Assembly Processes of Waterbird Communities Across Different Types of Wetlands in the Middle Reaches of the Huaihe River Basin

Abstract

Understanding the processes and potential mechanisms of species coexistence within biological communities is a key issue in biodiversity conservation. The Huaihe River Basin, situated in the transitional zone between northern and southern China, encompasses diverse wetland types in its middle reaches, including lakes, ponds, rivers, and subsidence areas. This unique combination of habitats provides an ideal natural laboratory for investigating the mechanisms governing bird community dynamics. Here, we quantified the species, taxonomic, functional, and phylogenetic diversity of waterbird communities across different wetland types. To assess patterns of species clustering or overdispersion, we compared the mean pairwise distance (MPD) and mean nearest taxon distance (MNTD) to null models, employing functional dendrograms and phylogenetic trees as analytical frameworks. Additionally, a hierarchical partitioning approach was employed to evaluate the independent contributions of multi-scale environmental variables to community assembly processes. The diversity indicators among different wetland types display asynchronous patterns, reflecting variations in ecological dynamics among these habitats, with the highest species, taxonomic, functional, and phylogenetic diversity in river wetlands. Our findings reveal that environmental filtering predominantly governs the dynamics of waterbird communities in monotonous open water bodies. In contrast, wetlands characterized by high environmental heterogeneity are primarily shaped by competitive exclusion, which emerges as a key mechanism influencing community structure. Moreover, our research demonstrates that increasing habitat diversity drives a shift in communities from functional and phylogenetic clustering to greater dispersion. Our study highlights the importance of habitat variables in structuring assemblages and suggests that increasing habitat heterogeneity will contribute to waterbird conservation.

1. Introduction

A core issue in ecology is to understand the processes and potential mechanisms of species coexistence in biological communities [1,2]. Community assembly hypotheses include environmental filtering [3], competitive exclusion [4,5], neutral assembly [6], macroevolutionary processes [7,8], and biogeographic effects [9,10]. Among them, two widely discussed but contrasting mechanisms are environmental filtering and competitive exclusion [11,12,13]. Environmental conditions act as a selective mechanism, favoring species that are well adapted to specific environmental settings. This selection leads to the coexistence of species that are more similar in morphological, physiological, or ecological traits, and exhibit tighter functional and phylogenetic clustering than expected by chance [14]. Conversely, ecological niche theory emphasizes the critical role of interspecific competition in assembly processes. Therefore, species with similar ecological characteristics or phylogenetic relationships often cannot coexist stably within communities, leading to functional or phylogenetic overdispersion (phylogenetic overdispersion refers to a pattern in which species in a community are more distantly related than would be expected by chance) [11,14].

In the progress of exploring assembly mechanisms in bio-communities, the use of traditional diversity metrics (i.e., species richness, taxonomic diversity) is insufficient to meet the need for a deeper understanding of the mechanisms. When only using species richness and taxonomic diversity to measure community structure, it is assumed that species are equivalent in terms of community ecological function. However, these approaches ignore the different roles, evolutionary positions, and ecological functions assumed by species in the community [15,16]. A growing number of studies have employed functional (FD) and phylogenetic diversity (PD) as new metrics for describing community structure [14,17,18]. FD refers to the variations in functional traits among species, which more accurately describes how species respond to diverse environmental conditions and assemble into distinct communities [19]. PD illustrates the evolutionary relationships of species within a community, thereby elucidating the community’s evolutionary context and the processes of species adapting to environmental changes [17]. Taxonomic diversity (TD) refers to the diversity of species within a community at the taxonomic level, commonly used to characterize the variation in species composition. By combining SR, TD, FD, and PD to quantify multiple biodiversity, we can achieve a more comprehensive understanding of community assembly and its implications for ecosystem functioning [20].

Numerous studies on community assembly have been conducted in Europe, South America, and North America [21,22,23]. In recent years, relevant research in China has been gradually increasing, mainly focusing on the Yangtze River basin, Yunnan–Guizhou Plateau, and Qinghai Tibet Plateau [24,25,26]. However, current studies of biotic community structure in the Huaihe River Basin are only limited to low-dispersal taxa, such as phytoplankton, benthic organisms, and indigenous fish [27,28,29], while research on high-dispersal groups, especially migratory waterbirds, is still relatively scarce.

The Huaihe River is located in eastern China, between the Yangtze River and the Yellow River, serving as a climate, flora, and fauna transition zone between northern and southern China [30]. The main stream of the Huaihe River and its tributaries (i.e., Ying River, Shi River, Guo River) provide essential water resources for the basin and have a variety of habitat types, including lakes, ponds, rivers, and subsidence areas, providing a stopover and overwintering sites for birds, especially long-distance migratory waterbirds from East Asia–Australia Flyway. Waterbirds have important ecological functions in wetland ecosystems, such as nutrient cycling, disease surveillance, pest control, and dispersal of seeds and invertebrates. In addition, waterbirds are sensitive to changes in wetland environments, and their numbers and behaviors can reflect changes in wetland plants and invertebrates [31]. Therefore, waterbirds, as a taxon with a high-dispersal capacity, are an optimal object for the study of community assembly mechanisms in response to habitat changes.

In this study, we conducted field surveys in the middle reaches of the Huaihe River Basin to collect data on overwintering waterbirds, analyzed variations in taxonomic, functional, and phylogenetic diversity across waterbird communities in different habitats, and examined the influence of multi-scale environmental variables on community assembly processes and the underlying environmental drivers. We propose the following hypotheses based on our understanding of habitat specificity in the study area:

(1)

Habitats such as lakes, ponds, and subsidence areas, primarily shaped by environmental filtering due to their homogenous open water characteristics, contrast with river wetlands, where the presence of diverse microhabitats (open water, mudflats, reed marshes, and forests) is more likely to lead to structuring through competitive exclusion;

(2)

The stronger influence of environmental variables on functional dimensions rather than phylogenetic dimensions likely arises because functional traits are more directly shaped by environmental filtering processes;

(3)

As habitat diversity increases, communities tend to shift from functional or phylogenetic clustering to overdispersion, as heterogeneous habitats provide a broader range of ecological niches, thereby facilitating species differentiation.

2. Methods

2.1. Study Area

The Huaihe River Basin is situated in eastern China, between the Yellow River and the Yangtze River, spanning latitudes between 30°55′ and 37°50′ N and longitudes between 111°55′ and 122°42′ E, with a total drainage area of approximately 2.7 × 10⁵ km² [32]. The main course of the Huaihe River originates from Tongbai Mountain in Henan Province and flows eastward toward the Yellow Sea. The basin’s topography is characterized by low hills in the western and northeastern regions, accounting for around one-third of the area, while the remaining two-thirds consist of a vast plain. The annual average discharge of the Huaihe River Basin is 62 km³, with a mean annual precipitation of 920 mm. The annual mean temperature in this basin is 15.1 °C, and over 60% of the annual precipitation occurs during the flood season from April to October. Traditionally, the Huaihe River serves as the geographical boundary between southern and northern China in terms of climate, precipitation, and flora and fauna [30].

This study focuses on the middle reaches of the Huaihe River Basin, including the main stream of the Huaihe River and its major tributaries. The topography of this area is mainly plain, with some low mountains and hills around it. The region is characterized by a diverse array of water bodies, including lakes, rivers, ponds, and coal mining subsidence wetlands. The area increasingly attracts wetland bird species, which utilize it for breeding and overwintering, particularly waterbirds migrating along the East Asian–Australasian Flyway. Winter represents a relatively stable period for avian communities, characterized by the predominance of wintering migrants and resident bird species in the region. This region provides an ideal setting for studying the biodiversity and community assembly mechanisms of migratory waterbirds wintering in various habitats. Based on recent Sentinel-2 satellite remote sensing maps, the location and number of lakes, rivers, ponds, and coal mining collapse wetlands were determined. Subsequently, 54 representative wetlands with clear boundaries were selected as survey sites according to the principle of randomness (Figure 1). To ensure the sampling intensity, it was required that the selected wetlands should account for more than 10% of the total wetland area in the district.

2.2. Bird Survey

From December 2023 to February 2024, We conducted three point-count surveys of waterbirds across these 54 wetlands, which include 9 national wetland parks and 9 provincially significant wetlands. Based on the size and accessibility of each wetland, between three and six sampling points were established along the wetland boundaries with unobstructed views. A radius of 1 km around each sampling point was defined as the observation area, with no overlap between these areas to avoid double counting.

Each field survey was conducted over a period of ten consecutive days with clear and calm weather conditions, during which three experienced bird observers employed the “look-see” total counting method, as used in the International Waterbird Census [33], to record the species and abundance of waterbirds at each sampling point. Each observation area was recorded for 15 min following the classification criteria proposed [34], using binoculars Olympus 10 × 42 PRO (Olympus, Tokyo, Japan) and a monocular ZEISS 30 × 60 Conquest Gavia 85 (ZEISS, Oberkochen, Germany). In accordance with the Ramsar Convention, we defined waterbird species as those “ecologically dependent on wetlands” [35]. Only waterbirds utilizing the wetlands were recorded, while those flying over the observation areas were excluded.

The 54 wetlands were classified into four habitat types based on an analysis of high-resolution remote sensing images from the Sentinel-2 satellite, processed using ArcGIS 10.8 and combined with field survey data. The four habitat types are lakes, rivers, ponds, and subsidence wetlands. The classification of habitats enabled a more comprehensive understanding of the variations in multiple diversity metrics, as influenced by assembly processes across the four habitat types.

During the analysis, the species count from all observations within each wetland was aggregated by wetland type, resulting in a ‘species × habitat’ matrix for assessing the structure of waterbird communities. A community was defined as the assemblage of waterbird species coexisting within a given habitat.

2.3. Habitat Variables

Throughout the field survey, we identified eight habitat variables based on both local and landscape scales of the wetlands, as well as human activities (

Table 1. Habitat variables identified as potential predictors of waterbird diversity patterns in the wetlands of the middle reaches of the Huaihe River, China.

Habitat Variable	Definition
SEW (ha)	The spatial extent of each wetland
EOW (ha)	Extent of open water in each wetland
HD	Habitat diversity within each wetland
TA (ha)	Total area of wetland (>1 ha) within a 5 km buffer zone surrounding each wetland
SW	Wetzel’s (1975) [37] shape index of wetlands. SW = Perimeter/circumference of a circle of equal area: L/2 $\sqrt{\mathsf{\pi }\times \mathrm{A}}$ (L: wetland perimeter, A: wetland area)
Boating Index	Defined by the frequency of boat traffic within the water body
Human Activity Index	Defined by the occurrence rate of fishermen and tourists within the study area

). Firstly, high-resolution remote sensing images of Sentinel-2 obtained on 2 January 2024 were used to determine the spatial extent of each wetland (SEW), the extent of open water (EOW), habitat diversity (HD), and total area of wetland (>1 ha) within a 5 km buffer zone surrounding each wetland (TA). These data were then processed using ArcGIS 10.8 and integrated with field survey observations to ensure accuracy. To calculate the HD, we used the inverse of Simpson’s index to assess the diversity of habitat types. The formula for HD is as follows:

$$\mathrm{H}\mathrm{D}=1/\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{p}}_{\mathrm{i}}^2$$

where p_i is the proportion of the wetland area occupied by the i-th of n habitat types, which include open water and non-aquatic areas [36].

Based on the field survey, we defined human disturbance factors using the following two indices: (1) Boating Index (BI), defined by the frequency of boat traffic within the water body; and (2) Human Activity Index (HAI), defined by the occurrence rate of fishermen and tourists within the study area.

Additionally, the shape index (SW), as defined by [37], was calculated to quantify the irregularity of each wetland and assess its deviation from a perfect circular shape. Given that no significant changes were observed in habitat variables during the field surveys, the same variable x plot matrix was employed in the subsequent analyses.

2.4. Functional Traits

In addition to habitat variables, functional traits related to resource use were incorporated into the analysis to further explore waterbird community structure (

Table 2. Traits used to measure functional diversity associated with resource use.

Trait Types	Traits	Value Type	Phylogentic Signal	PBrownian	Prandom
Resource quantity	Body mass	Continuous	λ = 0.905 **		0.008 **
Main food type	Vertebrates	Binary	D = 0.164 (N = 31)	0.319	<0.001 ***
	Invertebrates	Binary	D = 0.020 (N = 57)	0.496	<0.001 ***
	Plants	Binary	D = −0.153 (N = 35)	0.638	<0.001 ***
Main foraging method(s)	Pursuit	Binary	D = 0.123 (N = 17)	0.373	<0.001 ***
	Gleaning	Binary	D = 0.820 (N = 8)	0.030	0.212
	Pouncing	Binary	D = −0.015 (N = 23)	0.552	<0.001 ***
	Grazing	Binary	D = −0.516 (N = 12)	0.901	<0.001 ***
	Digging	Binary	D = 0.017 (N = 6)	0.537	0.004 **
	Scavening	Binary	D = 0.456 (N = 5)	0.225	0.06
	Probing	Binary	D = −0.031 (N = 35)	0.527	<0.001 ***
Main foraging substrate(s)	Water	Binary	D = −0.054 (N = 50)	0.589	<0.01 **
	Mud	Binary	D = −0.376 (N = 15)	0.861	<0.01 **
	Vegetation	Binary	D = 0.137 (N = 16)	0.359	<0.001 ***

Notes: Phylogenetic signals for functional traits were analyzed for the 69 waterbird species inhabiting the middle reaches of the Huaihe River in China. D indicates the phylogenetic signal for binary traits. λ represents the phylogenetic signal for body mass. N values in parentheses indicate the number of species with each trait. Both P_Brownian (for Brownian structure) and P_random (for random structure) are reported. ** p < 0.01; *** p < 0.001.

). We selected four commonly used functional traits to assess the functional diversity of waterbird assemblages [38]. These traits include one continuous attribute (body mass) and three categorical attributes (food type, foraging method, and foraging substrate). All traits related to food type, foraging method, and foraging substrate were converted into binary traits (1 or 0) [39,40,41].

2.5. Biodiversity Metrics

SR was defined as the total number of species recorded across all wetlands within each habitat type. Biodiversity metrics, including TD, FD, and PD, were calculated using the R packages ape and picante [42,43]. We adopted the TD definition proposed by Li [44], which incorporates pairwise differences between species within a community, specifically functional and phylogenetic distances, and weights these differences by the relative abundance of species [45]. The calculation of TD is based on Rao’s Quadratic Entropy [46], and the formula is as follows:

$$\mathrm{Q}=\sum _{\mathrm{i}=1}^{\mathrm{S}}\sum _{\mathrm{j}=1}^{\mathrm{S}}{\mathrm{p}}_{\mathrm{i}}{\mathrm{p}}_{\mathrm{j}}{\mathrm{d}}_{\mathrm{i}\mathrm{j}}$$

where d_ij represents the distance between species i and j in a community with a total of S species. The distances are weighted by the relative abundances of P_i and P_j of each species. The Rao index accounts for unequal differences between species pairs by incorporating d_ij, which can represent either functional or phylogenetic distances. When d_ij = 1 for all i ≠ j, Rao’s Q simplifies to the Simpson Diversity Index [2], indicating that all species in the community are equally distinct [46]. Rao’s index captures both functional trait differences and phylogenetic distances between species.

FD was calculated by measuring the functional dissimilarity between species pairs using Gower’s distance, as it can handle mixed data types, including continuous and binary variables [46]. Following the calculation of functional dissimilarities, a functional trait dendrogram was constructed using the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) [2]. The UPGMA algorithm first clusters the species pair with the smallest functional distance (randomly selecting in cases of ties) and then recalculates the distance matrix for the newly formed cluster with the remaining species, repeating this process until all species are aggregated into a complete dendrogram.

PD was based on the phylogenetic data collected from the BirdTree database (http://birdtree.org), which included 2000 phylogenetic trees covering all studied waterbird species from the “Hackett All species: a set of 10,000 trees with 9993 OTUs each” dataset [10,47]. We generated a maximum clade credibility tree using the BEAST 2 software package and the Tree Annotator v2.7.7 tool, with a posterior probability threshold of 0.5 and a burn-in percentage of 10% [48].

Using the functional and phylogenetic trees, we computed two relative abundance-weighted metrics: mean pairwise distance (MPD) and mean nearest taxon distance (MNTD), applied to both functional diversity (functional MPD and MNTD) and phylogenetic diversity (phylogenetic MPD and MNTD). MPD was computed by averaging the pairwise functional or phylogenetic distances between all co-occurring species, providing a measure of overall community divergence. MNTD quantifies the mean functional or phylogenetic distance between the nearest neighboring species, serving as an indicator of the extent of terminal clustering among co-occurring species within the community [49].

To assess phylogenetic or functional clustering or dispersion in habitat communities, we contrasted the observed MPD and MNTD values with those derived from null models generated by randomizing species associations [2]. The null models were established using an independent swap algorithm, producing 999 randomized communities with comparable species richness and occurrence frequencies [50]. By evaluating the observed values against the mean values of these null models, we derived the standardized MPD and MNTD (standardized effect size) as follows:

$$\text{Standardized effect size} ={\displaystyle \frac{{\mathrm{M}}_{\mathrm{o}\mathrm{b}\mathrm{s}}-{\mathrm{M}}_{\mathrm{n}\mathrm{u}\mathrm{l}\mathrm{l}}}{{\mathrm{S}\mathrm{D}}_{\mathrm{n}\mathrm{u}\mathrm{l}\mathrm{l}}}}$$

Here, M_obs represents the observed MPD or MNTD values, M_null denotes the mean MPD or MNTD values derived from 999 randomly generated communities, and SD_null refers to the standard deviation of these simulated values. The standardized MPD and MNTD correspond to the Nearest Relative Index (NRI) and Nearest Taxon Index (NTI), respectively. NRI assesses the overall phylogenetic clustering or dispersion within the community, with positive values indicating phylogenetic clustering and negative values suggesting phylogenetic dispersion. NTI, on the other hand, specifically evaluates clustering or dispersion at the terminal branches of the phylogenetic tree. Positive NTI values indicate clustering among closely related species, while negative values suggest greater evolutionary dispersion among nearest neighbors. Thus, positive NRI and NTI values indicate species clustering, while negative values reflect over dispersion [49,51].

2.6. Phylogenetic Signal

Measuring phylogenetic signals is crucial for understanding ecological similarity among species, or phylogenetic conservatism. Phylogenetic signals quantify the statistical dependence between species’ traits and their phylogenetic relationships. A significant phylogenetic signal indicates that traits are more similar among closely related species than would be expected by random chance [52]. This measurement is essential for estimating eco Phylogenetic functions, particularly through phylogenetic diversity (PD) as a predictor of species functions [17].

To assess phylogenetic signals in categorical traits, we applied the D-statistic [53]. The D-statistic reflects the distribution pattern of traits on the phylogenetic tree, where lower D values indicate a more conserved trait evolution, signifying a stronger phylogenetic signal. Specifically, a D value approaching 0 suggests that the trait’s distribution aligns with expectations under the Brownian motion model, indicating more conserved trait evolution. A D value less than 0 suggests strong clustering of the trait, while a D value of 1 or greater indicates either a random distribution, implying no phylogenetic signal, or an overdispersion of the trait along the phylogenetic tree [53].

Furthermore, we utilized Pagel’s λ [54,55] to assess the phylogenetic signal in body mass, a continuous trait. Pagel’s λ assesses the correlation between body mass evolution and phylogeny, where λ = 0 indicates no correlation, λ = 1 signifies that body mass evolution follows the Brownian motion model, and λ values between 0 and 1 suggest that phylogeny influences body mass evolution to a smaller degree the Brownian model [55].

These measurements help us understand how body mass varies with phylogenetic changes, thereby assessing its conservatism or diversity across different phylogenetic backgrounds. Both the calculation and significance testing of phylogenetic signals were conducted using the R package caper [56].

2.7. Statistical Analyses

For SR, TD, MPD, and MNTD, the differences between habitats were analyzed using one-way ANOVA with Tukey’s post hoc tests to compare metrics between habitats. For NRI and NTI, one-sample t-tests were employed to ascertain whether the values were significantly different from zero, with statistical significance set at p < 0.05. To quantify the independent contribution of habitat variables to NRI and NTI, hierarchical partitioning [57,58] was utilized, which calculates the goodness of fit (R²) of each model and considers all possible combinations of explanatory factors in the model. All possible combinations of explanatory factors were included in the models using the R package glmm.hp [59,60] were employed for hierarchical partitioning, and all analyses were conducted using R 4.4.3.

3. Results

Through three field surveys, a total of 69 waterbird species were documented across all wetland habitats, including 47 species in rivers (7 orders, 12 families), 56 species in lakes (6 orders, 12 families), 51 species in ponds (6 orders, 12 families), and 19 species in subsidence wetlands (7 orders, 12 families), comprising a total of 129,623 individuals. Among these, the baer’s pochard (Aythya baeri) is classified as critically endangered (CR) on the IUCN Red List. Similarly, the oriental white stork (Ciconia boyciana) and the swan goose (Anser cygnoides) are listed as endangered (EN). Several species, including the long-tailed duck (Clangula hyemalis), lesser white-fronted goose (Anser erythropus), common pochard (Aythya ferina), horned grebe (Podiceps auritus) and the white-naped crane (Grus vipio), are categorized as vulnerable (VU). Additionally, species classified as near threatened (NT) include the ferruginous duck (Aythya nyroca), falcated duck (Anas falcata), Dalmatian pelican (Pelecanus crispus), and the northern lapwing (Vanellus vanellus).

Phylogenetic signals demonstrated significant correlations with the majority of functional traits, revealing a pronounced pattern of phylogenetic niche conservatism (Table 2). SR was on average highest in lakes and lowest in subsidence wetlands with no difference between lakes, rivers, and ponds (F_3.50 = 6.276, p < 0.001). TD was on average highest in rivers, with lakes exhibiting significantly higher TD than ponds, but no significant differences were found between lakes and subsidence wetlands (F_3.50 = 71.71, p < 0.001). Phylogenetic MPD was on average highest in rivers and lowest in ponds, but there were no significant differences between lakes and subsidence wetlands (F_3.50 = 16.93, p = 0.014). While phylogenetic MNTD was higher in rivers and subsidence wetlands than those in lakes and ponds (F_3.50 = 19.92, p < 0.001). Functional MPD was significantly higher in rivers compared to lakes and ponds (F_3.50 = 14.81, p < 0.001), and in lakes compared to ponds, with no significant differences observed between subsidence wetlands and either lakes or ponds. Functional MNTD was found to be significantly higher in rivers and subsidence wetlands than in lakes and ponds (F_3.50 = 17.13, p < 0.001; Figure 2).

Phylogenetic NRI and NTI exhibited positive values in all habitats except rivers. Functional NRI did not differ significantly from zero in all habitats except rivers. The functional NTI was greater than 0 in lakes and ponds, but not different from 0 in the other two habitats (Figure 3). The proportion of variance explained by significant variables ranged from 15.1% to 99.9%. The influence of habitat variables on the standardized effect sizes of functional diversity was stronger than those of phylogenetic diversity (

Table 3. The independent contribution (%) of each habitat variable to the variations in the multifaceted diversity metrics of waterbird communities across wetlands in the middle reaches of the Huaihe River, China.

Habitat	Diversity Metrics	R2	SEW	EOW	HD	TA	SW	BI	HAI
lake	Phylogenetic NRI	0.550	21.87	−21.07	−4.51	−4.49	7.22	−15.98	24.87
	Phylogenetic NTI	0.433	−16.31	17.95	−33.45	−2.86	4.78	22.64	−2.01
	Functional NRI	0.487	10.3	−9.01	−15.94	−24.18	3.14	−28.06	9.37
	Functional NTI	0.786	16.97 *	−18.06 **	−32.54 **	−4.59	−3.64	−18.12	6.08
River	Phylogenetic NRI	0.941	8.59	−15.51	−5.14	−12.63	−5.41	7.21	45.51
	Phylogenetic NTI	0.837	−22.17	9.29	4.79	5.36	−44.66	10.97	2.76
	Functional NRI	0.919	22	−37.32	−2.98	4.47	−24.62	−2.3	−6.32
	Functional NTI	0.999	13.43 *	−14.11	−6.75	−21.14 *	−9.4 *	−22.26	12.92
Pond	Phylogenetic NRI	0.266	15.14	−15.66	−7.25	5.15	32.68	−11.98	−12.13
	Phylogenetic NTI	0.151	29.72	−25.02	−17.94	3.84	−3.57	−8.93	−10.99
	Functional NRI	0.468	−16.71	17.29	19.8	8.11	5.35	−30.67	−1.9
	Functional NTI	0.723	−9.76	−11.02	−7.67	3.58	−2.46	−57.64 **	−7.87
Subsidence Wetland	Phylogenetic NRI	0.999	17.03 *	−16.50 *	−20.16 *	24.97 *	−8.53 *	5.73	7.08 *
	Phylogenetic NTI	0.747	32.54	−31.35	−13.25	−12.85	3.18	−4.23	2.59
	Functional NRI	0.967	29.59	−28.70	−16.15	6.82	−12.47	−3.29	2.98
	Functional NTI	0.926	17.68	−16.78	−15.89	13.80	−10.18	−17.11	8.56

Notes: SEW: The spatial extent of each wetland. Negative correlations are denoted by a minus sign. EOW: extent of open water in each wetland. HD: habitat diversity within each wetland. TA: total area of wetland (>1 ha) within a 5 km buffer zone surrounding each wetland. SW: Wetzel’s (1975) shape index [37]. BI: defined by the frequency of boat traffic within the water body. HAI: defined by the occurrence rate of fishermen and tourists within the study area. * p < 0.05; ** p < 0.01.

). In rivers and subsidence wetlands, phylogenetic NRI and functional NTI were negatively impacted by HD. Functional NTI in lakes and rivers, along with phylogenetic NRI in subsidence wetlands, was positively influenced by AW. A negative association with phylogenetic NRI and functional NTI was observed for EOW in lakes and subsidence wetlands. The TA exerted a positively influenced phylogenetic NRI in subsidence wetlands, yet a negative effect on functional NTI in rivers. The SW negatively affected phylogenetic NRI in subsidence wetlands and functional NTI in rivers. The HAI showed a positive correlation with phylogenetic NRI in subsidence wetlands, while the BI negatively influenced functional NTI in ponds.

4. Discussion

Our findings showed that the waterbird community in river wetlands exhibited the highest species and taxonomic diversity on average among the four habitat types. The patterns of functional and phylogenetic diversity, as measured by MPD and MNTD, were found to align with those of TD. This suggested that the overall functional and phylogenetic divergence within the community increased with species and taxon richness, consistent with previous studies [61].

The positive phylogenetic NRI and NTI values observed in lakes, ponds, and subsidence wetlands indicated that the waterbird community in these habitats was mainly composed of species with close phylogenetic relationships, exhibiting a pattern of phylogenetic clustering. Similarly, the functional NTI values greater than zero in lakes and ponds revealed that the waterbird community in these wetlands was mainly composed of species with similar functional features, reflecting a pattern of functional clustering. The above three types of habitats are mainly open water surfaces with relatively simple environmental conditions. These homogeneous water bodies allow closely related species with similar functional characteristics to coexist, thus forming ecologically conserved phylogenetic clustering [62]. Our findings align with the environmental filtering hypothesis, which posits that habitat homogeneity promotes selective filtering of species with similar functional traits and ecological niches [63,64]. In contrast, the negative NRI and NTI values recorded in river wetlands indicated that the waterbird community in this habitat consisted of species with different functional characteristics and distant phylogenetic relationships. As a linear habitat, river wetlands have high heterogeneity and abundant microhabitats (open water, mudflats, reed marshes, and woodlands) that promote ecological diversity. However, resource and spatial limitations inevitably lead to interspecific competition, thereby constraining the coexistence of functionally similar species [65]. Our finding aligns with the limiting similarity hypothesis, which suggests that competition shapes community structure by restricting the similarity of coexisting species [11,12].

Our research found that SEW and EOW have different effects on the structure of waterbird communities. An increase in SEW improves resource availability and habitat diversity, reducing interspecific competition, and promoting the clustering of functional NTI, especially in lakes and wetlands, whereas the increase in EOW reduces wetland heterogeneity, limits the coexistence of birds with the same functional traits, intensifies competitive exclusion, and drives the dispersion of functional NTI [66,67,68].

HD plays an important role in increasing ecological niche resources. The wetlands with high heterogeneity encompass different microhabitats, such as aquatic plants, mudflats, forests, and reed marshes, supporting the coexistence of different functional groups and phylogenetically distant species, leading to an increase in functional and phylogenetic diversity, as well as a shift in community structure from clustering to dispersion [69]. The increase in the proportion of wetlands in buffer zones promotes resource diversity, allowing more species to coexist [70]. Our investigation showed that aquaculture or lotus cultivation ponds surrounding the studied river wetlands provide abundant food sources for various birds, including geese, ducks, cormorants, shorebirds, and herons. Additionally, an increased wetland SW enhances edge effects, thereby expanding the ecological niche [71]. This reduction in competition within the core area facilitates the coexistence of waterbird species with highly specialized functional traits, promoting a shift in community dynamics from aggregation to dispersion.

The assembly processes of waterbird communities were influenced by environmental factors, which exhibited varying effects across the four habitats (Table 3). Overall, environmental variables have a more pronounced influence on the functional dimension of community assembly than on the phylogenetic dimension. This disparity reflects the direct and immediate effects of local environmental conditions on functional traits, while phylogenetic patterns are primarily shaped by historical evolutionary processes [2,72].

In summary, our findings suggest that in homogeneous water bodies such as lakes, ponds, and subsidence wetlands, environmental filtering plays a dominant role in shaping waterbird community dynamics. In contrast, in river wetlands with high environmental heterogeneity, competitive exclusion emerges as the important force shaping community structure. Wetland habitat variables play important roles in the community assembly process, exerting a stronger influence on functional diversity than on phylogenetic diversity. As habitat diversity increases, assemblages shift from functional and phylogenetic clustering to some extent towards greater dispersion, suggesting that environmental heterogeneity contributes to enhancing bird diversity.

Wetlands in the middle reaches of the Huaihe River provide crucial wintering grounds for waterbirds migrating along the East Asian–Australasian Flyway. In order to better manage and protect the waterbirds in these wetlands, we propose three key strategies: (1) enhancing habitat diversity through the restoration of various microhabitats, such as mudflats, reed marshes, and forests, is crucial for supporting both the functional and phylogenetic diversity of waterbirds; (2) increasing the number of artificial wetlands in the Huaibei Plain, where natural wetlands are scarce, and enhancing the complexity of wetland boundaries, can provide abundant food resources and additional habitats for waterbirds, thereby promoting greater bird diversity; and (3) strengthening biodiversity monitoring in wetlands and improving ecosystem management are important for enhancing the ecological functions of wetlands.

Future research should examine the temporal fluctuations in waterbird community composition and dynamics, focusing on long-term trends and seasonal patterns, investigate fine-scale habitat preferences to uncover more intricate ecological patterns and species-specific requirements and integrate climate change models to assess how projected environmental shifts may impact waterbird communities and biodiversity in the region.