological strategies.\u0026nbsp;\u003cem\u003eArdea cinerea\u003c/em\u003e and\u0026nbsp;\u003cem\u003eArdea alba\u003c/em\u003e (Figure.5b \u0026amp; c) show strong negative response with distance from water, bare land, flooded vegetation, and EVI, indicating a preference for open water condition over densely vegetated areas. Tree cover had a positive effect on\u0026nbsp;\u003cem\u003eArdea cinerea\u003c/em\u003e occurrence, likely due to its role in providing roosting substrates as no nesting was observed. An increase in occurrence from 2021 to 2022 for both the species may reflect to the local management practices.\u0026nbsp;\u003cem\u003eEgretta garzetta\u003c/em\u003e (Figure.5d) exhibit a strong preference for areas near waterbodies, with a sharp decline in occurrence as distance increased. The species showed avoidance of dense vegetation and bare ground, likely due to reduced prey visibility or unsuitable habitat structure. Positive association with agricultural land and tree cover indicating its use of paddy field for foraging and nearby trees for roosting. The increase in occurrence in 2022 further indicates sensitivity to short-term habitat changes. Despite significant negative effects from several predictors,\u0026nbsp;\u003cem\u003eArdeola grayii\u003c/em\u003e (Figure.5e) maintained consistently high occurrence probabilities (\u0026gt;0.9), emphasizing its ecological plasticity and high adaptability in modified urban environments. The weak negative association with flooded vegetation may reflect a preference for open edges, but this did not substantially limit it presence.\u0026nbsp;\u003cem\u003eArdea intermedia\u003c/em\u003e (Figure.5f) showed a clear preference for habitat close to waterbodies, and avoided high EVI zones, indicating avoidance of densely vegetated wetlands. Its positive association with agricultural land highlights its use of semi modified habitats such as rice field, as an important foraging habitat\u0026nbsp;(Sundar and Kittur 2013).\u0026nbsp;\u003cem\u003eArdea purpurea\u003c/em\u003e (Figure.5g) showed relatively low occurrence across the study sites, with weak positive association with agricultural land and negative response to tree cover, and built-up areas. This indicates rice field may serve as critical refugia amid habitat loss.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict with humans and Conservation Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePiscivorous species are reported to damage local fish stocks\u0026nbsp;(Harris et al. 2008)\u0026nbsp;and we found herons and egrets were the most abundant (75.18%) species at Kamduni and EKW. Fishermen often cover the commercial fishponds with fine fishing lines or nets\u0026nbsp;(Barik et al. 2022). The same tendency was also observed at paddy cultivation lands during seed sowing and early vegetative phase at Baruipur. These activities of locals restrict the entry of the larger fish-eating birds for foraging. Such restricted access to preferred foraging habitats may adversely affects bird species belong to a specialized foraging group\u0026nbsp;(Burin et al. 2016). In the study area, locals even hunt herons and egrets for meat consumption and often as hobby. Fish farm owners frequently engage people to scare away fish-eating birds either by using slings or by beating steel plates or combination of both. Such strategies were employed only to minimize the fish loss, and maximize the financial gain. Over the years reduction in natural wetlands, and increase in aquaculture farms may led to more conflict between fish-eating birds and humans.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe distribution, and habitat use assessment information is often required for making conservation, and management decision. Unfortunately, these herons and egrets are least studied species in India. Here we attempted to understand how the occurrence probability and abundance of these species varied in an urban landscape. Although all six species are listed as the least concern species, \u0026nbsp;\u003cem\u003eArdea purpurea\u003c/em\u003e (BirdLife International 2019)\u0026nbsp;and\u0026nbsp;\u003cem\u003eArdea intermedia\u003c/em\u003e (BirdLife International 2020)\u0026nbsp;are projected to show global population decreasing trend. A similar trend was observed in our study, where these two species were the least abundant across the study sites. Furthermore, emergent vegetation patches are disappearing as the urban encroachment increases in the study areas. On the other hand, built-up areas negatively impact the bird population, yet the generalist species show a moderate to high level of tolerance towards such alternative environment\u0026nbsp;(Sultana et al. 2021). These condition leads to biotic homogenization\u0026nbsp;(McKinney 2002)\u0026nbsp;wherein specialist species are less adaptive and urban exploiters are thriving\u0026nbsp;(Palomino and Carrascal 2006).\u003c/p\u003e\n\u003cp\u003eWe suggest that maintaining multifunctional wetlands and setting\u0026ensp;urban sprawl limits requires immediate attention. Future conservation efforts should focus on conserving the remaining wetlands, promoting coexistence through community-based conflict mitigation strategies, and including wetland buffers into urban planning. Promoting non-lethal deterrents and long-term monitoring, and awareness campaigns might help to reduce the conflict. Policies on urban biodiversity should support the sustainable aquaculture and preservation of remaining wetlands. More studies on foraging ecology and habitat use of common Ardeidae are needed to support adaptive management and protect bird populations in rapidly urbanizing areas.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations and Future Research Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study provides valuable insights into the community structure of Ardeidae species in urban and peri urban wetlands; certain areas should be identified for future research. The study covered four representative wetlands and two years of data, providing insightful interannual comparisons but limited spatial and temporal scope. Incorporating more sites across varied urban gradient and extended period of monitoring would reveal the long-term trends. Additionally, sampling did not cover during the monsoon, a critical period of Ardeidae breeding\u0026nbsp;(Ali and Ripley 1978), although only two confirmed nesting of least abundant\u0026nbsp;\u003cem\u003eArdea purpurea\u003c/em\u003e was recorded. The models focused on broad habitat attributes; species specific habitat utilization and foraging studies may provide further critical insight. Based on these findings, future studies can provide more significant ecological insights and support adaptive wetland management in rapidly urbanizing landscapes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that urban expansion and aquaculture intensification in peri-urban landscape of Kolkata have altered the availability and structure of wetland habitat, leading to species-specific responses within the Ardeidae community. Commercial fish ponds with proper banks and dikes produce suitable feeding habitats, whereas a mix of agricultural and fallow lowlands can also increase the bird assemblage (Barik et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The aquaculture farms at Kamduni have the highest abundance of fish-eating birds. The submerged banks and biannual recycling process resulted in great variation in water levels, which offer suitable foraging habitats. In contrast, despite being a Ramsar Site with an aquaculture-dominated landscape, the EKW study areas showed comparatively lower abundance of Ardeidae species than Kamduni. This may be due to limited vegetation being allowed to grow except for the water hyacinth at the fringes of fish ponds and the maintenance of relatively high-water levels (Mookherjee and Chatterjee \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), which may reduce the foraging efficiency. Prior to land acquisition, the habitats of Baruipur and Rajarhat were characterised by agricultural and fallow land (Roy \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which became suitable for wetland-dependent birds during the transitional phase. However, with the initiation of large-scale urban development (Naskar et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mahata et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e), bird species assemblage declined sharply. The ongoing rapid loss of natural wetlands and presence of high density of fish in aquaculture farms likely increase the pressure of fish-eating birds on commercial ponds (Fasola and Brangi 2010; Feaga et al. 2015; Burr et al. 2020). Generalist species such as the \u003cem\u003eArdeola grayii\u003c/em\u003e and \u003cem\u003eEgretta garzetta\u003c/em\u003e show some adaptability to these modified environments. In contrast, specialist species such as the \u003cem\u003eArdea purpurea\u003c/em\u003e and \u003cem\u003eArdea intermedia\u003c/em\u003e, which depend largely on marshlands and, to some extent, agricultural fields may become more vulnerable. While species like \u003cem\u003eArdea cinerea\u003c/em\u003e and \u003cem\u003eArdea alba\u003c/em\u003e were found as predominantly open water foragers, and preferred aquaculture farms with dynamic water regimes, potentially heightening conflicts with fish farmers. Species-specific models revealed varied ecological responses, emphasising the need for habitat mosaics to support diverse assemblages. Urgent attention is needed to set the limit of urban sprawl and prioritise the conservation of multifunctional wetlands. Conservation and management strategies should focus on protecting the remaining wetlands, integrating ecological buffers into urban planning, enhancing public awareness, promoting community-based conflict mitigation, encouraging non-lethal deterrents, and implementation of continuous monitoring.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Director, Zoological Survey of India for support and encouragement. We are also thankful to the University Grants Commission, India, for fellowship support to the first author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGM and AN designed the study. AN carried-out field survey and did the data analyses. AN and GM wrote the draft manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by a fellowship to the first author by University Grants Commission (UGC), Government of India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaster data related to land use and spectral index variables were used for model building using open-source datasets. The abundance and occurrence data of the species can be made available upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe article submitted herewith contains the findings of our original research, is not under consideration for publication elsewhere, and is approved by all authors of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest in publishing the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAli and Ripley (1978) Handbook of Birds of India and Pakistan Vol 1\u003c/li\u003e\n\u003cli\u003eArora R, Balachander T, Agrawal I, et al (2024) Conserving Freshwater Ecosystems in India: A call to action. 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Regional Sustainability 5:100138. https://doi.org/10.1016/j.regsus.2024.100138\u003c/li\u003e\n\u003cli\u003eMahata D, Shekhar S, Ravi K (2024b) Urban expansion influences on land use and land cover changes in Kolkata metropolitan region: a geo-spatial study. GeoJournal 89:237. https://doi.org/10.1007/s10708-024-11236-x\u003c/li\u003e\n\u003cli\u003eMarzluff JM, Ewing K (2008) Restoration of Fragmented Landscapes for the Conservation of Birds: A General Framework and Specific Recommendations for Urbanizing Landscapes. In: Marzluff JM, Shulenberger E, Endlicher W, et al. (eds) Urban Ecology: An International Perspective on the Interaction Between Humans and Nature. Springer US, Boston, MA, pp 739\u0026ndash;755\u003c/li\u003e\n\u003cli\u003eMcFEETERS SK (1996) The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing 17:1425\u0026ndash;1432. https://doi.org/10.1080/01431169608948714\u003c/li\u003e\n\u003cli\u003eMcKinney ML (2002) Urbanization, Biodiversity, and Conservation: The impacts of urbanization on native species are poorly studied, but educating a highly urbanized human population about these impacts can greatly improve species conservation in all ecosystems. BioScience 52:883\u0026ndash;890. https://doi.org/10.1641/0006-3568(2002)052[0883:UBAC]2.0.CO;2\u003c/li\u003e\n\u003cli\u003eMcKinney ML (2006) Urbanization as a major cause of biotic homogenization. Biological Conservation 127:247\u0026ndash;260. https://doi.org/10.1016/j.biocon.2005.09.005\u003c/li\u003e\n\u003cli\u003eMookherjee K, Chatterjee S (1999) A guide to the birds of Salt Lake, Calcutta and change detection study. 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Springer, New York, NY, pp 101\u0026ndash;142\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":"Urbanisation, Peri-urban wetlands, Waders (Ardeidae), Google Earth Engine (GEE), Aquaculture farms, Generalized Linear Mixed Models (GLMMs)","lastPublishedDoi":"10.21203/rs.3.rs-7300140/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7300140/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrbanisation, a key indicator of socioeconomic development, often comes at the cost\u0026ensp;of natural habitats, particularly in peri-urban wetlands. Human-influenced wetlands may refuge diverse avian species, but the extent of their effectiveness remains uncertain. Ardeidae species are often considered effective bioindicators of wetland health, due to their high mobility and dependence on wetlands for foraging. This study assessed the influence of land use patterns on the Ardeidae community structure across four peri-urban regions of Kolkata, India. A total of 20,537 individuals belonging to six commonly found Ardeidae species were recorded. The aquaculture farms had the highest abundance (75.18% of observations) of Ardeidae species, indicating their importance as foraging habitats. Land Use and Land Cover changes over two decades from Kolkata and its surrounding landscapes revealed rapid urban expansion, increased waterbodies (primarily aquaculture farms), and substantial loss of tree cover. The generalist species comprised 65.26% of overall observations, suggesting higher resilience to urbanised habitats. Whereas marshland specialists showed vulnerability to urban-driven habitat changes. Conversely, open-water foragers were scarce in urban-fringed areas, but abundant in fish farming, which further heightens the conflict between aquaculture farms and the species. The Generalised Linear Mixed Models highlight the importance of habitat heterogeneity to support a wide range of species assemblages. This study emphasised that urban sprawl has negative impacts on Ardeidae community structure. Effective conservation in urbanising areas requires the protection of multifunctional wetlands, establishment of buffer zones, promotion of sustainable aquaculture, and involvement of local communities in conflict mitigation.\u003c/p\u003e","manuscriptTitle":"The Cost of Urban Expansion: Habitat Loss and Shifting Distribution of Long-Legged Wading Birds in a Peri-Urban landscape gradient","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 07:55:44","doi":"10.21203/rs.3.rs-7300140/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-06T02:46:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-04T07:25:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-29T08:34:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-17T22:26:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37103673271916879174954595999369542819","date":"2025-08-13T15:34:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334974824939174598058656586564122349529","date":"2025-08-11T17:58:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235292235834888614837810834405897046712","date":"2025-08-11T16:47:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36254742086283714020959043597664091577","date":"2025-08-11T06:21:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338717507521360744249201880916326761060","date":"2025-08-09T16:10:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147605241974172548181108990238598461053","date":"2025-08-09T13:33:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-09T13:11:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-08T00:15:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-07T23:53:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Urban Ecosystems","date":"2025-08-05T11:27:32+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":"bce15740-63f8-4fc3-951c-f2c9db1bd900","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:11:23+00:00","versionOfRecord":{"articleIdentity":"rs-7300140","link":"https://doi.org/10.1007/s11252-025-01845-w","journal":{"identity":"urban-ecosystems","isVorOnly":false,"title":"Urban Ecosystems"},"publishedOn":"2025-11-02 15:58:57","publishedOnDateReadable":"November 2nd, 2025"},"versionCreatedAt":"2025-08-21 07:55:44","video":"","vorDoi":"10.1007/s11252-025-01845-w","vorDoiUrl":"https://doi.org/10.1007/s11252-025-01845-w","workflowStages":[]},"version":"v1","identity":"rs-7300140","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7300140","identity":"rs-7300140","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}