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Article

MaxEnt Modeling for Predicting the Potential Wintering Distribution of Eurasian Spoonbill (Platalea leucorodia leucorodia) under Climate Change in China

by
Aihua Fu
1,2,
Erhu Gao
2,
Xiaoping Tang
2,
Zengli Liu
2,
Faxiang Hu
1,
Zhenjie Zhan
1,
Jiadong Wang
1 and
Xiaofeng Luan
1,*
1
School of Ecology and Nature Conservation, Beijing Forestry University, No. 35 Tsinghua East Road, Haidian District, Beijing 100083, China
2
Academy of Inventory and Planning, National Forestry and Grassland Administration, Beijing 100714, China
*
Author to whom correspondence should be addressed.
Animals 2023, 13(5), 856; https://doi.org/10.3390/ani13050856
Submission received: 23 December 2022 / Revised: 22 February 2023 / Accepted: 23 February 2023 / Published: 26 February 2023
(This article belongs to the Section Birds)
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Abstract

:

Simple Summary

Climate change has become an important cause of habitat loss. The Eurasian Spoonbill (Platalea leucorodia leucorodia) is a national grade II protected wildlife species in China. Few studies on the distribution and protection of Eurasian Spoonbill have been carried out in wintering grounds. We simulated the potential distribution of the Eurasian Spoonbill and analyzed the impact of climate change on this species, with occurrence sites recorded during the National Waterbird Simultaneous Survey of China in 2016 to support species conservation. We also analyzed the main environmental factors affecting its overwintering distribution. We conclude that the majority of suitable wintering habitats are concentrated in the middle and lower reaches of the Yangtze River and tend to shift northward under future emission scenarios. Moreover, the main factors affecting the suitable wintering habitat areas at present are water distance, altitude, precipitation, and mean temperature of the driest quarter. Therefore, future conservation work should pay considerable attention to the suitable habitat of the white spoonbill in the middle and high levels to effectively conserve this species over time. These findings offer a valuable reference for the conservation of the Eurasian Spoonbill and other endangered species under climate change. Furthermore, it provides a reference for wetland development, utilization, and protection.

Abstract

Global climate change has become a trend and is one of the main factors affecting biodiversity patterns and species distributions. Many wild animals adapt to the changing living environment caused by climate change by changing their habitats. Birds are highly sensitive to climate change. Understanding the suitable wintering habitat of the Eurasian Spoonbill (Platalea leucorodia leucorodia) and its response to future climatic change is essential for its protection. In China, it was listed as national grade II key protected wild animal in the adjusted State List of key protected wild animals in 2021, in Near Threatened status. Few studies on the distribution of the wintering Eurasian Spoonbill have been carried out in China. In this study, we simulated the suitable habitat under the current period and modeled the distribution dynamics of the wintering Eurasian Spoonbill in response to climate change under different periods by using the MaxEnt model. Our results showed that the current suitable wintering habitats for the Eurasian Spoonbill are mainly concentrated in the middle and lower reaches of the Yangtze River. Distance from the water, precipitation of the driest quarter, altitude, and mean temperature of the driest quarter contributed the most to the distribution model for the wintering Eurasian Spoonbill, with a cumulative contribution of 85%. Future modeling showed that the suitable distribution of the wintering Eurasian Spoonbill extends to the north as a whole, and the suitable area shows an increasing trend. Our simulation results are helpful in understanding the distribution of the wintering Eurasian Spoonbill under different periods in China and support species conservation.

1. Introduction

The Intergovernmental Panel on Climate Change (IPCC) report showed that global warming temperature may be 1.5 °C higher than the preindustrial level by 2040. Moreover, it is projected to increase by nearly 0.2 °C every ten years [1]. Climate change is considered one of the crucial factors affecting biodiversity patterns and species distribution; it is also a major issue for conservationists [2,3]. Thus far, the distribution and population of species have significantly changed, even related to species extinction [3]. Previous studies have shown that as the climate warms, some species change their current suitable habitats and mainly migrate to high altitudes and high latitudes to adapt to the changing environment, whereas others lose their existing suitable habitats [4,5,6,7]. As sensitive species, birds are particularly affected by climate change because they are highly sensitive to climate and weather [8,9]. Shifts in bird distribution have already been linked to climate change [10,11,12,13]. Moreover, some studies have suggested that global bird and mammal populations have decreased considerably in regions with high temperatures [11]. Besides knowledge of the natural history and biology of a species, an understanding of the suitable habitat of a species is essential for species conservation and conservation planning [14,15]. Comparing the current and future predicted distributions of species enables conservationists to develop protection and project plans [2,16,17,18]. Therefore, scientifically understanding the impact of climate change on species distribution is crucial to adequately protect species in the context of climate change [19,20].
Species distribution models (SDMs), also known as ecological niche models, are essential tools in basic ecology and biogeography and are widely used in analyzing the impacts of climate change on species [14,21,22]. SDMs are one method to correlate species distribution information and relevant environmental variables and obtain the potential distribution of species through multiple algorithms based on niche theory [23,24]. The development of SDMs originated from the BIOCLIM model [25]. Then, many niche models, such as the generalized linear model (GLM), genetic algorithm for rule set production (GARP), random forest (RF), and maximum entropy (MaxEnt) were developed to predict species distributions. MaxEnt is widely used because it only needs species occurrence data to make high stability and high precision predictions under incomplete data and small sample size conditions [26,27,28,29]. It is also used to predict suitable habitats for birds [14,30,31], biodiversity loss [32], species conservation, and options for nature-protected areas [33,34].
The Eurasian Spoonbill is a common species and belongs to the Least Concern category under the IUCN Red List [35]. In China, it was listed as national grade II key protected wild animal in the adjusted State List of key protected wild animals in 2021, in Near Threatened status [36,37]. There are three subspecies, namely: Platalea leucorodia leucorodia, Platalea leucorodia archeri, and Platalea leucorodia balsaci. Among them, Platalea leucorodia leucorodia is distributed in China. The global population of the Eurasian Spoonbill (Platalea leucorodia) is approximately 63,000–65,000 [35]. Most countries have only a few hundred breeding pairs with a decreasing trend [38]. The Eurasian Spoonbill likes to live near wetlands, such as the banks of rivers, lakes, and reservoirs in open plains and mountainous and hilly areas, swamp wetlands, and offshore and coastal areas. It mainly winters in the lower reaches of the Yangtze River, South China, and southeast coastal areas [39,40]. As a migratory species, the Eurasian Spoonbill is more vulnerable to a series of specific threats (such as habitat loss/fragmentation, overexploitation, and climate change) than nonmigratory species, thereby hindering the sustainable existence of its population [35,41,42]. Climate change is one of the main factors determining bird distribution and geographic range change [8]. Studies on the European and African Eurasian Spoonbill showed that climate change alters the wintering migration distance, the distribution of suitable habitats, and impacts the protection network [43]. Most of the previous studies regarding the Eurasian Spoonbill have focused on breeding behavior, geographical distribution, living habitats and habitat selection, overwintering ecology, and distribution in local areas [44,45,46,47,48,49]. However, studies on the distribution of wintering habitats and the response and adaptation to climate change of the Eurasian Spoonbill in China are limited. Thus, relevant research is necessary to develop the protection work appropriately.
In this study, we used the MaxEnt model to simulate the potential suitable wintering habitat of the Eurasian Spoonbill. We also predicted the future influences on suitable wintering habitats under climate change scenarios for the 2050s and 2070s. In particular, we aimed to address the following questions: (1) Where is the current potential wintering distribution of the Eurasian Spoonbill? (2) What environmental factors affect the wintering distribution? (3) What is the impact of climate change on the current suitable wintering habitats? Our study will provide guidance for future field surveys and make conservation workers aware of the potential threat of climate change to this important species and other endangered species, thereby allowing suitable conservation and management work.

2. Materials and Methods

2.1. Source of Species Occurrence Data

The Eurasian Spoonbill occurrence data used in this study were derived from the record during the Winter National Waterbird Simultaneous Survey of China in 2016 [50]. In this survey, 846 survey areas and 5255 observation points were determined based on the collection of historical data and literature, combined with the investigation and research on the migration and overwintering of migratory birds across the country, from 8 to 17 January 2016. The investigators conducted the survey synchronously using the unlimited distance point count method at the survey point with monocular (binocular) telescopes. The observation range is the longest distance that can be distinguished by a monocular telescope. Meanwhile, GPS devices were used to record the species’ locations. The synchronous survey here means that investigators used the same technical methods to conduct the investigation within 4 to 10 days [51]. A total of 105 sites of the Eurasian Spoonbills were found, involving 55 counties in 15 provinces. We selected only sites located at least 2 km from each to avoid spatial autocorrelation caused by clustered occurrences [52,53]. Finally, 87 locations for Eurasian Spoonbill occurrence points in China were obtained (Figure 1).

2.2. Environmental Variables

According to previous research reports, vegetation type, topography (altitude), climate, water distance, land-use type, and two human interferences (distance to road and villages) may affect the choice of wintering habitats for the Eurasian Spoonbill [54,55,56,57]. Therefore, we selected six categories of environmental datasets with 25 environmental variables, including 19 bioclimatic variables, 1 topographic variable (altitude), vegetation type variable, water distance variable, land-use type variable, and 2 human interference variables (distance to road and villages). Among these environmental variables, 19 bioclimatic layers were acquired from the WorldClim dataset under the current and future periods, with a resolution of 30″ (approximately 1 km2 on the ground) [58,59]. The future climate data for the 2050s (average for 2041–2060) and 2070s (average for 2061–2080) were downloaded from the Beijing Climate Center Climate System Modeling version 1.1 (BCC–CSM 1.1) for future model building [58,60,61]. In addition, we used four IPCC-CMIP5 representative concentration pathways (RCPs): one low mitigation gas emission scenario (RCP2.6), two moderate gas emission scenarios (RCP4.5 and RCP6.0), and one high greenhouse gas emission scenario (RCP8.5), which were labeled after the possible pathways of radiative forcing values (measured as +2.6, +4.5, +6.0, and +8.5 W/m2, respectively) in 2100 [62]. The vegetation type variable and land-use type were downloaded from the Resource and Environment Science and Data Center [63,64]. We downloaded the topographic variable (altitude) from the WorldClim dataset [58]. The water distance variables and human interference variables (distance to road and villages) were acquired from the 1:1 million National Catalogue Service for Geographic Information dataset [65]. Because land-use and distance to water are influenced by multiple socio-economic drivers, it is not easy to simply estimate the future condition of these two factors. Hence, we used the current datasets for land-use and distance to water as conservative prognoses for the future. Including dynamic and static variables can limit any extra uncertainty and improve the performance of the model under future climate scenarios [66,67].
In this study, all of these environmental variables were transformed into American Standard Code for Information Interchange (ASII) file format using ArcGIS 10.2 Conversion Tools. They were resampled at a 30″ spatial resolution. We used SPSS software 19 to reduce multicollinearity among these environmental variables by establishing the correlation coefficient matrix and eliminating highly correlated variables (r > 0.8) [68,69]. Ultimately, 13 environmental variables were selected (Table 1).

2.3. MaxEnt Model Prediction

The 87 occurrence site data and filtered environmental variable data were imported into MaxEnt software 3.3.3 [27]. In our model, we randomly selected 25% of the occurrence data for model testing, and the remaining 75% of the data were used for model training [27]. In addition, the number of iterations was set to 500 and 10,000 background points to reduce uncertainty. Other values were kept as default. The average value of 15 repeated modelings was selected as the final prediction result. The jackknife approach was used to analyze the contribution of each environmental variable to the model based on how significantly each environmental variable occupied the species distribution [70]. Meanwhile, we used the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to verify the accuracy of the model. The AUC value is not affected by the threshold, and the evaluation results are relatively objective. Therefore, it is widely used for the accuracy evaluation of SDMs [71]. The accuracy of model prediction is directly proportional to the AUC value, which ranges from 0 to 1. An AUC value of 0.50–0.60 indicates failure, 0.60–0.70 represents poor performance, 0.70–0.80 indicates moderate performance, 0.80–0.90 means the modeling results can be adopted, and a value >0.9 indicates high performance [27].
The results of the modeled species distribution are continuous values that vary between 0 and 1. We imported the predicted result of the model into ArcGIS10.2. We reclassified it to obtain four potential habitat categories: highly suitable habitat (p ≥ 0.6), moderately suitable habitat (0.4 ≤ p < 0.6), poorly suitable habitat (0.2 ≤ p < 0.4), and unsuitable habitat (p < 0.2). The area of each partition was also calculated.

2.4. Habitat Distribution Change and the Core Distributional Shifts

After using the current climate data to model the spatial extent of suitable habitat for the Eurasian Spoonbill, we obtained the future distribution of the species by performing model projections with eight future scenarios (RCP2.6-2050, RCP2.6-2070, RCP4.5-2050, RCP4.5-2070, RCP6.0-2050, RCP6.0-2070, RCP8.0-2050, and RCP8.5-2070). The change of suitable distribution area was obtained by comparing the distribution areas in the present and future periods through ArcGIS10.2. The results were classified into four categories: habitat loss, consistently unsuitable habitat, unchanged suitable habitat, and becoming suitable habitat. Afterward, we calculated the centroid of the suitable habitat in the current and future scenarios using the SDM toolbox, a Python-based GIS software10.7, to evaluate the trend of change in the suitable area [72].

3. Results

3.1. Model Performance and Potential Distribution of Current Wintering Habitat

The AUC value of the model showed that the MaxEnt model performed well (AUC = 0.985) (Figure 2). This finding indicates that the MaxEnt model exhibited a good performance that can be used to predict the potential wintering habitat for the Eurasian Spoonbill. The current suitable habitat total area was 106,621 km2. The highly suitable area, moderately suitable area, and poorly suitable area were 22,197, 30,185, and 54,239 km2, representing 21%, 28%, and 51% of the suitable habitat area, respectively. The suitable habitat of white spoonbills is wetlands, including natural wetlands such as offshore and coastal areas, rivers, lakes, and marshes, as well as artificial wetlands such as reservoirs, rice paddies, salt farms, and fish farms.
The suitable habitats are distributed in 21 provinces (autonomous regions and municipalities) in China, mainly located in Hubei, Hunan, Jiangxi, and Anhui provinces; a few are scattered in Jiangsu, Henan, Shaanxi, Shandong, and Xinjiang provinces; very few are scattered in Yunnan, Guangdong, Zhejiang, Shanghai, Chongqing, and Sichuan provinces (Figure 3).

3.2. Major Environmental Factors Affecting the Distribution of Wintering Spoonbills

The MaxEnt model’s internal jackknife test of factor importance showed that water_distance (52.2% contribution rate), precipitation of the driest quarter (Bio17, 15.3% contribution rate), altitude (Alt, 10.0% contribution rate), and mean temperature of the driest quarter (Bio9, 8.0% contribution rate) made the greatest contribution to the distribution model for the Eurasian Spoonbill. The cumulative contributions of these factors reached values as high as 85.5% (Table 2).
Figure 4 showed that the suitable altitude ranged from −100 m to 100 m. The highest suitability occurred when the area was near water. For hydrothermal conditions, the suitable range and optimal value of the precipitation of the driest quarter (Bio17) were from 110 mm to 190 mm. The suitable range of the mean temperature of the driest quarter (Bio9) was from 4 °C to 10 °C.

3.3. Future Changes in Suitable Habitat Area

The MaxEnt model predicted a dramatic range shift in the distribution of the suitable wintering habitat for Eurasian Spoonbill under four climate scenarios. The suitable distribution would expand to the northwest, while the suitable wintering habitat in the south would decrease. Compared with the current distribution, the suitable areas of northeastern Hunan, southeastern Hubei, Jiangxi, Jiangsu, and Anhui provinces would be lost. In contrast, the suitable areas of Shandong, Tianjin, Shaanxi, and the border with Henan Shanxi provinces greatly increase (Figure 5 and Figure 6).
The suitability of the Eurasian Spoonbill distribution range would improve with climate change (Table 3). Especially in the RCP6.0-2070s, the suitable wintering habitat was expected to increase by 77.28% (Table 4). In general, the suitable area showed a trend of increasing area and expanding northeast in the future.

3.4. The Shift of the Core Suitable Habitat Center under Different Climate Scenarios

The current habitat centroid of the Eurasian Spoonbill is at 114°43′ E and 31°12′ N in Hubei Province. The centroid of suitable habitat shifts to 114°48′ E and 32°43′ N in southeast Henan under RCP2.6-2050s. Moreover, it shifts to 113°59′ E and 33°17′ N in south central Henan under RCP2.6-2070s. Under RCP4.5-2050s, the centroid of suitable habitat is at 113°36′ E and 32°56′ N in southwest Henan. However, the centroid of suitable habitat shifts to the southeast (114°58′ E, 32°26′ N) under RCP4.5-2070s. The Eurasian Spoonbill migrates from southeast Henan (114°52′ E, 32°24′ N) under RCP6.0-2050s to west central Henan (113°32′ E, 33°14′ N) in the 2070s. The centroid of suitable habitat is at 114°6′ E, 33°53′ N in central Henan under RCP8.5-2050s. Finally, it shifts to northwest Henan (113°16′, E 34°39′ N) in the 2070s. Overall, the distribution center of the suitable habitat of the Eurasian Spoonbill generally moves from northeast Hubei to Henan from the present to the future. However, there was no significant correlation of the centroid shift observed between the different concentrations of greenhouse gas emissions (Figure 7).

4. Discussion

Our study provides the first look at the current and future species potential wintering distribution of the Eurasian Spoonbill in China. It shows that the suitable range changes dramatically under future climate scenarios, where the suitable habitat area would increase and shift to the northwest as a whole.
In this study, we found that the distance to water represents the environmental variable that contributes the most to the suitable wintering habitat and spatial distribution of the Eurasian Spoonbill. However, its effect on the probability of occurrence of the species is nearly constant. Only places close to water are suitable for the Eurasian Spoonbill. This finding is not very surprising because the Eurasian Spoonbill is a waterbird living and feeding in wetlands during winter [49,73]. The Eurasian Spoonbill is a specialist waterbird in shallow wetlands because it can only feed in water not deeper than 40 cm [74]. Furthermore, our results indicated that the suitable range of the precipitation of the driest quarter is from 80 mm to 230 mm. Thus, the precipitation of the driest quarter may limit the habitat selection of the Eurasian Spoonbill. Moreover, the mean temperature of the driest quarter may determine whether the temperature is suitable for the wintering Eurasian Spoonbill. For example, Poyang Lake and Dongting Lake are the main wintering areas for the Eurasian Spoonbill. The average temperature in January of Poyang Lake is 4.5 °C [75]. The average January temperature of Dongting Lake is 4.1 °C to 4.5 °C [76]. This finding is consistent with our results. Furthermore, this species prefers lower altitudes; this finding is also consistent with the results from a previous study [77]. It lives in wetlands below 100 m above sea level, and any significant change in temperature and precipitation may affect the potential distributions of the species, as shown in other research for other avian geographical distributions [78].
Climate change is one of the main factors affecting the distribution of birds [79,80]. The WWF (World Wide Fund for Nature) reported that global climate change threatens the survival of most birds [80,81]. Biologists need to predict the effect of climate change on species distribution, which has important implications for species conservation [2]. In our study, under a full dispersal assumption, we found that compared with the current situation, the average suitable wintering habitat area showed an increasing trend in the north. However, current suitable habitats will be lost, and some areas will experience a reduction in Eurasian Spoonbill habitat by up to 37.77% by 2070 These results are consistent with other studies; for example, Wu et al. reported that during the period from 1951 to 2010, more than 50% of the 14 species of birds in China migrated to the north, whereas Li et al. found that more than 40% of 108 species of birds in China will migrate approximately 90 km to the north by 2080 [82,83].
Birds are influenced by changes in temperature and precipitation [80]. Studies have shown that the average annual precipitation in China will increase (0%–20%), particularly in northern China and northwest China [84], and wetlands will provide more food and habitat for waterbirds [30]. In addition, climate warming will change the distribution pattern of birds and expand their distribution range [85]. This is consistent with our research results. At the same time, some studies have indicated that suitable habitats for some waterbirds decrease as the climate warms [86,87,88]. In extreme cases, the temperature in China is predicted to rise in the range of 2.5–5 °C by the end of the 21st century [89]. This scenario is likely to be one of the main contributors to the current loss of suitable habitat. We quantified the changes in the suitable wintering habitat of the Eurasian Spoonbill under four climate scenarios. We found that the area increases in comparison to the current distribution in the 2050s and 2070s, except in RCP4.5-2070s. No certain regular change is determined in the habitat area, probably because the relationship between temperature and precipitation in different climate scenarios is complex and nonlinear; this finding is similar to the findings of other studies [30,90,91], and the intrinsic reasons deserve further study in the future.
Our results indicated that some areas suitable for the Eurasian Spoonbill wintering at present (Jiangxi, Hunan Zhejiang, and Jiangsu) may not be suitable in the future because of climatic factors, such as dryness or high temperature. At the same time, in the future there will be some new suitable areas in the north and northwest (Shaanxi, north Shandong, Tianjin, the junction of Shaanxi, Shanxi, and Henan). Hence, climate change is an important factor affecting the overwintering distribution of birds [92,93]. When protecting the Eurasian Spoonbill and other waterbirds, we should pay attention to these areas and the phenomenon.
A large proportion of the population of the wintering Eurasian Spoonbill is mainly concentrated in the middle and lower reaches of the Yangtze River [94]. However, only some habitats are located in the protected area. These areas are under great pressure from human activities and habitat destruction. For instance, the wetland landscape pattern and ecological functions of Poyang Lake and Dongting Lake changed because of the expansion of water conservancy projects, agriculture, and aquaculture [95,96]. These changes may cause the Eurasian Spoonbill to move to smaller areas. Establishing protected areas is one of the effective means of protecting biodiversity and habitats. Therefore, some efforts, such as protecting the existing protected areas and establishing new reserves, should be undertaken in the areas identified as suitable habitats for Eurasian Spoonbills. At the same time, long-term surveys and monitoring in these areas can be considered in order to thoroughly understand the population status.
Climate change is an important factor, and bird species have responded to climate change [97]. It has also been shown that if the impact of climate change is not considered when establishing protected areas, some species may move out in the future [98]. Moreover, conservationists believe that it is very meaningful to consider the impact of short-term or long-term extreme weather when protecting and managing species in the context of climate change [99]. Therefore, we suggest integrating the current situation and future climate change effects when designing nature reserves and making management plans for the Eurasian Spoonbill or other waterbirds throughout China.
Many factors, such as interspecific relationships (competition and predation), land-use change, and other limited factors may affect the distribution of species. It is hard to model all impact factors. Although the MaxEnt model is widely used, the prediction results of this single model still have errors. Hence, in future research, we suggest that more factors affecting the distribution of species should be considered comprehensively, and the best model should be selected through comparative analysis of multiple models.

5. Conclusions

In this study, we predicted the distribution pattern and dynamic changes of the wintering Eurasian Spoonbill in response to climate change by using the MaxEnt model. Our results indicated that water distance, precipitation of the driest quarter, altitude, and mean temperature of the driest quarter were important factors shaping the distribution of the Eurasian Spoonbill. At present, suitable wintering habitats are mainly distributed in the middle and lower reaches of the Yangtze River in China. With climate change, the suitable wintering area showed an increasing trend in the future, and its suitable distribution would shift to the northwest as a whole. This suggests that species distribution could be affected by climate change. We suggest that the current situation and future impacts of climate change should be taken into account in nature reserve design and management planning for the Eurasian Spoonbill and other endangered waterbirds throughout China.

Author Contributions

Conceptualization, A.F. and X.L.; data curation, A.F. and E.G.; formal analysis, A.F.; funding acquisition, X.T. and Z.L.; investigation, X.T., Z.L. and E.G.; methodology, A.F.; writing—original draft, A.F.; supervision, X.L.; validation, X.L. and F.H.; visualization, A.F., J.W. and Z.Z.; writing—review and editing, A.F. and X.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Waterbird Simultaneous Survey of China in 2016 (20 April 2017) and the Second National Survey on Terrestrial Wildlife Resources in China (21 April 2017).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

This work was supported by the National Waterbird Simultaneous Survey of China in 2016 and the Second National Survey on Terrestrial Wildlife Resources in China. We thank the Academy of Inventory and Planning, National Forestry and Grassland Administration for supporting us in using the second national terrestrial wildlife resources survey winter waterbird simultaneous survey of China in 2016 data for our study. Furthermore, we are grateful to all the staff who conducted the investigation. We also would like to thank Majda and Chao Zhang for their valuable comments and linguistic assistance on the manuscript.

Conflicts of Interest

No conflict of interest exits in the submission of this manuscript, and the manuscript is approved by all authors for publication.

References

  1. Skea, J.; Sjukla, P.; Reisinger, A.; Slade, R.; Pathak, M.; Some, P.; Vyas, P.; Fradera, R.; Belkacemi, M.; Hasija, A.; et al. IPCC Climate Change 2022: Mitigation of Climate Change, Working Group III; Cambridge University Press: Cambridge, UK, 2022. [Google Scholar]
  2. Araújo, M.B.; Rahbek, C. How Does Climate Change Affect Biodiversity? Science 2006, 313, 1396–1397. [Google Scholar] [CrossRef]
  3. Thomas, C.D.; Cameron, A.; Green, R.E.; Bakkenes, M.; Beaumont, L.J.; Collingham, T.C.; Erasmus, B.F.N.; de Siqueira, M.F.; Grainger, A.; Hannah, L.; et al. Extinction risk from climate change. Nature 2004, 427, 145–148. [Google Scholar] [CrossRef] [Green Version]
  4. Hickling, R.; Roy, D.B.; Hill, J.K.; Fox, R.; Thomas, C.D. The distributions of a wide range of taxonomic groups are expanding polewards. Glob. Chang. Biol. 2006, 12, 450–455. [Google Scholar] [CrossRef]
  5. Pecl, G.T.; Araujo, M.B.; Bell, J.D.; Blanchard, J.; Bonebrake, T.C.; Chen, I.C.; Clark, T.D.; Colwell, R.K.; Danielsen, F.; Evengard, B.; et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science 2017, 355, 6332. [Google Scholar] [CrossRef] [PubMed]
  6. Steen, V.A.; Skagen, S.K.; Melcher, C.P. Implications of Climate Change for Wetland Dependent Birds in the Prairie Pothole Region. Wetlands 2016, 36 (Suppl. 2), 445–459. [Google Scholar] [CrossRef]
  7. Valerie, S.; Skagen, S.K.; Noon, B.R.; Mark, B.R. Vulnerability of breeding waterbirds to climate change in the Prairie Pothole Region, U.S.A. PLoS ONE 2014, 9, e96747. [Google Scholar] [CrossRef] [Green Version]
  8. Borger, L.; Nudds, T.D. Fire, humans, and climate: Modeling distribution dynamics of boreal forest waterbirds. Ecol. Appl. 2014, 24, 121–141. [Google Scholar] [CrossRef]
  9. Sparks, T.; Crick, H.; Elkins, N.; Moss, R.; Moss, S.; Mylne, K. Birds, weather and climate. Weather 2002, 57, 399–410. [Google Scholar] [CrossRef]
  10. Gregory, R.D.; Willis, S.G.; Jiguet, F.; Voříšek, P.; Klvaňová, A.; Strien, A.V.; Huntley, B.; Collingham, Y.C.; Couvet, D.; Green, R.E. An indicator of the impact of climatic change on European bird populations. PLoS ONE 2009, 4, e4678. [Google Scholar] [CrossRef] [Green Version]
  11. Niven, D.K.; Butcher, G.S.; Bancroft, G.T. Birds and Climate Change: Ecological Disruption in Motion; National Audubon Society: New York, NY, USA, 2009. [Google Scholar]
  12. Chen, I.C.; Hill, J.K.; Ohlemüller, R.; Roy, D.B.; Thomas, C.D. Rapid range shifts of species associated with high levels of climate warming. Science 2011, 333, 1024–1026. [Google Scholar] [CrossRef]
  13. Maclean, I.M.D.; Austin, G.E.; Rehfisch, M.M.; Blew, J.; Crowe, O.; Delany, S.; Devos, K.; Deceuninck, B.; Günther, K.; Laursen, K.; et al. Climate change causes rapid changes in the distribution and site abundance of birds in winter. Glob. Chang. Biol. 2008, 14, 2489–2500. [Google Scholar] [CrossRef]
  14. Hu, J.; Hu, H.; Jiang, Z. The impacts of climate change on the wintering distribution of an endangered migratory bird. Oecologia 2010, 164, 555–565. [Google Scholar] [CrossRef] [PubMed]
  15. Papeş, M.; Gaubert, P. Modelling ecological niches from lownumber of occurrences: Assessment of the conservation status of poorly known viverrids (Mammalia, Carnivora) across two continents. Divers. Distrib. 2007, 13, 890–902. [Google Scholar] [CrossRef]
  16. Elith, J.; Graham, C.H.; Anderson, R.P.; Dudı´k, M.; Ferrier, S.; Guisan, A.; Hijmans, R.J.; Huettmann, F.; Leathwick, J.R.; Lehmann, A.; et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 2006, 29, 129–151. [Google Scholar] [CrossRef] [Green Version]
  17. Wiensa, J.A.; Stralberg, D.; Jongsomjit, D.; Howell, C.A.; Snyder, M.A. Niches, models, and climate change: Assessing the assumptions and uncertainties. Proc. Natl. Acad. Sci. USA 2009, 106 (Suppl. 2), 19729–19736. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Guisan, A.; Thuiller, W. Predicting species distribution: Offering more than simple habitat models. Ecol. Lett. 2005, 8, 993–1009. [Google Scholar] [CrossRef] [PubMed]
  19. Williams, P.; Hannah, L.; Andelman, S.; Midgley, G.; Araújo, M.; Hughes, G.; Manne, L.; Martinez-Meyer, E.; Pearson, R. Planning for Climate Change: Ide-ntifying Minimum Dispersal Corridors for the Cape Proteaceae. Conserv. Biol. 2010, 19, 1063–1074. [Google Scholar] [CrossRef]
  20. Pyke, C.R.; Andelman, S.J.; Midgley, G. Identifying priority areas for bioclimatc representation under climate change: A case study for Proteaceae in the Cape Floristic Region, South Africa. Biol. Conserv. 2005, 125, 1–9. [Google Scholar] [CrossRef]
  21. Peterson, A.T.; Ortega-Huerta, M.A.; Bartley, J.; Sanchez-Cordero, V.; Soberon, J.; Buddemeier, R.H.; Stockwell, D.R.B. Future projections for Mexican faunas under global climate change scenarios. Nature 2002, 416, 626–629. [Google Scholar] [CrossRef]
  22. Hijmans, R.J.; Graham, C.H. The ability of climate envelope models to predict the effect of climate change on species distributions. Glob. Chang. Biol. 2006, 12, 2272–2281. [Google Scholar] [CrossRef]
  23. Elith, J.; Leathwick, J.R. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 2009, 40, 677–697. [Google Scholar] [CrossRef]
  24. Elith, J.; Graham, C.H. Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models. Ecography 2009, 32, 66–77. [Google Scholar] [CrossRef]
  25. Busby, J.R. BIOCLIM: A bioclimate analysis and prediction system. Plant Prot. Q. 1991, 6, 8–9. [Google Scholar]
  26. Elith, J.; Phillips, S.J.; Hastie, T.; Dudík, M.; Chee, T.E.; Yates, C.J. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 2011, 17, 43–57. [Google Scholar] [CrossRef]
  27. Phillips, S.J.; Anderson, R.P.; Schapire, R.E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 2006, 190, 231–259. [Google Scholar] [CrossRef] [Green Version]
  28. Pearson, R.G.; Raxworthy, C.J.; Nakamura, M.; Peterson, A.T. Predicting species distri-butions from small numbers of occurrence records: A test case using cryptic geckos in madagascar. J. Biogeogr. 2007, 34, 102–117. [Google Scholar] [CrossRef]
  29. Wintle, B.A.; Mccarthy, M.A.; Parris, K.M.; Burgman, M.A. Precisionand bias of methods for estimating point survey detectionprobabilities. Ecol. Appl. 2004, 14, 703–712. [Google Scholar] [CrossRef]
  30. Li, M.; Zhou, H.; Bai, J.; Zhang, T.; Liu, Y.; Ran, J. Distribution of Breeding Population and Predicting Future Habitat under Climate Change of Black-Necked Crane (Grus nigricollis Przevalski, 1876) in Shaluli Mountains. Animals 2022, 12, 2594. [Google Scholar] [CrossRef]
  31. Almalki, M.; AlRashidi, M.; O’Connell, M.; Shobrak, M.; Székely, T. Modelling the distribution of wetland birds on the Red Sea coast in the Kingdom of Saudi Arabia. Appl. Ecol. Environ. Res. 2015, 13, 67–84. [Google Scholar] [CrossRef]
  32. Bertrand, R.; Perez, V.; Gégout, G.C. Disregarding the edaphic dimension in species distribution models leads to the omission of crucial spatial information under climate change: The case of Quercus pubescens in France. Glob. Chang. Biol. 2012, 18, 2648–2660. [Google Scholar] [CrossRef]
  33. He, P.; Li, J.; Li, Y.; Xu, N.; Gao, Y.; Guo, L.; Huo, T.; Peng, C.; Meng, F. Habitat protection and planning for three Ephedra using the MaxEnt and Marxan models. Ecol. Indic. 2021, 133, 108399. [Google Scholar] [CrossRef]
  34. Mafuwe, K.; Broadley, S.; Moyo, S. Use of maximum entropy (Maxent) niche modelling to predict the occurrence of threatened freshwater species in a biodiversity hotspot of Zimbabwe. Afr. J. Ecol. 2022, 60, 557–565. [Google Scholar] [CrossRef]
  35. The IUCN Red List of Threatened Species 2016. Available online: https://www.iucnredlist.org/species/22697555/155460986 (accessed on 20 November 2022).
  36. List of National Key Protected Wildlife in China. Available online: http://www.forestry.gov.cn/main/5461/20210205/122418860831352.html (accessed on 20 November 2022).
  37. Jiang, Z.; Jiang, J.; Wang, Y.; Zhang, E.; Zhang, Y.; Li, L.; Xie, F.; Cai, B.; Cao, L.; Zheng, G.; et al. Red List of China’s Vertebrates. Biodivers. Sci. 2016, 24, 500–551. [Google Scholar] [CrossRef] [Green Version]
  38. Overdijk, O.; Lok, T.; Olff, H.; Piersma, T. Home Range, Habitat Selection, and Foraging Rhythm in Mauritanian Spoonbills (Platalea leucorodia balsaci): A Satellite Tracking Study. Waterbirds 2013, 36, 277–286. [Google Scholar] [CrossRef] [Green Version]
  39. Song, C.; Xing, B.J.; Yu, H.C.; Chen, H.; Gao, Z.S.; Zhang, Y.X.; Cui, S.B. Study on brooding behavior of White Spoonbill in Qixinghe National Nature Reserve, Heilongjiang Province. Wetl. Sci. 2019, 17, 166–171. [Google Scholar] [CrossRef]
  40. Zheng, G.M. A Checklist on the Classification and Distribution of the Birds of the China, 3rd ed.; Science Press: Beijing, China, 2018; (In Chinese with English Abstract). [Google Scholar]
  41. Sanderson, F.J.; Donald, P.F.; Pain, D.J.; Burfield, I.J.; van Bommel, F.P.J. Long-term population declines in Afro-Palearctic migrant birds. Biol. Conserv. 2006, 131, 93–105. [Google Scholar] [CrossRef]
  42. Mantyka-Pringle, C.S.; Martin, T.G.; Rhodes, J.R. Interactions between climate and habitat loss effects on biodiversity: A systematic review and meta-analysis. Glob. Chang. Biol. 2012, 18, 1239–1252. [Google Scholar] [CrossRef] [Green Version]
  43. Bellisario, B. Conserving migration in a changing climate, a case study: The Eurasian Spoonbill, Platalea leucorodia leucorodia. Biol. Conserv. 2018, 217, 222–231. [Google Scholar] [CrossRef]
  44. Wu, C.S.; Xu, T. Observation on ecological habits of White Spoonbill. Wild Anim. 1983, 5, 5–7, (In Chinese with English Abstract). [Google Scholar] [CrossRef]
  45. Li, B.S.; Gao, Z.S.; Cui, S.B.; Zhang, Y.X.; Chen, H. Breeding season behavior ethogram and PAE coding system of Eurasian Spoonbill (Platalea leucorodia). Chin. Agric. Sci. Bull. 2014, 30, 259–265, (In Chinese with English Abstract). [Google Scholar]
  46. Liu, J.S.; Wang, D.H.; Sun, R.Y. Growth and Development of Homeothermy in Nestlings of Eurasian Spoonbill (Platalea eucorodia). Zool. Res. 2003, 24, 249–253, (In Chinese with English Abstract). [Google Scholar] [CrossRef]
  47. Garaita, R.; Arizaga, J. The benefits of a constructed lagoon for the conservation of Eurasian Spoonbills (Platalea leucorodia) in a tidal marsh. J. Nat. Conserv. 2015, 25, 35–41. [Google Scholar] [CrossRef]
  48. Li, J.; Li, Y.K.; Miu, L.J.; Xie, G.Y.; Yuan, F.K.; Huang, Y.; Xu, P. The effect of cli-mate in wintering area on the population size of the Eurasian Spoonbill (Platalea leucorodia) in the Poyang Lake National Nature Reserve, China. Acta Ecol. Sin. 2014, 34, 5522–5529, (In Chinese with English Abstract). [Google Scholar] [CrossRef] [Green Version]
  49. Hu, W.H.; Yu, G.Q.; He, S.C.; Liu, Z.Y.; Xu, W.B. Overwintering ecology of white Spoonbill. Territ. Nat. Resour. Study 1999, 1, 69–71, (In Chinese with English Abstract). [Google Scholar]
  50. Gao, E.H.; (Academy of Inventory and Planning, National Forestry and Grassland Administration, Beijing, China); Wang, Z.C.; (Academy of Inventory and Planning, National Forestry and Grassland Administration, Beijing, China). Personal communication, Unpublished work. 2022.
  51. Geng, G.B. Take Efficient Efforts To Protect Endangered Wildlife. Green China 2016, 3, 24–27, (In Chinese with English Abstract). [Google Scholar]
  52. Sun, L.; Xu, H.G.; Wu, J.; Lei, J.C.; Cui, P. Impact of Climate Change on Distribution of Breeding Places of White-Naped Crane in China. J. Ecol. Rural. Environ. 2014, 30, 595–600, (In Chinese with English Abstract). [Google Scholar]
  53. Son, S.J.; Kang, J.H.; Lee, S.K.; Kim, I.K.; Yoo, J.C. Breeding and wintering home ranges of the black-faced spoonbill Platalea minor. J. Asia-Pac. Biodivers. 2020, 13, 7–12. [Google Scholar] [CrossRef]
  54. Zhu, B.R. Research on Breeding Habitat of Eurasian Spoonbill (Platalea leucorodia) Based on NDVI. Master’s Thesis, Northeast Forestry University, Heilongjiang, China, 2014. (In Chinese with English Abstract). [Google Scholar]
  55. Deng, Q.H.; Niu, E.W. Research on the Breeding Ecology of Platalea leucorodia in Dalaihu Ulan Noel Natural Reserve. J. Anhui Agric. Sci. 2012, 40, 6559–6560, (In Chinese with English Abstract). [Google Scholar] [CrossRef]
  56. Jin, X.; Ren, X.T.; Peng, H.B.; Ma, Q.; Tang, C.D.; Niu, D.L.; Ma, Z.J. Habitat use and factors affecting distribution of wintering waterbirds in the wetland restoration areaat Chongming Dongtan. Chin. J. Zool. 2013, 48, 686–692, (In Chinese with English Abstract). [Google Scholar] [CrossRef]
  57. Cui, S.B. The preliminary study on numerical distribution and breeding behaboirs of spoonbill Platalea leucorodia in Qixinghe Nation Natural Reserve of Heilongjiang province. Heilongjiang Sci. 2017, 8, 57–59, (In Chinese with English Abstract). [Google Scholar] [CrossRef]
  58. Fick, S.E.; Hijmans, R.J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 2017, 37, 4302–4315. [Google Scholar] [CrossRef]
  59. Hijmans, R.J.; Cameron, S.E.; Parra, J.L.; Jones, P.G.; Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 2005, 25, 1965–1978. [Google Scholar] [CrossRef]
  60. Wu, T.; Li, W.; Ji, J.; Xin, X.; Li, L.; Wang, Z.; Zhang, Y.; Li, J.; Zhang, F.; Wei, M.; et al. Global carbon budgets simulated by the Beijing Climate Center C-limate System Model for the last century. J. Geophys. Res. Atmos. 2013, 118, 4326–4347. [Google Scholar] [CrossRef]
  61. Xin, X.G.; Wu, T.W.; Li, J.L.; Wang, Z.Z.; Li, W.P.; Wu, F.H. How well does BCC_CSM1.1 reproduce the 20th Century Climate Change over China? Atmos. Ocean. Sci. Lett. 2013, 6, 21–26. [Google Scholar] [CrossRef] [Green Version]
  62. Stocker, T.F.; Qin, D.; Plattner, G.-K.; Tignor, M.; Allen, S.K.; Boschung, J.; Nauels, A.; Xia, Y.; Bex, V.; Midgley, P.M. Climate Change 2013: The Physical Science Basis; Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: New York, NY, USA, 2013; Volume 1535. [Google Scholar]
  63. Resource and Environment Science and Data Center. Spatial Distribution of 1 Million Vegetation Types in China. Available online: https://www.resdc.cn/data.aspx?DATAID=122 (accessed on 6 August 2022).
  64. Resource and Environment Science and Data Center. Multi-Period Land Use Remote Sensing Monitoring Data in China (CNLUCC). Available online: http://www.resdc.cn/DOI (accessed on 6 August 2022).
  65. National Catalogue Service for Geographic Information. 1 Million Public Version of Basic Geographic Information Data. Available online: http://www.webmap.cn/ (accessed on 6 August 2022).
  66. Thuiller, W.; Broennimann, O.; Hughes, G.; Alkemade, J.R.M.; Midgley, G.F.; Corsi, F. Vulnerability of African mammals to anthropogenic climate change under conservative land transformation assumptions. Glob. Chang. Biol. 2006, 12, 424–440. [Google Scholar] [CrossRef]
  67. Stanton, J.C.; Pearson, R.G.; Horning, N.; Ersts, P.; Akcakaya, H.R. Combining static and dynamic variables in species distribution models under climate change. Methods Ecol. Evol. 2012, 3, 349–357. [Google Scholar] [CrossRef]
  68. Lemke, D.; Hulme, P.E.; Brown, J.A.; Tadesse, W. Distribution modelling of Japanese honeysuckle (Lonicera japonica) invasion in the Cumberland Plateau and Mountain Region, USA. For. Ecol. Manag. 2011, 262, 139–149. [Google Scholar] [CrossRef]
  69. Yang, X.Q.; Kushwaha, S.P.S.; Saran, S.; Xu, J.C.; Roy, P.S. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills. Ecol. Eng. 2013, 51, 83–87. [Google Scholar] [CrossRef]
  70. Baldwin, R.A. Use of Maximum Entropy Modeling in Wildlife Research. Entropy 2009, 11, 854–866. [Google Scholar] [CrossRef]
  71. Raes, N.; Steege, H.T. A null model for significance testing of presence only species distribution models. Ecography 2007, 30, 727–736. [Google Scholar] [CrossRef]
  72. Brown, J.L. SDMtoolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 2014, 5, 694–700. [Google Scholar] [CrossRef]
  73. Wang, Y.X.; Xie, Y.B.; Batbayar, N.; Zhu, B.G.; Dong, S.B.; Barma, A. Discussion of existing protection for three waterbirds’ habitats in the Yellow River basin nature reserves, based on satellite tracking. Biodivers. Sci. 2020, 28, 1483–1495, (In Chinese with English Abstract). [Google Scholar] [CrossRef]
  74. Sullender, B.K.; Barzen, J.; Silbernagel, J. Foraging success and habitat selection of the Eurasian Spoonbill (Platalea leucorodia) at Poyang Lake, China. Waterbirds 2016, 39, 356–364. [Google Scholar] [CrossRef]
  75. Liu, J.S.; Yang, X.Z.; Li, Y.F.; Han, Z.Y.; Wang, Y.J. Observation on the Breeding Habit of White Spoonbill and the Development of Its Nesting. Chin. J. Zool. 1997, 32, 249–253, (In Chinese with English Abstract). [Google Scholar]
  76. Guan, L.; Jing, L.; Lei, J.L.; Lei, J.C. Distribution of wintering waterbirds and assessment of their habitat in Dongting Lake, China. J. Beijing For. Univ. 2016, 38, 64–70, (In Chinese with English Abstract). [Google Scholar]
  77. Zhang, Y.G. A Comparative Study on the Habitats of the White Spoonbill (Platalea leucorodia) in Different Periods. Master’s Thesis, Northeast Forestry University, Heilongjiang, China, 2018. (In Chinese with English Abstract). [Google Scholar]
  78. Banda, L.B.; Tassie, N. Modeling the distribution of four-bird species under climate change in Ethiopia. Ethiop. J. Biol. Sci. 2018, 17, 1–17, (In Chinese with English Abstract). [Google Scholar]
  79. Cao, M.C.; Wu, J.; Wu, W.W.; Xu, H.G. The impact of climate change on birds: A review. Biodivers. Sci. 2012, 20, 108–115, (In Chinese with English Abstract). [Google Scholar] [CrossRef]
  80. Wormworth, J.; Mallon, D.K. Bird Species and Climate Change: A Summary of The Global Status Report, Austrialia, November 2006. Available online: https://wwfint.awsassets.panda.org/downloads/wwfsummaryfinal.pdf (accessed on 6 August 2022).
  81. Wormworth, J.; Mallon, D.K. Bird Species and Climate Change-The Global Status Report, Austrialia, August 2006. Available online: https://wwfint.awsassets.panda.org/downloads/birdsclimatereportfinal.pdf(accessed on 6 August 2022).
  82. Wu, J.G.; Shi, Y.J. Attribution index for changes in migratory bird distributions:The role of climate change over the past 50 years in China. Ecol. Inform. 2016, 31, 147–155. [Google Scholar] [CrossRef]
  83. Li, X.Y.; Clinton, N.; Si, Y.L.; Liao, J.S.; Liang, l.; Gong, P. Projected impacts of climate change on protected birds and nature reserves in China. Sci. Bull. 2015, 19, 1644–1653. [Google Scholar] [CrossRef] [Green Version]
  84. Liu, L.; Guan, L.; Zhao, H.; Huang, Y.; Hu, J. Modeling habitat suitability of Houttuynia cordata Thunb (Ceercao) using MaxEnt under climate change in China. Ecol. Inform. 2021, 63, 101324. [Google Scholar] [CrossRef]
  85. Tingley, M.W.; Monahan, W.B.; Beissinger, S.R.; Moritz, C. Birds track their Grinnellian niche through a century of climate change. Proc. Natl. Acad. Sci. USA 2009, 106 (Suppl. 2), 19637–19643. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  86. Beale, C.M.; Baker, N.E.; Brewer, M.J.; Lennon, J.J. Protected area networks and savannah bird biodiversity in the face of climate change and land degradation. Ecol. Lett. 2013, 16, 1061–1068. [Google Scholar] [CrossRef] [PubMed]
  87. Steen, V.; Powell, A.N. Potential effects of climate change on the distribution of waterbirds in the prairie pothole region, USA. Waterbirds 2012, 35, 217–229. [Google Scholar] [CrossRef]
  88. La Sorte, F.A.; Thompson, F.R., III. Poleward shifts in winter ranges of North American birds. Ecology 2007, 88, 1803–1812. [Google Scholar] [CrossRef]
  89. Gao, X.J.; Wang, M.L.; Filippo, G. Climate Change over China in the 21st Century as Simulated by BCC_CSM1.1-RegCM4.0. Atmos. Ocean. Sci. Lett. 2013, 6, 381–386. [Google Scholar]
  90. Bai, X.H.; Wang, W.J.; Jiang, W.G.; Shi, H.D.; Chen, k.; Chen, M. Simulation of Potential Suitable Distribution of Endangered Waterfowl and Its Gap Analysis of National Nature Reserves in Beijing-Tianjin-Hebei Region under the background of Climate Change and analysis of protection gap. Scenar. Res. Environ. Sci. 2019, 32, 1001–1011. [Google Scholar] [CrossRef]
  91. Yuan, Y.J.; Zou, X.; Shi, F.; Gao, X.; Hu, L.; Zhang, Z.Y. Bird Habitat Suitability Distribution in Dongting Lake Basin Under Different Climate Change Scenarios. J. Hydroecology 2022, 4, 56–62, (In Chinese with English Abstract). [Google Scholar] [CrossRef]
  92. Princé, K.; Zuckerberg, B. Climate change in our backyards: The reshuffling of North America’s winter bird communities. Glob. Chang. Biol. 2015, 21, 572–585. [Google Scholar] [CrossRef]
  93. Lehikoinen, A.; Jaatinen, K.; Vähätalo, A.V.; Clausen, P.; Crowe, O.; Deceuninck, B.; Hearn, R.; Holt, C.A.; Hornman, M.; Keller, V.; et al. Rapid climate driven shifts i-n wintering distributions of three common waterbird species. Glob. Chang. Biol. 2013, 19, 2071–2081. [Google Scholar] [CrossRef]
  94. Li, G.F.; Yin, W.P.; Li, X.M. The Research Status and Forecast of Threskiornithidae in China. Chin. J. Wildl. 2016, 37, 234–238, (In Chinese with English Abstract). [Google Scholar] [CrossRef]
  95. Lu, H.W.; Hu, W.M.; Yu, J.Y.; Zeng, W.; Song, Y.B. Study on the influence of Ecological Poplar Withdrawal on the la-ndscape pattern of Dongting Lake wetland. J. Nanjing For. Univ. (Nat. Sci. Ed.) 2020, 44, 171–178, (In Chinese with English Abstract). [Google Scholar] [CrossRef]
  96. Liu, X.B.; Han, Z.; Wang, S.Y.; Peng, W.Q. A research thinking of Poyang Lake wetland from the perspective of Yang-tze River protection. J. China Inst. Water Resour. Hydropower Res. 2021, 19, 201–209, (In Chinese with English Abstract). [Google Scholar] [CrossRef]
  97. Case, M.J.; Lawler, J.J.; Tomasevic, J.A. Relative sensitivity to climate change of species in northwestern North America. Biol. Conserv. 2015, 187, 127–133. [Google Scholar] [CrossRef]
  98. Araújo, M.B.; Cabeza, M.; Thuiller, W.; Hannah, L.; Williams, P.H. Would climate change drive species out of reserves? An assessment of existing reserve-selection methods. Glob. Chang. Biol. 2004, 10, 1618–1626. [Google Scholar] [CrossRef]
  99. Lawler, J.; Watson, J.; Game, E. Conservation in the face of climate change: Recent developments. F1000Research 2015, 4, 1158. [Google Scholar] [CrossRef] [PubMed] [Green Version]
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Figure 1. Eurasian Spoonbill occurrence sites in the National Waterbird Simultaneous Survey of China in 2016.
Figure 1. Eurasian Spoonbill occurrence sites in the National Waterbird Simultaneous Survey of China in 2016.
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Figure 2. ROC curve and AUC value (note: ROC represents receiver operating characteristic; AUC represents the area under the ROC curve).
Figure 2. ROC curve and AUC value (note: ROC represents receiver operating characteristic; AUC represents the area under the ROC curve).
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Figure 3. The current potential distribution of suitable areas for the Eurasian Spoonbill.
Figure 3. The current potential distribution of suitable areas for the Eurasian Spoonbill.
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Figure 4. Response curves of probability of presence (based on the logistic output of the MaxEnt model) for the Eurasian Spoonbill (vertical axis) to Distance to water (a), Precipitation of the driest quarter (b), Altitude (c), and Mean temperature of the driest quarter (d); the curves are shown as means (red lines) with standard deviation (SD, blue buffers).
Figure 4. Response curves of probability of presence (based on the logistic output of the MaxEnt model) for the Eurasian Spoonbill (vertical axis) to Distance to water (a), Precipitation of the driest quarter (b), Altitude (c), and Mean temperature of the driest quarter (d); the curves are shown as means (red lines) with standard deviation (SD, blue buffers).
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Figure 5. Potential wintering habitat of Eurasian Spoonbill under different climate change scenarios. (a) Climate scenario RCP2.6 in 2050; (b) Climate scenario RCP2.6 in 2070; (c) Climate scenario RCP4.5 in 2050; (d) Climate scenario RCP4.5 in 2070; (e) Climate scenario RCP6.0 in 2050; (f) Climate scenario RCP6.0 in 2070; (g) Climate scenario RCP8.5 in 2050; (h) Climate scenario RCP8.5 in 2070.
Figure 5. Potential wintering habitat of Eurasian Spoonbill under different climate change scenarios. (a) Climate scenario RCP2.6 in 2050; (b) Climate scenario RCP2.6 in 2070; (c) Climate scenario RCP4.5 in 2050; (d) Climate scenario RCP4.5 in 2070; (e) Climate scenario RCP6.0 in 2050; (f) Climate scenario RCP6.0 in 2070; (g) Climate scenario RCP8.5 in 2050; (h) Climate scenario RCP8.5 in 2070.
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Figure 6. Spatial shifts for the Eurasian Spoonbill under different climate change scenarios. (a) Climate scenario RCP2.6 in 2050; (b) Climate scenario RCP2.6 in 2070; (c) Climate scenario RCP4.5 in 2050; (d) Climate scenario RCP4.5 in 2070; (e) Climate scenario RCP6.0 in 2050; (f) Climate scenario RCP6.0 in 2070; (g) Climate scenario RCP8.5 in 2050; (h) Climate scenario RCP8.5 in 2070.
Figure 6. Spatial shifts for the Eurasian Spoonbill under different climate change scenarios. (a) Climate scenario RCP2.6 in 2050; (b) Climate scenario RCP2.6 in 2070; (c) Climate scenario RCP4.5 in 2050; (d) Climate scenario RCP4.5 in 2070; (e) Climate scenario RCP6.0 in 2050; (f) Climate scenario RCP6.0 in 2070; (g) Climate scenario RCP8.5 in 2050; (h) Climate scenario RCP8.5 in 2070.
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Figure 7. The centroid distributional shifts under different climate scenarios for the Eurasian Spoonbill.
Figure 7. The centroid distributional shifts under different climate scenarios for the Eurasian Spoonbill.
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Table
Table 1. Environmental variables used to drive the MaxEnt model in this study.
Table 1. Environmental variables used to drive the MaxEnt model in this study.
VariablesDescriptionUnit
Bio2Monthly mean diurnal temperature range°C × 10
Bio3IsothermalityDimensionless
Bio6Min. temperature of the coldest month°C × 10
Bio9Mean temperature of the driest quarter°C × 10
Bio10Mean temperature of the warmest quarter°C × 10
Bio15CV of precipitationDimensionless
Bio17Precipitation of the driest quartermm
AltAltitudem
VegVegetation typeDimensionless
LUCCLand-use typeDimensionless
Water_distanceDistance to waterm
Road_distanceDistance to roadm
Village_distanceDistance to villagem
Table
Table 2. Relative contributions of the environmental variables to the MaxEnt model.
Table 2. Relative contributions of the environmental variables to the MaxEnt model.
VariableContribution Rate
(%)		VariableContribution Rate
(%)
Water_distance52.2CV of precipitation1.8
Precipitation of the driest quarter15.3Land-use0.7
Altitude10Vegetation type0.5
Mean temperature of the driest quarter8Village_distance0.3
Min. temperature of the coldest month6.8Road_distance0.1
Mean temperature of the warmest quarter2.5Isothermality 0.1
Monthly mean diurnal temperature range1.8
Table
Table 3. Suitable distribution areas for Eurasian Spoonbill under different climate scenarios (note: RCP, representative concentration pathway. RCP2.6 represents the minimum greenhouse gas emission scenario, RCP4.5 represents the medium greenhouse gas emission scenario, RCP6.0 represents the high greenhouse gas emission scenario, RCP8.5 represents the highest greenhouse gas emission scenario).
Table 3. Suitable distribution areas for Eurasian Spoonbill under different climate scenarios (note: RCP, representative concentration pathway. RCP2.6 represents the minimum greenhouse gas emission scenario, RCP4.5 represents the medium greenhouse gas emission scenario, RCP6.0 represents the high greenhouse gas emission scenario, RCP8.5 represents the highest greenhouse gas emission scenario).
PeriodClimate ScenarioHighly
Suitable		Moderately
Suitable		Poorly
Suitable		Suitable Area
Area
km2		Percentage
%		Area
km2		Percentage
%		Area
km2		percentage
%		Area
km2
Current 22,1972130,1852854,23951106,621
2050sRCP2.622,6581839,7253262,19150124,574
RCP4.517,9391631,9662959,66955109,574
RCP6.023,4862129,4092659,73553112,630
RCP8.539,4912348,6112883,62549171,727
2070sRCP2.622,1211932,0632763,05354117,237
RCP4.519,0371832,6163153,15751104,810
RCP6.045,2432448,4332695,49950189,175
RCP8.520,5561731,5972572,13758124,290
Table
Table 4. Dynamics of changes in suitable wintering habitat area for the Eurasian Spoonbill under different climate scenarios (note: RCP, representative concentration pathway. RCP2.6 represents the minimum greenhouse gas emission scenario, RCP4.5 represents the medium greenhouse gas emission scenario, RCP6.0 represents the high greenhouse gas emission scenario, RCP8.5 represents the highest greenhouse gas emission scenario).
Table 4. Dynamics of changes in suitable wintering habitat area for the Eurasian Spoonbill under different climate scenarios (note: RCP, representative concentration pathway. RCP2.6 represents the minimum greenhouse gas emission scenario, RCP4.5 represents the medium greenhouse gas emission scenario, RCP6.0 represents the high greenhouse gas emission scenario, RCP8.5 represents the highest greenhouse gas emission scenario).
Climate ScenarioYearArea (km2)Proportion of Area (%)
GainLossStableTotal
Change		GainLossStableTotal
Change
Representative concentration
Pathway (RCP)2.6		205040,32021,68085,73718,64037.8220.3380.4117.49
207046,76435,80571,56510,95943.8633.5867.1210.28
Representative concentration
Pathway (RCP)4.5		205031,61127,45979,735415229.6525.7574.783.9
207030,04130,75976,379−71828.1828.8571.64−0.67
Representative concentration
Pathway (RCP)6.0		205030,09023,80183,424628928.2222.3278.245.9
207091,603920098,67982,40385.918.6392.5577.28
Representative concentration
Pathway (RCP)8.5		205085,27419,82187,74165,45379.9818.6082.3061.38
207058,91140,26966,82118,64255.2537.7762.6717.48
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Fu, A.; Gao, E.; Tang, X.; Liu, Z.; Hu, F.; Zhan, Z.; Wang, J.; Luan, X. MaxEnt Modeling for Predicting the Potential Wintering Distribution of Eurasian Spoonbill (Platalea leucorodia leucorodia) under Climate Change in China. Animals 2023, 13, 856. https://doi.org/10.3390/ani13050856

AMA Style

Fu A, Gao E, Tang X, Liu Z, Hu F, Zhan Z, Wang J, Luan X. MaxEnt Modeling for Predicting the Potential Wintering Distribution of Eurasian Spoonbill (Platalea leucorodia leucorodia) under Climate Change in China. Animals. 2023; 13(5):856. https://doi.org/10.3390/ani13050856

Chicago/Turabian Style

Fu, Aihua, Erhu Gao, Xiaoping Tang, Zengli Liu, Faxiang Hu, Zhenjie Zhan, Jiadong Wang, and Xiaofeng Luan. 2023. "MaxEnt Modeling for Predicting the Potential Wintering Distribution of Eurasian Spoonbill (Platalea leucorodia leucorodia) under Climate Change in China" Animals 13, no. 5: 856. https://doi.org/10.3390/ani13050856

APA Style

Fu, A., Gao, E., Tang, X., Liu, Z., Hu, F., Zhan, Z., Wang, J., & Luan, X. (2023). MaxEnt Modeling for Predicting the Potential Wintering Distribution of Eurasian Spoonbill (Platalea leucorodia leucorodia) under Climate Change in China. Animals, 13(5), 856. https://doi.org/10.3390/ani13050856

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