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Article

Spatio-Temporal Distribution Patterns and Determinant Factors of Wintering Hooded Cranes (Grus monacha) Population

by
Xuetao Sun
1,2,3,
Lizhi Zhou
1,2,3,*,
Zhongfang Zhang
1,2,3 and
Lei Meng
1,2,3
1
School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
2
Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, Anhui University, Hefei 230601, China
3
Anhui Shengjin Lake Wetland Ecology National Long-Term Scientific Research Base, Chizhou 247110, China
*
Author to whom correspondence should be addressed.
Diversity 2022, 14(12), 1091; https://doi.org/10.3390/d14121091
Submission received: 10 October 2022 / Revised: 6 December 2022 / Accepted: 7 December 2022 / Published: 9 December 2022
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Abstract

:
Hooded Cranes (Grus monacha) rely on wetlands for the majority of their life cycle and respond to the environmental conditions during the wintering period. Future conservation planning should be driven by an understanding of how cranes respond to environmental factors at degraded wetland sites and the changes in their spatio-temporal distribution. In recent years, the spatial and temporal distribution of waterbirds and determinant factors have become a research focus. However, research on the specific factors influencing the relative abundance of Hooded Cranes from multiple perspectives in the different habitat patches at Shengjin and Caizi Lakes is lacking. Therefore, from 2021 to 2022, we investigated the quantity and distribution of Hooded Cranes in the upper part of Shengjin and Baitu Lake part of Caizi Lakes. We considered multiple habitat variables, including patch size, food biomass, food availability, and human disturbance, and analyzed the dynamic changes in the distribution of the population in different wintering periods. We used model selection and averaging to select the best model and identify key variables. During different wintering periods, the spatio-temporal distribution of the crane population differed in the upper part of Shengjin Lake, but the crane was mainly distributed in the northern part of the Baitu Lake part of Caizi Lake. The model that included food biomass and patch size was the best for predicting the relative abundance of Hooded Cranes. Cranes foraged in areas with large patches and abundant food resources. Therefore, we suggest reserving patch integrity and availability in the current habitats and protecting and restoring the main food resources to provide high-quality habitat patches and plentiful food resources for wintering populations of Hooded Cranes.

1. Introduction

In recent years, the conservation of endangered species and their habitats has become a focus of studies aiming to conserve biodiversity and restore wetland ecosystems [1,2]. Lakes in the middle and lower Yangtze River floodplain are important stopover sites for wintering waterbirds. Unreasonable agricultural and fishery exploitation, hydraulic engineering construction, and water level control [3,4] have resulted in the reduction of wetland areas, serious habitat degradation and fragmentation [5,6]. These smaller and more fragmented habitats make it more difficult for many species to survive. The wintering period is an important part of the annual life cycle of waterbirds, and the quantity and spatial distribution of waterbirds during winter can fully reflect habitat quality [7]. Changes to the environmental conditions of wetland habitats in winter can directly affect the spatial distribution and body condition of waterbirds [8,9], which also affects the departure time and breeding success of migratory birds in spring [10]. Studying the spatio-temporal distribution of birds and the determinant factors can effectively reveal the ecological adaptive characteristics of waterbirds and provide theoretical suggestions for the conservation of endangered waterbirds and restoration of their wetland habitats.
Waterbirds depend on wetlands for all or part of their life cycles and are extremely sensitive to habitat changes around those ecosystems, making them become good indicators of environmental changes in wetland ecosystems [11,12]. Food biomass and availability, patch size, and human disturbances can affect foraging patch selection during wintering period [13,14,15]. Foraging habitat patch size is one of the most important factors influencing avian habitat selection [15,16,17,18], thus affecting species abundance. Spatio-temporal variation in food resources will affect habitat selection [19,20], especially during the wintering period when food resources are limited [21]. Waterbirds prefer patches with high biomass and food availability during the wintering period so they can extract the maximum amount of energy, which constitutes a complex decision for patch utilization [22]. In addition, seasonal variation in vegetation biomass has been reported as one of the most important predictors of waterbird species richness [23,24]. With economic development, increasing population size, and unsustainable use of wetland resources, the negative effects of human factors on birds are becoming increasingly prominent [25]. Understanding how ecological and human factors interact is necessary because this interaction can directly or indirectly affect the spatio-temporal distribution [26,27] and relative abundance [28,29,30] of waterbirds in wetlands.
Hooded Crane is a migratory wader bird species for which the lakes in the middle and lower Yangtze River floodplain are important wintering sites. It is a vulnerable (VU) species in the IUCN Red List of Threatened Species, classified as a first-class national protected wild animal species on the Chinese Wildlife Protection List. The cranes are mainly distributed across two adjacent lakes in the floodplain wetlands, Shengjin and Caizi Lakes, which are located on the East Asian-Australasian flyway. Owing to the degradation and loss of lake wetlands, submerged plants have largely disappeared in recent decades, resulting in the cranes shifting from natural habitats to artificial habitats where they forage for enough food to satisfy nutrient demands [13,31]. With the aim of enhancing protection and management practices, research on the population ecology of Hooded Cranes has increased in recent years. The factors affecting the spatio-temporal distribution of birds are complex [8]. However, previous studies have focused on single variables [13,20,32] and have been limited to observations and descriptive studies [33,34,35]. For example, researchers have analyzed the determinant factors of habitat selection of Hooded Cranes, but these studies examined the stopover sites only [36] and ignored the importance of patch size for cranes [37]. Against the background of habitat fragmentation, there is sparse information regarding the analysis of determinant factors on the relative abundance of cranes from multiple perspectives in different patches. Multiple perspectives must be considered to understand the determinant factors and to propose effective measures for population and habitat protection.
To this end, in the present study, we aimed to: (1) determine the spatio-temporal distribution of wintering population of Hooded Cranes in different periods, and (2) to identify key environmental variables that are significantly related to the relative abundance of Hooded Cranes in different habitat patches. The findings are expected to improve our understanding of the important environmental variables which affect the abundance of Hooded Cranes in different habitat patches. This information can help land managers to develop effective conservation measures, which will contribute to the conservation of cranes and the management and restoration of their habitats.

2. Materials and Methods

2.1. Study Area

The study sites were chosen in two lakes connected to the Yangtze River (Figure 1), in the upper part of Shengjin Lake (30.25°–30.50° N, 116.92°–117.25° E) and the Baitu Lake part of Caizi Lakes (30.75°–30.97° N, 117.00°–117.15° E) where wintering Hooded Cranes mainly foraged. The number of Hooded Cranes observed in the survey areas accounted for the majority of the wintering population. The lake habitats in the study areas are highly heterogeneous, and these diverse landscapes provide suitable habitats for the cranes [38]. In recent years, the number of submerged plants in the lakes of Yangtze River floodplain has declined sharply, and cranes have had to change their food sources, forcing them to move from natural habitats to artificial habitats (paddy fields) for daily food security [19]. Paddy fields play an important role as alternative habitats for the cranes. According to the natural climate, and the migratory rhythm of the cranes, we defined November–December as the early wintering period, January–February as the middle wintering period, and March as the late wintering period.

2.2. Bird Survey

The survey was conducted from November 2021 to March 2022 monthly. According to a previous survey [13], we used the fixed-point observation method to survey the number of Hooded Cranes. The observation sites covered the study area. Direct counting was used to record the number of the cranes observed per month. To record the location of the cranes, we divided the study area into 1 × 1 km2 grid cells. Once the cranes were found, the entire visible site was scanned clockwise for direct individual counting using a binocular (EL 10 × 32 WB, SWAROVSKI, Wattens, Austria) and a spotting scope (ATS 20–60 × 80 HD, SWAROVSKI). The number of the cranes at each site, habitat types, and the location on the grid were recorded. To reduce the effect of severe weather on our results, observations were postponed to the following day when strong winds, thick fogs, or heavy snow occurred.

2.3. Food Biomass and Availability

Following the crane survey, we collected food data in each habitat patch, including biomass and availability until the cranes flew away. At the sites occupied by the cranes within their home ranges, we established quadrats of 1 × 1 m. As the crane beaks are approximately 15 cm long, two 0.25 × 0.25 m sub-quadrats were randomly allocated. We repeated this process five times and obtained 10 sub-quadrats for indicating food biomass and availability of each habitat patch through a monthly survey. In each sub-quadrat, we collected the tubers and roots of aquatic vegetation, such as those of Potentilla supine, Polygonum hydropiper, Ranunculus polii, Sagittaria sagittifolia and other plants, including rice grains, and mollusks within the typical 15 cm foraging depth [39]. Food resources were washed and dried in an oven (YHG-9050A, Dreip, Suzhou, China) at 60 °C to a constant weight for ≥72 h. We defined the dry weight of the food resources as the food biomass (g). To test the availability of food, we used a straightedge (Deli, Beijing, China) to measure the burial depth of roots and tubers in each sub-quadrat. The thickness of the straw covering the rice was considered as the food availability of rice grain. Before collecting all food, we used a soil compactness meter (TYD-2; Alisun, Hangzhou, China), inserted vertically into the sediment surface to a depth of 15 cm and its peak value was recorded as sediment permeability (N∙cm−2) [13]. The soil hardness tests were performed twice per sub-quadrat. Finally, we calculated the average food biomass and availability to represent the food biomass and availability of each habitat patch. During the winter seasons, we collected information from a total of 190 plots, performing 380 soil hardness tests, and 110 plots, performing 220 soil hardness tests, at Shengjin Lake and Caizi Lakes, respectively.

2.4. Patch Size Delineation

The observers measured the patch size in each habitat patch when measuring the food biomass. According to previous studies [19], we classified habitat patches into three types: meadows, mudflats, and paddy fields. Habitat patches were defined as areas of contiguous habitat in three habitat types, and roads, rivers, farmland edges, and ditches as delineating patches. The boundary between meadows and mudflats was not obvious, and we defined 10% vegetation coverage as the boundary between mudflats and meadows. Each observer estimated the distance to the habitat edge (defined as the point where land use changed) from the center of the circle. We checked those estimates, together with patch size (hectares) using ArcMap 10.7 [40]. Finally, we identified a total of 30 habitat patches through the monthly surveying.

2.5. Human Disturbance

The distance from the center of each flock to the nearest road or settlement was considered to represent human disturbance. The distance to the nearest residential area or road was measured using high-resolution Google Earth images.

2.6. Data Analysis

To address multicollinearity among explanatory variables, we calculated the variance inflation factor (VIF) for each variable in the model. If the value of this factor was greater than 5, then the variable was removed from the model.
We estimated the relative abundance of cranes in each patch as the ratio of the number of cranes in the patch (Ni) to the total number of cranes in all patches every month (N), using the following equation:
Relative abundance = Ni/N,
We used model selection and model averaging to analyze the factors influencing the abundance of Hooded Cranes in each habitat patch. Information theory has been increasingly applied to analysis in the field of ecology, including the use of the corrected Akaike’s information criterion (AICc) [41,42,43]. This analytical approach allows the comparison and ranking of multiple models to determine the best one. Therefore, we performed model selection and averaging based on information theory to elucidate the influence of various environmental factors on the relative abundance of the cranes. We calculated the difference between the AICc values of the best model and the AICc values of each of the other models (ΔAICc) to select the best model. The model with the lowest ΔAICc was considered to be the best among the candidates tested. Models with ΔAICc values < 2 were considered to be essentially as good as the best model. The second metric was the Akaike weight (wi), which can be considered the relative importance of the variables. We obtained the Akaike weight (wi) of each model by calculating ΔAICc, based on which the best model (wi > 0.9) can be selected. The model selection results show that none of the models were optimal; therefore, we used model averaging to eliminate uncertainty in model selection at 95% confidence intervals [43]. All statistical analyses were performed using R Software v.4.1.0.

3. Results

3.1. Spatial and Temporal Distribution of Wintering Hooded Crane Population

The population dynamics of cranes in the study area during the survey showed that the number of cranes initially increased and then decreased (Figure 2). The number of cranes peaked in January at 174 and 244 birds in the study area at Caizi and Shengjin Lakes, respectively.
The cranes were recorded in eight grids in the study area at Caizi Lake and 13 grids in the study area at Shengjin Lake, accounting for 3.9% and 15.12% of all grids in the study area, respectively. The distribution of cranes changed during different wintering periods at Shengjin Lake (Figure 3d–f) but was limited at Caizi Lake (Figure 3a–c) where they were mainly distributed in the northern region of the lake.

3.2. Model Selection and Model Averaging

The characteristics of patches we surveyed during the wintering period in the study area are shown in the Appendix B (Table A3 and Table A4). According to the results of selection based on the ΔAICc model, the model containing both food biomass and patch size was the best model for predicting the relative abundance of cranes (ΔAICc = 0, wi = 0.418) (Table 1). However, the wi of the best model was not high. The model average results showed that food biomass and patch size (wi = 1, wi = 0.998) were the most important factors affecting the relative abundance of cranes (Table 2).
We selected important environmental factors for general linear regression and found two factors that had a significant impact on the relative abundance of cranes (Figure 4). The result of model selection was the same as that of general linear regression, indicating that patch size and food biomass could explain the relative abundance of wintering cranes.

4. Discussion

Reclamation, high density purse seine culture, and hydrologic rhythm disturbance lead to habitat degradation. Owing to habitat fragmentation and the large decrease in submerged plants, waterbird populations were regionally mobile during the wintering period [44]. Although our study area is not the whole lake, the number of wintering Hooded Cranes in our study area accounts for the majority of the whole lake. The occasional discovery of a small number of the cranes outside the study area is considered to have little impact on this study. The Hooded Cranes adopted flexible strategies to gather and disperse in large numbers according to their specific food needs and use the lake habitat to meet their energy needs [13,32,37,45].
Our results showed that habitat patch size was the most important predictor of the abundance of the cranes. These results are consistent with some previous habitat fragmentation studies [15,46,47]. Larger patches can provide more ecological space, and thus more food resources for birds [48]. The cranes could change foraging habitat patches over time and space in order to get enough nutrient supplies. Owing to the decline of suitable habitat areas, mixed species foraging inevitably occurs at the habitat patches, especially for geese. Although we cannot rule out the interspecific competition, previous studies have shown that geese have effective ways to avoid excessive competition with cranes in winter: based on food niche separation and regulation of niche width to reduce niche overlap [20,49]. Therefore, coexistence among geese and cranes may have been considered an evolutionary strategy to partition limited resources and minimize potential interspecific competition.
We found that abundance of food resources was a critical factor for determining crane distribution, and similar patterns to those reported herein have been found in other crane species [50,51]. Waterbirds, especially cranes can identify patches with rich food resources to meet their nutritional and energy needs and reduce the adverse impact of habitat degradation and loss [52] during the wintering period. Cranes are highly flexible regarding habitat patch utilization, and they can adjust their temporal and spatial distribution according to the food biomass in potential foraging sites. Hooded Cranes have been found to select foraging sites in response to higher food biomass to increase foraging efficiency [13]. Other birds have also been shown to select sites with higher food biomass to increase foraging efficiency. For instance, some insectivorous birds select trees with the highest arthropod biomass [53] in which they exert higher foraging activity [54]. When food resources change over time, the cranes can adjust their spatial and temporal distribution to select alternative food sources to increase their fitness.
Previous studies have highlighted the importance of food availability [55,56]. For Hooded Cranes, food at deeper substrate depths that is more difficult to reach is avoided to optimize the amount of energy and nutrients obtained for minimal foraging effort [57,58]. We did not observe this phenomenon in the present study, possibly because the measured food burial depth was lower than the range of crane beak lengths (Table A3 and Table A4). Previous studies have chosen to directly record the type and duration of human disturbance to represent the intensity of human disturbance [34,59], which seems more reasonable. However, some recent studies on the wintering population ecology of birds have found that the metrics we use are important for habitat selection [60]. Human disturbance had no significant effect on the relative abundance of cranes, possibly due to the development of behavioral adaptations to human disturbance. They can adjust the time budget allocated to vigilance and foraging behaviors to adapt the relative degradation of habitats rather than giving up the habitat patch, which indicates flexible adaptation strategies in the face of different degrees of human disturbances [34,61]. The cranes can maximize their energy benefits by keenly recognizing the changes in environmental factors and adjusting their daily activity rhythms in a timely manner. Owing to the sharp decline in the number of submerged plants, the paddy fields which contain relatively substantial amounts of food have become important alternative foraging habitats (Table A1 and Table A2). After the rice harvest, a large amount of rice grain is scattered throughout the paddy field, providing rich food resources for the Hooded Cranes. However, human disturbance has become one of the key components of habitats in paddy fields. In recent years, the administrator has negotiated with local villages to lease some of the un-harvested paddy fields as foraging sites for the Hooded Cranes. The abundance of food resources may have reduced the vigilance of cranes to human disturbances [62].

5. Conclusions

Our study revealed the spatio-temporal distribution patterns of wintering Hooded Crane in different wintering periods and confirmed that food biomass and foraging patch size play key roles in determining the relative abundance of the cranes. The model that included both food biomass and patch size was the best for predicting the relative abundance of cranes. Here, we present key guiding principles for the crane conservation of cranes. Due to the unique climate and geographical characteristics, the distribution of submerged plants foraged by the cranes varies greatly across the lake and may often be concentrated in patches within certain periods. The submerged vegetation (Potentilla supina, Polygonum criopolitanum, Ranunculus polii) in the foraging area of the cranes should be protected and restored. The paddy fields considered as the alternative habitats for the cranes should be artificially supplemented with rice grain to provide them with a constant source of food during the wintering period. Furthermore, we also should optimize land use and cultivation methods in paddy fields by delaying plowing to protect the integrity and availability of larger habitat patches.

Author Contributions

Conceptualization, X.S. and L.Z.; methodology, X.S.; software, X.S. and L.M.; validation, L.Z.; formal analysis, X.S.; investigation, X.S. and Z.Z.; resources, L.Z.; data curation, X.S.; writing—original draft preparation, X.S. and L.Z.; writing—review and editing, X.S and L.Z.; visualization, X.S.; supervision, L.Z.; project administration, L.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Natural Science Foundation of China, Grant Number 32171530, 31172117.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

We thank Zhenhua Wei, Zunyi Huang, Shanshan Xia, Xianglin Ji, Yundong Zhong, and Jindi Han for their assistance with this study.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. The number of cranes, food biomass and item in the grids at Shengjin Lake (The food item was the main food resources for cranes).
Table A1. The number of cranes, food biomass and item in the grids at Shengjin Lake (The food item was the main food resources for cranes).
Wintering PeriodGrid CodeFood BiomassFood ItemSampleNumber of Cranes
Early wintering periodG11.2 ± 0.22Oryza sativaN = 10233
G24.8 ± 1.3Polygonum criopolitanumN = 1059
G30.34 ± 0.09Polygonum criopolitanumN = 104
G40.41 ± 0.10Polygonum criopolitanumN = 104
G51.05 ± 0.12Polygonum criopolitanumN = 28
G61.23 ± 0.15Polygonum criopolitanumN = 680
G71.32 ± 0.18Polygonum criopolitanumN = 28
Middle wintering periodG80.75 ± 0.35Oryza sativaN = 30242
G90.25 ± 0.07Oryza sativaN = 204
G103.29 ± 0.26Potentilla supina
Polygonum criopolitanum		N = 6180
G113.05 ± 0.32Potentilla supina
Polygonum criopolitanum		N = 415
G122.67 ± 0.52Potentilla supina
Polygonum criopolitanum		N = 2013
Late wintering
period		G130.85 ± 0.19Oryza sativaN = 103
G141.45 ± 0.18Ranunculus polii
Potentilla supina		N = 1028
G152.7 ± 0.58Ranunculus polii
Potentilla supina		N = 1041
G160.31 ± 0.12Ranunculus polii
Potentilla supina		N = 105
G170.22 ± 0.05Ranunculus polii
Potentilla supina		N = 109
G180.27 ± 0.07Ranunculus polii
Potentilla supina		N = 108
Table
Table A2. The number of cranes, food biomass and item in the grids at Caizi Lakes (The food item was the main food resources for cranes).
Table A2. The number of cranes, food biomass and item in the grids at Caizi Lakes (The food item was the main food resources for cranes).
Wintering PeriodGrid CodeFood BiomassFood ItemSampleNumber of Cranes
Early wintering periodG12.1 ± 0.26Oryza sativaN = 10110
G20.26 ± 0.07Polygonum criopolitanumN = 102
G30.28 ± 0.07Polygonum criopolitanumN = 103
G40.21 ± 0.06Polygonum criopolitanumN = 102
Middle wintering periodG50.81 ± 0.29Oryza sativaN = 105
G60.87 ± 0.26Oryza sativaN = 2064
G71.15 ± 0.18Oryza sativaN = 1076
G81.12 ± 0.37Oryza sativaN = 10161
Late wintering
period		G90.78 ± 0.26Oryza sativaN = 1031
G100.19 ± 0.06Potentilla supinaN = 103

Appendix B

Table
Table A3. Characteristics of the surveyed patches at Shengjin Lake (The food item was the main food resources for cranes).
Table A3. Characteristics of the surveyed patches at Shengjin Lake (The food item was the main food resources for cranes).
MonthPatch
Code		Hardness (N∙cm−2)
N = 380		FoodDistance to the Road (m)Distance to the
Village (m)		Patch
Size (ha)		Depth (cm)
N = 190		Relative Abundance of Cranes
Biomass (g) N = 190Food Item
November131.36 ± 10.24.8 ± 1.13Polygonum criopolitanum4164420.073 10.9 ± 1.90.347
251.06 ± 10.541.2 ± 0.27Polygonum criopolitanum157815010.435 6.8 ± 2.40.576
369.51 ± 12.760.41 ± 0.10Polygonum criopolitanum5084950.264 7.29 ± 1.20.024
468.31 ± 14.680.34 ± 0.09Polygonum criopolitanum91213500.086 6.75 ± 1.160.024
December5112.93 ± 20.381.85 ± 0.23Oryza sativa2994860.570 9.2 ± 2.380.675
January690.88 ± 25.041.65 ± 0.22Oryza sativa2515130.182 9.67 ± 2.130.557
7118.41 ± 23.610.19 ± 0.06Polygonum criopolitanum6237500.047 9.6 ± 1.660.005
8125.23 ± 30.000.18 ± 0.05Polygonum criopolitanum115311840.320 9.1 ± 1.360.010
987.05 ± 18.710.28 ± 0.05Oryza sativa5735830.254 8.9 ± 1.730.012
February1098.51 ± 15.930.24 ± 0.09Oryza sativa3585870.135 9.7 ± 2.010.012
1188.68 ± 10.673.28 ± 0.26Potentilla supina
Polygonum criopolitanum		112611490.288 6.5 ± 1.470.564
1285.43 ± 9.672.67 ± 0.52Potentilla supina
Polygonum criopolitanum		5526250.027 6.1 ± 1.330.032
1390.46 ± 25.040.22 ± 0.07Oryza sativa3052310.112 8.3 ± 2.050.012
March14100.28 ± 16.480.85 ± 0.19Oryza sativa2891960.135 8.7 ± 2.010.023
1585.43 ± 9.671.45 ± 0.18Ranunculus polii
Potentilla supina		4087420.036 6.4 ± 1.320.219
1688.45 ± 10.672.7 ± 0.58Ranunculus polii
Potentilla supina		8495390.288 7 ± 1.550.320
1788.12 ± 11.200.31 ± 0.12Ranunculus polii
Potentilla supina		8525350.195 6.5 ± 1.150.039
1872.05 ± 9.800.22 ± 0.05Ranunculus polii
Potentilla supina		8538160.240 5.9 ± 1.100.070
1970.19 ± 9.720.27 ± 0.07Ranunculus polii
Potentilla supina		4864730.110 6.8 ± 1.230.063
Table
Table A4. Characteristics of the surveyed patches at Caizi Lakes (The food item was the main food resources for cranes).
Table A4. Characteristics of the surveyed patches at Caizi Lakes (The food item was the main food resources for cranes).
MonthPatch CodeHardness (N∙cm−2)
N = 220		FoodDistance to the Road (m)Distance to the
Village (m)		Patch
Size (ha)		Depth (cm)
N = 110		Relative Abundance of Cranes
Biomass (g) N = 110Food Item
November1115.67 ± 11.690.26 ± 0.07Polygonum criopolitanum2042500.091 6.13 ± 0.750.012
2110.42 ± 10.630.28 ± 0.07Polygonum criopolitanum3573730.144 6.2 ± 0.650.018
December3122.78 ± 28.452.1 ± 0.26Oryza sativa1339020.228 10.7 ± 2.80.319
4108.81 ± 10.100.21 ± 0.06Polygonum criopolitanum2965730.089 5.7 ± 1.290.006
January5111.78 ± 21.601.35 ± 0.27Oryza sativa7815440.227 10.8 ± 1.660.234
6108.38 ± 19.231.15 ± 0.18Oryza sativa4209410.413 9.9 ± 1.370.182
February797.65 ± 25.370.81 ± 0.29Oryza sativa8914600.409 10.4 ± 2.780.182
896.64 ± 22.680.87 ± 0.26Oryza sativa12714420.166 10.8 ± 2.830.185
996.05 ± 21.420.79 ± 0.31Oryza sativa8910670.156 10.5 ± 2.860.014
March1085.05 ± 10.190.19 ± 0.06Potentilla supina1463020.082 7.9 ± 1.700.023
1193.88 ± 21.880.78 ± 0.26Oryza sativa919740.161 9.5 ± 1.220.242

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Figure 1. Location of the study area at Shengjin and Caizi Lakes.
Figure 1. Location of the study area at Shengjin and Caizi Lakes.
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Figure 2. Population changes of Hooded Cranes in different months in the study area at (a) Caizi and (b) Shengjin Lakes.
Figure 2. Population changes of Hooded Cranes in different months in the study area at (a) Caizi and (b) Shengjin Lakes.
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Figure 3. Spatio-temporal distribution of Hooded Cranes population in different wintering periods. (a–c) at Caizi Lakes and (d–f) at Shengjin Lake, presented the crane individuals in the kilometer grids in the early, middle, and late wintering periods.
Figure 3. Spatio-temporal distribution of Hooded Cranes population in different wintering periods. (a–c) at Caizi Lakes and (d–f) at Shengjin Lake, presented the crane individuals in the kilometer grids in the early, middle, and late wintering periods.
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Figure 4. General linear regression between relative abundance of cranes and (a) patch size (ha) and (b) food biomass (g). (Blue areas represent 95% confidence intervals).
Figure 4. General linear regression between relative abundance of cranes and (a) patch size (ha) and (b) food biomass (g). (Blue areas represent 95% confidence intervals).
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Table
Table 1. Model selection-related relative abundance to variables using ΔAICc.
Table 1. Model selection-related relative abundance to variables using ΔAICc.
ModelAICcΔAICcwi
Patch size + food biomass−34.700.418
Patch size + food biomass + depth−32.12.60.111
Patch size + food biomass + distance to village−32.02.70.108
Patch size + food biomass + hardness−31.92.80.102
wi, Akaike weight; ΔAICc, corrected Akaike’s information criterion. Models with ΔAICc > 3 are not shown.
Table
Table 2. Model-averaged parameter estimate, standard error, and relative importance (Akaike weight, wi) for important variables.
Table 2. Model-averaged parameter estimate, standard error, and relative importance (Akaike weight, wi) for important variables.
Environment FactorsEstimateStandard Errorwi
Patch size0.790.071
Food biomass0.110.180.998
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Sun, X.; Zhou, L.; Zhang, Z.; Meng, L. Spatio-Temporal Distribution Patterns and Determinant Factors of Wintering Hooded Cranes (Grus monacha) Population. Diversity 2022, 14, 1091. https://doi.org/10.3390/d14121091

AMA Style

Sun X, Zhou L, Zhang Z, Meng L. Spatio-Temporal Distribution Patterns and Determinant Factors of Wintering Hooded Cranes (Grus monacha) Population. Diversity. 2022; 14(12):1091. https://doi.org/10.3390/d14121091

Chicago/Turabian Style

Sun, Xuetao, Lizhi Zhou, Zhongfang Zhang, and Lei Meng. 2022. "Spatio-Temporal Distribution Patterns and Determinant Factors of Wintering Hooded Cranes (Grus monacha) Population" Diversity 14, no. 12: 1091. https://doi.org/10.3390/d14121091

APA Style

Sun, X., Zhou, L., Zhang, Z., & Meng, L. (2022). Spatio-Temporal Distribution Patterns and Determinant Factors of Wintering Hooded Cranes (Grus monacha) Population. Diversity, 14(12), 1091. https://doi.org/10.3390/d14121091

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