Trait-Mediated Metacommunity Internal Structure: Insights from Waterbirds Assemblages in a Dynamic Estuarine System     

Trait-Mediated Metacommunity Internal Structure: Insights from Waterbirds Assemblages in a Dynamic Estuarine System | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 25 March 2026 V2 Latest version Share on Trait-Mediated Metacommunity Internal Structure: Insights from Waterbirds Assemblages in a Dynamic Estuarine System Authors : Tawane Nunes 0000-0001-9660-7410 [email protected] , Jeffrey Mintz 0000-0003-4345-366X , Maiara Miotto , Camila Domit , Mathew Leibold 0000-0003-3954-3187 , and Andre Padial Authors Info & Affiliations https://doi.org/10.22541/au.175509004.41150819/v2 279 views 183 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Aim Understanding how ecological processes shape species assemblages in dynamic environments is central to community ecology. Estuarine waterbirds represent a useful system for metacommunity analysis due to their high mobility, functional diversity, and sensitivity to environmental variation. We investigate the internal structure of a waterbird metacommunity and assess the relative roles of environmental filtering, spatial processes, and species co-distribution. Location Paranaguá Estuarine Complex, southern Brazil, as a model system for dynamic subtropical estuarine systems. Taxon Estuarine waterbirds Methods Using joint species distribution models (JSDMs), we partitioned variation in species occurrence among environmental predictors, spatial structure, and species co-distribution. Our analysis included 32 waterbird species grouped into functional foraging guilds. Models included environmental field data, land-cover variables, and Moran’s eigenvector spatial predictors derived from 36 transects surveyed monthly between March 2020 to February 2021. Results Environmental filtering and species co-distribution explained most of the variation in community composition, whereas spatial effects were comparatively weak. Environmental variables, particularly salinity and air temperature, showed contrasting effects across foraging guilds. Migratory and resident ground predators exhibited different seasonal tendencies, suggesting potential temporal niche partitioning. In contrast to our expectations, aerial predators showed positive intraguild co-distributions, possibly reflecting heterospecific attraction or shared responses to unmeasured factors. Landscape context also influenced community structure as assemblages in bays subject to higher anthropogenic pressure exhibited greater contribution from species co-distribution, potentially indicating weaker species–environment relationships. Main Conclusions Our results emphasize the importance of environmental gradients and species associations in shaping waterbird metacommunity structure in a dynamic estuarine system. By integrating species-level and guild-level responses across landscape contexts, this study illustrates how the internal structure framework can help disentangle multiple assembly processes in highly mobile taxa. Birds are highly mobile organisms sensitive to environmental changes and are often involved in many ecological interactions (Şekercioğlu et al., 2012). Estuarine waterbirds (hereafter waterbirds), exemplify this dynamism as they continuously move between favorable patches in wetlands in search of resources (Andrade et al., 2018; Miotto et al., 2023). This ecological complexity makes them a compelling system for studying how assembly processes shape biodiversity patterns from a metacommunity perspective (Henry & Cumming, 2016). Although the spatial extent of estuarine systems may be limited relative to the high dispersal capacity of birds, a metacommunity perspective provides a useful framework to organize the multiscale ecological forces shaping these assemblages. Metacommunity theory suggests that community assembly is driven by a combination of local mechanisms, such as environmental filtering and species interactions, and regional drivers like dispersal (Leibold et al., 2004). While traditionally categorized into four distinct paradigms (patch dynamics, species sorting, mass effects, and neutral theory), these are not mutually exclusive and often interact across space and time (Leibold et al., 2022; Leibold & Chase, 2018). To better address this interactive complexity, the 'internal structure of metacommunities' framework (Leibold et al., 2022) provides a powerful lens. By partitioning the variation in species distributions into environmental, spatial and co-distribution components, these fractions can be cautiously interpreted as reflecting environmental filtering, dispersal processes and ecological interactions, thereby capturing nuanced contributions of different assembly mechanisms at multiple scales. Empirical studies on waterbird and other avian metacommunities consistently demonstrate that environmental gradients and spatial configuration can independently and jointly structure community composition and beta diversity (De Almeida et al., 2016; Gianuca et al., 2013). In many cases, environmental species sorting emerges as a dominant mechanism, often overriding spatial effects, but relative strength can shift through time (Henry & Cumming, 2016; Özkan et al., 2013). Additionally, species co-occurrence at broader scales may reflect shared environmental responses, particularly among species with similar foraging habits (Royan et al., 2016). However, most studies rely on approaches that treat species as independent responses, limiting the ability to account for residual species associations and leaving the relative contributions of environmental filtering, spatial structure, and species co-distribution to bird metacommunity structure still not fully understood, particularly in Neotropical coastal systems. To address this research gap, we applied the ‘internal structure of metacommunities’ framework to quantify the relative contributions of environmental factors, spatial configuration, and species co-distribution in shaping waterbird assembly (Leibold et al., 2022). We used a Joint Species Distribution Model to analyze the waterbird assemblage of the Paranaguá Estuarine Complex (PEC), in southern Brazil, a dynamic estuarine system characterized by strong environmental gradients and high species turnover (Miotto et al., 2024; Miura & Noernberg, 2020). Because waterbirds encompass functionally diverse species that may respond differently to environmental conditions, we further evaluated the emerging patterns within functional foraging guilds to assess how ecological traits shape metacommunity assembly. Given the high dispersal capacity of waterbirds and the absence of major barriers within the study area, we expect spatial effects associated with dispersal limitation to play a relatively minor role in structuring the metacommunity compared to environmental gradients and species co-distribution. We further predict that species responses will vary among functional foraging guilds, reflecting trait-based differences in sensitivity to environmental conditions along the estuarine gradient and across seasons. In addition, we expect that landscape sectors characterized by distinct environmental conditions will modulate the strength of environmental filtering, with stronger effects toward the extremes of the estuarine gradient. Finally, we anticipate that anthropogenic pressure may alter these patterns by weakening species–environment relationships and increasing the relative importance of species co-distribution. The PEC comprises a network of interconnected bays and channels organized into three main sectors (Paranaguá–Antonina, Laranjeiras, and Pinheiros bays) (Figure 1), which differ in size, landscape composition, and levels of anthropogenic influence (Miura & Noernberg, 2020). The system consists of a mosaic of habitats, including tidal flats, sand beaches, and mangroves, which provide shelter, nesting, and feeding sites for at least 46 waterbird species, both resident and migratory, representing multiple functional foraging guilds (Miotto et al., 2023, 2024). We analyzed an existing dataset of waterbird occurrences collected from March 2020 to February 2021, across 36 fixed transects located along margins of PEC (Miotto et al., 2023). Surveys were conducted monthly using standardized boat transects (1400 m in length) at an average speed of 10 km/h and maximum duration of 15 min. Transects were equally distributed among the three main bays: Pinheiros, Laranjeiras, and Paranaguá-Antonina bays, with 12 transects each (Figure 1). All birds sighted within a 200 m radius were recorded and categorized as either foraging, resting, or flying over. Birds that were flying over were excluded because they did not necessarily use the surveyed sites. To ensure numerical stability of the models, we excluded species with fewer than four occurrences study-wide and excluded sampling events (site-month combinations) with no detections. The final dataset comprised 32 waterbird species from 13 families (Table S1) and 425 sampling events. We used field-measured variables, seasonal information, and land cover derived from remote sensing as environmental predictors. Field variables were collected during bird surveys and included pH, salinity, turbidity, water and air temperature (°C), dissolved oxygen, and wind speed (m/s). Because dissolved oxygen and water temperature were highly correlated with air temperature (Spearman |ρ| > 0.7), we only retained air temperature. All predictors were centered and scaled prior to analysis. Seasonality was incorporated as categorical predictor, divided into dry (May to October) and rainy (November to April) seasons (Miotto et al., 2023). Land cover information was extracted from a thematic map of the PEC developed by Miotto et al. (in prep), based on an ISODATA unsupervised classification of a Landsat-8 multispectral image (30 x 30 m resolution, 7 June 2020). Seven land cover classes were defined: water, sandbanks, mangroves, beaches, exposed land, urban area, and vegetation (Figure 1). We extracted the proportions of each class within a radius of 3 km around each of the 36 sampling sites using the R packages raster (Hijmans, 2023), sf (Pebesma, 2018; Pebesma & Bivand, 2023), and exactextractr (Baston, 2023). We retained all classes except ‘water’ and the rare class ‘exposed land’ as environmental predictors. We incorporated average spatial configuration using distance-based Moran’s Eigenvector Maps (MEM), computed from unique geographic coordinates of transect midpoints using the R package adespatial (Dray et al., 2025). From the resulting 11 MEMs, we performed a global redundancy analysis (RDA) using the vegan R package to assess overall spatial structure (permutations = 999). To avoid overparameterization, MEMs were subsequently screened using forward selection with a double-stopping criterion (α = 0.05 and adjusted R² of the global model) implemented in the adespatial package (Dray et al., 2025). Alternative spatial specifications, including models with all MEMs, multi-scale MEM combinations, and raw spatial coordinates as predictors, were also evaluated. Joint species distribution model To test our hypotheses regarding the relative contributions of environmental filtering, dispersal limitation, and species associations, we fitted a Joint Species Distribution Model (JSDM) to explain spatial variation in community composition (Pichler & Hartig, 2021). We partitioned the explained variance into environmental, spatial, and co-distribution components and their shared fraction, following the internal structure of metacommunities framework (Leibold et al., 2022). Model tuning parameters (penalization and learning rate) were determined through a grid search and cross-validation. The final model was a probit formulation with 32 degrees of freedom and a light ridge penalization only to the co-distribution. All analyses were conducted using R software version 4.4.1 (R Core Team, 2024), and the JSDMs utilized the sjSDM package (Pichler & Hartig, 2021). To aid interpretation of the relative importance of the components, we compiled species trait information from the AVONET dataset (Tobias et al., 2022), including body size (mm), trophic niche, and primary foraging behavior. Trophic niches were classified based on at least 60% of the diet composition and categorized as predator, herbivore, or omnivore. Primary foraging behaviors were classified as ground, aquatic, aerial, insessorial (perching birds), or generalist. We additionally classified species according to their migratory status. Single-species guilds were merged with the most ecologically similar guild. We used bootstrap resampling to examine how guilds differed in their reliance on environmental and spatial variables or the co-distribution of species. We resampled with replacement from our dataset and refit models for occupancy 100 times. For each iteration, we calculated the variance explained by each component and summarized guild-level responses as the median across species within the same guild. We then computed 0.025, 0.5, and 0.975 quantiles across bootstrap iterations to estimate guild centers and their 95% confidence regions. Seasonal difference in occurrence probability was summarized using odds ratios (OR) derived from model predictions to provide an intuitive and scale-independent measure of the magnitude and direction of seasonal effects. Species were considered seasonally neutral when OR values ranged between 1.5 and 0.67 (~1/1.5) corresponded to less than a 50% change in odds between seasons. Odds ratios were plotted on the logarithmic scale to ensure symmetry around the null value. Species covariances fit by sJSDM were converted into correlations for visualization and arranged into guild-ordered blocks by body size. To test whether within-guild correlations were more similar than expected by chance, we performed a permutation test. The observed species correlation matrix was permuted 1,000 times by randomly shuffling off-diagonal elements. For each permuted matrix k , we calculated the within-guild agreement statistic and quantified overall variation as . The observed value was compared to the null distribution of . To investigate spatial patterns in the relative importance of community assembly processes, each sampling site was categorized by bay and estuarine zone. Bays were used as a proxy for anthropogenic pressure, reflecting the cumulative influence of port activities, artisanal fisheries, and coastal infrastructure: Paranaguá-Antonina (high pressure), Laranjeiras (medium pressure), and Pinheiros (low pressure) (Miura & Noernberg, 2020). Estuarine zones were defined according to dominant environmental influence as lower (marine-dominated), middle (mixing zone), and upper (freshwater-dominated) estuary (Figure 1). These zones represent broad spatial divisions without fixed physical boundaries and may shift with tidal and seasonal dynamics. We calculated the 0.025, 0.5, and 0.975 quantiles of the variation components across sites and monthly samplings for each grouping factor, allowing us to graphically represent group centers and their 95% confidence regions for comparison. Group medians (0.5 quantiles) were then used to generate spatial pie charts representing the proportion of variation explained by each component across sampling sites. Maps were produced using QGis version 3.34.5 (QGIS Development Team, 2024). Overall, we found that environmental and co-distribution covariates are primarily responsible for internally structuring the waterbird metacommunity (R² = 0.21). Spatial effects were generally weak across species, as indicated by low R² values (Figure 2). Consistent with this pattern, the global RDA analysis indicated marginally non-significant spatial effects (p = 0.084; adjR² = 0.004). Forward selection retained a single spatial predictor (MEM2) representing broad-scale spatial variation. Alternative spatial specifications did not improve model performance or reduced residual spatial autocorrelation compared to this parsimonious configuration (Table S2). The spatial predictor retained primarily captured differences among the three main bays within PEC (Figure S1). Specifically, 17 species were better explained by environmental covariates, while 15 species were more influenced by co-distribution, reflecting responses to unmeasured environmental variables and/or potential species interactions. All species exhibited some degree of contribution from environmental factors, but the Striated Heron ( Butorides striata ), Southern Crested Caracara ( Caracara plancus ), Yellow-headed Caracara ( Milvago chimachima ), Green Kingfisher ( Chloroceryle americana ), and Lesser Yellowlegs ( Tringa flavipes ) showed no contribution from co-distribution factors (Figure 2). The waterbird metacommunity assessed is composed of six guilds: aerial predators, generalist omnivore, ground omnivore, ground predator, migratory ground predator and insessorial predator (perching birds). Ground predators were the most representative, composed of 18 species of which four were migratory Nearctic waterbirds, and 14 were resident species. To avoid a single-species guild, the Neotropical cormorant ( Nannopterum brasilianum ), primarily classified as a generalist predator, was grouped with aerial predators. Consequently, aerial predators included six species, while generalist omnivores, ground omnivores, and insessorial predators comprised two, three, and three species, respectively. Three of the guilds exhibited relatively balanced numbers of species influenced by environmental and co-distribution components (Figure 2). In contrast, ground omnivores and insessorial predators were predominantly associated with environmental predictors, with two of the three ground omnivores and all insessorial predators showing stronger environmental influences. Conversely, most aerial predators were better explained by co-distribution factors, with four out of six species in this guild showing higher contribution from this component. This pattern was particularly evident for the Cabot’s tern ( Thalasseus acuflavidus ), whose distribution was largely associated with co-distribution effects. Despite the differences in the number of species influenced by each component, bootstrap confidence intervals overlapped to some degree across guilds. Aerial predators showed a greater contribution from co-distribution factors compared to other guilds, whereas ground predators exhibited a more homogeneous contribution from both environmental and co-distribution factors. In contrast, migratory ground feeders showed the widest confidence intervals, indicating high variability in the relative influence of environmental, spatial, and co-distribution components on their distributions. Most species exhibited a low frequency of occurrence across sampling events (<50%), except for N. brasilianum and the Little Blue Heron ( Egretta caerulea ), which showed the highest frequencies of occurrence (80% and 69%, respectively; Table S1). Consistently, average predicted probabilities of occurrence were generally below or close to 50% across seasons, except for these two species. Significant seasonal differences in probability of occurrence were detected for only seven ground predators and one generalist omnivore (Figure S2). Therefore, odds ratios were used to explore general tendencies across foraging guilds. Migratory ground predators tended to occur more frequently during the rainy season, with three of four species exhibiting higher odds of occurrence during this period and one showing no clear seasonal preference (Figure 3). In contrast, resident ground predators were more often associated with the dry season, only two favored the rainy season, and four showed no seasonal tendencies. Other guilds exhibited weaker or mixed patterns. Generalist omnivores showed contrasting responses between seasons (one rainy, one dry), whereas aerial predators and ground omnivores were predominantly neutral or slightly associated with the dry season. Insessorial predators tended to favor the rainy season (two of three species), with one showing no seasonal preference. Among the remaining environmental predictors, salinity significantly affected three out of six aerial predators, three out of 14 ground predators and one insessorial predator. While its effect was positive for aerial predators it was negative for the other two guilds (Figure 4). pH also influenced several species, showing positive effects for ground feeders (two predators and one omnivore) and negative effects for two aerial predators and one generalist omnivore. Air temperature showed predominantly negative effects, mainly affecting ground-feeding species, including one omnivore and four predators. Other environmental variables significantly affected one to four species, while tidal flat proportion had no significant effect on any species (Figure 4). After accounting for environmental and spatial effects on species distributions, the residual co-distribution patterns were transformed into a correlation matrix, where large absolute correlation values correspond to higher co-distribution R² in the ternary plots (Figure 5). Overall, most intraguild correlations were positive or neutral, with a few weak negative correlations among ground predators. Within aerial predators, N. brasilianum showed largely neutral correlations with other species in the guild, possibly reflecting its generalist diet and high abundance. The species correlation matrix showed a small but significant intraguild agreement ( V = 0.01, permutation test, p = 0.03), suggesting that species correlations are more homogeneous when organized by guild than expected by chance. Regarding landscape configuration, most sites demonstrated stronger contributions from environmental covariates and species co-distribution, with weak influence from spatial factors (Figure 6). Mean contributions of environment and co-distribution were similar across estuarine zones, as indicated by the largely overlapping centers and confidence intervals (Figure S3). In contrast, patterns differed among bays: assemblages in Laranjeiras and Pinheiros bays showed similar environmental structuring, whereas assemblages in Paranaguá-Antonina bays exhibited stronger contribution from species co-distribution, with minimal overlap in confidence intervals relative to the other bays (Figure 6, Figure S3). To our knowledge, this study represents one of the first empirical applications of the internal structure framework to jointly examine environmental filtering, species associations, and dispersal limitation driving waterbird metacommunity assembly. By employing this framework, we were able to capture the heterogeneous species- and guild-level responses contributing to the overall metacommunity patterns. This approach helps disentangle how different assembly processes operate within a highly mobile taxonomic group. Overall, our findings align with previous research indicating that environmental filtering primarily drives waterbird community assembly, with comparatively weaker spatial effect (De Almeida et al., 2016; Gianuca et al., 2013; Henry & Cumming, 2016; Özkan et al., 2013). However, when examining functional foraging guilds, we observed variation in the relative contribution of these components and in the direction of responses to environmental covariates. The importance of the environmental filter in structuring waterbird assemblages has been consistently observed across diverse wetland ecosystems. In highly mobile taxa such as waterbirds, species-sorting may therefore play a more prominent role in determining species occurrences (Leibold et al., 2004). Although aspects of the sampling design, such as the reduced coverage of central bay areas, may have influenced the strength of certain patterns, the general trends remain consistent with this expectation. Furthermore, by employing the internal structure approach, our results further clarify how environmental filtering contributes to community organization while also revealing species-level heterogeneity that may be obscured in traditional community-level summaries (e.g. Miotto et al., 2023). Importantly, despite the general metacommunity trend, our results reinforce that species exhibit different responses to environmental gradients, species co-distribution, and spatial structure. Species responses to environmental gradients were not homogeneous across the metacommunity. Such heterogeneity likely reflects differences in ecological requirements, making functional foraging guilds a valuable framework for interpreting how ecologically similar species respond to environmental variation (Royan et al., 2016). For instance, migratory and most resident ground predators exhibited contrasting seasonal tendencies, which may suggest temporal niche partitioning (Bellefontaine & Hamilton, 2023; Faria et al., 2024; Lunardi et al., 2012). In addition, salinity exerted significant but opposing effects on aerial and ground predators, a pattern possibly associated with tidal dynamics influencing prey availability and habitat accessibility for these groups (Aarif et al., 2021; Rose & Nol, 2010; Russell et al., 2014). Although environmental filtering emerged as the dominant driver of species distributions, the limited spatial extent of the study area and the high mobility of the waterbirds suggest that dispersal constraints are less influential at this scale. Consistent with this expectation, the spatial component showed the lowest contribution across functional guilds. This pattern likely reflects the limited spatial scale and absence of major dispersal barriers within the study area relative to the high dispersal capability of waterbirds. Consequently, spatial effects arising from dispersal limitations may become more evident only at broader spatial scales. The reduced contribution from spatial structures likely emphasizes the relative importance of environmental co-variates and species co-distribution, as species distributions become more closely tied to local habitat conditions and biotic interactions rather than dispersal constraints. In this context, approximately half of the species showed higher contribution from co-distribution components, indicating a potential role for species associations and/or unmeasured environmental factors. Contrary to classical expectations of negative co-occurrence among ecologically similar species driven by competition (MacArthur & Levins, 1967), intraguild associations were predominantly positive after accounting for measured environmental covariates. Such patterns may be consistent with heterospecific attraction or shared use of profitable habitat patches (Royan et al., 2016). It is important to acknowledge that biotic covariance can emerge from unmeasured environmental factors and shared responses (Leibold et al., 2022; Ovaskainen et al., 2010; Royan et al., 2016). Nevertheless, positive associations among aerial predators are consistent with observations of mixed-species foraging groups (mixed flocks), that may be linked to social facilitation helping them locate and exploit ephemeral common resources (Giokas et al., 2020; Veit & Harrison, 2017). At the same time, such aggregations may involve trade-offs, including kleptoparasitism (stealing prey from others) and interference competition (Schreffler et al., 2010; Sridhar et al., 2009). In contrast, N. brasilianum showed largely neutral associations with other aerial predators. Its high abundance, opportunistic foraging behavior, and gregarious behavior in the study area (Miotto et al., 2024) may lead to different spatial dynamics compared to other species in the guild, potentially reducing consistent co-occurrence patterns. Beyond species- and guild-level responses, we expected assembly processes to vary across broader landscape sectors. However, estuarine zones did not differ substantially in their dominant structuring forces. This pattern may reflect the natural gradients within the estuary, which may smooth out differences between zones, alongside the confounding influence of anthropogenic pressures. In contrast, clearer differences emerged among bay divisions. The higher contribution of species co-distribution observed in the Paranaguá-Antonina bays may reflect the influence of unmeasured environmental conditions or anthropogenic disturbances that obscure species-environment relationships. This region encompasses two major ports, including the Paranaguá Port, one of the largest public ports in Brazil, where activities, such as heavy navigation, dredging, and infrastructure maintenance continuously modify local environmental conditions (Miura & Noernberg, 2020). Such human-driven disturbances can disrupt expected species-environment associations by modifying habitat structure and food availability, increasing noise and sedimentation, and potentially facilitating community homogenization (Chase et al., 2020). These results suggest anthropogenic pressures may influence metacommunity structure, leading to emergent and unpredictable patterns. 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Positive Interactions among Foraging Seabirds, Marine Mammals and Fishes and Implications for Their Conservation. Frontiers in Ecology and Evolution , 5 , 121. https://doi.org/10.3389/fevo.2017.00121 Figure 1 – Map of Paranaguá Estuarine Complex, in southern Brazil, showing the location of the 36 sampling transects equally distributed across the three main bays (12 in each) and the land use classification. Dashed lines represent divisions between the lower (L), middle (M) and upper (U) estuarine zones. The ship symbol indicates port locations. Figure 2 – Ternary plots describing the three components of metacommunity internal structure for species (left panel) and foraging guilds (right panel) of waterbirds in the Paranaguá Estuarine Complex, southern Brazil. In both panels, the position of each symbol represents the proportion of explained variation attributed to environmental factors (E – lower left), spatial effects (S – lower right), and co-distributions (C – upper apex). In the left panel, the symbol size is proportional to the R² of the model. In the right panel, each symbol represents the bootstrapped median of species within a foraging guild, and the shaded polygon denotes the confidence interval for the median. Colors indicate different foraging guilds. Figure 3 - Odds ratios of species occurrence in the Paranaguá Estuarine Complex, southern Brazil, comparing the rainy and dry season, grouped by foraging guild. Values greater than 1 indicate higher occurrence in the rainy season, and values lower than 1 indicate higher occurrence in the dry season. Intervals along the y-axis represent a five-fold change in the odds ratio. Asterisks after species names indicate significant differences between seasons (* p < 0.05, ** p < 0.01, *** p < 0.001). Figure 4 – Model coefficients showing the effects of environmental variables, and land cover on waterbird species in the Paranaguá Estuarine Complex, southern Brazil. Each panel represents a different factor, with species grouped by foraging guilds (colors as shown in Figure 2) and ordered by body size. Error bars represent standard errors. Asterisks indicate significance levels (* p < 0.05, ** p < 0.01, *** p < 0.001). Figure 5 – Correlation matrix of waterbird species co-distribution in the Paranaguá Estuarine Complex, southern Brazil. Color intensity represents pairwise correlation strength (orange: positive, purple: negative, ranging from -1 to +1). Gray cells (r = 1) indicate species compared with themselves. Species are grouped by foraging guilds and ordered by body size. Facet colors represent guilds, as shown in Figure 2: GO – Generalist omnivores; AP – Aerial predators; GDO – Ground omnivores; GDP – Ground predators; MGP – Migratory ground predators; IP – Insessorial predators. Figure 6 – Relative contribution of variation components across sampling sites in the Paranaguá Estuarine Complex (southern Brazil), including the main estuarine axis (Paranaguá–Antonina) and the lateral embayment of Laranjeiras (central–north) and Pinheiros (west). Dashed lines indicate divisions among the lower (L), middle (M), and upper (U) estuarine zones. Ship symbols denote port locations. Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 5 Information & Authors Information Version history V1 Version 1 13 August 2025 V2 Version 2 25 March 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords community assembly environmental filtering foraging guilds jsdms landscape heterogeneity species co-distribution Authors Affiliations Tawane Nunes 0000-0001-9660-7410 [email protected] Universidade Federal do Paraná View all articles by this author Jeffrey Mintz 0000-0003-4345-366X University of Florida View all articles by this author Maiara Miotto Universidade Federal do Paraná Centro de Estudos do Mar View all articles by this author Camila Domit Universidade Federal do Paraná Centro de Estudos do Mar View all articles by this author Mathew Leibold 0000-0003-3954-3187 University of Florida View all articles by this author Andre Padial Universidade Federal do Paraná View all articles by this author Metrics & Citations Metrics Article Usage 279 views 183 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Tawane Nunes, Jeffrey Mintz, Maiara Miotto, et al. 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