Waterbird Use of Wetlands Along a Conserved Rural to Urban Landscape Gradient

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McCall, Deborah Kunkel, Andrew Hopkins, Joseph D. Lanham, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7716046/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Wetlands are ecologically valuable due to their high biological diversity and productivity, with many avian species depending on them. Understanding why species use different habitats is essential to successful wildlife management and can enhance predictions of animal responses to land use change. Despite the importance of coastal wetlands for waterbirds, habitat use studies are limited, especially along the rural-to-urban gradient. Our primary objective was to determine what environmental factor(s) are contributing to waterbird wetland use along the northcentral coast of South Carolina on conserved and developed lands. Between January and April 2022 and 2023, we conducted point-count surveys, secretive marshbird surveys, macroinvertebrate surveys, and collected other wetland-level data in a total of 156 wetlands. We found that waterbird diversity is influenced by water level, flooding regime of the wetlands and the proximity to other nearby wetlands. The results of this project have helped to identify key variable in both rural and urban landscapes that wetland managers can focus on to promote waterbird diversity. This information can be highly useful for wetland managers and professionals looking to protect to increase habitat for rare and threatened birds that frequent their lands. Avian Coastal Heron Wetland Wood Duck Anseriformes Figures Figure 1 Introduction Understanding a species' specific habitat use and niche selection is critical for wildlife management. Exploration of these species or taxon-habitat relationships enhances understanding of animals' interaction with their environment, which often influences abundance and distribution patterns (Masto et al. 2023 ). In our context and for the purposes of this paper, we define habitat and habitat use as the combination of resources (both biotic and abiotic) that species require for survival and reproductive success, as well as how they use these resources to fulfill those goals (Krausman 1999 , Leopold 1933 , Thomas 1979 , Block and Brennan 1993 , Jones 2001 ). Not only does understanding habitat use of species reveal how they interact with the environment, but it can also enhance predictions of species responses to climate change (Arthur et al. 2018 ; Teitelbaum et al. 2020 ). Wetlands are ecologically valuable due to their high biological diversity and productivity (Paracuellos 2006 ). Studies show that wetlands are capable of supporting a wide range of communities (Kingsford et al. 2016 , Junk et al. 2006 ). One factor that leads to this diverse assemblage of communities is the various niches that different species are able to fill and specialize in depending on their feeding behaviour or location in the wetland trophic web (Parcuellos 2006). Avian species are capable of using a wide range of these niches, which contributes to a wetland’s diversity (Hafner 1997 , Kushlan et al. 1985 , Chatterjee et al. 2020 ). Different factors that influence waterbird biodiversity include, but are not limited to, variations in water depth and vegetation composition, which influence the amount of available food, nesting, and thermal cover for waterbirds (Baschuk et al. 2012 ). Not only do these environmental variables influence avian diversity and abundance in wetlands, but they also influence the invertebrate diversity, which can be a critical food source for many avian species (Baschuk et al. 2012 ). Invertebrates fill the role of keystone species both through their use as a food source to numerous wetland species and by playing a critical component in many wetland ecosystem services (Balcombe et al. 2005 , Brraich and Kaur 2017 ). Specifically, invertebrates represent a critical source of nutrients for many waterfowl species, making up a significant portion of their diet (Anderson and Smith 1998 , 1999 ). Furthermore, invertebrates contribute to many wetland functions, including but not limited to litter decomposition, nutrient cycling, and plant community regulation (Balcombe et al. 2005 , Gingerich et al. 2015 ). Benthic macroinvertebrates are larger invertebrates (~ 6 mm) that represent a critical group of invertebrates that reside in the substratum of lakes, ponds, streams, and rivers. These invertebrates represent numerous different families, including worms, insects, sponges, and mollusks. Due to their sensitivity to various levels and varieties of environmental pollution, benthic macroinvertebrates are valuable tools for biomonitoring studies (Anderson et al. 2013 , Braich and Kaur 2017). The composition and distribution of macroinvertebrate communities reflect the health and productivity of the wetland, and thus, information on their composition can be used to design further management strategies (Brraich and Kaur 2017 ). Wetlands are often not isolated environments, but form larger complexes that share similar chemical, biological, hydrologic, and geomorphic characteristics (Cowardin et al. 1979 ). These complexes serve as vital areas for waterbird habitat as well as provide critical stopover points during migration (Anderson et al. 1996 , 1999 , 2000 ; Veselka et al. 2010 ). Wetlands can be classified based on their salinity and tides, which can shape their species and community composition. The most common wetland systems along the northcentral South Carolina coast are estuarine, palustrine, and lacustrine wetlands (Cowardin et al. 1979 ). Estuarine wetlands represent one of the most productive ecosystems in the world and host a diverse assemblage of bird species (Anderson et al. 1996 , Kingsford et al. 2016 ). Their ephemeral nature and influx of tidal influences lead these coastal wetlands to act as an amazing example to study the interactions between fresh and saltwater (Estades and Vukasovic 2013 ). The environmental dynamics of these wetlands, in turn, attract a variety of rarer waterbirds such as willets ( Tringa semipalmata ), clapper rails ( Rallus crepitans ), snowy egrets ( Egretta thula ), American oystercatchers ( Haematopus palliatus ), and roseate spoonbills ( Platalea ajaja ) (Anderson et al. 1996 ; Masto et al. 2023 ; Suthar et al. 2025 ). Palustrine wetlands are predominantly nontidal and freshwater and are commonly used by species such as mallards ( Anas platyrhynchos ), American wigeon ( Mareca americana ), least bitterns ( Ixobrychus exilis ), yellow-crowned night herons ( Nyctanassa violacea ), and tricolored herons ( Egretta tricolor ) (Anderson et al. 1996 ; Anderson and Smith 1999 , Cowardin et al. 1979 ). Conversely, lacustrine wetlands can be tidal or nontidal but are also predominantly freshwater and mostly open water and are typically dominated by double-crested cormorants ( Phalacrocorax auritus ), anhingas ( Anhinga anhinga ), and green herons ( Butorides virescens ) (Anderson et al. 1996 , Cowardin et al. 1979 ). Wetlands are dynamic habitats that can be largely shaped by environmental factors, further influencing the species that utilize these unique habitats. The objectives of this study are to 1) determine which wetland type(s) are positively affecting waterbirds in terms of species presence and abundance, and 2) determine what environmental factor(s) are driving this favorable attraction for waterbirds in coastal South Carolina. We predict that wetland classification (i.e., estuarine, palustrine, lacustrine) will have a significant effect on species and guilds, as well as tidal stage and wetland area (Anderson et al. 1996 ; Erwin et al. 2006 ; Paracuellos 2006 ). Specifically, we predict that waterfowl abundance would be highest in palustrine wetlands, shorebird abundance would be highest in estuarine wetlands, and wading birds would be highest in lacustrine wetlands. Methods Study Area We conducted the study at the Hobcaw Barony (~ 7,000 ha, 33.364° N, 79.227° W) and the DeBordieu Colony in Georgetown County, South Carolina, USA (~ 1,000 ha, 33.369° N, 79.152° W, Fig. 1 ). The study area comprises estuarine and marine deepwater systems, estuarine and marine wetlands, freshwater emergent wetlands, freshwater forested and scrub-shrub wetlands, freshwater ponds, lakes, and riverine wetlands (Cowardin et al. 1979 ) that follow a gradient of development intensity. Georgetown County features a development gradient from untouched tracts of old plantation lands (Hobcaw Barony) to densely human-developed housing communities (DeBordieu Colony; Buyck 2016 , Hapke et al. 2012 ). The Hobcaw Barony is located on the southern tip of the Waccamaw peninsula north of the city of Georgetown, SC, and is managed under a conservation easement (Heaton et al. 2023 ). The property contains about 3,000 ha of forest, 3,000 ha of salt marsh, and 1,000 ha of freshwater or brackish marshes and abandoned rice fields (Kale et al. 2008 ). The DeBordieu Colony is a private coastal housing community located 6.5 km from U.S. Highway 17 to the Atlantic Ocean and 7.25 km north of the city of Georgetown. The site was developed in 1969, and today, it has over 1,200 home sites with natural features including maritime forests, marshes, and beaches (Vernberg 1996). Our field sites accumulated 57.9 cm of rainfall in 2022 and 76.3 cm in 2023 over 7 months. Tidal height at the field sites ranged from 2.60 m (March) to 4.65 m (May) in 2022 and from 2.72 m (February) to 4.76 m (June) in 2023. The forest composition varies from xeric to hydric soil association and includes loblolly pine ( Pinus taeda ) stands to cypress-gum ( Taxodium-Nyssa spp.) swamps (Barry and Batson 1969 ). Salt marsh plant communities are dominated by Juncus spp. and Sporobolus spp., while the freshwater marsh community is dominated by Scirpus spp. and Typha spp. (Stalter et al. 2021 ). Wetland Mapping and Site Selection We used ArcGIS Pro (ESRI 2022) to identify and map wetland types using the National Wetland Inventory (NWI) classification (Cowardin et al. 1979 ; USFWS 2019), roads from USGS (2020) to identify survey routes and access points, and the property boundary for both the Hobcaw Barony and DeBordieu Colony from Georgetown County GIS by ESRI ( 2022a ,b). The area of each wetland class and the number of polygons were used to delineate the relative number of sites by wetland class to survey. We selected sampled wetland locations randomly using a random number generator. Survey points were placed at the edge of wetlands to minimize observer disturbance that would be caused by walking through the wetland (Anderson et al. 1999 , 2000 ). Wetlands for playback surveys were chosen similarly from a pool of emergent wetlands only. We then field-verified each survey point to ensure the accessibility and accuracy of the NWI GIS layer. Sample wetlands were randomly chosen by class each year, so some wetlands surveyed in year 1 were also surveyed in year 2. Avian Point-count Surveys We performed point-count surveys from January to April of 2022 and 2023, four days a week, alternating between DeBordieu Colony and Hobcaw Barony. Surveys started before sunrise with playback calls used to detect secretive marshbirds (Conway 2011 ). We alternated the survey route starting point (2022: n 1 = 7; 2023: n 2 = 8), every other time the wetland was visited to avoid morning bias. At each survey point (2022: n 1 = 95; 2023: n 2 = 98), during a 10-minute, 100-m radius point, we identified all waterbird species (i.e., those that are ecologically dependent on wetlands, Mott et al. 2023 ) by sight or sound using binoculars and a spotting scope. We allowed for a two-minute settling period before the point count started to account for any disturbance we may have caused by navigating to the target wetland. We clarified on the datasheet whether we were observing one side of the survey point (i.e., we were at the edge of a wetland) or both sides (i.e., the target wetland surrounded us or there were multiple wetland types at the point) to later accurately estimate densities using the wetland area surveyed. We used an elevated surface (e.g., the back of the ATV) to reduce potential visibility bias from emergent vegetation (Hagy et al. 2016 ). Birds that were observed flying over the wetland were not included in our counts to avoid bias and ensure accurate counts. However, individuals that aerially foraged in the study wetland, such as gulls or terns, or landed in the wetland during the survey period were recorded. At each wetland, we recorded the alphanumeric code of each survey point, start/stop time of the survey, date, estimated percent (%) of wetland covered with water, and estimated % of wetland covered with vegetation. Wetlands were surveyed once every two weeks during the course of the study. We conducted point-count surveys in all weather, aside from heavy rainfall or when wind exceeded 12 km/hr (Hamel et al. 1996 ). Secretive Marshbird Surveys We conducted secretive marshbird surveys on select emergent wetlands at the Hobcaw Barony (~ 320 ha) and the DeBordieu Colony (~ 7 ha) between March and July 2022 and 2023 using playback surveys and standardized USGS protocols (Conway 2011 ). We used playback calls to survey for secretive marshbirds, with these surveys beginning at approximately 0800 each time. We obtained the 10-minute playback recording from the Cornell Lab of Ornithology's All About Birds' database and broadcast it through a Bluetooth speaker at 80–90 dB. Audio for the playback calls included black rails ( Laterallus jamaicensis ), clapper rails ( Rallus crepitans ), king rails ( Rallus elegans ), sora ( Porzana carolina ), Virginia rails ( Rallus limicola ), American bittern ( Botaurus lentiginosus ), least bittern ( Ixobrychus exilis ), and limpkin ( Aramus guarauna ). Surveys were completed once per month from March to July in a subset of emergent wetlands at both sites (2022: n 1 = 10; 2023: n 2 = 8). To account for the timing required to survey both secretive marshbirds as well as complete the point count surveys, the number of survey location points varied by site (Conway 2011 ). To decrease the sampling variation created by diurnal variation, we surveyed points in the same order every time, which can lead to decreases in the vocalization probability of each marshbird as the morning progressed (Conway 2011 ). Macroinvertebrate Surveys We collected macroinvertebrate samples once at each wetland during the avian survey period, starting in March and continuing through July. We used a 5-cm diameter PVC core sampler for benthic invertebrates and surveyed to a depth of 15 cm (Sherfy et al. 2000 ; Anderson et al. 2013 ) and a 5-cm diameter water column sampler for water column invertebrates (Anderson et al. 2013 ). We took both the benthic and water column samples at 10 random points along a line transect that bisected the center of the wetland (or a portion of the wetland surveyed for birds). In 2023, the sampling protocol for benthic samples remained consistent, but we increased the sample size for water column samples due to the smaller volume captured by the aquatic sampler. In wetlands that contained water > 1 m deep, we took benthic samples along a transect near the shore and aquatic samples with the use of a kayak. We placed benthic samples in Ziploc bags labeled with the survey point, sample number, and date of collection and kept them in a cooler while in the field before transport to the laboratory (Anderson and Smith 1998 ). Samples were kept refrigerated to prevent degradation before being washed through a number 35 (500 micron) sieve. All samples were sorted within 10 days of collection, and once sorted, invertebrates were stored in 95% ethanol (Anderson and Smith 1998 ). During water column invertebrate sampling, we collected 10 samples/wetland along the predetermined transect lines. However, in 2023, we increased the water column samples to two samples at 10 random locations on the transect line, for a total of 20 aquatic samples in every wetland. We filtered each water column sample through a number 35 (500 micron) sieve to carefully separate detritus and other materials from the invertebrates, then placed any invertebrates in 95% ethanol for preservation (Huener and Kadlect 1992). Each sample was immediately labeled with the survey site, survey point, sampling date, and type of sample (i.e., benthic versus aquatic). Macroinvertebrate samples were not taken in wetlands that did not contain any water or in wetlands where conditions were unsafe to walk through alone. We identified all invertebrates to order at a minimum and to family when possible (Voshell Jr. 2022; Louw et al. 2024 ). We calculated macroinvertebrate richness as the number of orders per wetland. Wetland-level Data Collection During the survey period, we collected environmental data and metrics for each wetland. These data included vegetation distribution patterns (Stewart and Kantrud 1971 ), hydrologic regimes (Cowardin et al. 1979 ), distance to the nearest wetland, tidal class, and tidal stage. The hydrologic regime and tidal class were determined by visual observation across the surveyed portion of the wetland and using the NWI from the U.S. Fish and Wildlife Service (USFWS 2019). The tidal stage was later determined using the time of survey and USGS water data (USGS 2024). Vegetation distribution patterns were also assessed in the field (Stewart and Kantrud 1971 ). We determined ‘distance to the nearest wetland’ using the measure tool in ArcGIS at each survey point. Statistical Analyses We used a generalized linear mixed model with a Poisson distribution in JMP® 17.0.0 (SAS Institute) to determine which environmental factor(s) were driving waterbird use (Millspaugh et al. 2006 , Kloskowski et al. 2010). We analyzed our point count and environmental data at the guild level [Ardeidae and Threskiornithidae (wading birds), Charadriiformes (shorebirds), Anseriformes (waterfowl), Gruiformes (marshbirds), and Suliformes and Pelicanus (pelicans, anhingas, cormorants)], a group of species that exploit the same class of environmental resources similarly (Adams 1985 ; Boros 2021 ), and individual species level. Individual species were assigned to guilds based on a mix of methodology from previous similar studies, as well as feeding or behavioral habits (Anderson et al. 2000 , Conway 2011 ). All surveyed wetlands were included in the analysis, regardless of whether the species was present or not, to account for species' absence. With the intent to simplify the analysis, we reduced wetland classification from class/subclass to the system level: estuarine (deepwater and estuarine wetlands), palustrine (freshwater emergent, freshwater forested/shrub, freshwater pond, and riverine wetlands), and lacustrine (lakes) (Cowardin et al. 1979 ). We included guilds and individual species that were detected ≥ 85 times throughout both field seasons, as this was the minimum number of observations required to be able to include the nine core predictor variables we identified in the model (Fieberg and Johnson 2015). Using species count as the response variable, we evaluated the impact of the different predictor variables on species responses. Our original surveys recorded 15 different variables; however, we were able to reduce this to nine key predictor variables that described the distribution of waterbirds throughout wetlands. The predictor variables included wetland classification (system), tidal class, water regime, tidal stage, wetland area (ha), macroinvertebrate species richness, percent water cover, and percent vegetation cover. We included survey date (a repeated measure) as a random effect to account for temporal variation in habitat use/associations. After standardizing all numerical variables, we screened for multicollinearity (| r |= 0.6) among predictors using a correlation matrix (Burnham and Anderson 2002 ). None were correlated for species guilds or individual species' analyses, so all variables were included. Results Waterbird guilds include Ardeidae and Threskiornithidae (wading birds), Charadriiformes (shorebirds), Anseriformes (waterfowl), Gruiformes (secretive marshbirds), and Suliformes and Pelicanus (pelicans, anhingas, and cormorants). Individual species that were investigated include great egrets ( Ardea alba ), tricolored herons, willets, wood ducks ( Aix sponsa ), clapper rails, and double-crested cormorants. Although the gulls and terns guild (American wigeon ( Mareca americana ), hooded mergansers ( Lophodytes cucullatus ), and sanderlings ( Calidris alba )) were frequently detected (> 85 times), they were not widely detected enough to demonstrate meaningful patterns. Waterbirds on these properties used 20.4 of the 50.5 ha (40.1%) surveyed in 2022 and 26.1 of the 42.5 ha (61.4%) surveyed in 2023. Over both field seasons, 2,388 waterbirds were detected, with 993 occurring on Hobcaw (natural site) and 1,395 occurring on DeBordieu (urban site). Ardeidae and Threskiornithidae From January to April of 2022 and 2023, we detected a total of 628 wading birds, with the most numerous species being great egrets, 266 (42.4%), and tricolored herons, 174 (27.7%) (Table 1 ). The environmental variables that best explained the distribution and site selection for this guild were wetland classification (p < 0.0001), water regime (p < 0.0001), and tidal stage (p = 0.0483) (Table 2 ). They had the strongest positive relationship with permanently flooded (β = 0.63) estuarine wetlands (β = 13.98) during lower tides (β = 0.13) and a negative relationship with palustrine wetlands (β= -0.16). Ardeidae and Threskiornithidae also had a significant relationship with smaller (p < 0.0001; β= -0.33), non-isolated (p < 0.0001; β= -1.69) wetlands that were higher in water cover (p < 0.0001; β = 0.65) than vegetation cover (p = 0.0148; β= -0.24) (Table 2 ). Table 1 Total waterbird guild and waterbird species detected and included in the analysis based on wetland classification between January and April of 2022 and 2023 on Hobcaw Barony and DeBordieu Colony, South Carolina, USA. No. Birds Waterbird Estuarine Palustrine Lacustrine Wading birds 281 282 65 Great egret ( Ardea alba ) 53 176 37 Tricolored heron ( Egretta tricolor ) 156 13 5 Shorebirds 485 0 0 Willet ( Tringa semipalmata ) 85 0 0 Waterfowl 135 174 468 Wood duck ( Aix sponsa ) 19 78 11 Secretive marshbirds 98 8 11 Clapper rail ( Rallus crepitans ) 81 0 5 Pelicans, anhingas, cormorants 212 66 73 Double-crested cormorant ( Phalacrocorax auritus ) 151 50 60 Table 2 Results from a generalized linear mixed model analysis with Poisson distribution for the Wading bird guild and two most common species guild on Hobcaw Barony and DeBordieu Colony in Georgetown, South Carolina, USA, January to April 2022 and 2023. DF denominator degrees of freedom = 1,205 for all tests, DF numerator are the same for all tests, * represents significant variables Guild Great Egret Tricolored Heron Term DFNum F Ratio Prob > F F Ratio Prob > F F Ratio Prob > F Wetland Classification 3 6.10 0.0004* 0.84 0.4714 5.04 0.0018* Tidal Class 1 0.00 0.9787 0.00 0.9992 0.96 0.3268 Water Regime 2 18.84 < 0.0001* 1.91 0.1482 24.35 < 0.0001* Tidal Stage 3 2.64 0.0483* 1.60 0.1883 11.31 < 0.0001* Distance to nearest wetland 1 136.50 < 0.0001* 95.96 < 0.0001* 30.29 < 0.0001* Wetland Areas 1 19.27 < 0.0001* 8.29 0.0041* 0.01 0.9030 Macro. Species Richness 1 0.41 0.5228 12.34 0.0005* 86.26 < 0.0001* % Water Cover 1 63.0 < 0.0001* 58.63 < 0.0001* 0.36 0.5488 % Veg. Cover 1 5.96 0.0148* 23.94 < 0.0001* 0.55 0.4602 Great egrets were closely associated with smaller wetlands (p = 0.0041; β= -0.61) in close proximity to other wetlands (p < 0.0001; β= -2.63), that held more water (p < 0.0001; β = 1.43) and less vegetative cover (p < 0.0001; β= -1.11) (Table 2 ). They also selected wetlands that had lower macroinvertebrate richness (p = 0.0005; β= -0.53) (Table 2 ). Tricolored heron habitat usage was significantly affected by wetland classification (p = 0.0018), water regime (p < 0.0001), and tidal stage (p < 0.0001) (Table 2 ). They were most frequently found in permanently flooded (β = 1.25) lacustrine wetlands (β = 4.26) when the tide was falling (β = 0.82) and a strong negative relationship with high tide (β= -0.55). They were also frequently associated with wetlands in close proximity to other wetlands (p < 0.0001; β= -7.12), and those that had high macroinvertebrate richness (p < 0.0001; β = 0.98) (Table 2 ). Charadriiformes We detected a total of 485 shorebirds, willets representing the most numerous species at 85 (17.5%) (Table 1 ). The Charadriiformes guild displayed significant associations with the water regime (p < 0.0001) and tidal stage (p = 0.0002) (Table 3 ). They had a strong negative relationship with seasonally flooded wetlands (β= -1.08) and a positive relationship with wetlands in high tide (β = 0.05). Charadriiformes also had a significant relationship with larger wetlands (p < 0.001; β = 0.09) near other wetlands (p = 0.0018; β= -11.71) and wetlands that had more water (p < 0.0001; β = 0.65) than vegetative cover (p < 0.0001; β= -0.83) (Table 3 ). Table 3 Results from a generalized linear mixed model analysis with Poisson distribution for the Charadriiformes (shorebird) species guild and the most commonly found species on Hobcaw Barony and DeBordieu Colony in Georgetown, South Carolina, USA. The denominator degrees of freedom = 1,205 for all tests for the guild, numerator degrees of freedom is the same for all tests. *significant variables Guild Willet Term DFNum F Ratio Prob > F F Ratio Prob > F DF Den Wetland Classification 3 0.00 1.0000 0.00 0.9997 1204.0 Tidal Class 1 0.00 0.9997 0.55 0.5523 156.5 Water Regime 2 49.24 < 0.0001* 6.83 0.0011* 1204.0 Tidal Stage 3 6.80 0.0002* 4.73 0.0028* 1204.0 Distance to nearest wetland 1 9.78 0.0018* 2.58 0.1088 1204.0 Wetland Areas 1 96.0 < 0.0001* 2.65 0.1040 1204.0 Macro. Species Richness 1 1.11 0.2931 0.07 0.7907 1204.0 % Water Cover 1 47.76 < 0.0001* 9.40 0.0022* 1204.0 % Veg. Cover 1 46.72 < 0.0001* 8.31 0.0040* 1204.0 Willets specifically were significantly affected by the water regime and tidal stage (Table 3 ). They had the strongest positive relationship with permanently flooded (β = 1.15) wetlands at low tide (β = 0.52) and the strongest negative relationship with seasonally flooded (β= -1.02) wetlands at high tide (β= -1.18). They also had significant relationships with wetlands that were higher in % water cover (p = 0.0022; β = 0.46) and % vegetation cover (p = 0.004; β = 0.78) (Table 3 ). Anseriformes We detected a total of 777 waterfowl, with wood ducks representing the most commonly found species at 108 (13.9%) (Table 1 ). The Anseriformes guild was significantly affected by wetland classification (p < 0.0001), water regime (p < 0.0001), and tidal stage (p < 0.0001) (Table 4 ). They had the strongest positive relationship with permanently flooded (β = 1.88) estuarine wetlands (β = 24.2) at high tide (β = 0.59) and the strongest negative relationship with seasonally flooded wetlands (β= -2.45) when the tide was falling (β= -0.6). Anseriformes also had a significant relationship with wetlands of high water cover (p < 0.0001; β = 1.07) and vegetation percentage (p < 0.0001; β = 0.74) but a low macroinvertebrate richness (p < 0.0001; β= -2.38) (Table 4 ). Table 4 Results from a generalized linear mixed model analysis with Poisson distribution for the Anseriformes (waterfowl) species guild and the most commonly found species on Hobcaw Barony and DeBordieu Colony in Georgetown, South Carolina, USA. The denominator degrees of freedom = 1,205 for all guild tests, numerator degrees of freedom is the same for all tests. *significant variable Guild Wood Duck Term DFNum F Ratio Prob > F F Ratio Prob > F DF Den Wetland Classification 3 11.01 < 0.0001* 1.25 0.2887 949.8 Tidal Class 1 0.00 0.9999 1.32 0.2509 1204.0 Water Regime 2 60.43 < 0.0001* 2.87 0.0569 1204.0 Tidal Stage 3 19.84 < 0.0001* 7.63 < 0.0001* 1204.0 Distance to nearest wetland 1 2.23 0.1352 6.57 0.0105* 1204.0 Wetland Areas 1 1.34 0.2477 0.02 0.8768 1204.0 Macro. Species Richness 1 165.96 < 0.0001* 7.59 0.0060* 1204.0 % Water Cover 1 77.84 < 0.0001* 8.29 0.0041* 1204.0 % Veg. Cover 1 59.89 < 0.0001* 0.49 0.4891 827.2 Wood ducks specifically were significantly affected by the tidal stage (p < 0.0001), having the strongest positive relationship with a falling tide level (β = 1.16) (Table 4 ). They also had a significant relationship with wetlands that were high in water percentage (p = 0.0041; β = 1.64) and those close to other wetlands (p = 0.0105; β= -0.36), but wetlands low in macroinvertebrate richness (p = 0.006; β= -0.86) (Table 4 ). Gruiformes We detected a total of 117 Gruiformes, secretive marshbirds, with 86 (73.5%) of those being clapper rails (Table 1 ). Gruiformes were significantly affected by the water regime (p = 0.0009), having the strongest positive relationship with permanently flooded wetlands (β = 1.07) (Table 5 ). They also had a significant relationship with wetlands that were in close proximity to other wetlands (p = 0.0233; β= -2.2) and those that were high in water percentage (p = 0.0026; β = 0.49) (Table 5 ). Clapper rails specifically were significantly affected by the tidal stage (p = 0.003), having the strongest positive relationship with low tide (β = 0.47) (Table 5 ). They also had a significant relationship with larger wetlands (p = 0.0036; β = 0.41) that were high in water percentage (p = 0.0435; β = 0.34) (Table 5 ). Table 5 Results from a generalized linear mixed model analysis with Poisson distribution for the Gruiformes (secretive marshbird) species guild and the most commonly found species on Hobcaw Barony and DeBordieu Colony in Georgetown, South Carolina, USA. The denominator degrees of freedom = 1,205 for all tests for the guild, numerator degrees of freedom is the same for all tests. *significant variable Guild Clapper Rails Term DFNum F Ratio Prob > F F Ratio Prob > F DF Den Wetland Classification 3 1.56 0.1965 0.21 0.8886 1203.0 Tidal Class 1 0.00 0.9679 2.24 0.1365 170.1 Water Regime 2 7.08 0.0009* 0.54 0.5849 1203.0 Tidal Stage 3 0.28 0.8406 4.67 0.0030* 1203.0 Distance to nearest wetland 1 5.16 0.0233* 0.89 0.3470 1203.0 Wetland Areas 1 2.33 0.1276 8.49 0.0036* 1203.0 Macro. Species Richness 1 0.09 0.7636 0.68 0.4089 1203.0 % Water Cover 1 9.13 0.0026* 4.08 0.0435* 1203.0 % Veg. Cover 1 1.49 0.2225 0.02 0.8786 1203.0 Suliformes and Pelicanus We detected a total of 351 Suliformes and Pelicanus : pelicans (n = 36), anhingas (n = 54), and cormorants (n = 261), with 261 (74.4%) of those being double-crested cormorants (Table 1 ). This guild overall was significantly affected by the water regime (p < 0.0001) and tidal stage (p = 0.0156) (Table 6 ). They were strongly associated with permanently flooded (β = 1.5) wetlands at high tide (β = 0.42) and a negative relationship with seasonally flooded (β= -2.44) wetlands at low tide (β= -0.52). They also had a significant relationship with larger wetlands (p < 0.0001; β = 1.1) that were near other wetlands (p < 0.0001; β= -0.73), low in macroinvertebrate species richness (p = 0.0001; β= -0.42), and a greater percent of water (p = 0.0307; β = 0.4) rather than vegetation (p < 0.0001; β= -0.71) (Table 6 ). Table 6 Results from a generalized linear mixed model analysis with Poisson distribution for Suliformes and Pelicanus (pelicans, anhingas, and cormorants) on Hobcaw Barony and DeBordieu Colony in Georgetown, South Carolina, USA. The denominator degrees of freedom = 1,205 for all tests. Guild Double-breasted Cormoorants Term DFNum F Ratio Prob > F F Ratio Prob > F Wetland Classification 3 0.91 0.4375 3.38 0.0178* Tidal Class 1 0.00 0.9871 0.954 0.3298 Water Regime 2 32.80 < 0.0001* 27.08 < 0.0001* Tidal Stage 3 3.47 0.0156* 4.08 0.0068* Distance to nearest wetland 1 29.75 < 0.0001* 18.30 < 0.0001* Wetland Areas 1 62.66 < 0.0001* 45.87 < 0.0001* Macro. Species Richness 1 15.14 0.0001* 8.73 0.0032* % Water Cover 1 4.68 0.0307* 4.13 0.0423* % Veg. Cover 1 16.22 < 0.0001* 3.41 0.0652 Double-crested cormorants specifically were significantly influenced by wetland classification (p = 0.0178), water regime (p < 0.0001), and tidal stage (p = 0.0068) (Table 6 ). They had the strongest positive relationship with permanently flooded (β = 1.95) lacustrine wetlands (β = 4.99) while the tide was falling (β = 0.44). Similar to the guild, they had a significant relationship with larger wetlands (p < 0.0001; β = 0.94) that were near other wetlands (p < 0.0001; β= -0.59), low in macroinvertebrate species richness (p = 0.0035; β= -0.35), and high in water coverage (p = 0.0423; β = 0.43) (Table 6 ). Discussion We conducted several types of surveys (i.e., point counts, marsh bird playbacks, invertebrate richness) to form an understanding of wetland and waterbird relationships in coastal South Carolina. While studies on waterbird habitat usage have occurred on the South Carolina coast (Weber and Haig 1996 ; Boettcher et al. 1995 ; Masto et al. 2023 ; Suthar et al. 2025 ), few have evaluated the rural-to-urban gradient in coastal wetlands. Our study focused on the rural-to-urban gradient between the Hobcaw Barony and the nearby DeBordieu plantation. During the course of our study, we found a large portion of the waterbirds at DeBordieu, likely due to the abundance of freshwater ponds and lacustrine wetlands. Overall, our predictions for the relationships between waterbirds and habitats were supported. However, our specific predictions for guild preference in wetland type were not supported. The results of this study do support different habitats and habitat modifications that wetland managers can do to increase wetland quality and species diversity. Ardeidae and Threskiornithidae Wading birds were found to be closely associated with estuarine wetlands, likely because these wetlands support their primary prey items, among other factors (McKinney and Raposa 2010 ), which was further evidenced and supported by our results. The environmental factor that was most strongly predictive and correlated to wading bird abundance at our sites was tidal timing, specifically low tide, which matches previously reported trends (Maccarone and Brzorad. 2005, Raposa et al. 2005, Calle et al. 2016 ). The primary reason for this correlation is likely due to low tides increasing the activity and access to nektonic prey items (fish and decapod crustaceans) that these guilds prefer (Raposa et al. 2009 ). Furthermore, if wetlands are too densely covered with vegetation, the falling tide will not expose sufficient areas of mudflats for wading birds to forage. Hence, our data indicate that wading birds prefer less vegetative cover (Granadeiro et al. 2006 ). Wading birds on our study sites were also selected for smaller wetlands that were close to other wetlands. Although large, isolated wetlands support wading bird diversity (Paracuellos 2006 ), several studies have suggested that a mosaic of small wetlands can hold just as much ecological value as a single large wetland (Scheffer et al. 2006 ; Ma et al. 2010 ). Overall, great egrets were predominantly found in wetlands that contained features favored by other members of their larger guild. However, they had a significant negative relationship with macroinvertebrate species richness, consistent with Beerens et al. ( 2011 ), who suggest that they are 'visual exploiter' species that require lower prey concentrations. Whereas tricolored herons had a positive relationship with macroinvertebrate species richness, suggesting they require higher prey concentrations. Tricolored herons utilize one of the most energy-demanding feeding methods, ‘running’ or ‘openwing’, which requires a large amount of protein to maintain (Rodgers 1983 , Kent 1986 ). Tricolored herons also selected wetland conditions similar to the overall guild but showed a preference for lacustrine wetlands over estuarine. Although tricolored herons will feed in estuarine wetlands, their presence in lacustrine wetlands could be attributed to the use of shallow areas for roosting (Anderson et al. 1996 ; Essian 2022 ). Charadriiformes Shorebirds were frequently found during high tide (p < 0.001) and had a negative relationship with flooded wetlands (β=-1.08). Shorebirds forage during low tide but use roost sites at high tide to find refuge, rest, and preen while their foraging habitat is inundated (Weber and Haig 1996 ; Ramli and Norazlimi 2016 ). We detected most shorebirds at high tide, suggesting these sites could be primarily used for maintenance behaviors. They also selected larger wetlands close to surrounding wetlands, suggesting that shorebirds are dependent on the mosaic as a whole because it allows them to locate high-quality food patches while minimizing the energetic cost of searching for food at longer distances (Long and Ralph 2001 ). Similar to wading birds, shorebirds preferred wetlands with higher water cover than vegetation cover, potentially because they often utilize mudflats as opposed to densely vegetated patches (Burger et al. 1997 ). Unlike the overall shorebird guild, willets selected wetlands at low tide, consistent with Long and Ralph ( 2001 ), who suggest they are 'salt marsh opportunists' utilizing the mudflats that occur in these environments at lower tide for optimal foraging sites. Salt marsh opportunists are less selective when foraging, not relying on a single source of food (Brittingham and Temple 1992 ). Willets also preferred a high percentage of water cover for foraging on mudflats during low tide (Burger et al. 1997 ; Ramli and Norazlimi 2016 ; Fonseca et al. 2017 ) and a high percentage of vegetation cover (Specht 2018 ). While the high vegetation cover may limit open areas for optimal foraging, increased vegetative cover could be linked to higher prey densities, protection from predators, or easier escape from predators, suggesting there are trade-offs and differences between site selection ultimately (Ryan and Renken 1987 ). Anseriformes Waterfowl presence during this time was limited, which could be explained by the migration patterns of this guild. Our surveys occurred during late winter and spring migrations, a time when many of the local waterfowl species migrate from this region and likely don’t use these ponds and wetlands for extended periods. Their overall preference was for permanently flooded estuarine wetlands, which is uncommon considering they historically prefer tidal freshwater marshes (Odum 1988 ; Thompson and Baldassarre 1988 ). One potential explanation for this difference in habitat preference could be that the freshwater wetlands at our study sites rarely had > 5 cm of surface water (J. McCall, personal observation), so waterfowl were pushed into other wetland types to seek food resources (i.e., estuarine wetlands; Ringelman et al. 2015 ). As waterfowl, depending on whether they are dabblers or divers, require 13–25 cm of water for appropriate foraging, a lack of freshwater wetlands meeting this criterion could have led these species to occupy flood estuarine wetlands instead (Colwell and Taft 2000 ). Additionally, water coverage and depth can influence the composition and abundance of submersed aquatic vegetation in wetlands (Seabloom et al. 1998 ; Baschuk et al. 2012 ). The establishment of submerged aquatic vegetation can provide support and structure for different species in the system, which can affect the amount of food material available and the cover for waterfowl (Baschuk et al. 2012 ; Silver et al. 2012 ). In support of this water and vegetation requirement, our data indicated that waterfowl preferred a high percentage of water cover and vegetation cover. They also preferred high tide, which did not come as a surprise, as this was the best time for them to forage. However, the waterfowl guild and wood ducks specifically had a negative relationship with macroinvertebrate richness. This could be because our surveyed sites did not have macroinvertebrate richness. Instead, they were more influenced by habitat structure and stability. The individuals we detected could have been drawn to the area because of the vegetative coverage, which can include suitable plant-based food sources but does not provide a good structural habitat for macroinvertebrates (Krull 1970 ). It is possible that they continued from our surveyed ponds and proceeded to search the surrounding properties for higher protein material. Wood ducks also had the strongest relationship with wetlands when the tide was falling, supporting data from Colwell and Taft ( 2000 ), who claim that habitats that range from open water to mud flats can support greater bird diversity due to the greater diversity of microhabitats. It is possible that waterfowl selected non-isolated (less than 0.5 km apart) sites that provide a mix of food sources and nesting habitats (Hartke and Hepp 2004 , Gilmer 1971 ). This ensures that they have a nearby food source and reduces the likelihood of encountering other nesting pairs (Gilmer 1971 ). Gruiformes The secretive marshbird guild preferred permanently flooded wetlands with a high percentage of water coverage. Wetlands with permanent standing water allow for the establishment of robust emergent vegetation communities, which provide required nesting and predator protection for marshbirds (Kantrud and Stewart 1984 ; Harms and Dinsmore 2013 ). Access to open water allows for some species to build nests in areas that are not easily accessible to mammalian predators (Harms and Dinsmore 2013 ). Furthermore, marshbirds on our sites also preferred non-isolated wetlands in a complex as opposed to a single isolated wetland. These findings are consistent with other studies that found that populations in isolated wetlands have a decreased chance of migration and recolonization and a limited amount of nesting and feeding habitat (Brown and Dinsmore 1986 ; Smith and Chow-Fraser 2010 ). Clapper rails specifically made up > 70% of the marshbirds we detected, so their preferences largely overlapped with the guild overall. Our study shows they preferred wetlands with high water percentages because they encourage the more permanent growth of emergent vegetation in which the species can hide. Clapper rails were more frequently detected during low tide, likely because they make use of edge habitat during periods of low tide, making them easier to detect during this time (Rush et al. 2010 ). They were also detected more often in larger wetlands, likely because they provided a larger foraging area. Fiddler crab densities, a primary food source of clapper rails, are highest in large marsh areas where cordgrass is present (Ricketts 2011 ), and this is where most of these individuals were observed (J. McCall, personal observation). Suliformes and Pelicanus The pelican-anhinga-cormorant guild utilizes a prolonged underwater foraging strategy, often feeding on fish in freshwater (Johansen et al. 2001 ; Nelson 2005 ). This strategy involves diving from the surface, as opposed to diving from the air, and catching several fish on one dive, thus expending less energy (Nelson 2005 ). Our data revealed that they preferred extensive permanently flooded wetlands during high tide, as well as those that are dominated by water rather than vegetation. These data align with their foraging habits, as all of these variables create the ideal feeding habitat. This group also preferred non-isolated wetlands, potentially because they require less energy to travel and forage between wetlands. Anhingas' and cormorants' plumage will absorb water rather than repel it (Hennemann III 1988 ), so they are greatly weighed down while foraging and must take the time to dry out their feathers after foraging by selecting wetlands that are connected in a complex; they could potentially save more energy compared to traveling larger distances (Sellers 1995 ; Johansen et al. 2001 ). Double-crested cormorants made up nearly 75% of the detections we had in this guild, so they had similar preferences while also revealing a preference for lacustrine wetlands over other wetland types. This is consistent with Anderson et al. ( 1996 ), who found that this trend is likely because freshwater fish are abundant in large, deep, stable water bodies (i.e., lacustrine wetlands), and open water areas allow cormorants an unobstructed view when they pursue their prey. The guild and double-crested cormorants specifically had a negative relationship with macroinvertebrate species richness, likely because they forage on larger prey items that will replenish their energy quicker than macroinvertebrates (Johansen et al. 2001 ). Conclusions Our study found that several species used the wetlands at the Hobcaw Barony and DeBordieu Colony due to the mix of habitat types and conditions that support a wide range of behaviors and niches. Although some of these waterbirds were detected minimally, our results indicate that these study sites hold some ecological value. The three wetland classifications we selected were all used by waterbirds, estuarine being the most used at Hobcaw and lacustrine being the most used at DeBordieu, highlighting the need for further management and preservation actions in this region. The Hobcaw Barony is uninhabited and unmanaged for waterbirds and has been subjected to hurricane damage, sea level rise, and invasive species (Stalter and Baden 1994 ; Stalter et al. 2021 ; Regmi 2024 ). Therefore, it could lack some of the key areas that build desirable structures or resources for these species (habitat structure, food availability, and space). The DeBordieu Colony is more urbanized compared to Hobcaw but is subject to disturbances such as noise pollution, potential fertilizer runoff, or habitat degradation (White 2003 ; White and Main 2005 ). Despite the value that natural landscapes offer (Huner et al. 2002 ; Bellio et al. 2009 ), we recommend that these properties be more intensely managed for waterbirds specifically. For example, waterfowl require 5–25 cm of water (Cross 1988), and shorebirds require < 5 cm of water (Weber and Haig 1966). Future managers of these properties could deploy water manipulation technology and ensure the species are getting the foraging habitat they require (Bauer et al. 2020 ). Our data are invaluable to wetland managers here and on surrounding properties as they indicate essential waterbird needs. Declarations The authors have no relevant financial or non-financial interests to disclose. Funding The DeBordieu Colony (Georgetown, SC, USA), the Clemson University James C. Kennedy Waterfowl and Wetlands Conservation Center (Georgetown, SC, USA), and the National Institute of Food and Agriculture/U.S. Department of Agriculture (Washington, D.C., USA), project number SC-1700590, provided funding for this research. Author Contributions Jordan E. McCall, Deborah Kunkel, Joseph D. Lanham, and James T. Anderson contributed to the study design. Jordan E. McCall conducted data collection and wrote the initial draft of the manuscript. Jordan E. 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Environ Manage 45:1040–1051. https://doi.org/10.1007/s00267-010-9475-5 Specht H (2018) Habitat use and reproductive success of waterbirds in the human-dominated landscape of North America's prairies: Using sparse data to inform management. Dissertation, University of Minnesota. 204 pp. https://doi.org/10.13020/2vq4-fn73 Stalter R, Baden J (1994) A twenty-year comparison of vegetation of three abandoned rice fields, Georgetown County, South Carolina. South Appalach Bot Soc 59:1:69–77. https://www.jstor.org/stable/4033763 Stalter R, Rachlin J, Baden J (2021) A forty-seven year comparison of the vascular flora at three abandoned rice fields, Georgetown, South Carolina, U.S.A. Journal of the Botanical Research Institute of Texas 15:1:271–282. https://www.jstor.org/stable/27082517 Stewart RE, Kantrud HA (1971) Classification of Natural Ponds and Lakes in the Glaciated Prairie Region. United States Department of the Interior: Fish and Wildlife Service. The Bureau of Sport Fisheries and Wildlife, Washington Stolen ED, Brininger DR, Frederick PC (2004) Using waterbirds as indicators in estuarine systems: Successes and perils. In: Bortone SA (ed) Estuarine Indicators. CRC, Boca Raton, Florida, pp 431–444 Suthar AR, Biggs AR, Anderson JT (2025) A decadal change in shorebird populations in response to temperature, wind, and precipitation at Hilton Head Island, South Carolina, USA. Birds 6(1):14. https://doi.org/10.3390/birds6010014 Teitelbaum CS, Sirén APK, Coffel E, Foster JR, Frair JL, Hinton JW, Horton RM, Kramer DW, Lesk C, Raymond C, Wattles DW, Zeller KA, Morelli TL (2020) Habitat use as indicator of adaptive capacity to climate change. Divers Distrib 27:655–667. https://doi.org/10.1111/ddi.13223 Thomas JW (1979) Wildlife habitats in managed forests: The Blue Mountains of Oregon and Washington. USDA, Forest Service Handbook 553, Washington, D.C. Thompson JD, Baldassarre GA (1988) Postbreeding habitat preference of wood ducks in Northern Alabama. J Wildl Manag 52:1:80–85. https://doi.org/10.2307/3801063 U.S. Fish and Wildlife Service (2019) National Wetlands Inventory Website, Published 20191004. U.S. Department of the interior, Fish and Wildlife Service, Washington, D.C. https://fwsprimary.wim.usgs.gov/wetlands/apps/wetlands-mapper/ Geological Survey US, National Geospatial Technical Operations Center (2020). Georgetown County, SC, All Roads County-based. Accessed January 21, 2022 at URL https://apps.nationalmap.gov/downloader/ Geological Survey US (2024) Pee Dee River at Georgetown, SC – 02136350, Water Data. Accessed July 31, 2024 at https://waterdata.usgs.gov/monitoring-location/02136350/#parameterCode=00065.=P7D&showMedian=false Veselka WV, Anderson JT, Kordek WS (2010) Using dual classifications in the development of avian wetlands indices of biological integrity for wetlands in West Virginia, USA. Environ Monit Assess 164:533–548. https://doi.org/10.1007/s10661-009-0911-z Voshell JR Jr. (2002) A Guide to Common Freshwater Invertebrates of North America. The McDonald & Woodward Publishing Company. Blacksburg, Virginia Weber LM, Haig SM (1996) Shorebird use of South Carolina managed and natural coastal wetlands. J Wildl Manag 60:73–82. https://doi.org/10.2307/3802042 White CL (2003) Habitat value of created wetlands to waterbirds in golf course landscapes. Thesis, University of Florida. 130 pp. https://www.researchgate.net/publication/255670357 White CL, Main MB (2005) Waterbird use of created wetlands in golf-course landscapes. Wildl Soc Bull 33:2:411–421. https://doi.org/10.2193/0091-7648(2005)33 [411:WUOCWI]2.0.CO;2 Supplementary Files APPENDICES.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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18:17:56","extension":"html","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":281806,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7716046/v1/5723a036820d904ef89b9bb5.html"},{"id":94587552,"identity":"9bee5fe3-4d03-491e-a7f7-1390dfae0232","added_by":"auto","created_at":"2025-10-28 18:18:22","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":441109,"visible":true,"origin":"","legend":"\u003cp\u003eWaterbird habitat selection study area and its wetland types in Georgetown, South Carolina, USA: Hobcaw Barony (~7,000 ha, 33.3639° N, 79.2272° W) and DeBordieu Colony (~1,000 ha, 33.3691° N, 79.1517° W) between February and July 2022 and January and July 2023.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7716046/v1/74697911900d03ebd0f55c39.jpeg"},{"id":100737343,"identity":"ade486a0-7ab9-404a-9233-031e89e31f29","added_by":"auto","created_at":"2026-01-20 22:53:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1704641,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7716046/v1/e482d124-22f3-4f99-9c08-03529772de55.pdf"},{"id":94586428,"identity":"8e2a7b10-6545-4dae-ba6f-30b2e7107e75","added_by":"auto","created_at":"2025-10-28 18:16:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18824,"visible":true,"origin":"","legend":"","description":"","filename":"APPENDICES.docx","url":"https://assets-eu.researchsquare.com/files/rs-7716046/v1/08eef700e6c01186bff888a9.docx"}],"financialInterests":"","formattedTitle":"Waterbird Use of Wetlands Along a Conserved Rural to Urban Landscape Gradient","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnderstanding a species' specific habitat use and niche selection is critical for wildlife management. Exploration of these species or taxon-habitat relationships enhances understanding of animals' interaction with their environment, which often influences abundance and distribution patterns (Masto et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In our context and for the purposes of this paper, we define habitat and habitat use as the combination of resources (both biotic and abiotic) that species require for survival and reproductive success, as well as how they use these resources to fulfill those goals (Krausman \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, Leopold \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1933\u003c/span\u003e, Thomas \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e1979\u003c/span\u003e, Block and Brennan \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1993\u003c/span\u003e, Jones \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Not only does understanding habitat use of species reveal how they interact with the environment, but it can also enhance predictions of species responses to climate change (Arthur et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Teitelbaum et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWetlands are ecologically valuable due to their high biological diversity and productivity (Paracuellos \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Studies show that wetlands are capable of supporting a wide range of communities (Kingsford et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Junk et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). One factor that leads to this diverse assemblage of communities is the various niches that different species are able to fill and specialize in depending on their feeding behaviour or location in the wetland trophic web (Parcuellos 2006). Avian species are capable of using a wide range of these niches, which contributes to a wetland\u0026rsquo;s diversity (Hafner \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1997\u003c/span\u003e, Kushlan et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1985\u003c/span\u003e, Chatterjee et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Different factors that influence waterbird biodiversity include, but are not limited to, variations in water depth and vegetation composition, which influence the amount of available food, nesting, and thermal cover for waterbirds (Baschuk et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Not only do these environmental variables influence avian diversity and abundance in wetlands, but they also influence the invertebrate diversity, which can be a critical food source for many avian species (Baschuk et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInvertebrates fill the role of keystone species both through their use as a food source to numerous wetland species and by playing a critical component in many wetland ecosystem services (Balcombe et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Brraich and Kaur \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Specifically, invertebrates represent a critical source of nutrients for many waterfowl species, making up a significant portion of their diet (Anderson and Smith \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Furthermore, invertebrates contribute to many wetland functions, including but not limited to litter decomposition, nutrient cycling, and plant community regulation (Balcombe et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Gingerich et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Benthic macroinvertebrates are larger invertebrates (~\u0026thinsp;6 mm) that represent a critical group of invertebrates that reside in the substratum of lakes, ponds, streams, and rivers. These invertebrates represent numerous different families, including worms, insects, sponges, and mollusks. Due to their sensitivity to various levels and varieties of environmental pollution, benthic macroinvertebrates are valuable tools for biomonitoring studies (Anderson et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Braich and Kaur 2017). The composition and distribution of macroinvertebrate communities reflect the health and productivity of the wetland, and thus, information on their composition can be used to design further management strategies (Brraich and Kaur \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWetlands are often not isolated environments, but form larger complexes that share similar chemical, biological, hydrologic, and geomorphic characteristics (Cowardin et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). These complexes serve as vital areas for waterbird habitat as well as provide critical stopover points during migration (Anderson et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Veselka et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Wetlands can be classified based on their salinity and tides, which can shape their species and community composition. The most common wetland systems along the northcentral South Carolina coast are estuarine, palustrine, and lacustrine wetlands (Cowardin et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). Estuarine wetlands represent one of the most productive ecosystems in the world and host a diverse assemblage of bird species (Anderson et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, Kingsford et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Their ephemeral nature and influx of tidal influences lead these coastal wetlands to act as an amazing example to study the interactions between fresh and saltwater (Estades and Vukasovic \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The environmental dynamics of these wetlands, in turn, attract a variety of rarer waterbirds such as willets (\u003cem\u003eTringa semipalmata\u003c/em\u003e), clapper rails (\u003cem\u003eRallus crepitans\u003c/em\u003e), snowy egrets (\u003cem\u003eEgretta thula\u003c/em\u003e), American oystercatchers (\u003cem\u003eHaematopus palliatus\u003c/em\u003e), and roseate spoonbills (\u003cem\u003ePlatalea ajaja\u003c/em\u003e) (Anderson et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Masto et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Suthar et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Palustrine wetlands are predominantly nontidal and freshwater and are commonly used by species such as mallards (\u003cem\u003eAnas platyrhynchos\u003c/em\u003e), American wigeon (\u003cem\u003eMareca americana\u003c/em\u003e), least bitterns (\u003cem\u003eIxobrychus exilis\u003c/em\u003e), yellow-crowned night herons (\u003cem\u003eNyctanassa violacea\u003c/em\u003e), and tricolored herons (\u003cem\u003eEgretta tricolor\u003c/em\u003e) (Anderson et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Anderson and Smith \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, Cowardin et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). Conversely, lacustrine wetlands can be tidal or nontidal but are also predominantly freshwater and mostly open water and are typically dominated by double-crested cormorants (\u003cem\u003ePhalacrocorax auritus\u003c/em\u003e), anhingas (\u003cem\u003eAnhinga anhinga\u003c/em\u003e), and green herons (\u003cem\u003eButorides virescens\u003c/em\u003e) (Anderson et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, Cowardin et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). Wetlands are dynamic habitats that can be largely shaped by environmental factors, further influencing the species that utilize these unique habitats.\u003c/p\u003e\u003cp\u003eThe objectives of this study are to 1) determine which wetland type(s) are positively affecting waterbirds in terms of species presence and abundance, and 2) determine what environmental factor(s) are driving this favorable attraction for waterbirds in coastal South Carolina. We predict that wetland classification (i.e., estuarine, palustrine, lacustrine) will have a significant effect on species and guilds, as well as tidal stage and wetland area (Anderson et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Erwin et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Paracuellos \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Specifically, we predict that waterfowl abundance would be highest in palustrine wetlands, shorebird abundance would be highest in estuarine wetlands, and wading birds would be highest in lacustrine wetlands.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Area\u003c/h2\u003e\u003cp\u003eWe conducted the study at the Hobcaw Barony (~\u0026thinsp;7,000 ha, 33.364\u0026deg; N, 79.227\u0026deg; W) and the DeBordieu Colony in Georgetown County, South Carolina, USA (~\u0026thinsp;1,000 ha, 33.369\u0026deg; N, 79.152\u0026deg; W, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The study area comprises estuarine and marine deepwater systems, estuarine and marine wetlands, freshwater emergent wetlands, freshwater forested and scrub-shrub wetlands, freshwater ponds, lakes, and riverine wetlands (Cowardin et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) that follow a gradient of development intensity. Georgetown County features a development gradient from untouched tracts of old plantation lands (Hobcaw Barony) to densely human-developed housing communities (DeBordieu Colony; Buyck \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Hapke et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Hobcaw Barony is located on the southern tip of the Waccamaw peninsula north of the city of Georgetown, SC, and is managed under a conservation easement (Heaton et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The property contains about 3,000 ha of forest, 3,000 ha of salt marsh, and 1,000 ha of freshwater or brackish marshes and abandoned rice fields (Kale et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The DeBordieu Colony is a private coastal housing community located 6.5 km from U.S. Highway 17 to the Atlantic Ocean and 7.25 km north of the city of Georgetown. The site was developed in 1969, and today, it has over 1,200 home sites with natural features including maritime forests, marshes, and beaches (Vernberg 1996).\u003c/p\u003e\u003cp\u003eOur field sites accumulated 57.9 cm of rainfall in 2022 and 76.3 cm in 2023 over 7 months. Tidal height at the field sites ranged from 2.60 m (March) to 4.65 m (May) in 2022 and from 2.72 m (February) to 4.76 m (June) in 2023. The forest composition varies from xeric to hydric soil association and includes loblolly pine (\u003cem\u003ePinus taeda\u003c/em\u003e) stands to cypress-gum (\u003cem\u003eTaxodium-Nyssa\u003c/em\u003e spp.) swamps (Barry and Batson \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1969\u003c/span\u003e). Salt marsh plant communities are dominated by \u003cem\u003eJuncus\u003c/em\u003e spp. and \u003cem\u003eSporobolus\u003c/em\u003e spp., while the freshwater marsh community is dominated by \u003cem\u003eScirpus\u003c/em\u003e spp. and \u003cem\u003eTypha\u003c/em\u003e spp. (Stalter et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eWetland Mapping and Site Selection\u003c/h3\u003e\n\u003cp\u003eWe used ArcGIS Pro (ESRI 2022) to identify and map wetland types using the National Wetland Inventory (NWI) classification (Cowardin et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; USFWS 2019), roads from USGS (2020) to identify survey routes and access points, and the property boundary for both the Hobcaw Barony and DeBordieu Colony from Georgetown County GIS by ESRI (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e,b). The area of each wetland class and the number of polygons were used to delineate the relative number of sites by wetland class to survey. We selected sampled wetland locations randomly using a random number generator. Survey points were placed at the edge of wetlands to minimize observer disturbance that would be caused by walking through the wetland (Anderson et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Wetlands for playback surveys were chosen similarly from a pool of emergent wetlands only. We then field-verified each survey point to ensure the accessibility and accuracy of the NWI GIS layer. Sample wetlands were randomly chosen by class each year, so some wetlands surveyed in year 1 were also surveyed in year 2.\u003c/p\u003e\n\u003ch3\u003eAvian Point-count Surveys\u003c/h3\u003e\n\u003cp\u003eWe performed point-count surveys from January to April of 2022 and 2023, four days a week, alternating between DeBordieu Colony and Hobcaw Barony. Surveys started before sunrise with playback calls used to detect secretive marshbirds (Conway \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). We alternated the survey route starting point (2022: n\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;7; 2023: n\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;8), every other time the wetland was visited to avoid morning bias. At each survey point (2022: n\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;95; 2023: n\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;98), during a 10-minute, 100-m radius point, we identified all waterbird species (i.e., those that are ecologically dependent on wetlands, Mott et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) by sight or sound using binoculars and a spotting scope. We allowed for a two-minute settling period before the point count started to account for any disturbance we may have caused by navigating to the target wetland. We clarified on the datasheet whether we were observing one side of the survey point (i.e., we were at the edge of a wetland) or both sides (i.e., the target wetland surrounded us or there were multiple wetland types at the point) to later accurately estimate densities using the wetland area surveyed. We used an elevated surface (e.g., the back of the ATV) to reduce potential visibility bias from emergent vegetation (Hagy et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Birds that were observed flying over the wetland were not included in our counts to avoid bias and ensure accurate counts. However, individuals that aerially foraged in the study wetland, such as gulls or terns, or landed in the wetland during the survey period were recorded.\u003c/p\u003e\u003cp\u003eAt each wetland, we recorded the alphanumeric code of each survey point, start/stop time of the survey, date, estimated percent (%) of wetland covered with water, and estimated % of wetland covered with vegetation. Wetlands were surveyed once every two weeks during the course of the study. We conducted point-count surveys in all weather, aside from heavy rainfall or when wind exceeded 12 km/hr (Hamel et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eSecretive Marshbird Surveys\u003c/h3\u003e\n\u003cp\u003eWe conducted secretive marshbird surveys on select emergent wetlands at the Hobcaw Barony (~\u0026thinsp;320 ha) and the DeBordieu Colony (~\u0026thinsp;7 ha) between March and July 2022 and 2023 using playback surveys and standardized USGS protocols (Conway \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). We used playback calls to survey for secretive marshbirds, with these surveys beginning at approximately 0800 each time. We obtained the 10-minute playback recording from the Cornell Lab of Ornithology's All About Birds' database and broadcast it through a Bluetooth speaker at 80\u0026ndash;90 dB. Audio for the playback calls included black rails (\u003cem\u003eLaterallus jamaicensis\u003c/em\u003e), clapper rails (\u003cem\u003eRallus crepitans\u003c/em\u003e), king rails (\u003cem\u003eRallus elegans\u003c/em\u003e), sora (\u003cem\u003ePorzana carolina\u003c/em\u003e), Virginia rails (\u003cem\u003eRallus limicola\u003c/em\u003e), American bittern (\u003cem\u003eBotaurus lentiginosus\u003c/em\u003e), least bittern (\u003cem\u003eIxobrychus exilis\u003c/em\u003e), and limpkin (\u003cem\u003eAramus guarauna\u003c/em\u003e). Surveys were completed once per month from March to July in a subset of emergent wetlands at both sites (2022: n\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;10; 2023: n\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;8). To account for the timing required to survey both secretive marshbirds as well as complete the point count surveys, the number of survey location points varied by site (Conway \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). To decrease the sampling variation created by diurnal variation, we surveyed points in the same order every time, which can lead to decreases in the vocalization probability of each marshbird as the morning progressed (Conway \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eMacroinvertebrate Surveys\u003c/h3\u003e\n\u003cp\u003eWe collected macroinvertebrate samples once at each wetland during the avian survey period, starting in March and continuing through July. We used a 5-cm diameter PVC core sampler for benthic invertebrates and surveyed to a depth of 15 cm (Sherfy et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Anderson et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and a 5-cm diameter water column sampler for water column invertebrates (Anderson et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). We took both the benthic and water column samples at 10 random points along a line transect that bisected the center of the wetland (or a portion of the wetland surveyed for birds). In 2023, the sampling protocol for benthic samples remained consistent, but we increased the sample size for water column samples due to the smaller volume captured by the aquatic sampler. In wetlands that contained water\u0026thinsp;\u0026gt;\u0026thinsp;1 m deep, we took benthic samples along a transect near the shore and aquatic samples with the use of a kayak.\u003c/p\u003e\u003cp\u003eWe placed benthic samples in Ziploc bags labeled with the survey point, sample number, and date of collection and kept them in a cooler while in the field before transport to the laboratory (Anderson and Smith \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Samples were kept refrigerated to prevent degradation before being washed through a number 35 (500 micron) sieve. All samples were sorted within 10 days of collection, and once sorted, invertebrates were stored in 95% ethanol (Anderson and Smith \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDuring water column invertebrate sampling, we collected 10 samples/wetland along the predetermined transect lines. However, in 2023, we increased the water column samples to two samples at 10 random locations on the transect line, for a total of 20 aquatic samples in every wetland. We filtered each water column sample through a number 35 (500 micron) sieve to carefully separate detritus and other materials from the invertebrates, then placed any invertebrates in 95% ethanol for preservation (Huener and Kadlect 1992). Each sample was immediately labeled with the survey site, survey point, sampling date, and type of sample (i.e., benthic versus aquatic). Macroinvertebrate samples were not taken in wetlands that did not contain any water or in wetlands where conditions were unsafe to walk through alone. We identified all invertebrates to order at a minimum and to family when possible (Voshell Jr. 2022; Louw et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We calculated macroinvertebrate richness as the number of orders per wetland.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eWetland-level Data Collection\u003c/h2\u003e\u003cp\u003eDuring the survey period, we collected environmental data and metrics for each wetland. These data included vegetation distribution patterns (Stewart and Kantrud \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e1971\u003c/span\u003e), hydrologic regimes (Cowardin et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1979\u003c/span\u003e), distance to the nearest wetland, tidal class, and tidal stage. The hydrologic regime and tidal class were determined by visual observation across the surveyed portion of the wetland and using the NWI from the U.S. Fish and Wildlife Service (USFWS 2019). The tidal stage was later determined using the time of survey and USGS water data (USGS 2024). Vegetation distribution patterns were also assessed in the field (Stewart and Kantrud \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e1971\u003c/span\u003e). We determined \u0026lsquo;distance to the nearest wetland\u0026rsquo; using the measure tool in ArcGIS at each survey point.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eWe used a generalized linear mixed model with a Poisson distribution in JMP\u0026reg; 17.0.0 (SAS Institute) to determine which environmental factor(s) were driving waterbird use (Millspaugh et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Kloskowski et al. 2010). We analyzed our point count and environmental data at the guild level [Ardeidae and Threskiornithidae (wading birds), Charadriiformes (shorebirds), Anseriformes (waterfowl), Gruiformes (marshbirds), and Suliformes and \u003cem\u003ePelicanus\u003c/em\u003e (pelicans, anhingas, cormorants)], a group of species that exploit the same class of environmental resources similarly (Adams \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Boros \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and individual species level. Individual species were assigned to guilds based on a mix of methodology from previous similar studies, as well as feeding or behavioral habits (Anderson et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Conway \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). All surveyed wetlands were included in the analysis, regardless of whether the species was present or not, to account for species' absence. With the intent to simplify the analysis, we reduced wetland classification from class/subclass to the system level: estuarine (deepwater and estuarine wetlands), palustrine (freshwater emergent, freshwater forested/shrub, freshwater pond, and riverine wetlands), and lacustrine (lakes) (Cowardin et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). We included guilds and individual species that were detected\u0026thinsp;\u0026ge;\u0026thinsp;85 times throughout both field seasons, as this was the minimum number of observations required to be able to include the nine core predictor variables we identified in the model (Fieberg and Johnson 2015).\u003c/p\u003e\u003cp\u003eUsing species count as the response variable, we evaluated the impact of the different predictor variables on species responses. Our original surveys recorded 15 different variables; however, we were able to reduce this to nine key predictor variables that described the distribution of waterbirds throughout wetlands. The predictor variables included wetland classification (system), tidal class, water regime, tidal stage, wetland area (ha), macroinvertebrate species richness, percent water cover, and percent vegetation cover. We included survey date (a repeated measure) as a random effect to account for temporal variation in habitat use/associations. After standardizing all numerical variables, we screened for multicollinearity (|\u003cem\u003er\u003c/em\u003e|= 0.6) among predictors using a correlation matrix (Burnham and Anderson \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). None were correlated for species guilds or individual species' analyses, so all variables were included.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWaterbird guilds include Ardeidae and Threskiornithidae (wading birds), Charadriiformes (shorebirds), Anseriformes (waterfowl), Gruiformes (secretive marshbirds), and Suliformes and \u003cem\u003ePelicanus\u003c/em\u003e (pelicans, anhingas, and cormorants). Individual species that were investigated include great egrets (\u003cem\u003eArdea alba\u003c/em\u003e), tricolored herons, willets, wood ducks (\u003cem\u003eAix sponsa\u003c/em\u003e), clapper rails, and double-crested cormorants. Although the gulls and terns guild (American wigeon (\u003cem\u003eMareca americana\u003c/em\u003e), hooded mergansers (\u003cem\u003eLophodytes cucullatus\u003c/em\u003e), and sanderlings (\u003cem\u003eCalidris alba\u003c/em\u003e)) were frequently detected (\u0026gt;\u0026thinsp;85 times), they were not widely detected enough to demonstrate meaningful patterns. Waterbirds on these properties used 20.4 of the 50.5 ha (40.1%) surveyed in 2022 and 26.1 of the 42.5 ha (61.4%) surveyed in 2023. Over both field seasons, 2,388 waterbirds were detected, with 993 occurring on Hobcaw (natural site) and 1,395 occurring on DeBordieu (urban site).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eArdeidae and Threskiornithidae\u003c/h2\u003e\u003cp\u003eFrom January to April of 2022 and 2023, we detected a total of 628 wading birds, with the most numerous species being great egrets, 266 (42.4%), and tricolored herons, 174 (27.7%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The environmental variables that best explained the distribution and site selection for this guild were wetland classification (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), water regime (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and tidal stage (p\u0026thinsp;=\u0026thinsp;0.0483) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). They had the strongest positive relationship with permanently flooded (β\u0026thinsp;=\u0026thinsp;0.63) estuarine wetlands (β\u0026thinsp;=\u0026thinsp;13.98) during lower tides (β\u0026thinsp;=\u0026thinsp;0.13) and a negative relationship with palustrine wetlands (β= -0.16). Ardeidae and Threskiornithidae also had a significant relationship with smaller (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; β= -0.33), non-isolated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; β= -1.69) wetlands that were higher in water cover (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; β\u0026thinsp;=\u0026thinsp;0.65) than vegetation cover (p\u0026thinsp;=\u0026thinsp;0.0148; β= -0.24) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTotal waterbird guild and waterbird species detected and included in the analysis based on wetland classification between January and April of 2022 and 2023 on Hobcaw Barony and DeBordieu Colony, South Carolina, USA.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eNo. Birds\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWaterbird\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstuarine\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePalustrine\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLacustrine\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWading birds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreat egret (\u003cem\u003eArdea alba\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTricolored heron (\u003cem\u003eEgretta tricolor\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShorebirds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWillet (\u003cem\u003eTringa semipalmata\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWaterfowl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e468\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWood duck (\u003cem\u003eAix sponsa\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecretive marshbirds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClapper rail (\u003cem\u003eRallus crepitans\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePelicans, anhingas, cormorants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDouble-crested cormorant (\u003cem\u003ePhalacrocorax auritus\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults from a generalized linear mixed model analysis with Poisson distribution for the Wading bird guild and two most common species guild on Hobcaw Barony and DeBordieu Colony in Georgetown, South Carolina, USA, January to April 2022 and 2023. DF denominator degrees of freedom\u0026thinsp;=\u0026thinsp;1,205 for all tests, DF numerator are the same for all tests, * represents significant variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eGuild\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eGreat Egret\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eTricolored Heron\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTerm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eDFNum\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eF Ratio\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eF Ratio\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eF Ratio\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWetland Classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0004*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.4714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.0018*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTidal Class\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9787\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.9992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.3268\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater Regime\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.1482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e24.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTidal Stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0483*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.1883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e11.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to nearest wetland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e136.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e30.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWetland Areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0041*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.9030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMacro. Species Richness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0005*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e86.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Water Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e58.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.5488\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Veg. Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0148*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.4602\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGreat egrets were closely associated with smaller wetlands (p\u0026thinsp;=\u0026thinsp;0.0041; β= -0.61) in close proximity to other wetlands (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; β= -2.63), that held more water (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; β\u0026thinsp;=\u0026thinsp;1.43) and less vegetative cover (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; β= -1.11) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). They also selected wetlands that had lower macroinvertebrate richness (p\u0026thinsp;=\u0026thinsp;0.0005; β= -0.53) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Tricolored heron habitat usage was significantly affected by wetland classification (p\u0026thinsp;=\u0026thinsp;0.0018), water regime (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and tidal stage (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). They were most frequently found in permanently flooded (β\u0026thinsp;=\u0026thinsp;1.25) lacustrine wetlands (β\u0026thinsp;=\u0026thinsp;4.26) when the tide was falling (β\u0026thinsp;=\u0026thinsp;0.82) and a strong negative relationship with high tide (β= -0.55). They were also frequently associated with wetlands in close proximity to other wetlands (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; β= -7.12), and those that had high macroinvertebrate richness (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; β\u0026thinsp;=\u0026thinsp;0.98) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCharadriiformes\u003c/h2\u003e\u003cp\u003eWe detected a total of 485 shorebirds, willets representing the most numerous species at 85 (17.5%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Charadriiformes guild displayed significant associations with the water regime (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and tidal stage (p\u0026thinsp;=\u0026thinsp;0.0002) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). They had a strong negative relationship with seasonally flooded wetlands (β= -1.08) and a positive relationship with wetlands in high tide (β\u0026thinsp;=\u0026thinsp;0.05). Charadriiformes also had a significant relationship with larger wetlands (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; β\u0026thinsp;=\u0026thinsp;0.09) near other wetlands (p\u0026thinsp;=\u0026thinsp;0.0018; β= -11.71) and wetlands that had more water (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; β\u0026thinsp;=\u0026thinsp;0.65) than vegetative cover (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; β= -0.83) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults from a generalized linear mixed model analysis with Poisson distribution for the Charadriiformes (shorebird) species guild and the most commonly found species on Hobcaw Barony and DeBordieu Colony in Georgetown, South Carolina, USA. The denominator degrees of freedom\u0026thinsp;=\u0026thinsp;1,205 for all tests for the guild, numerator degrees of freedom is the same for all tests. *significant variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"16\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eGuild\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e\u003cp\u003eWillet\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTerm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eDFNum\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eF Ratio\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eF Ratio\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eDF Den\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWetland Classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.9997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1204.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTidal Class\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.5523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e156.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater Regime\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e49.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0011*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1204.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTidal Stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0002*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0028*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1204.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to nearest wetland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0018*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.1088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1204.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWetland Areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.1040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1204.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMacro. Species Richness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.2931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.7907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1204.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Water Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e47.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0022*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1204.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Veg. Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e46.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0040*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1204.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWillets specifically were significantly affected by the water regime and tidal stage (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). They had the strongest positive relationship with permanently flooded (β\u0026thinsp;=\u0026thinsp;1.15) wetlands at low tide (β\u0026thinsp;=\u0026thinsp;0.52) and the strongest negative relationship with seasonally flooded (β= -1.02) wetlands at high tide (β= -1.18). They also had significant relationships with wetlands that were higher in % water cover (p\u0026thinsp;=\u0026thinsp;0.0022; β\u0026thinsp;=\u0026thinsp;0.46) and % vegetation cover (p\u0026thinsp;=\u0026thinsp;0.004; β\u0026thinsp;=\u0026thinsp;0.78) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eAnseriformes\u003c/h2\u003e\u003cp\u003eWe detected a total of 777 waterfowl, with wood ducks representing the most commonly found species at 108 (13.9%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Anseriformes guild was significantly affected by wetland classification (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), water regime (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and tidal stage (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). They had the strongest positive relationship with permanently flooded (β\u0026thinsp;=\u0026thinsp;1.88) estuarine wetlands (β\u0026thinsp;=\u0026thinsp;24.2) at high tide (β\u0026thinsp;=\u0026thinsp;0.59) and the strongest negative relationship with seasonally flooded wetlands (β= -2.45) when the tide was falling (β= -0.6). Anseriformes also had a significant relationship with wetlands of high water cover (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; β\u0026thinsp;=\u0026thinsp;1.07) and vegetation percentage (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; β\u0026thinsp;=\u0026thinsp;0.74) but a low macroinvertebrate richness (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; β= -2.38) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults from a generalized linear mixed model analysis with Poisson distribution for the Anseriformes (waterfowl) species guild and the most commonly found species on Hobcaw Barony and DeBordieu Colony in Georgetown, South Carolina, USA. The denominator degrees of freedom\u0026thinsp;=\u0026thinsp;1,205 for all guild tests, numerator degrees of freedom is the same for all tests. *significant variable\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"16\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eGuild\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e\u003cp\u003eWood Duck\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTerm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eDFNum\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eF Ratio\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eF Ratio\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eDF Den\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWetland Classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.2887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e949.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTidal Class\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.2509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1204.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater Regime\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1204.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTidal Stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1204.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to nearest wetland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0105*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1204.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWetland Areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.2477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.8768\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1204.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMacro. Species Richness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e165.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0060*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1204.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Water Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e77.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0041*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1204.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Veg. Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e59.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.4891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e827.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWood ducks specifically were significantly affected by the tidal stage (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), having the strongest positive relationship with a falling tide level (β\u0026thinsp;=\u0026thinsp;1.16) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). They also had a significant relationship with wetlands that were high in water percentage (p\u0026thinsp;=\u0026thinsp;0.0041; β\u0026thinsp;=\u0026thinsp;1.64) and those close to other wetlands (p\u0026thinsp;=\u0026thinsp;0.0105; β= -0.36), but wetlands low in macroinvertebrate richness (p\u0026thinsp;=\u0026thinsp;0.006; β= -0.86) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eGruiformes\u003c/h2\u003e\u003cp\u003eWe detected a total of 117 Gruiformes, secretive marshbirds, with 86 (73.5%) of those being clapper rails (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Gruiformes were significantly affected by the water regime (p\u0026thinsp;=\u0026thinsp;0.0009), having the strongest positive relationship with permanently flooded wetlands (β\u0026thinsp;=\u0026thinsp;1.07) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). They also had a significant relationship with wetlands that were in close proximity to other wetlands (p\u0026thinsp;=\u0026thinsp;0.0233; β= -2.2) and those that were high in water percentage (p\u0026thinsp;=\u0026thinsp;0.0026; β\u0026thinsp;=\u0026thinsp;0.49) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Clapper rails specifically were significantly affected by the tidal stage (p\u0026thinsp;=\u0026thinsp;0.003), having the strongest positive relationship with low tide (β\u0026thinsp;=\u0026thinsp;0.47) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). They also had a significant relationship with larger wetlands (p\u0026thinsp;=\u0026thinsp;0.0036; β\u0026thinsp;=\u0026thinsp;0.41) that were high in water percentage (p\u0026thinsp;=\u0026thinsp;0.0435; β\u0026thinsp;=\u0026thinsp;0.34) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults from a generalized linear mixed model analysis with Poisson distribution for the Gruiformes (secretive marshbird) species guild and the most commonly found species on Hobcaw Barony and DeBordieu Colony in Georgetown, South Carolina, USA. The denominator degrees of freedom\u0026thinsp;=\u0026thinsp;1,205 for all tests for the guild, numerator degrees of freedom is the same for all tests. *significant variable\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"16\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eGuild\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e\u003cp\u003eClapper Rails\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTerm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eDFNum\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eF Ratio\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eF Ratio\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eDF Den\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWetland Classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.8886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1203.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTidal Class\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.1365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e170.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater Regime\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0009*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.5849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1203.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTidal Stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0030*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1203.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to nearest wetland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0233*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.3470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1203.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWetland Areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0036*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1203.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMacro. Species Richness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.7636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.4089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1203.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Water Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0026*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0435*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1203.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Veg. Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.2225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.8786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1203.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSuliformes and\u003c/b\u003e \u003cb\u003ePelicanus\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe detected a total of 351 Suliformes and \u003cem\u003ePelicanus\u003c/em\u003e: pelicans (n\u0026thinsp;=\u0026thinsp;36), anhingas (n\u0026thinsp;=\u0026thinsp;54), and cormorants (n\u0026thinsp;=\u0026thinsp;261), with 261 (74.4%) of those being double-crested cormorants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This guild overall was significantly affected by the water regime (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and tidal stage (p\u0026thinsp;=\u0026thinsp;0.0156) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). They were strongly associated with permanently flooded (β\u0026thinsp;=\u0026thinsp;1.5) wetlands at high tide (β\u0026thinsp;=\u0026thinsp;0.42) and a negative relationship with seasonally flooded (β= -2.44) wetlands at low tide (β= -0.52). They also had a significant relationship with larger wetlands (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; β\u0026thinsp;=\u0026thinsp;1.1) that were near other wetlands (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; β= -0.73), low in macroinvertebrate species richness (p\u0026thinsp;=\u0026thinsp;0.0001; β= -0.42), and a greater percent of water (p\u0026thinsp;=\u0026thinsp;0.0307; β\u0026thinsp;=\u0026thinsp;0.4) rather than vegetation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; β= -0.71) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults from a generalized linear mixed model analysis with Poisson distribution for Suliformes and \u003cem\u003ePelicanus\u003c/em\u003e (pelicans, anhingas, and cormorants) on Hobcaw Barony and DeBordieu Colony in Georgetown, South Carolina, USA. The denominator degrees of freedom\u0026thinsp;=\u0026thinsp;1,205 for all tests.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"16\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eGuild\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e\u003cp\u003eDouble-breasted Cormoorants\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTerm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eDFNum\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eF Ratio\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eF Ratio\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWetland Classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.4375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0178*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTidal Class\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.3298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater Regime\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e27.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTidal Stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0156*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0068*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to nearest wetland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e18.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWetland Areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e62.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e45.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMacro. Species Richness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0032*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Water Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0307*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0423*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Veg. Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDouble-crested cormorants specifically were significantly influenced by wetland classification (p\u0026thinsp;=\u0026thinsp;0.0178), water regime (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and tidal stage (p\u0026thinsp;=\u0026thinsp;0.0068) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). They had the strongest positive relationship with permanently flooded (β\u0026thinsp;=\u0026thinsp;1.95) lacustrine wetlands (β\u0026thinsp;=\u0026thinsp;4.99) while the tide was falling (β\u0026thinsp;=\u0026thinsp;0.44). Similar to the guild, they had a significant relationship with larger wetlands (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; β\u0026thinsp;=\u0026thinsp;0.94) that were near other wetlands (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; β= -0.59), low in macroinvertebrate species richness (p\u0026thinsp;=\u0026thinsp;0.0035; β= -0.35), and high in water coverage (p\u0026thinsp;=\u0026thinsp;0.0423; β\u0026thinsp;=\u0026thinsp;0.43) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe conducted several types of surveys (i.e., point counts, marsh bird playbacks, invertebrate richness) to form an understanding of wetland and waterbird relationships in coastal South Carolina. While studies on waterbird habitat usage have occurred on the South Carolina coast (Weber and Haig \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Boettcher et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Masto et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Suthar et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), few have evaluated the rural-to-urban gradient in coastal wetlands. Our study focused on the rural-to-urban gradient between the Hobcaw Barony and the nearby DeBordieu plantation. During the course of our study, we found a large portion of the waterbirds at DeBordieu, likely due to the abundance of freshwater ponds and lacustrine wetlands. Overall, our predictions for the relationships between waterbirds and habitats were supported. However, our specific predictions for guild preference in wetland type were not supported. The results of this study do support different habitats and habitat modifications that wetland managers can do to increase wetland quality and species diversity.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eArdeidae and Threskiornithidae\u003c/h2\u003e\u003cp\u003eWading birds were found to be closely associated with estuarine wetlands, likely because these wetlands support their primary prey items, among other factors (McKinney and Raposa \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), which was further evidenced and supported by our results. The environmental factor that was most strongly predictive and correlated to wading bird abundance at our sites was tidal timing, specifically low tide, which matches previously reported trends (Maccarone and Brzorad. 2005, Raposa et al. 2005, Calle et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The primary reason for this correlation is likely due to low tides increasing the activity and access to nektonic prey items (fish and decapod crustaceans) that these guilds prefer (Raposa et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Furthermore, if wetlands are too densely covered with vegetation, the falling tide will not expose sufficient areas of mudflats for wading birds to forage. Hence, our data indicate that wading birds prefer less vegetative cover (Granadeiro et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Wading birds on our study sites were also selected for smaller wetlands that were close to other wetlands. Although large, isolated wetlands support wading bird diversity (Paracuellos \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), several studies have suggested that a mosaic of small wetlands can hold just as much ecological value as a single large wetland (Scheffer et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOverall, great egrets were predominantly found in wetlands that contained features favored by other members of their larger guild. However, they had a significant negative relationship with macroinvertebrate species richness, consistent with Beerens et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), who suggest that they are 'visual exploiter' species that require lower prey concentrations. Whereas tricolored herons had a positive relationship with macroinvertebrate species richness, suggesting they require higher prey concentrations. Tricolored herons utilize one of the most energy-demanding feeding methods, \u0026lsquo;running\u0026rsquo; or \u0026lsquo;openwing\u0026rsquo;, which requires a large amount of protein to maintain (Rodgers \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1983\u003c/span\u003e, Kent \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Tricolored herons also selected wetland conditions similar to the overall guild but showed a preference for lacustrine wetlands over estuarine. Although tricolored herons will feed in estuarine wetlands, their presence in lacustrine wetlands could be attributed to the use of shallow areas for roosting (Anderson et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Essian \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eCharadriiformes\u003c/h2\u003e\u003cp\u003eShorebirds were frequently found during high tide (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and had a negative relationship with flooded wetlands (β=-1.08). Shorebirds forage during low tide but use roost sites at high tide to find refuge, rest, and preen while their foraging habitat is inundated (Weber and Haig \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Ramli and Norazlimi \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). We detected most shorebirds at high tide, suggesting these sites could be primarily used for maintenance behaviors. They also selected larger wetlands close to surrounding wetlands, suggesting that shorebirds are dependent on the mosaic as a whole because it allows them to locate high-quality food patches while minimizing the energetic cost of searching for food at longer distances (Long and Ralph \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Similar to wading birds, shorebirds preferred wetlands with higher water cover than vegetation cover, potentially because they often utilize mudflats as opposed to densely vegetated patches (Burger et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnlike the overall shorebird guild, willets selected wetlands at low tide, consistent with Long and Ralph (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), who suggest they are 'salt marsh opportunists' utilizing the mudflats that occur in these environments at lower tide for optimal foraging sites. Salt marsh opportunists are less selective when foraging, not relying on a single source of food (Brittingham and Temple \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Willets also preferred a high percentage of water cover for foraging on mudflats during low tide (Burger et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Ramli and Norazlimi \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fonseca et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and a high percentage of vegetation cover (Specht \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While the high vegetation cover may limit open areas for optimal foraging, increased vegetative cover could be linked to higher prey densities, protection from predators, or easier escape from predators, suggesting there are trade-offs and differences between site selection ultimately (Ryan and Renken \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eAnseriformes\u003c/h2\u003e\u003cp\u003eWaterfowl presence during this time was limited, which could be explained by the migration patterns of this guild. Our surveys occurred during late winter and spring migrations, a time when many of the local waterfowl species migrate from this region and likely don\u0026rsquo;t use these ponds and wetlands for extended periods. Their overall preference was for permanently flooded estuarine wetlands, which is uncommon considering they historically prefer tidal freshwater marshes (Odum \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Thompson and Baldassarre \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). One potential explanation for this difference in habitat preference could be that the freshwater wetlands at our study sites rarely had\u0026thinsp;\u0026gt;\u0026thinsp;5 cm of surface water (J. McCall, personal observation), so waterfowl were pushed into other wetland types to seek food resources (i.e., estuarine wetlands; Ringelman et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As waterfowl, depending on whether they are dabblers or divers, require 13\u0026ndash;25 cm of water for appropriate foraging, a lack of freshwater wetlands meeting this criterion could have led these species to occupy flood estuarine wetlands instead (Colwell and Taft \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Additionally, water coverage and depth can influence the composition and abundance of submersed aquatic vegetation in wetlands (Seabloom et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Baschuk et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The establishment of submerged aquatic vegetation can provide support and structure for different species in the system, which can affect the amount of food material available and the cover for waterfowl (Baschuk et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Silver et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In support of this water and vegetation requirement, our data indicated that waterfowl preferred a high percentage of water cover and vegetation cover. They also preferred high tide, which did not come as a surprise, as this was the best time for them to forage.\u003c/p\u003e\u003cp\u003eHowever, the waterfowl guild and wood ducks specifically had a negative relationship with macroinvertebrate richness. This could be because our surveyed sites did not have macroinvertebrate richness. Instead, they were more influenced by habitat structure and stability. The individuals we detected could have been drawn to the area because of the vegetative coverage, which can include suitable plant-based food sources but does not provide a good structural habitat for macroinvertebrates (Krull \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1970\u003c/span\u003e). It is possible that they continued from our surveyed ponds and proceeded to search the surrounding properties for higher protein material. Wood ducks also had the strongest relationship with wetlands when the tide was falling, supporting data from Colwell and Taft (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), who claim that habitats that range from open water to mud flats can support greater bird diversity due to the greater diversity of microhabitats. It is possible that waterfowl selected non-isolated (less than 0.5 km apart) sites that provide a mix of food sources and nesting habitats (Hartke and Hepp \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Gilmer \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1971\u003c/span\u003e). This ensures that they have a nearby food source and reduces the likelihood of encountering other nesting pairs (Gilmer \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1971\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eGruiformes\u003c/h2\u003e\u003cp\u003eThe secretive marshbird guild preferred permanently flooded wetlands with a high percentage of water coverage. Wetlands with permanent standing water allow for the establishment of robust emergent vegetation communities, which provide required nesting and predator protection for marshbirds (Kantrud and Stewart \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Harms and Dinsmore \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Access to open water allows for some species to build nests in areas that are not easily accessible to mammalian predators (Harms and Dinsmore \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Furthermore, marshbirds on our sites also preferred non-isolated wetlands in a complex as opposed to a single isolated wetland. These findings are consistent with other studies that found that populations in isolated wetlands have a decreased chance of migration and recolonization and a limited amount of nesting and feeding habitat (Brown and Dinsmore \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Smith and Chow-Fraser \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eClapper rails specifically made up \u0026gt;\u0026thinsp;70% of the marshbirds we detected, so their preferences largely overlapped with the guild overall. Our study shows they preferred wetlands with high water percentages because they encourage the more permanent growth of emergent vegetation in which the species can hide. Clapper rails were more frequently detected during low tide, likely because they make use of edge habitat during periods of low tide, making them easier to detect during this time (Rush et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). They were also detected more often in larger wetlands, likely because they provided a larger foraging area. Fiddler crab densities, a primary food source of clapper rails, are highest in large marsh areas where cordgrass is present (Ricketts \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and this is where most of these individuals were observed (J. McCall, personal observation).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSuliformes and\u003c/b\u003e \u003cb\u003ePelicanus\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe pelican-anhinga-cormorant guild utilizes a prolonged underwater foraging strategy, often feeding on fish in freshwater (Johansen et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Nelson \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This strategy involves diving from the surface, as opposed to diving from the air, and catching several fish on one dive, thus expending less energy (Nelson \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Our data revealed that they preferred extensive permanently flooded wetlands during high tide, as well as those that are dominated by water rather than vegetation. These data align with their foraging habits, as all of these variables create the ideal feeding habitat. This group also preferred non-isolated wetlands, potentially because they require less energy to travel and forage between wetlands. Anhingas' and cormorants' plumage will absorb water rather than repel it (Hennemann III \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), so they are greatly weighed down while foraging and must take the time to dry out their feathers after foraging by selecting wetlands that are connected in a complex; they could potentially save more energy compared to traveling larger distances (Sellers \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Johansen et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDouble-crested cormorants made up nearly 75% of the detections we had in this guild, so they had similar preferences while also revealing a preference for lacustrine wetlands over other wetland types. This is consistent with Anderson et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), who found that this trend is likely because freshwater fish are abundant in large, deep, stable water bodies (i.e., lacustrine wetlands), and open water areas allow cormorants an unobstructed view when they pursue their prey. The guild and double-crested cormorants specifically had a negative relationship with macroinvertebrate species richness, likely because they forage on larger prey items that will replenish their energy quicker than macroinvertebrates (Johansen et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study found that several species used the wetlands at the Hobcaw Barony and DeBordieu Colony due to the mix of habitat types and conditions that support a wide range of behaviors and niches. Although some of these waterbirds were detected minimally, our results indicate that these study sites hold some ecological value. The three wetland classifications we selected were all used by waterbirds, estuarine being the most used at Hobcaw and lacustrine being the most used at DeBordieu, highlighting the need for further management and preservation actions in this region. The Hobcaw Barony is uninhabited and unmanaged for waterbirds and has been subjected to hurricane damage, sea level rise, and invasive species (Stalter and Baden \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Stalter et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Regmi \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, it could lack some of the key areas that build desirable structures or resources for these species (habitat structure, food availability, and space). The DeBordieu Colony is more urbanized compared to Hobcaw but is subject to disturbances such as noise pollution, potential fertilizer runoff, or habitat degradation (White \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; White and Main \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Despite the value that natural landscapes offer (Huner et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Bellio et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), we recommend that these properties be more intensely managed for waterbirds specifically. For example, waterfowl require 5\u0026ndash;25 cm of water (Cross 1988), and shorebirds require\u0026thinsp;\u0026lt;\u0026thinsp;5 cm of water (Weber and Haig 1966). Future managers of these properties could deploy water manipulation technology and ensure the species are getting the foraging habitat they require (Bauer et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our data are invaluable to wetland managers here and on surrounding properties as they indicate essential waterbird needs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe DeBordieu Colony (Georgetown, SC, USA), the Clemson University James C. Kennedy Waterfowl and Wetlands Conservation Center (Georgetown, SC, USA), and the National Institute of Food and Agriculture/U.S. Department of Agriculture (Washington, D.C., USA), project number SC-1700590, provided funding for this research.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\u003cp\u003eJordan E. McCall, Deborah Kunkel, Joseph D. Lanham, and James T. Anderson contributed to the study design. Jordan E. McCall conducted data collection and wrote the initial draft of the manuscript. Jordan E. McCall conducted data analyses with input from Deborah Kunkel. James T. Anderson and Andrew P. Hopkins edited and commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eWe thank the Belle W. Baruch Institute of Coastal Ecology and Forest Science, Nemours Wildlife Foundation, Belle W. Baruch Foundation, and Clemson's Department of Forestry and Environmental Conservation for logistical support. We appreciate Jack Corbin, Anna Koon, Carly Sprott, and Blair Abernathy for their assistance in the field. This manuscript is technical contribution number 7469 of the Clemson University Experiment Station.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData are available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdams J (1985) The definition and interpretation of guild structure in ecological communities. J Anim Ecol 54:1:43\u0026ndash;59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/4619\u003c/span\u003e\u003cspan address=\"10.2307/4619\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnderson JT, Smith LM (1998) Protein and energy production in playas: Implications for migratory bird management. 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Wildl Soc Bull 33:2:411\u0026ndash;421. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2193/0091-7648(2005)33\u003c/span\u003e\u003cspan address=\"10.2193/0091-7648(2005)33\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e[411:WUOCWI]2.0.CO;2\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Avian, Coastal, Heron, Wetland, Wood Duck, Anseriformes","lastPublishedDoi":"10.21203/rs.3.rs-7716046/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7716046/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWetlands are ecologically valuable due to their high biological diversity and productivity, with many avian species depending on them. Understanding why species use different habitats is essential to successful wildlife management and can enhance predictions of animal responses to land use change. Despite the importance of coastal wetlands for waterbirds, habitat use studies are limited, especially along the rural-to-urban gradient. Our primary objective was to determine what environmental factor(s) are contributing to waterbird wetland use along the northcentral coast of South Carolina on conserved and developed lands. Between January and April 2022 and 2023, we conducted point-count surveys, secretive marshbird surveys, macroinvertebrate surveys, and collected other wetland-level data in a total of 156 wetlands. We found that waterbird diversity is influenced by water level, flooding regime of the wetlands and the proximity to other nearby wetlands. The results of this project have helped to identify key variable in both rural and urban landscapes that wetland managers can focus on to promote waterbird diversity. This information can be highly useful for wetland managers and professionals looking to protect to increase habitat for rare and threatened birds that frequent their lands.\u003c/p\u003e","manuscriptTitle":"Waterbird Use of Wetlands Along a Conserved Rural to Urban Landscape Gradient","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-28 16:36:11","doi":"10.21203/rs.3.rs-7716046/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"694b76ad-8b70-4590-bca7-ef6e9b84798a","owner":[],"postedDate":"October 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-20T22:50:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-28 16:36:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7716046","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7716046","identity":"rs-7716046","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}