Assessing Habitat Suitability and Connectivity of Black Storks in China: Integrating Species Distribution Models and Landscape Connectivity Analysis

Assessing Habitat Suitability and Connectivity of Black Storks in China: Integrating Species Distribution Models and Landscape Connectivity Analysis | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Ecology and Evolution This is a preprint and has not been peer reviewed. Data may be preliminary. 15 April 2025 V1 Latest version Share on Assessing Habitat Suitability and Connectivity of Black Storks in China: Integrating Species Distribution Models and Landscape Connectivity Analysis Authors : Zhiheng zhang , Jinyu Yang , Xiaohan Yu , Yuerong Jia , Lei zhang , and Dongmei Wan 0000-0002-1465-6110 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174473860.04927117/v1 Published Ecology and Evolution Version of record Peer review timeline 333 views 233 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The black stork (Ciconia nigra), recognized as a wetland umbrella species and biological indicator, plays a crucial role in maintaining ecosystem balance and biodiversity conservation. However, it faces significant threats from habitat fragmentation and degradation. This study employed the MaxEnt model and landscape connectivity analysis to evaluate suitable habitats for black storks in China, designed an ecological corridor network, and identified key ecological nodes. The findings reveal that areas of high habitat suitability are primarily located in North China, the northwestern region of Xinjiang, and the middle and lower reaches of the Yangtze River. The ecological corridor network connects regions between North China and the Yangtze River Basin, forming a rectangular network with vertices in Gansu-Qinghai, Shanxi-Beijing-Tianjin-Hebei, the lower Yangtze, and Sichuan-Yunnan Province, respectively, totaling 29,099 kilometers in length. Additionally, four ecological nodes requiring priority protection and management were identified. The study proposes conservation strategies which improve habitat connectivity and ecological functionality to ensure long-term stability of black stork populations. These include prioritizing the protection of highly suitable habitats, enhancing ecological restoration in the Hexi Corridor, and optimizing the management of nature reserves. Zhiheng Zhang 1 , Jinyu Yang 1 , Xiaohan Yang 2 , Yuerong Jia 3 , Lei Zhang 1,* , Dongmei Wan 1,* * Dongmei Wan and Lei Zhang be considered joint senior author 1 Laboratory of Animal Resources and Epidemic Disease Prevention, School of Life Sciences, Liaoning University, Shenyang 110036 China 2 Shanghai University of Finance and Economics, Shanghai 200433 China 3 School of Basic Medical Sciences, Hebei Medical University, Shijiazhuang 050017 China Contact information: first author: Zhiheng Zhang ( [email protected] ) second author: Jinyu Yang ( [email protected] ) third author: Xiaohan Yu ( [email protected] ) fourth author: Yuerong Jia ( [email protected] ) Correspondence: Dongmei Wan ( [email protected] ) | Lei Zhang ( [email protected] ) Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Conflicts of Interest The authors declare no conflicts of interest. Author Contributions Zhiheng zhang: Conceptualization (Equal), Data curation (Equal), Formal analysis (Equal), Methodology (Equal), Visualization (Equal), Writing - original draft (Equal), Writing - review and editing (Equal). Jinyu Yang: Data curation (Equal), Visualization (Equal). Xiaohan Yu: Data curation (Equal). Yuerong Jia: Data curation (Equal). Lei zhang: Conceptualization (Equal), Supervision (Equal), Writing - review and editing (Equal). Dongmei Wan: Conceptualization (Equal), Supervision (Equal), Writing - review and editing (Equal) Data Availability Statement Our bird site data come from eBird (https://ebird.org/home), GBIF (GBIF,https://www.gbif.org), and iNaturalist (https://www.inaturalist.org). Environmental variable data comes from the National Cryosphere Desert Data Center (http://www.ncdc.ac.cn), the National Catalogue Service for Geographic Information (https://www.webmap.cn), NASA’s Earth Science Data (https://www.earthdata.nasa.gov), and WorldClim (https://worldclim.org). Abstract: The black stork ( Ciconia nigra ), recognized as a wetland umbrella species and biological indicator, plays a crucial role in maintaining ecosystem balance and biodiversity conservation. However, it faces significant threats from habitat fragmentation and degradation. This study employed the MaxEnt model and landscape connectivity analysis to evaluate suitable habitats for black storks in China, designed an ecological corridor network, and identified key ecological nodes. The findings reveal that areas of high habitat suitability are primarily located in North China, the northwestern region of Xinjiang, and the middle and lower reaches of the Yangtze River. The ecological corridor network connects regions between North China and the Yangtze River Basin, forming a rectangular network with vertices in Gansu-Qinghai, Shanxi-Beijing-Tianjin-Hebei, the lower Yangtze, and Sichuan-Yunnan Province, respectively, totaling 29,099 kilometers in length. Additionally, four ecological nodes requiring priority protection and management were identified. The study proposes conservation strategies which improve habitat connectivity and ecological functionality to ensure long-term stability of black stork populations. These include prioritizing the protection of highly suitable habitats, enhancing ecological restoration in the Hexi Corridor, and optimizing the management of nature reserves. Keywords: black stork ( Ciconia nigra ); MaxEnt model; landscape connectivity; species conservation; Introduction Biodiversity is the material foundation of and survival guarantee for human development. It maintains ecosystem functions, enhances resource utilization efficiency, and provides crucial ecosystem services (Duffy, 2009). However, with the rapid development of human society, global biodiversity has become increasingly vulnerable, threatened by environmental issues such as habitat fragmentation (Ramírez-Delgado et al. 2022), resource overexploitation, pollution (Eni et al. 2025) and climate change (de Souza et al. 2025) Of these, habitat fragmentation has emerged as a major threat (Pereira et al. 2010). Continuous habitats are divided into isolated and spatially heterogeneous patches because of human activities (Collinge and Forman, 1998; Ewers and Didham, 2006). This does not just affect species distribution (Haddad et al. 2015; Keinath et al. 2017), but could significantly increase their risk of extinction as it hinders species dispersal and exacerbates genetic isolation (Deb et al. 2019; Wilcox and Murphy, 1985). China is one of the most biodiverse countries in the world (Wang et al. 2020). However, this diversity is at risk from habitat fragmentation caused by agressive expansion of cities and agriculture, as well as transportation infrastructure networks (Zhou et al. 2021). Wetlands, which are influenced by both aquatic and surrounding terrestrial environment, are hit disproportionally hard (Terrado et al. 2016). In particular, over the past 40 years, China’s wetland area has decreased by more than 12% (Mao et al. 2025), severely impacting rare and endangered bird species, including oriental white stork ( Ciconia boyciana ) (Kavana et al. 2024), red-crowned crane ( Grus japonensis ) (Li et al. 2025), and crested ibis ( Nipponia nippon ) (Sun et al. 2016). The black stork ( Ciconia nigra ), a large and endangered waterbird and an umbrella species in wetland ecosystems (Eaton and Cano-Alonso, 2022), is widely distributed across the Eurasian temperate regions (Gula et al. 2023), with scattered populations in southern Africa (Bobek et al. 2008). It plays a crucial role in maintaining food web balance and assessing the health of wetlands and forests (Eaton and Cano-Alonso, 2022). However, its population is at risk due to wetland habitat fragmentation, forcing it to relocate to suboptimal habitats. This increases their survival pressure because foraging efficiency is lower and energy consumption in migration is higher (Xing et al. 2025). Research on the black stork mainly focuses on behaviors (Cano Alonso, 2013; Freschi et al. 2023; Xiaojing et al. 2011) and genetics (Lanzarot et al. 2005; Liang et al. 2019; Liu et al. 2016), with few resources devoted to habitat fragmentation and suitability study (Tuohetahong et al. 2023). Studies on the mechanisms of habitat fragmentation and connectivity are also scarce. But these are vital aspects in protecting its core habitats and promoting population dispersal and gene flow (Margules and Pressey, 2000). In the global context of habitat fragmentation, the core strategy for species conservation is building ecological networks and enhancing landscape connectivity (Cao et al. 2020; Saura et al. 2019). Consequently, relavant research has largely focused on species distribution prediction (Pearson and Dawson, 2003) and landscape connectivity analysis (Rao et al. 2025). Ecological corridors, linear landscapes connecting isolated habitats (Puth and Wilson, 2001), are important tools for mitigating habitat fragmentation and strengthening population connectivity (Jia et al. 2023). Building frequently utilized corridors relies on accurate identification of ecological source areas and the establishment of resistance surfaces (Ding et al. 2023). Ecological source areas refer to core patches in a regional ecosystem that have major ecological function, sustain biodiversity, and provide key habitats (Cao et al. 2022). Resistance surfaces are spatial distribution models reflecting the resistance encountered by species during movement across the landscape, quantifying factors such as land use type, elevation, slope, and aspect (Zhou and Song, 2021). Ecological pinch points are the narrowest, most vulnerable, and critical areas for species migration/connectivity within ecological corridors (McRae et al. 2008). Typically there are few or no alternative pathways there. Once damaged (e.g., through road construction, urbanization, etc.), it may result in corridor fragmentation, threatening species migration, gene flow, and habitat connectivity. Therefore they are a priority of habitat protection (Rahimi and Dong, 2023). Obstacle points are areas in the landscape that impede species migration, typically caused by human activities (such as roads and cities) or natural factors (such as rivers and mountains) (Wei et al. 2022)。 The MaxEnt model has gained widespread recognition among researchers for its high accuracy (Duan et al. 2014), low data volume required, and ease of use (Tsiftsis et al. 2019). In terms of ecological corridor planning, Linkage Mapper, the ArcGIS-based tool built on the least-cost path model, is very useful. It can be used not only to identify ecological corridors between species’ core distribution areas, but also to detect ecological pinch points and barrier points combined with the Circuitscape tool (Cao et al. 2020; Zhu et al. 2025). This helps select corridors most suitable for practical application and identify priority areas for restoration or protection (Xu et al. 2022). It is among the most widely employed methods for ecological corridor construction. This study identifies potential core habitats of the black stork (Ciconia nigra) in China and determines key environmental drivers using the MaxEnt model based on distribution data and environmental variables. Subsequently, the Linkage Mapper tool, in combination with the Circuitscape plugin, is employed to construct an ecological corridor network and identify ecological pinch points and barrier points. The objectives are: (a) to analyze the spatial distribution patterns of potential suitable habitats and identify the dominant environmental factors influencing this distribution and (b) to identify core habitats and ecological corridors, locate pinch points and barrier points within the corridor network, based on which to propose recommendations for habitat restoration and protection. These efforts aim to facilitate inter-population connectivity and maintain functioning migration routes for the species. Materials and methods Distribution Data Collection and Processing In this study, distribution records of the black stork were obtained from three species occurrence databases: eBird (https://ebird.org/home) (Sullivan et al. 2009), the Global Biodiversity Information Facility (GBIF,https://www.gbif.org) (Luo et al. 2021) and iNaturalist (https://www.inaturalist.org) (Van Horn et al. 2018), covering the period from January 2015 to January 2025. To ensure the accuracy of distribution points, only occurrence records with precise geographic coordinates were retained. For habitat suitability analysis, occurrence data from all four seasons were used to develop MaxEnt models to predict potential suitable habitats of the black stork and to delineate its overall habitat extent. Since avian migration involves seasonal movements between breeding and wintering grounds, the construction of ecological corridors in this study was based exclusively on occurrence records from non-migratory periods,namely, the breeding season (April to July) (Zhao, 2001) and the wintering season (December to February of the following year) (Liu et al. 2013). During these two periods, areas with high habitat suitability identified by the MaxEnt model were extracted as ecological source areas. These core habitat patches were then used as the foundation for constructing ecological corridors, aiming to optimize the spatial connectivity of black stork habitats. To reduce spatial redundancy among occurrence records, the distribution data were filtered using ENMTools software in combination with climate variables (Warren et al. 2010). Specifically, only one presence point was retained within each ~1 km grid cell (Elith* et al. 2006), minimizing spatial autocorrelation and sampling bias. As a result, a total of 717 unique black stork distribution points were retained for modeling, of which 529 were recorded during the breeding and wintering seasons (Fig. 1). Fig. 1. Distribution map of black stork occurrence points. Green dots represent records during the migratory period, purple dots indicate records from the breeding season, and red dots denote records from the wintering season. Acquisition and Processing of Environmental Variables The altitude data used in this study were from National Cryosphere Desert Data Center (http://www.ncdc.ac.cn) (Kang, 2020). Slope and aspect information were extracted from the altitude data using ArcGIS 10.8.2. Distance-related variables were sourced from the 1:1,000,000 public version of the basic geographic information data available through the National Catalogue Service For Geographic Information (https://www.webmap.cn), which includes water bodies such as lakes, reservoirs, and rivers, and roads such as highways and railways. Land cover data were obtained from the National Cryosphere Desert Data Center (http://www.ncdc.ac.cn) (Yang and Huang, 2021), and this dataset classified land types into cropland, forest, shrub, grassland, water, snow and ice, barren, impervious, and wetland. NDVI data were sourced from NASA’s The Earth Science Data (https://www.earthdata.nasa.gov). Climate variable data (19 variables) were extracted from the WorldClim (https://worldclim.org). Specific environmental variable information is presented in Table 1. All environmental variables were standardized using ArcGIS 10.8.2, with the geographic coordinate system set to GCS_WGS_1984 and the projection system set to WGS_1984_UTM_Zone_50N. The boundary size and pixel size of all variables were unified. The correlation analysis results between the variables are shown in Figure 2. To reduce the impact of multicollinearity, variables with a correlation ≥ R 4.4.0 (Xu et al. 2019). All variables and their usage are listed in Table 1. Tab 1 Environmental Variable Names Topographic variables Altitude Altitude (m) yes Slope Slope (°) yes Aspect Aspect yes Distance-based variables Distance to water source Distance to water source(m) yes Distance to the roads Distance to the roads(m) yes Distance to the urban area Distance to the roads(m) yes Habitat variables NDVI NDVI yes Land cover Land cover yes Climate variables bio1 Annual mean temperature (℃) no bio2 Mean diurnal range[Mean of monthly (maxtemp-mintemp)] (℃) no bio3 Isothermality (bio_2/bio_7) (×100) yes bio4 Temperature seasonality (standard deviation×100) no bio5 Maximum temperature of warmest month(℃) no bio6 Minimum temperature of coldest month(℃) yes bio7 Temperature annual range(℃) yes bio8 Mean temperature of wettest quarter(℃) no bio9 Mean temperature of driest quarter(℃) no bio10 Mean temperature of warmest quarter(℃) no bio11 Mean temperature of coldest quarter(℃) no bio12 Annual precipitation(mm) no bio13 Precipitation of wettest month(mm) no bio14 Precipitation of driest month(mm) yes bio15 Precipitation seasonality(mm) yes bio16 Precipitation of wettest quarter(mm) no bio17 Precipitation of driest quarter (mm) no bio18 Precipitation of warmest quarter(mm) no bio19 Precipitation of coldest quarter(mm) no Fig. 2. Correlation matrix of environmental variables. In the figure, larger circles indicate stronger correlations between variables. Red represents positive correlations, while blue indicates negative correlations. Among all factors, Water denotes the distance to water sources, Road represents the distance to roads, and Urban area indicates the distance to the urban area. MaxEnt Model Parameter Settings The parameter settings for the MaxEnt model used in habitat suitability prediction and source area reconstruction were as follows: the ”create response curves” option was selected to generate response curves illustrating the relationship between environmental variables and species occurrence probability. The jackknife method was employed to evaluate the relative importance of each environmental variable in predicting species distribution. The output format was set to Logistic. 75% of the occurrence records were randomly selected to train the model, while the remaining 25% were used to validate model accuracy. The model was replicated 10 times using the bootstrap method, and all other parameters were kept at their default values. The predictive performance of the model is evaluated using the Area Under the Curve of the Receiver Operating Characteristic Curve. The AUC value ranges from 0 to 1, with a higher value indicating better model prediction accuracy (Fawcett, 2006). When the AUC value is between 0.9 and 1.0, the model’s predictive capability is considered excellent (Wan et al. 2019). Habitat Suitability Zoning and Ecological Corridor Construction The results from the two MaxEnt model outputs were imported into ArcGIS, and the habitat suitability of the black stork was classified into four categories using the natural breaks method (Bao et al. 2024): unsuitable habitat, low suitability habitat, medium suitability habitat, and high suitability habitat. Distribution of Suitable Habitats and Its Influencing Factors In this study, R 4.4.0 was used to enhance the Receiver Operating Characteristic curve, jackknife plot, and variable response curve. ArcGIS 10.8.2 was employed for the visualization of the black stork’s suitable habitat distribution. Ecological Corridors, Pinch points, and Barrier Points Although the maximum migration distance of black storks in Europe and Africa can exceed 2,000 km (Chevallier et al. 2013), the longest recorded migration distance of black storks in China is currently 956 km (Wang et al. 2018). To avoid functional failure of ecological corridors caused by exceeding the species’ migration capacity, the maximum corridor length threshold in this study was set to 1,500 km. In addition, to identify ecological pockets and barrier points, the natural breaks classification method was applied to divide these features into four levels, with the highest levels of current density and resistance values selected as the final ecological pockets and barrier points (Peng et al. 2018). Considering that the black stork has an activity range of approximately 20 km (Yao et al. 2009), search radii for barrier point identification were set at 10 km, 15 km, and 20 km to ensure both spatial analytical accuracy and ecological relevance. The inverse of the Habitat Suitability Index (HSI) during the breeding and wintering periods was used as the resistance surface (LaRue and Nielsen, 2008), and areas classified as highly suitable habitats were designated as ecological sources for corridor construction (Tian et al. 2023). However, as there is no definitive record of the minimum habitat area required for black storks, this study adopted the core habitat area of a closely related species, the white stork ( Ciconia ciconia ), approximately 50 km² (Zurell et al. 2018), as the minimum threshold for ecological source areas. The Linkage Pathways Tool within the Linkage Mapper toolbox was used to construct ecological corridors for the black stork in China (Dickson et al. 2013). In addition, ecological pinch points and barrier points along the corridors were identified using the Pinchpoint Mapper and Barrier Mapper tools in Circuitscape (Zhou et al. 2023). Spatial visualizations of ecological corridors, pinch points, and barrier points were all completed in ArcGIS 10.8.2. Results and Analyses Accuracy of the MaxEnt Model The ROC curve of the habitat suitability prediction model based on black stork occurrence records from all four seasons is shown in Figure 3 (Fig. 3a), with an average AUC value of 0.915. The ROC curve of the model based on breeding and wintering period records is shown in Figure 3 (Fig. 3b), with an average AUC value of 0.932. Both models yielded AUC values greater than 0.9, indicating excellent predictive performance and demonstrating that the model is suitable for identifying suitable habitats and ecological source areas. Fig. 3. ROC curves (a). Model based on occurrence records from all seasons; (b). Model based on occurrence records from the breeding and wintering periods. Contribution Rates of Environmental Variables The Jackknife test was used to identify the key environmental variables with the strongest explanatory power for the distribution of suitable habitats, as shown in Figure 4. The regularized training gain for the distance to built-up areas was significantly higher than that of other environmental variables, indicating that this factor had the greatest influence on the distribution of black storks. The remaining variables, in descending order of importance, were land cover type, distance to roads, bio15, NDVI, bio6, distance to water bodies, elevation, bio7, bio3, bio14, slope, and aspect. In contrast, the terrain variables—slope and aspect—as well as precipitation of the driest month (bio14), had relatively low regularized training gain values, suggesting a lesser influence on black stork distribution. Fig.4. Jackknife test results based on year-round data The black stork shows a significant preference for mid-to-low elevations, with a tendency to select areas between 1,000 and 3,500 meters above sea level (Fig. 5a). There is no clear preference for slope or aspect (Fig. 5b, c). Among the distance-related factors, the black stork prefers habitats that are close to water sources and built-up areas while avoiding roads (Fig. 5d, e, f). Its distribution probability is negatively correlated with the distance to water sources and built-up areas, and positively correlated with the distance to roads. Regarding vegetation cover, the black stork prefers areas with moderate vegetation index values (Fig. 5g). In terms of climate factors, both temperature and precipitation influence the distribution probability of the black stork (Fig. 5h, I, j, k, l). Among land use types, water bodies have the most significant impact on the distribution of the black stork, while the influence of other land use types is relatively consistent (Fig. 6). Fig. 5. Response curves for continuous environmental variables. Fig. 6. Response curves for categorical environmental variables. Distribution of Suitable Habitats for the black stork The distribution of suitable habitats for the black stork in China is shown in Fig 7. The area of highly suitable habitat reaches 490,000 km², accounting for approximately 5% of the country’s total land area. These highly suitable areas are mainly concentrated in North China, the northwestern part of Xinjiang, and the middle and lower reaches of the Yangtze River. Moderately suitable areas are primarily located around the highly suitable zones, while marginally suitable areas are mainly distributed on the periphery of the moderately suitable zones and in Northeast China. Fig. 7. Distribution of suitable habitats for the black stork Ecological Corridors A total of 60 ecological source areas were identified, along with 109 ecological corridors, with a combined length of 29,099 km. The longest corridor measures 1,429.31 km, while the shortest spans just 1.35 km (Fig. 8). The spatial distribution of ecological corridors exhibits distinct regional patterns, primarily concentrated between northern China and the Yangtze River Basin. These corridors form a rectangular ecological network with four key nodes: the Gansu–Qinghai region, the Shanxi–Beijing-Tianjin-Hebei region, the lower reaches of the Yangtze River, and the Sichuan–Yunnan region. This network provides critical ecological pathways for the migration of the black stork, particularly the corridors between the Gansu–Qinghai and Shanxi–Beijing-Tianjin-Hebei regions, which represent the most densely connected ecological zones. In contrast, ecological corridors in the northwestern region are relatively sparse. Xinjiang is connected to Gansu via a single corridor located in the Hexi Corridor (hereafter referred to as the Hexi Corridor), which serves as a key route linking the black stork’s ecological source areas in northwest China with those in central and east China. Additionally, only one ecological corridor was identified in the Tibet region, connecting two ecological source areas. Ecological Pinch Points and Barriers Based on the natural breaks classification method, a total of 75 ecological pinch points were identified. Among them, 60 were concentrated along the Hexi Corridor, while the remaining 15 were located between ecological source areas within Xinjiang. In addition, six ecological barrier points were detected, including three along the Hexi Corridor, two within ecological corridors in Xinjiang, and one in the ecological corridor of Jiangsu Province (Fig. 8). To facilitate the development of conservation and management strategies for the black stork, both ecological pinch points and barrier points were collectively referred to as ecological nodes. By clustering and integrating adjacent pinch points and barriers, four ecological nodes were ultimately identified. These nodes are located along ecological corridors in Xinjiang, the Hexi Corridor, and Jiangsu Province (Fig. 8). Fig. 8. Ecological corridors, ecological pinch points, barrier points, and ecological nodes. The labels a, b, c, and d represent different ecological node regions. Discussion Distribution of Suitable Habitats for the black stork and Its Influencing Factors The habitat selection of the black stork is influenced by multiple environmental factors, and its distribution in China shows significant regional characteristics. High-suitability habitats are mainly concentrated in the Yangtze River Basin, North China, and the northwestern region of Xinjiang. Based on environmental variable analysis, the black stork prefers areas of moderate to low elevation, which generally have a mild climate (Pu et al. 2025) and moderate vegetation cover (Zhang et al. 2022), providing sufficient concealment and stable food resources. The black stork typically nests in mountain or cliff areas during the breeding season (Lee et al. 2023), and the abundant mountain ranges and complex terrain in North China and northwest Xinjiang meet the stork’s need for concealed nesting sites. Additionally, studies show that the core activity areas of the black stork are often located within 2,500 meters of water sources (Tuohetahong et al. 2023). Thus ecosystems with rich water resources could be good habitat options for storks, such as the lake wetland systems of the Yangtze River Basin, the Yellow River system, and the glacial meltwater networks in northwestern Xinjiang. These water bodies provide rich foraging sites for the storks. Furthermore, the black stork’s response to human activity is dualistic. On one hand, its distribution probability is negatively correlated with distance from roads, reflecting its tendency to avoid areas with high human disturbance, which is consistent with the findings of Chevallier (2010), who also found that the black stork tends to avoid dense human-activity areas. On the other hand, the black stork is capable of utilizing agricultural landscapes and artificial wetlands as supplementary foraging sites, demonstrating a habitat selection trade-off mechanism (de Resende et al. 2024). In summary, the distribution of the black stork is jointly determined by proximity to water sources, elevation, terrain conditions, climate suitability, and human disturbance. The environmental characteristics of high-suitability habitats are highly aligned with those in the stork’s breeding and wintering habits. Ecological Corridors, Pinch Points, and Barrier Points for the black stork The spatial distribution of ecological corridors and their driving mechanisms are influenced by the combined effects of natural geography, ecological source area patterns, and human activities. For example, dense rectangular network of ecological corridors was formed between North China and the Yangtze River Basin because there are natural geographic advantages here (the North China Plain has flat terrain and the Yangtze River has intricate tributary systems), which provides a physical foundation for species migration. Moreover, the dense distribution of ecological source areas within these basins reduces the distance between source areas, thereby decreasing the resistance to corridor construction. In other cases, corridors benefited from regional ecological policies, such as the establishement of the Beijing-Tianjin-Hebei ecological barrier (Liu et al. 2008) and the ecological protection work along the Yangtze River Economic Belt (Peng and Yu, 2024). These endeavor has effectively mitigated the impact of urbanization on corridor fragmentation. It showcased that human activities, when properly guided and planned, can make positive ecological impacts In contrast, the single remaining corridor from Xinjiang to central and east China highlights the ecological fragility of the northwest region. Although the Hexi Corridor serves as a transitional zone and provides a migration route, the area is characterized by sparse rainfall and intense evaporation (Zhu et al. 2022), resulting in severe fragmentation of ecological source areas. Furthermore, factors such as poor soil quality (Luo et al. 2024; Ting et al. 2025), air pollution (Guan et al. 2019), and excessive water resource exploitation (Wang et al. 2025) further weaken the connectivity of this potential corridor, making it a critical bottleneck on the migration route connecting the black stork’s eastern and western populations. To restore the connectivity of black stork ecological nodes, we analyzed key factors influencing their distribution (Fig 5, 6) and found that the formation of ecological nodes was mainly affected by the barrier effect of mountains and land use types. These factors spatially blocked migration paths and restricted the habitat distribution of the black stork, creating obstacles along the corridors. Ecological nodes c and d are primarily affected by the barrier effect of the Tianshan Mountains and the Bogda Mountains (Fig. 9a). The main peak of the Tianshan Mountains has an elevation between 4,000 and 6,000 meters (Aizen et al. 1997), and the average elevation of the Bogda Mountains is as high as 5,445 meters (Du et al. 2021). The mountainous barriers formed by these two mountain ranges increase the migration cost of birds (Henningsson and Alerstam, 2005). The physical barriers created by the complex mountainous terrain pose significant obstacles to bird migration, forcing them to expend more energy to detour or ascend. Additionally, the mountains can alter local wind conditions, reducing wind support during bird flight and increasing the risk of energy expenditure during migration (Aurbach et al. 2018). Given that birds instinctively adjust their migration strategies when encounter topographic obstacles (Biebach et al. 2000), this study proposes an ecological corridor optimization plan to address the issues black storks face in this migration route. As shown in Fig. 9, the two new ecological corridor routes (indicated by black dashed lines) provide better water resource supply compared with the original ecological nodes c and d, while avoiding the high mountain barriers, thus effectively meeting black stork’s needs along the migration. Therefore, by protecting water resources along the new corridor routes, ecological conservation efforts can be further promoted, enhancing the sustainability of migration. Fig. 9. Ecological nodes a and b. ①③ represent the original locations of the two regions’ ecological corridors, while ②④ represent the locations of the redirected ecological corridors. The main issue affecting ecological node c is the extensive barren land (Fig. 10a) and low vegetation coverage (He et al. 2024), which is highly detrimental to the migration and stopover of black storks. Additionally, the Hexi Corridor is located in an arid climate zone with annual precipitation of only 110–350 mm (Li et al. 2024), further exacerbating the difficulty of migration. Therefore, constructed wetlands are recommended, for example in the Gansu Anxi Extreme Arid Desert Conservation Area, to conserve water resources and support wildlife habitat, such as providing stopover points for migratory species including the black stork. These treatment systems have the additional benefit of protecting China’s afforestation and desertification combating effort in its northwest (Qi et al. 2023). In contrast, the restoration of ecological node d is relatively simple, as it is not only close to the Su Sihong Hongze Lake Wetland National Nature Reserve (Fig. 10b), but also has alternative corridors nearby. Therefore, this node does not hinder the migration of the black stork. For the isolated population in Tibet, we recommend protecting this small population separately to prevent its extinction. Fig. 10. Ecological nodes c and d. ① represents the Gansu Anxi Extreme Arid Desert National Nature Reserve, and ② represents the Su Sihong Hongze Lake Wetland National Nature Reserve. Protection Recommendations for the black stork Based on the ecological habits and environmental preferences of the black stork, combined with the needs for ecological corridors and connectivity in China, this study proposes three protection recommendations. First, protect the high-suitability areas for the black stork. This includes building ecological buffer zones around the Beijing-Tianjin-Hebei urban cluster, protecting corridors from being squeezed by urbanization and strengthening the dynamic monitoring and protection of wetlands in the Yellow River and Yangtze River basins. In addition, to ensure critical source area are connected, ecological red lines should be established in key ecological nodes such as the northern oasis belt of the Tarim Basin in Xinjiang, balancing biodiversity conservation and agricultural expansion and energy development. Second, prioritize the protection of the Hexi Corridor in the northwest region, with a focus on enhancing ecological restoration. Wetland restoration and vegetation reconstruction should be carried out to provide better foraging and stopover habitats for the black stork. Third, strengthen the management and protection of rivers and other water bodies in existing nature reserves near ecological corridors to provide safe stopover points and foraging areas for black storks. References Aizen, V. B., Aizen, E. M., Melack, J. M., and Dozier, J. (1997). Climatic and hydrologic changes in the Tien Shan, central Asia. Journal of Climate , 10 (6), 1393-1404. Aurbach, A., Schmid, B., Liechti, F., Chokani, N., and Abhari, R. 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Collection Ecology and Evolution Keywords ecological experiment multiple population ecology vertebrate Authors Affiliations Zhiheng zhang Liaoning University View all articles by this author Jinyu Yang Liaoning University View all articles by this author Xiaohan Yu Shanghai University of Finance and Economics View all articles by this author Yuerong Jia Hebei Medical University View all articles by this author Lei zhang Liaoning University View all articles by this author Dongmei Wan 0000-0002-1465-6110 [email protected] Liaoning University View all articles by this author Metrics & Citations Metrics Article Usage 333 views 233 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Zhiheng zhang, Jinyu Yang, Xiaohan Yu, et al. Assessing Habitat Suitability and Connectivity of Black Storks in China: Integrating Species Distribution Models and Landscape Connectivity Analysis. Authorea . 15 April 2025. 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