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Article

Annual Dynamics of Bird Community at a Coastal Wetland and Their Relation to Habitat Types: The Example of Beidagang Wetland, Northern China

by
Mengxuan He
1,
Ziling Dai
2,
Xunqiang Mo
1,*,
Zhengwang Zhang
3,*,
Jin Liu
3,
Weipan Lei
3,
Weiqing Meng
1,
Beibei Hu
1 and
Wenbin Xu
1
1
School of Geographic and Environmental Science, Tianjin Normal University, Tianjin 300387, China
2
School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China
3
Key Laboratory for Biodiversity Science and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing 100875, China
*
Authors to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2023, 11(2), 342; https://doi.org/10.3390/jmse11020342
Submission received: 28 November 2022 / Revised: 15 January 2023 / Accepted: 17 January 2023 / Published: 3 February 2023
(This article belongs to the Section Marine Ecology)
Browse Figures
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Abstract

:
In order to provide more scientific guidance for wetland bird protection, this study addressed the dynamics of the bird community sorted by ecotypes, classifications and threat categories from 2015 to 2019, and non-metric multidimensional scaling analysis, generalized additive models and the Mantel test were used to examine the relationships between bird communities and habitat types. The results showed that: (1) The abundance of birds peaked in 2017 at 88,258 individuals and then declined. Moreover, there was an inverse trend between species richness and abundance of birds, meaning greater abundance is associated with fewer species. (2) Swimming birds were dominant ecotypes and Anseriformes possessed the highest abundance. It was noteworthy that the abundance of critically endangered birds (Aythya baeri and Grus leucogeranus) and the species richness of endangered birds increased. (3) Building land and farmland had dominant impacts on the composition of bird community. Wading birds and birds in Gruiformes were significantly impacted by building land and farmland, and near threatened species were substantially influenced by farmland. Therefore, maintaining good connectivity between protected areas and surrounding areas is one of the best ways to effectively manage biodiversity of the target area. This research may provide a broader insight for coastal wetland bird habitat management and bird diversity preservation.

1. Introduction

Wetlands have the most plentiful biodiversity worldwide, characterized by abundant animal and vegetation resources [1,2,3]. Moreover, wetland biodiversity contributes not only to ecosystem stability, but also to human sustainable development [4,5,6]. Among other things, birds are considered as the most active components of wetlands and play a critical role in wetland stability and biodiversity. Owing to the fact that bird community composition is highly sensitive to habitat changes [7,8], birds have emerged as indicator species in monitoring wetland habitat quality [9]. Revealing the bird community traits and studying its relations to wetland habitats can provide a sound basis for biodiversity conservation [10].
Currently, the acceleration of urbanization and intense human activities pose a tremendous threat to wetland ecosystems, resulting in fragmented and even disappearing wetlands [11,12,13], further threatening the survival of birds. On the one hand, industrial land-use have occupied water bodies on which birds depend for resting and food [14,15,16]. Wang [17] discovered that cranes in the Shengjin Wetland had decreased considerably during the past 30 years and land use change was the main cause. On the other hand, port and power plant development has also caused the segmentation of land, thus making habitats isolated [18,19,20]. Consequently, birds have to exhibit distinct adjustment to wetland changes, and some prefer to stay in their original areas, whereas others have to choose to change their migratory routes. On this condition, wetland habitat change substantially affects community composition and dominant species, during which bird diversities decrease sharply [21,22,23,24,25]. In the past two decades, more than 450 km2 of coastal wetlands in Bohai Bay have been reclaimed, including 218 km2 of intertidal wetlands [26]. The dramatic decrease in intertidal wetlands has caused a decline in waterbird’s abundance and species richness at any stop-over on the East Asian-Australasian Flyway [27]. To maintain biodiversity in human-affected regions, it is of great significance to understand the relations between biodiversity and habitats.
Located on the west coast of Bohai Bay in Tianjin, the Beidagang wetland is an important stop-over on the East Asian-Australasian Flyway which is one of the nine major bird migratory routes in the world [28,29], attracting hundreds of thousands of birds to stop and breed every year. Until now, 279 species of birds were recorded here [28] and Beidagang wetland is highly appraised by wetland specialists for its immense ecological values in maintaining biodiversity and was listed as the International Wetland in 2020 by National Forestry and Grassland Administration in China instructed by Convention on Wetlands of International Importance Especially as Waterfowl Habitat (http://www.forestry.gov.cn (accessed on 1 September 2022)). Nevertheless, bird community traits and its reactions to habitat change are still not well documented, particularly after wetland ecological restoration [30]. In this regard, this paper aimed to study the dynamics of bird communities in five years and show its relations to habitat change. The result can provide support for wetland bird protection as well as habitat management in coastal wetland.

2. Materials and Methods

2.1. Study Area

Beidagang wetland (117°11′ E–117°37′ E, 38°36′ N–38°57′ N) is a brackish wetland located on the west coast of Bohai Bay and in southern Tianjin, and it is the largest national nature reserve with a total area of 34,887.13 hectares. This area belongs to the warm temperate monsoon continental semi-humid climate zone with an average temperature of 11 °C, an average rainfall of 550 mm, and annual evaporation of 1120.5 mm.
Beidagang wetland is a significant stopover on the East Asian-Australasian Flyway which is one of the nine major migratory paths in the world. Hitherto, the number of migratory birds in fall and spring can even reach more than scores of thousands annually. Additionally, more than 200 species were discovered there, among which twenty-two species belong to the first-class national protected animals, such as Ciconia boyciana, and fifty species belong to the second-class national protected animals (http://www.forestry.gov.cn (accessed on 26 September 2022)).

2.2. Bird Survey

The bird surveys adopted a stratified design to determine bird survey sites, and a total of 35 sites were picked in Beidagang Reservoir which was the main area of Beidagang wetland (Figure 1). The survey regions involved open water, reed meadows, perennial meadows, building land, farmland and woodland. Monthly bird surveys were carried out from 2015 to 2019. Bird surveys started at 6:00 a.m. on each sampling day and lasted four hours until 10:00 a.m. For each site, the point count method was used. We stayed at the sampling point for 20 min, observed and recorded all the birds we watched or heard within a radius of 300 m with binoculars and a monocular telescope. Birds of prey, climbing birds, songbirds, swimming birds, terrestrial birds and wading birds which were stationary or in flight have been recorded during the surveys.
Observation dates, site numbers, locations and weather conditions were also recorded. Since birds in this region have two migration periods, which was in the first and second half of a year, respectively, the annual and semi-annual changes of bird community traits were both analyzed in this paper. To avoid repetitive records, each bird’s annual abundance was calculated through dividing the whole abundance of per species by the number of observations. For bird identification, we mainly referred to “China Bird Field Manual”, “China Bird Classification and Distribution List” [31], China Bird Report (www.birdreport.cn (accessed on 23 March 2022)), eBird (ebird.org/home (accessed on 3 March 2022)) and other relevant materials.

2.3. Habitat Change

A remote sensing image was selected by source of Landsat 8 OLI image, which has high precision and can cover research time ranging from 2015 to 2019. We chose images taken in the summer and autumn for their convenient for us to process them because of lush vegetation and less cloud coverage. ENVI (5.3) was used for image preprocessing, including cropping of study area and radiometric correction.
Habitat types were identified through a combination of remote sensing image interpretation and field survey. The area of each habitat type was measured annually. According to the land use classification and the wetland classification [32], habitat types of Beidagang wetland and its surrounding area (117°11′ E–117°37′ E, 38°36′ N–38°57′ N) were sorted into the following categories, namely building land, farmland, reed meadows, perennial meadows, open water and woodland. In 2015, the woodland area was 0.120 km2, the building land area 220.152 km2, the farmland area 301.154 km2, the reed meadows area 29.942 km2, the perennial meadows area 200.540 km2 and the open water area 266.709 km2. Area changes of habitat types from 2015 to 2019 were revealed in Table S1.

2.4. Statistical Analysis

In this paper, analysis of variance (ANOVA) was used to exam the effects of independent variables (years, semi-years, ecotypes, order and threat categories) on dependent variables (species richness and abundance of birds) and their significance.
Additionally, non-metric multidimensional scaling (NMDS) based on Bray–Curtis distance was used to reflect the ordination information of bird community composition in different years. With the first and second principal components being as coordinate axis, namely NMDS1 and NMDS2, the community information was mapped from the multidimensional scale to the two-dimensional scale by dimension reduction.
To account for the impact of environmental variables on community, we used generalized additive models (GAMs), which could handle nonlinear relationships between response variables and multiple explanatory variables. GAMs fitted habitat types as a smooth response surface over bird community ordination diagram. Residual maximum likelihood was used as the smoothing parameter estimation method to explain to what degree the variation in the bird community was explained by habitat types.
Meanwhile, the Mantel test was used to examine the liner quantitative relationship between habitat types and community traits (ecotypes, orders, and threat categories) using Pearson correlation coefficients.
All data processing were performed in R (4.1.1). Furthermore, we used the aov function for analysis of variance and vegdist function for non-metric multidimensional scaling analysis. GAMs were calculated by using the ordisurf function and the Mantel test was carried out by using the mantel-test function.
NMDS and GAMs analysis were implemented by using “vegan”, “scales”, “ggrepel”, “tidyverse”, “ggpubr”, “rstatix” and “ggpmisc” packages. The Mantel test was realized by using “vegan”, “ggcor” and “dplyr” packages.

3. Results

3.1. Dynamics of Bird Communities

From 2015 to 2019, the abundance of birds was 45,162, 54,806, 88,258, 59,492 and 28,766 individuals. When birds were divided into terrestrial birds, birds of prey, climbing birds, songbirds, wading birds and swimming birds by ecotypes [33,34,35,36,37], the results were shown in Figure 2. Swimming birds, wading birds and songbirds were the dominant ecotypes. Analysis of variance (Table 1) indicated that there were significant differences in the abundance of birds among different ecotypes. From 2015 to 2019, the abundance of swimming birds first increased and then decreased, which were 24,533, 41,734, 71,943, 44,118 and 19,807 individuals, respectively. An overall decreasing trend was found in the abundance of wading birds, climbing birds, birds of prey, whereas terrestrial birds increased. The abundance of songbirds decreased first and then increased, with 3232 individuals in 2015 which increased to 5016 individuals in 2019. From Figure S1, we also concluded that in the second half of 2017, the abundance of swimming bird reached the highest of 46,594 individuals and then decreased to 15,026 and 9334 individuals in the second half of 2018 and 2019.
There were significant differences in the effects of years and ecotypes on species richness with p < 0.01 (Table 1). Swimming birds had the highest species richness of 55 in 2018 and 2019. Songbirds were the second-highest bird species, and their species richness has declined from 43 in 2015 to 34 in 2019 over the past five years. The abundance of wading birds was higher than that of songbirds, but its species richness was lower. Species richness of swimming birds was 48, 47, 37, 47 and 49 in the second half, and 47, 39, 41, 49 and 46 in the first half from 2015 to 2019 (Figure S1) and there was no significant difference for species richness between the first half and the second half of a year with p > 0.05.
Moreover, the top seven orders were chosen, among which Anseriformes possessed the highest abundance (Figure 3). From 2015 to 2019, abundance of Anseriformes first increased and then decreased, which was 19,166, 35,534, 69,915, 41,449 and 18,512 individuals. Abundance of birds in Charadriiformes has decreased, reaching its lowest level of 896 individuals in 2019. Passeriformes had the highest abundance in 2018 with 6383 individuals. In the second half, the abundance of Anseriformes peaked at 45,030 individuals in 2017 and declined greatly to 8583 individuals in 2019, where Mareca strepera, Anas zonorhyncha, Anser fabalis, Anser anser and Aythya ferina were included (Figure S2).
Obviously, lower species richness was discovered in 2017 and 2019. Passeriformes had the highest species richness of 43, 33, 33, 36 and 34 in five years. The species richness in Anseriformes and Charadriiformes was 32 and 34, respectively in 2018, and the species richness in Pelecaniformes and Podicipediformes first decreased and then increased. Analysis of variance also showed that there were significant differences in abundance of birds and species richness among different orders (Table 2).
As illustrated in Figure 4, most birds in Beidagang wetland were considered least concern species in IUCN Red List. The abundance of least concern birds rose first and then decreased. In 2017, the abundance of least concern birds had peaked to 82,819 individuals. Nonetheless, the species richness of least concern first declined and then slightly increased. Again, the abundance of near threatened birds reached the highest point of 3918 in 2018, and the abundance of five near threatened species (Limnodromus semipalmatus, Limosa limosa, Calidris ruficollis, Mareca falcata, Calidris ferruginea) reached as high as 161, 340, 516, 837 and 945 individuals, respectively. The abundance of critically endangered birds (Aythya baeri and Grus leucogeranus) and species richness of endangered birds increased in five years. Analysis of variance also showed that there were significant differences in the abundance of birds and species richness among different threat categories (Table 3). The species richness of endangered birds grew significantly in 2019, reaching 3 species.
The total abundance of birds counted annually from 2015 to 2019, as shown in Figure 5, kept increasing and then fell after 2017. The abundance was at its highest level in 2017 with 88,258 individuals, which was 48.82% and 37.9% higher than that in 2015 and 2016. Whereas, a total of 28,766 individuals were found in 2019 with a decline rate of 67.41% compared with that in 2017. Interestingly, there was an inverse trend between annual species richness and abundance of birds. Less species was discovered in 2016 and 2017 and specifically only 119 species were discovered in 2017. Nevertheless, 147 species were identified in 2018, which was 28 species more than in 2017 [38].

3.2. Influence of Habitat Types on Bird Community Composition

Deviance can reflect the extent to which different habitat types explain community composition in generalized additive models. As shown in Table 4 and Figure 6, bird community composition was significantly influenced by changes of habitat types with p < 0.01. Building land was a stronger determinant of bird community composition with deviance of 31.5% in the generalized additive models and bird compositions in 2018 and 2019 were related to an increased building land. Reed meadows and farmland were other important determinants with deviance explained of 31.4% and 30.3%. Bird compositions in 2018 and 2019 were also associated with an increased reed meadows and bird compositions in 2015 and 2016 were linked to an increased farmland. Additionally, woodland explained a relative lower amount of deviance within the model.

3.3. Influence of Habitat Types on Bird Community Traits

As shown in Figure 7, wading birds significantly correlated with the changes of farmland and building land, such as Anas zonorhyncha, Fulica atra, Limosa limosa, while the changes of reed meadows, perennial meadows and open water did not exert significant influence on the changes of bird ecotypes. From the perspective of orders (Figure 8), Gruiformes highly correlated with farmland and building land, and Fulica atra had the highest abundance in Gruiformes, which decreased sharply within five years. In addition, near threatened species were found to have significant correlation with the changes in farmland (Figure 9).

4. Discussion

4.1. Bird Community Dynamics

The abundance of birds was inversely proportional to species richness, namely, the higher abundance of birds, the lower species richness. Species richness was the lowest at 119 in 2017, but abundance of birds was the highest at 88,258 individuals, and this was mainly because the abundance of Anser anser was 18,894 individuals in 2017. When checking changes of habitat types, we discovered that area of perennial meadows where Aanser anser enjoyed inhabiting was the largest, being 213.954 km2 that year. Mo [39] demonstrated that birds in Beidagang wetland were mainly waterfowl regardless of spring or autumn migration season, and our study indicated that, Anser serrirostris, along with Anser anser, was another abundant bird in five years and the annual volatility of both species was significant.
After five years, the abundance of swimming birds, wading birds, climbing birds all declined, likely due to the loss of open water area. Surveys of weather conditions during the study period showed an increased temperature, which could be an important factor in changing open water aera and water level. Since most of the birds in Beidagang wetland preferred to inhabit and forage in the water, changes in aquatic habitats could also affect the stopover of birds. However, the abundance of songbirds and terrestrial birds increased, and this was mainly because the growth of shrubs and trees inside or around wetland provided more habitats for songbirds.
Until now, many species in Anseriformes and Charadriiformes have been illustrated to take their regular long-distance migration [40], and Beidagang wetland, as the transit station for wintering bird, attracted many Anseriformes to inhabit, making Anseriformes the most numerous among birds.

4.2. Bird Community Composition and Traits Response to Habitat Types

Several studies have illustrated that the bird community has high correlations with habitats [41,42,43]. In this study, changes in farmland, building land, reed meadows, perennial meadows, open water and woodland had extremely significant effects on bird community composition (p < 0.01). Birds were particularly vulnerable to habitat changes driven by anthropogenic land conversions [44,45]. This study confirmed that building land (deviance explained = 31.5%) and farmland exerted (deviance explained = 30.3%) significant impacts on bird community composition inside Beidagang wetland. In addition, the Mantel test (Figure 7, Figure 8 and Figure 9) also proved that building land could have positive effects on wading birds and birds in Gruiformes significantly, and farmland had an influence on wading birds, birds in Gruiformes and near threatened species.
The Beidagang wetland is adjacent to the coast, with oil fields and towns in between. Most wading birds observed inside the Beidagang wetland were waders, and they also preferred to stay on the mudflats along the seashore. Nevertheless, due to urbanization, plenty of farmlands were turned into construction land with the area of construction land expanding and the area of farmland diminishing in the past five years. In the process of migrating from coastline to Beidagang wetland, shallow tidal flats suitable for waders to stop at have been destroyed, resulting in a decrease in the landscape connectivity [46,47]. Consequently, wading birds had positive correlations with building land and farmland significantly. Liu’s [48] research in Honghe National Nature Reserve confirmed that the decline of landscape connectivity between Reserve and the surrounding areas has weakened habitat’s function for wading birds. In our study, a large number of waders belonged to near threatened species, so near threatened species were also substantially correlated with the changes in farmland. Limosa limosa and Mareca falcata were near threatened species, which were abundant in Beidagang wetland. Nevertheless, the abundance of both species was in decline due to the loss of farmland. Although farmland lied outside of the Beidagang wetland, it was still an important resting and feeding habitat for these species [49]. Since the fact that large areas of agricultural lands have converted to commercial use, this has exerted important impacts on near threatened species.
Compared with other bird species, waterbirds are extremely sensitive to habitat changes along their migration paths. To protect waterbirds, especially threatened and endangered birds, despite the protection of habitats inside wetland reserves, maintaining a good landscape-scale connection between the protected area and surrounding areas is also one of the best ways to effectively manage and protect the biodiversity of the target area.

4.3. Research Insufficiency and Prospects

The bird community in the Beidagang wetland was dominated by waterbirds, and since the survey method or survey time was set mainly depending on the investigation of waterbirds, it was inevitable to affect the abundance and species richness of songbirds. Yet, songbirds could be underestimated due to the following reasons: (1) When watching birds aligned in approximately the same direction, it was hard to tell apart birds which were far away from observers [50]. (2) Songbirds that stayed in reed meadows could not be detected in a timely manner.
Many factors have been reported to affect bird diversity [38,49], such as vegetation types and water-level fluctuations [51,52]. In this paper, we mapped six habitats on bird community traits and found that habitats related to anthropogenic activities were the dominant factors. In the future, other factors affecting the bird community, such as, vegetation cover and food resources, should be looked into. Despite this, the conclusions drawn from this study can provide a more accurate scientific basis for wetland biodiversity protection and ecological restoration management.

5. Conclusions

By studying bird community dynamics as well as its relations to habitats, we found that:
(1)
The abundance of birds rose to the maximum of 88,258 in 2017 and then fell and an inverse trend was shown between species richness and abundance of birds.
(2)
Swimming birds were the dominant bird ecotype, followed by wading birds and songbirds. Anseriformes possessed the highest abundance, including Mareca strepera, Anas zonorhyncha, Anser fabalis, Anser anser and Aythya ferina. The abundance of critically endangered species and the species richness of endangered birds had increased.
(3)
Building land and farmland exerted significant impacts on the bird community composition inside the Beidagang wetland. That was, building land could significantly impact wading birds and birds in Gruiformes, and farmland substantially influenced wading birds, birds in Gruiformes and near threatened species.
(4)
To protect waterbirds, especially threatened and endangered birds, in addition to protecting habitats inside wetland reserves, maintaining a good landscape-scale connection between the wetland area and its surrounding areas is also one of the most effective ways to protect bird diversity.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jmse11020342/s1, Table S1: Changes in habitat types from 2015 to 2019; Table S2: Ecotypes of bird species; Figure S1: Semi-annual abundance of birds and species richness sorted by ecotypes; Figure S2: Semi-annual abundance of birds and species richness sorted by orders; Figure S3: Semi-annual abundance of birds and species richness sorted by threat categories.

Author Contributions

All authors contributed to the study conception and design. Material preparation and data collection were performed by Z.Z., J.L., W.L. and X.M., while analysis was performed by M.H., Z.D., W.X., W.M. and B.H. The first draft of the manuscript was written by M.H. and X.M., and all authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (32171853), the Doctoral Funding Project of Tianjin Normal University (52XB1902), Science and Technology Popularization Project of Tianjin (22KPHDRC00140).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets generated during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Maps of bird survey sites. The red line is Beidagang Reservoir’s boundary line, and the blue dots are sampling sites.
Figure 1. Maps of bird survey sites. The red line is Beidagang Reservoir’s boundary line, and the blue dots are sampling sites.
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Figure 2. Annual abundance of birds and species richness sorted by ecotypes. Data for abundance of birds were presented in logarithmic form. TB: terrestrial birds; BP: birds of prey; CB: climbing birds; SBD: songbirds; WB: wading birds; SB: swimming birds.
Figure 2. Annual abundance of birds and species richness sorted by ecotypes. Data for abundance of birds were presented in logarithmic form. TB: terrestrial birds; BP: birds of prey; CB: climbing birds; SBD: songbirds; WB: wading birds; SB: swimming birds.
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Figure 3. Annual abundance of birds and species richness sorted by orders. Data for abundance of birds were presented in logarithmic form. AC: Accipitriformes; AN: Anseriformes; CH: Charadriiformes; GR: Gruiformes; PA: Passeriformes; PE: Pelecaniformes; PO: Podicipediformes.
Figure 3. Annual abundance of birds and species richness sorted by orders. Data for abundance of birds were presented in logarithmic form. AC: Accipitriformes; AN: Anseriformes; CH: Charadriiformes; GR: Gruiformes; PA: Passeriformes; PE: Pelecaniformes; PO: Podicipediformes.
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Figure 4. Annual abundance of birds and species richness sorted by threat categories. Data for abundance of birds were presented in logarithmic form. CR: critically endangered; EN: endangered; LC: least concern; NR: not recognized; NT: near threatened; VU: vulnerable.
Figure 4. Annual abundance of birds and species richness sorted by threat categories. Data for abundance of birds were presented in logarithmic form. CR: critically endangered; EN: endangered; LC: least concern; NR: not recognized; NT: near threatened; VU: vulnerable.
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Figure 5. Total abundance of birds counted annually.
Figure 5. Total abundance of birds counted annually.
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Figure 6. Non-metric multidimensional scaling (NMDS) plots of bird community composition, with smooth response curves of habitat types using generalized additive models. Points indicating bird community composition on NMDS plots are colored by years. Green curved splines show the nonlinear fit of habitat types from high levels to low levels (from dark green to light green). (a) Building land; (b) open water; (c) woodland; (d) farmland; (e) perennial meadows; (f) reed meadows.
Figure 6. Non-metric multidimensional scaling (NMDS) plots of bird community composition, with smooth response curves of habitat types using generalized additive models. Points indicating bird community composition on NMDS plots are colored by years. Green curved splines show the nonlinear fit of habitat types from high levels to low levels (from dark green to light green). (a) Building land; (b) open water; (c) woodland; (d) farmland; (e) perennial meadows; (f) reed meadows.
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Figure 7. Correlation between ecotypes and habitat types by Mantel test. Pearson’s r represents the correlation coefficients among different habitat types; Mantel’s r reveals the correlation coefficients between ecotypes and habitat types, and the thicker the line is, the larger the correlation coefficients are; p-value represents the significance of the correlation between ecotypes and habitat types.
Figure 7. Correlation between ecotypes and habitat types by Mantel test. Pearson’s r represents the correlation coefficients among different habitat types; Mantel’s r reveals the correlation coefficients between ecotypes and habitat types, and the thicker the line is, the larger the correlation coefficients are; p-value represents the significance of the correlation between ecotypes and habitat types.
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Figure 8. Correlation between orders and habitat types by Mantel test. Pearson’s r represents the correlation coefficients among different habitat types; Mantel’s r reveals the correlation coefficients between ecotypes and habitat types, and the thicker the line is, the larger the correlation coefficients are; p-value represents the significance of the correlation between ecotypes and habitat types.
Figure 8. Correlation between orders and habitat types by Mantel test. Pearson’s r represents the correlation coefficients among different habitat types; Mantel’s r reveals the correlation coefficients between ecotypes and habitat types, and the thicker the line is, the larger the correlation coefficients are; p-value represents the significance of the correlation between ecotypes and habitat types.
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Figure 9. Correlation between threat categories and habitat types by Mantel test. Pearson’s r represents the correlation coefficients among different habitat types; Mantel’s r reveals the correlation coefficients between ecotypes and habitat types, and the thicker the line is, the larger the correlation coefficients are; p-value represents the significance of the correlation between ecotypes and habitat types.
Figure 9. Correlation between threat categories and habitat types by Mantel test. Pearson’s r represents the correlation coefficients among different habitat types; Mantel’s r reveals the correlation coefficients between ecotypes and habitat types, and the thicker the line is, the larger the correlation coefficients are; p-value represents the significance of the correlation between ecotypes and habitat types.
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Table
Table 1. Effects of years, semi-years and ecotypes on species richness and abundance of birds.
Table 1. Effects of years, semi-years and ecotypes on species richness and abundance of birds.
VariableYearsSemi-YearsEcotypes
FpFpFp
Species Richness5.1480.00153 **0.5120.47774169.234<0.01 ***
Abundance of Birds1.1870.3281.920.17218.776<0.01 ***
Notes: *** is p-value less than 0.001; ** is p-value less than 0.01.
Table
Table 2. Effects of years, semi-years and orders on species richness and abundance of birds.
Table 2. Effects of years, semi-years and orders on species richness and abundance of birds.
VariableYearsSemi-YearsOrders
FpFpFp
Species Richness1.2210.3151.2020.27991.104<0.01 ***
Abundance of Birds1.2810.2910.5670.45513.1<0.01 ***
Notes: *** is p-value less than 0.001.
Table
Table 3. Effects of years, semi-years and threat categories on species richness and abundance of birds.
Table 3. Effects of years, semi-years and threat categories on species richness and abundance of birds.
VariableYearsSemi-YearThreat Categories
FpFpFp
Species Richness0.6680.6180.0070.935270.81<0.01 ***
Abundance of Birds1.2580.31.1810.28314.816<0.01***
Notes: *** is p-value less than 0.001.
Table
Table 4. Importance of habitat types in determining bird community compositions.
Table 4. Importance of habitat types in determining bird community compositions.
Habitat TypesDeviance Explainedp-Value
Farmland30.3%<0.01
Building land31.5%<0.01
Reed meadows31.4%<0.01
Perennial meadows29%<0.01
Open water27.9%<0.01
Woodland27.7%<0.01
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MDPI and ACS Style

He, M.; Dai, Z.; Mo, X.; Zhang, Z.; Liu, J.; Lei, W.; Meng, W.; Hu, B.; Xu, W. Annual Dynamics of Bird Community at a Coastal Wetland and Their Relation to Habitat Types: The Example of Beidagang Wetland, Northern China. J. Mar. Sci. Eng. 2023, 11, 342. https://doi.org/10.3390/jmse11020342

AMA Style

He M, Dai Z, Mo X, Zhang Z, Liu J, Lei W, Meng W, Hu B, Xu W. Annual Dynamics of Bird Community at a Coastal Wetland and Their Relation to Habitat Types: The Example of Beidagang Wetland, Northern China. Journal of Marine Science and Engineering. 2023; 11(2):342. https://doi.org/10.3390/jmse11020342

Chicago/Turabian Style

He, Mengxuan, Ziling Dai, Xunqiang Mo, Zhengwang Zhang, Jin Liu, Weipan Lei, Weiqing Meng, Beibei Hu, and Wenbin Xu. 2023. "Annual Dynamics of Bird Community at a Coastal Wetland and Their Relation to Habitat Types: The Example of Beidagang Wetland, Northern China" Journal of Marine Science and Engineering 11, no. 2: 342. https://doi.org/10.3390/jmse11020342

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