Study on suitability assessment of waterbird habitats along Jiangsu coastal wetland of China

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It features vast natural mudflats, serving as an important "rest stop" for migratory birds, with hundreds of species and over ten million migratory birds stopping or wintering here each year. The more than 900 kilometers of coastline presents a diverse landscape, including wind farms, photovoltaic installations, fishing ports, and areas invaded by Spartina alterniflora . This diverse land use creates potential conflicts between bird habitat selection and human activities. This study integrates 11 environmental factors, including estuaries, fishing ports, wind power, photovoltaics, Spartina alterniflora , climatic conditions, and vegetation normalized difference indices, to comprehensively analyze the suitability of habitats for migratory waterbirds along the Jiangsu coast. According to species distribution models, the environmental factor that most significantly affects the habitat suitability for migratory waterbirds is Spartina alterniflora , followed by the distribution of fisheries, chemical plants, and estuaries. The main suitable distribution areas for migratory waterbirds in Jiangsu's coastal region are located in the Yancheng Rare Bird Protection Zone, Dongtai Tiaozi Mud Wetland, Lianyungang Linhong Estuary, Rudong Xiaoyangkou, and Dongling. In the context of the ongoing reduction and degradation of natural wetlands, the findings provide a scientific basis for the creation, restoration, and management of artificial habitats for coastal waterbirds. Jiangsu coast migratory waterbirds habitat distribution model suitability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The spatial distribution of species is one of the research hotspots in disciplines such as ecology, biogeography, and conservation biology(Navarro et al., 2015 ). In particular, with the increasing impact of global changes in recent years, issues such as habitat degradation, fragmentation, and loss of species have become increasingly severe, posing unprecedented threats to global biodiversity(Rands et al., 2010 ; Barnosky et al., 2011 ). In this context, studying and understanding the spatial distribution patterns of biodiversity not only provides a foundation for predicting the impacts of global changes on biodiversity but also helps to identify priority conservation areas, thereby utilizing limited resources to achieve optimal conservation outcomes. The spatial distribution of biodiversity is a comprehensive result of the spatial distribution of various species. However, traditional species distribution is mostly based on simple descriptions using administrative divisions, relying on expert subjective judgment, and neglecting environmental heterogeneity(Underwood et al., 2004 ). As a result, the accuracy of the results obtained from such data is often criticized. To address this, ecologists have proposed using Ecological Niche Models (ENMs), which utilize species distribution points and their associated environmental variables to infer the ecological requirements of species and simulate their distribution(Wiens et al., 2010 ). Among these, the Maximum Entropy model (Maxent) is particularly advantageous because it only requires information on species occurrence points, avoiding the need for "non-occurrence" data that regression models require(Hucks and Leberg, 2024 ). This makes simulating potential species distributions more convenient and easier, while also being more tolerant of data biases such as small sample sizes and irregular sampling(Li and Ding, 2016 ). Maxent has shown excellent predictive performance and is widely used in various fields, including the distribution prediction of rare and endangered species(Zhang et al., 2018 ), management of invasive species(Fernandes et al., 2019 ), assessing the impact of global climate change on species distribution(Anderson and Raza, 2010 ), and biodiversity conservation assessments(Franklin, 2010 ). It has become an indispensable tool in large-scale research. Waterbirds, as a unique group of higher organisms in wetlands, are an important component of wetland ecosystems and play a crucial role in maintaining wetland biodiversity(Franklin, 2010 ; Furness and Greenwood, 2013 ; Qiu et al., 2024 ). The coastal areas of Jiangsu have a dense network of rivers, with intricate waterways and rich coastal and river wetlands, resulting in abundant waterbird resources. Additionally, the Jiangsu coastline is located at a mid-point along the important migratory route for migratory birds between East Asia and Australia, serving as a significant stopover and wintering ground for migratory waterbirds, especially many rare and endangered species. However, in recent decades, rapid urbanization and industrialization, along with the concentration of population and industrial activities, have led to increased development of coastal wetlands. Activities such as wind power, photovoltaic projects, fishing port construction, and land reclamation have severely degraded wetlands, resulting in a significant reduction of nearshore and coastal wetlands, as well as prominent ecological issues such as sediment accumulation and loss of biodiversity(Xu et al., 2011 ; Li et al., 2014 ; Cui et al., 2018 ). Understanding the spatial distribution characteristics of waterbird diversity and prioritizing the protection of the most urgently needed areas with limited resources has become a core issue that needs to be addressed in the ecological civilization construction of Jiangsu's coastal wetlands. Therefore, this study combines field surveys with the Maxent model to simulate suitable areas for waterbirds along the Jiangsu coast. Based on this, it analyzes the suitable distribution areas for migratory waterbirds in Jiangsu's coastal region, aiming to provide a scientific basis for the planning of waterbird diversity conservation in Jiangsu's coastal areas. Materials and Methods Study Area The southern coastal region of Jiangsu is a transitional zone from the southern warm temperate zone to the northern subtropical zone, situated in the alluvial plain of the lower Yangtze River. It is located in a transition area between land and sea, with an average annual temperature of 15°C, average annual precipitation of 850 to 1280 mm, and average annual sunshine hours of 1900 to 2250. The Jiangsu coastal area is located in the middle section of the migratory route for birds between "East Asia and Australasia," featuring vast natural tidal flats that serve as important "stops" for migratory birds. The coastline, which stretches over 900 kilometers, has diverse landscape patterns, including wind farms, photovoltaic installations, fishing ports, and areas affected by the invasion of Spartina alterniflora . This research area includes the cities of Lianyungang, Yancheng, and Nantong. It encompasses Yancheng Wetland and Rare Birds NNR, Jiangsu Dafeng Elk NNR, and the first and second phases of the Yellow (Bo) Sea Migratory Bird Habitat. Data Sources and Preprocessing of Waterbird Distribution Sites A total of 72 waterbird cluster distribution sites were obtained from field surveys along the coast. To reduce the spatial autocorrelation effect of the distribution points, points within a 1 km range of adjacent distribution points were removed through buffer zone analysis, resulting in 24 valid distribution data points (Figure 1). The coordinates of these distribution points were organized into CSV format and imported into ArcGIS 10.2 for subsequent analysis. Data Sources and Preprocessing of Environmental factors Three types of environmental factors were selected as predictive variables for the distribution model of coastal migratory waterbirds: bioclimatic factors, land use types, and human activity factors(Chang et al., 2022; Fu et al., 2023). There is a certain correlation among ecological factors, so a correlation analysis of the environmental factors was conducted before applying them to the MaxEnt model. A multicollinearity analysis (SPSS 22.0) was performed on the bioclimatic factors, land use types, and human activity factors to test the correlation between ecological factors. If the Pearson correlation index between two ecological factors was greater than ±0.9, the more representative ecological factor was selected. Ultimately, 11 environmental factors were obtained, including 2 bioclimatic factors (annual average temperature bio_1, annual average precipitation bio_12), 4 land use types (Normalized Difference Vegetation Index NDVI, estuary River, protected area Reserve, and smooth cordgrass Sporobolus), and 5 human activity factors (fishing port Fishing_port, photovoltaic Photovoltaic, fishery production Fishery, chemical plant Chemistry, and wind farm Windpower). All environmental variable layers were unified to the WGS-1984 coordinate system on the ArcGIS platform, with a raster size of 30S (approximately 835 meters), and converted to the ASC file format required by the MaxEnt model. The types and descriptions of ecological factors are detailed in Table 1. Table 1. Environmental variables used for the MaxEnt model and their description Environmental Type Environmental Variables Short name Unit Climate Annual average temperature Bio_1 ℃ Annual average precipitation Bio_12 mm Land-use type Normalized Difference Vegetation Index NDVI / Distance from the estuary River m Distance from Spartina alterniflora Sporobolus m Distance from the protected area Reserve m Human activity factors Distance from Fishing Port Terminal Fishing_port m Distance from photovoltaic system Photovoltaic m Distance from fishery production Fishery m Distance from chemical factory Chemistry m Distance from wind farm Windpower m Model Construction and Calculation The distribution point data of migratory waterbirds along the coast and environmental variable data were imported into MaxEnt software. A random selection of 75% of the distribution point data was used to construct the maximum entropy model for suitable habitat distribution of migratory waterbirds along the Jiangsu coast, while the remaining 25% of the points were used for model validation. The model for suitable habitat distribution of migratory waterbirds was constructed, and the average of the results from 10 repeated simulations was taken as the final result(Chang et al., 2022). The model employed Jackknife tests to analyze the importance of environmental factors and evaluated the model's accuracy using the area under the ROC curve (AUC). The use of ROC curve analysis to evaluate the suitable habitat model for waterbirds is due to the fact that AUC is not affected by the judgment threshold and is currently recognized as one of the best methods for assessing the quality of distribution model predictions(Phillips and Dudík, 2008). The AUC value ranges from 0 to 1.0, with values closer to 1 indicating a greater correlation between the environmental variables and the geographic distribution model of the predicted objects, thus indicating better predictive performance. Generally, an AUC value less than 0.5 indicates that the prediction results are not reliable, an AUC value between 0.5 and 0.7 indicates average predictive ability, an AUC value between 0.7 and 0.9 indicates good predictive ability, and an AUC value above 0.9 is considered excellent(Araújo and New, 2007). Import the ASC file output from the model into ArcGIS 10.2 and convert it into a raster file to obtain the probability distribution map of suitable areas for waterbirds. The probability values range from 0 to 1.0, with higher values indicating greater suitability for waterbirds in that area. Using the Nature Break (Jean) method for reclassification, the probability distribution map of suitable areas for waterbirds along the Jiangsu coast is divided into three levels: 0 to 0.2 is classified as low suitability, 0.2 to 0.5 as medium suitability, and 0.5 to 1.00 as high suitability. Results and Analysis Model Prediction Accuracy 25% of the distribution data was selected as the validation dataset, while the remaining site data was used as the training dataset for model computation. The model was run randomly 10 times, and the Receiver Operating Characteristic (ROC) curve was plotted. The AUC values for the training and validation data of the MaxEnt prediction model were 0.893 and 0.877, respectively, which are significantly greater than the AUC value of random distribution (0.5). This indicates that the MaxEnt model's predictions of coastal waterbird distribution have good accuracy and reliability. Key Environmental Factors Affecting the Distribution of Coastal Waterbirds According to the results of the Jackknife method analysis in the MaxEnt model, the weights of different ecological factors contributing to the model predictions can be displayed (Figure 4) along with their contribution rates to the distribution of waterbirds (Table 2). The results of the ecological factor contribution rates indicate that there are six ecological factors with contribution rates greater than 5% to the predictive model results, listed from highest to lowest as follows: distance to Spartina alterniflora (contribution rate of 39.09%), distance to fishery working areas (contribution rate of 25.99%), distance to chemical plants (contribution rate of 8.94%), distance to estuaries (contribution rate of 8.92%), distance to wind farms (contribution rate of 6.11%), and annual average precipitation (Bio_12) (contribution rate of 5.31%). The cumulative contribution rate reaches 94.32%, indicating that Spartina alterniflora , fishery production, chemical plants, estuaries, and wind farms are the main ecological factors influencing the distribution of migratory waterbirds along the Jiangsu coast. Table 2 Importance rate of environmental variables in MaxEnt modeling and potential distribution of shorebirds Environmental Variables Importance percent (%) Environmental Variables Importance percent (%) Sporobolus 39.03 Reserve 2.88 Fishery 25.99 Fishing_port 1.83 Chemistry 8.94 NDVI 0.72 River 8.92 Photovoltaic 0.23 Windpower 6.11 Bio_1 0.02 Bio_12 5.31 From the perspective of six important environmental factors affecting the distribution of suitable areas for migratory waterbirds along the Jiangsu coast, in terms of land use types, the high suitability areas are located between 500 to 3600 meters from Spartina alterniflora and less than 2600 meters from estuaries. Regarding human disturbance types, the high suitability areas are less than 4700 meters from fishing work areas, more than 14 kilometers from chemical plants, and over 8500 meters from wind farms. In terms of climate parameters, the high suitability areas have an average annual precipitation of less than 765 mm (Table 3). Table 3 Statistical analysis of five main environmental variables in areas of each suitable classes of shorebirds Ecological factors high suitability areas P≥0.5 Unit Sporobolus 500 ~ 3600 m Fishery ＜ 4700 m Chemistry ＞ 14000 m River ＜ 2600 m Windpower ＞ 8500 m Bio_12 ＜ 765 mm Prediction of Potential Suitable Areas for Migratory Waterbirds The geographic coordinate information of migratory waterbird distribution along the Jiangsu coast obtained from field surveys, along with ecological factor data from distribution sites, was imported into the MaxEnt model. The ASC layers outputted by the model were then imported into ArcGIS, where resampling was conducted to extract the suitability rankings for migratory waterbirds. Based on the results of the field surveys of species distribution, the suitability rankings for migratory waterbirds along the Jiangsu coast were classified into low suitability areas (P < 0.2), medium suitability areas (0.2 ≤ P < 0.5), and high suitability areas (P ≥ 0.5). The results showed that the suitable areas for migratory waterbirds along the Jiangsu coast (Figure 5) were highly consistent with the distribution areas identified in field surveys (Figure 1). The model prediction results indicated that the high suitability areas (P ≥ 0.5) for migratory waterbirds along the Jiangsu coast are mainly concentrated in places such as Yancheng wetland and rare bird NNR, Tiaozini Wetland, Xiaoyangkou Wetland, Dongling Wetland, and Linhong River Estuary Wetland. The area of high suitability zones for migratory waterbirds along the Jiangsu coast is 928.20 km², accounting for 10.81% of the coastal monitoring area; the area of medium suitability zones is 3819.05 km², accounting for 44.47% of the coastal monitoring area; and the area of low suitability zones is 3840.30 km², accounting for 44.72% of the coastal monitoring area. Conclusions and Discussion The Importance of Jiangsu Coastal Wetlands for Migratory Waterbirds The Jiangsu coastal area is located at a mid-point along the East Asia-Australasia migratory route for birds, serving as an important stopover and wintering site for migratory waterbirds. Research has shown a significant distribution of migratory waterbirds in wetland habitats such as coastlines, estuaries, and Nature Reserve. Analysis of the contribution rates of various environmental factors in waterbird distribution models indicates that the main influences on waterbird distribution include factors such as Spartina alterniflora , fisheries production, chemical plants, estuaries, and wind farms. The habitat characteristics of waterbirds differ from those of other bird species, being more significantly affected by land use(Sun et al., 2023). The Jiangsu coastal region features diverse wetland types and a rich variety of waterbird species, and it occupies an important geographical position along migratory routes. During the autumn and winter seasons, a large number of wintering waterbirds inhabit this area. Climate change has become a significant threat to biodiversity conservation, and waterbirds, as sensitive species of wetlands, respond particularly strongly to habitat changes(Ma et al., 2010; Wang et al., 2021). Studies have shown that changes in habitat can alter the migratory routes and wintering ranges of waterbirds(Wang et al., 2021). Given the important geographical position of the Jiangsu coastal region in the migration of waterbirds, we need to pay more attention to the impact of land use and landscape changes on waterbirds, in order to provide better stopover and wintering sites for migratory waterbirds. Environmental Factors Affecting the Suitability of Waterbird Habitats The population size and distribution of waterbirds are important indicators for assessing the ecological environment of wetlands(Qiu et al., 2024). Factors such as body size, bill length, and leg length influence the foraging habitats of different types of waterbirds, which are determined by water area and water depth conditions. Coastal waterbirds primarily rely on natural intertidal wetlands for foraging, but they also depend on artificial supratidal wetlands. During high tide, seawater inundates the intertidal mudflats, forcing geese, ducks, and shorebirds to seek refuge in nearby supratidal habitats (such as artificial wetlands)(Ge et al., 2006; Lilleyman et al., 2016). Each year, hundreds of bird species, totaling over ten million individuals, migrate through this area during the spring and autumn migration seasons(Melville et al., 2016). Due to economic development and population pressure, land use in the coastal areas of Jiangsu has changed due to activities such as land reclamation, the invasion of Spartina alterniflora , and the construction of wind farms, solar power facilities, and fishing ports(Xu et al., 2011). Existing wetlands are still facing varying degrees of loss and degradation, which will impact the community structure and habitat conditions of wintering bird populations. Research indicates that the invasion of Spartina alterniflora has complex ecological impacts on wintering waterbirds in southern Jiangsu. The vegetation in Spartina wetlands is relatively dense, with low food diversity and abundance, making it unsuitable for shorebirds and other waterfowl(Ma et al., 2011; Okoye et al., 2020). Additionally, the rapid spread of Spartina populations has led to the fragmentation of local reed community habitats. If not controlled, Spartina habitats will replace reed habitats, significantly affecting populations of waterfowl, reed warblers, and other species such as the Black-faced Bunting(Gan et al., 2009; Qu et al., 2023). Surveys show that the bird diversity index in Spartina habitats is the lowest, and Spartina mudflats lack the capacity to provide habitat for various bird species. The rapid expansion of Spartina has altered the structure of local plant communities, significantly reducing habitat quality for coastal birds, and the negative impacts on coastal bird populations are gradually becoming apparent(Li et al., 2009; Ma et al., 2011). Coastal migratory waterbirds primarily inhabit natural intertidal zones, where mudflats exposed during ebb tides and early flood phases provide critical foraging grounds for species such as shorebirds (Charadriiformes) and waterfowl (Anatidae) by sustaining abundant benthic macroinvertebrate prey(Ma et al., 2010). During high tides, these waterbirds predominantly utilize anthropogenic wetlands (e.g., reclaimed aquaculture ponds) behind seawalls as temporary roosting habitats(Lilleyman et al., 2016). However, large-scale coastal development (e.g., wind farms, photovoltaic installations, and aquaculture operations) has induced significant habitat alterations, adversely affecting waterbird habitat selection(Ma et al., 2019). Rapidly rotating wind turbines now pose a barrier effect along migratory flyways. Excessive reclamation has precipitated population declines in intertidal-dependent species due to the progressive loss of suitable foraging and roosting habitats. Consequently, within coastal development frameworks, it is imperative to establish protected areas or conservation zones to maintain critical stopover sites and wintering grounds for waterbirds along the East Asian-Australasian Flyway. Management Suggestions Based on species distribution modeling analyses, waterbird conservation in Jiangsu's coastal zone necessitates region-specific strategies: Within existing protected areas including the Yellow-Bohai Sea World Heritage Site, regulatory frameworks should be enhanced to preserve critical habitats, implement science-based restoration (e.g., eradicating invasive Spartina alterniflora with sustained monitoring), and increase ecological carrying capacity; in economically developed regions supporting high waterbird densities, spatial planning must reconcile industrial activities (offshore wind, solar, aquaculture) with habitat integrity; for areas with fragmented suitable habitat, establishing localized wetland parks or conservation zones is advised to maintain high-tide roosting areas during reclamation, alongside optimized water-level management in engineered wetlands (particularly reclamation impoundments) to provide roosting and wintering refugia; ultimately, ecological corridors should integrate nature reserves, wetland parks, and community conservation areas to establish a cohesive protected area network aligned with waterbird movement ecology. Declarations Funding This paper is financially supported by following projects: Youth Fund Project of Jiangsu Academy of Forestry (JAF-2022-01), and Jiangsu Forestry Science Technology Innovation and Extension Project (LYKJ[2023]08, LYKJ[2020]21). Author Contribution Xuan Wang : Conceptualisation, Formal analysis, Investigation, Writing—Original draft, Writing—Review & editing, Visualisation.Lei Wang: Conceptualisation, Methodology, Validation, Writing—Original draft, Xue Wang: Investigation, Writing—Review & editing. Qing Chang: Conceptualisation, Writing—Review & editing, Supervision, Funding acquisition.Jingjing Ding: Conceptualisation, Funding acquisition, Supervision, Writing—Review & editing,All authors reviewed and edited the manuscript. Acknowledgement We thank Weiming Zou, Ping Yuan, and Mingzhi Zhang for their assistance during the field surveys. 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Cite Share Download PDF Status: Published Journal Publication published 09 Oct, 2025 Read the published version in Wetlands Ecology and Management → Version 1 posted Editorial decision: Revision requested 26 Aug, 2025 Reviews received at journal 26 Aug, 2025 Reviewers agreed at journal 26 Aug, 2025 Reviews received at journal 08 Aug, 2025 Reviewers agreed at journal 03 Aug, 2025 Reviewers invited by journal 02 Aug, 2025 Editor assigned by journal 25 Jul, 2025 Submission checks completed at journal 24 Jul, 2025 First submitted to journal 24 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7202578","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":494822479,"identity":"3273d2f5-637e-4d40-a89e-8ff51f14515d","order_by":0,"name":"Xuan Wang","email":"","orcid":"","institution":"Jiangsu Academy of Forestry","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Wang","suffix":""},{"id":494822481,"identity":"589e8961-c573-4f92-be67-371a84b4f693","order_by":1,"name":"Lei Wang","email":"","orcid":"","institution":"Jiangsu Academy of 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Ding","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYJACZgYDIHngQOKDhIoa0rQkGzw4c4xYLSBwgIFN8mELM2Hl8jNyD38uKLgjx3fwwLOKxAY2Bv727gS8Wgxu5KVJzzB4Zix54EDajcQdMgwSZ85uwK9FIseMmcfgcOIGsJYzbECRXPxa5GfkGH8GaqkHaSlIbGMmrIXhRo6BNFBLggFQCwNRWgzOvDEDaTGcCQxkiYQzx3gI+kW+HeSwP4fl+W6cSfz4o6JGjr+9l4DD4EDiTAKI4iFSOQjwtx8gQfUoGAWjYBSMJAAASu5QYBalQlsAAAAASUVORK5CYII=","orcid":"","institution":"Jiangsu Academy of Forestry","correspondingAuthor":true,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Ding","suffix":""}],"badges":[],"createdAt":"2025-07-24 07:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7202578/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7202578/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11273-025-10096-7","type":"published","date":"2025-10-09T15:57:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88607280,"identity":"754b402e-fb6a-4563-bb42-b43ab51680f2","added_by":"auto","created_at":"2025-08-08 09:01:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":13745710,"visible":true,"origin":"","legend":"\u003cp\u003eThe location of Jiangsu coast and waterbird distribution sites based on observation\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7202578/v1/4f79a5b94d2a76926adc6253.png"},{"id":88608206,"identity":"cc1dabc2-43e9-4e47-9c50-a9bd66f532f0","added_by":"auto","created_at":"2025-08-08 09:09:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":253502,"visible":true,"origin":"","legend":"\u003cp\u003eEnvironmental variables used for the MaxEnt model\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7202578/v1/7c4ae51f819d75871453c787.png"},{"id":88607240,"identity":"36fbaf38-b01f-416d-8451-2480cb2cadd1","added_by":"auto","created_at":"2025-08-08 09:01:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":34339,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of potential distribution prediction of shorebirds\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7202578/v1/d0227cfaa9c39fcb543fd8ed.png"},{"id":88608685,"identity":"ab8b627b-f71a-43f3-922f-abe252237548","added_by":"auto","created_at":"2025-08-08 09:17:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":16099,"visible":true,"origin":"","legend":"\u003cp\u003eContribution arte of environment factors in MaxEnt modeling prediction the potential distribution of shorebirds\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7202578/v1/7e833978846fbc010879351a.png"},{"id":88608686,"identity":"3028fcb0-3d87-4cfa-9169-10ee2581624e","added_by":"auto","created_at":"2025-08-08 09:17:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1050301,"visible":true,"origin":"","legend":"\u003cp\u003eResponse curves of distribution probability of shorebirds for distance to Sporobolus (A), distance to fishery (B), distance to Chemistry (C), distance to River (D), distance to Windpower (E), Bio_12 (F) of the MaxEnt model.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-7202578/v1/78c3645b12e7b59feeb10266.png"},{"id":88608687,"identity":"d35c82ad-d069-437b-ace6-1e8897ee9da8","added_by":"auto","created_at":"2025-08-08 09:17:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":12792225,"visible":true,"origin":"","legend":"\u003cp\u003ePotential distribution area of shorebirds along Jiangsu coast based on MaxEnt\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-7202578/v1/b185aa2d30f262c51f08a41e.png"},{"id":93421301,"identity":"1314b69c-8a50-4f48-bdf9-e41ae2f7ecda","added_by":"auto","created_at":"2025-10-13 16:10:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":26218092,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7202578/v1/90ad918c-aac5-408f-b62c-f92ed877ca00.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Study on suitability assessment of waterbird habitats along Jiangsu coastal wetland of China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe spatial distribution of species is one of the research hotspots in disciplines such as ecology, biogeography, and conservation biology(Navarro et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In particular, with the increasing impact of global changes in recent years, issues such as habitat degradation, fragmentation, and loss of species have become increasingly severe, posing unprecedented threats to global biodiversity(Rands et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Barnosky et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In this context, studying and understanding the spatial distribution patterns of biodiversity not only provides a foundation for predicting the impacts of global changes on biodiversity but also helps to identify priority conservation areas, thereby utilizing limited resources to achieve optimal conservation outcomes.\u003c/p\u003e\u003cp\u003eThe spatial distribution of biodiversity is a comprehensive result of the spatial distribution of various species. However, traditional species distribution is mostly based on simple descriptions using administrative divisions, relying on expert subjective judgment, and neglecting environmental heterogeneity(Underwood et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). As a result, the accuracy of the results obtained from such data is often criticized. To address this, ecologists have proposed using Ecological Niche Models (ENMs), which utilize species distribution points and their associated environmental variables to infer the ecological requirements of species and simulate their distribution(Wiens et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Among these, the Maximum Entropy model (Maxent) is particularly advantageous because it only requires information on species occurrence points, avoiding the need for \"non-occurrence\" data that regression models require(Hucks and Leberg, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This makes simulating potential species distributions more convenient and easier, while also being more tolerant of data biases such as small sample sizes and irregular sampling(Li and Ding, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Maxent has shown excellent predictive performance and is widely used in various fields, including the distribution prediction of rare and endangered species(Zhang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), management of invasive species(Fernandes et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), assessing the impact of global climate change on species distribution(Anderson and Raza, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and biodiversity conservation assessments(Franklin, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). It has become an indispensable tool in large-scale research.\u003c/p\u003e\u003cp\u003eWaterbirds, as a unique group of higher organisms in wetlands, are an important component of wetland ecosystems and play a crucial role in maintaining wetland biodiversity(Franklin, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Furness and Greenwood, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Qiu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The coastal areas of Jiangsu have a dense network of rivers, with intricate waterways and rich coastal and river wetlands, resulting in abundant waterbird resources. Additionally, the Jiangsu coastline is located at a mid-point along the important migratory route for migratory birds between East Asia and Australia, serving as a significant stopover and wintering ground for migratory waterbirds, especially many rare and endangered species.\u003c/p\u003e\u003cp\u003eHowever, in recent decades, rapid urbanization and industrialization, along with the concentration of population and industrial activities, have led to increased development of coastal wetlands. Activities such as wind power, photovoltaic projects, fishing port construction, and land reclamation have severely degraded wetlands, resulting in a significant reduction of nearshore and coastal wetlands, as well as prominent ecological issues such as sediment accumulation and loss of biodiversity(Xu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cui et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Understanding the spatial distribution characteristics of waterbird diversity and prioritizing the protection of the most urgently needed areas with limited resources has become a core issue that needs to be addressed in the ecological civilization construction of Jiangsu's coastal wetlands. Therefore, this study combines field surveys with the Maxent model to simulate suitable areas for waterbirds along the Jiangsu coast. Based on this, it analyzes the suitable distribution areas for migratory waterbirds in Jiangsu's coastal region, aiming to provide a scientific basis for the planning of waterbird diversity conservation in Jiangsu's coastal areas.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eStudy Area\u003c/p\u003e\n\u003cp\u003eThe southern coastal region of Jiangsu is a transitional zone from the southern warm temperate zone to the northern subtropical zone, situated in the alluvial plain of the lower Yangtze River. It is located in a transition area between land and sea, with an average annual temperature of 15\u0026deg;C, average annual precipitation of 850 to 1280 mm, and average annual sunshine hours of 1900 to 2250. The Jiangsu coastal area is located in the middle section of the migratory route for birds between \u0026quot;East Asia and Australasia,\u0026quot; featuring vast natural tidal flats that serve as important \u0026quot;stops\u0026quot; for migratory birds. The coastline, which stretches over 900 kilometers, has diverse landscape patterns, including wind farms, photovoltaic installations, fishing ports, and areas affected by the invasion of \u003cem\u003eSpartina alterniflora\u003c/em\u003e. This research area includes the cities of Lianyungang, Yancheng, and Nantong. It encompasses Yancheng Wetland and Rare Birds NNR, Jiangsu Dafeng Elk NNR, and the first and second phases of the Yellow (Bo) Sea Migratory Bird Habitat.\u003c/p\u003e\n\u003cp\u003eData Sources and Preprocessing of Waterbird Distribution Sites\u003c/p\u003e\n\u003cp\u003eA total of 72 waterbird cluster distribution sites were obtained from field surveys along the coast. To reduce the spatial autocorrelation effect of the distribution points, points within a 1 km range of adjacent distribution points were removed through buffer zone analysis, resulting in 24 valid distribution data points (Figure 1). The coordinates of these distribution points were organized into CSV format and imported into ArcGIS 10.2 for subsequent analysis.\u003c/p\u003e\n\u003cp\u003eData Sources and Preprocessing of Environmental factors\u003c/p\u003e\n\u003cp\u003eThree types of environmental factors were selected as predictive variables for the distribution model of coastal migratory waterbirds: bioclimatic factors, land use types, and human activity factors(Chang et al., 2022; Fu et al., 2023). There is a certain correlation among ecological factors, so a correlation analysis of the environmental factors was conducted before applying them to the MaxEnt model. A multicollinearity analysis (SPSS 22.0) was performed on the bioclimatic factors, land use types, and human activity factors to test the correlation between ecological factors. If the Pearson correlation index between two ecological factors was greater than \u0026plusmn;0.9, the more representative ecological factor was selected. Ultimately, 11 environmental factors were obtained, including 2 bioclimatic factors (annual average temperature bio_1, annual average precipitation bio_12), 4 land use types (Normalized Difference Vegetation Index NDVI, estuary River, protected area Reserve, and smooth cordgrass Sporobolus), and 5 human activity factors (fishing port Fishing_port, photovoltaic Photovoltaic, fishery production Fishery, chemical plant Chemistry, and wind farm Windpower). All environmental variable layers were unified to the WGS-1984 coordinate system on the ArcGIS platform, with a raster size of 30S (approximately 835 meters), and converted to the ASC file format required by the MaxEnt model. The types and descriptions of ecological factors are detailed in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1. Environmental variables used for the MaxEnt model and their description\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnvironmental Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnvironmental Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShort name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 156px;\"\u003e\n \u003cp\u003eClimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eAnnual average temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eBio_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e℃\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eAnnual average precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eBio_12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 156px;\"\u003e\n \u003cp\u003eLand-use type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eNormalized Difference Vegetation Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNDVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eDistance from the estuary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eRiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eDistance from \u003cem\u003eSpartina alterniflora\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSporobolus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eDistance from the protected area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eReserve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 156px;\"\u003e\n \u003cp\u003eHuman activity factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eDistance from Fishing Port Terminal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eFishing_port\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eDistance from photovoltaic system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ePhotovoltaic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eDistance from fishery production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eFishery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eDistance from chemical factory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eChemistry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eDistance from wind farm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eWindpower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel Construction and Calculation\u003c/p\u003e\n\u003cp\u003eThe distribution point data of migratory waterbirds along the coast and environmental variable data were imported into MaxEnt software. A random selection of 75% of the distribution point data was used to construct the maximum entropy model for suitable habitat distribution of migratory waterbirds along the Jiangsu coast, while the remaining 25% of the points were used for model validation. The model for suitable habitat distribution of migratory waterbirds was constructed, and the average of the results from 10 repeated simulations was taken as the final result(Chang et al., 2022). The model employed Jackknife tests to analyze the importance of environmental factors and evaluated the model\u0026apos;s accuracy using the area under the ROC curve (AUC). The use of ROC curve analysis to evaluate the suitable habitat model for waterbirds is due to the fact that AUC is not affected by the judgment threshold and is currently recognized as one of the best methods for assessing the quality of distribution model predictions(Phillips and Dud\u0026iacute;k, 2008). The AUC value ranges from 0 to 1.0, with values closer to 1 indicating a greater correlation between the environmental variables and the geographic distribution model of the predicted objects, thus indicating better predictive performance. Generally, an AUC value less than 0.5 indicates that the prediction results are not reliable, an AUC value between 0.5 and 0.7 indicates average predictive ability, an AUC value between 0.7 and 0.9 indicates good predictive ability, and an AUC value above 0.9 is considered excellent(Ara\u0026uacute;jo and New, 2007).\u003c/p\u003e\n\u003cp\u003eImport the ASC file output from the model into ArcGIS 10.2 and convert it into a raster file to obtain the probability distribution map of suitable areas for waterbirds. The probability values range from 0 to 1.0, with higher values indicating greater suitability for waterbirds in that area. Using the Nature Break (Jean) method for reclassification, the probability distribution map of suitable areas for waterbirds along the Jiangsu coast is divided into three levels: 0 to 0.2 is classified as low suitability, 0.2 to 0.5 as medium suitability, and 0.5 to 1.00 as high suitability.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Results and Analysis","content":"\u003cp\u003eModel Prediction Accuracy\u003c/p\u003e\n\u003cp\u003e25% of the distribution data was selected as the validation dataset, while the remaining site data was used as the training dataset for model computation. The model was run randomly 10 times, and the Receiver Operating Characteristic (ROC) curve was plotted. The AUC values for the training and validation data of the MaxEnt prediction model were 0.893 and 0.877, respectively, which are significantly greater than the AUC value of random distribution (0.5). This indicates that the MaxEnt model\u0026apos;s predictions of coastal waterbird distribution have good accuracy and reliability.\u003c/p\u003e\n\u003cp\u003eKey Environmental Factors Affecting the Distribution of Coastal Waterbirds\u003c/p\u003e\n\u003cp\u003eAccording to the results of the Jackknife method analysis in the MaxEnt model, the weights of different ecological factors contributing to the model predictions can be displayed (Figure 4) along with their contribution rates to the distribution of waterbirds (Table 2). The results of the ecological factor contribution rates indicate that there are six ecological factors with contribution rates greater than 5% to the predictive model results, listed from highest to lowest as follows: distance to \u003cem\u003eSpartina alterniflora\u003c/em\u003e (contribution rate of 39.09%), distance to fishery working areas (contribution rate of 25.99%), distance to chemical plants (contribution rate of 8.94%), distance to estuaries (contribution rate of 8.92%), distance to wind farms (contribution rate of 6.11%), and annual average precipitation (Bio_12) (contribution rate of 5.31%). The cumulative contribution rate reaches 94.32%, indicating that \u003cem\u003eSpartina alterniflora\u003c/em\u003e, fishery production, chemical plants, estuaries, and wind farms are the main ecological factors influencing the distribution of migratory waterbirds along the Jiangsu coast.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 Importance rate of environmental variables in MaxEnt modeling and potential distribution of shorebirds\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"561\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eEnvironmental Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eImportance percent (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eEnvironmental Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eImportance percent (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eSporobolus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e39.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eReserve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eFishery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e25.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eFishing_port\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eChemistry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e8.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eNDVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eRiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e8.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003ePhotovoltaic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eWindpower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e6.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eBio_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eBio_12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e5.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFrom the perspective of six important environmental factors affecting the distribution of suitable areas for migratory waterbirds along the Jiangsu coast, in terms of land use types, the high suitability areas are located between 500 to 3600 meters from \u003cem\u003eSpartina alterniflora\u003c/em\u003e and less than 2600 meters from estuaries. Regarding human disturbance types, the high suitability areas are less than 4700 meters from fishing work areas, more than 14 kilometers from chemical plants, and over 8500 meters from wind farms. In terms of climate parameters, the high suitability areas have an average annual precipitation of less than 765 mm (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 Statistical analysis of five main environmental variables in areas of each suitable classes of shorebirds\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"561\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 243px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEcological factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ehigh suitability areas P\u0026ge;0.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 243px;\"\u003e\n \u003cp\u003eSporobolus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e500 ~ 3600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 243px;\"\u003e\n \u003cp\u003eFishery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e＜\u0026nbsp;4700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 243px;\"\u003e\n \u003cp\u003eChemistry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e＞\u0026nbsp;14000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 243px;\"\u003e\n \u003cp\u003eRiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e＜\u0026nbsp;2600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 243px;\"\u003e\n \u003cp\u003eWindpower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e＞ 8500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 243px;\"\u003e\n \u003cp\u003eBio_12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e＜ 765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePrediction of Potential Suitable Areas for Migratory Waterbirds\u003c/p\u003e\n\u003cp\u003eThe geographic coordinate information of migratory waterbird distribution along the Jiangsu coast obtained from field surveys, along with ecological factor data from distribution sites, was imported into the MaxEnt model. The ASC layers outputted by the model were then imported into ArcGIS, where resampling was conducted to extract the suitability rankings for migratory waterbirds. Based on the results of the field surveys of species distribution, the suitability rankings for migratory waterbirds along the Jiangsu coast were classified into low suitability areas (P \u0026lt; 0.2), medium suitability areas (0.2 \u0026le; P \u0026lt; 0.5), and high suitability areas (P \u0026ge; 0.5). The results showed that the suitable areas for migratory waterbirds along the Jiangsu coast (Figure 5) were highly consistent with the distribution areas identified in field surveys (Figure 1).\u003c/p\u003e\n\u003cp\u003eThe model prediction results indicated that the high suitability areas (P \u0026ge; 0.5) for migratory waterbirds along the Jiangsu coast are mainly concentrated in places such as Yancheng wetland and rare bird NNR, Tiaozini Wetland, Xiaoyangkou Wetland, Dongling Wetland, and Linhong River Estuary Wetland. The area of high suitability zones for migratory waterbirds along the Jiangsu coast is 928.20 km\u0026sup2;, accounting for 10.81% of the coastal monitoring area; the area of medium suitability zones is 3819.05 km\u0026sup2;, accounting for 44.47% of the coastal monitoring area; and the area of low suitability zones is 3840.30 km\u0026sup2;, accounting for 44.72% of the coastal monitoring area.\u003c/p\u003e"},{"header":"Conclusions and Discussion","content":"\u003cp\u003e\u003cstrong\u003eThe Importance of Jiangsu Coastal Wetlands for Migratory Waterbirds\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Jiangsu coastal area is located at a mid-point along the East Asia-Australasia migratory route for birds, serving as an important stopover and wintering site for migratory waterbirds. Research has shown a significant distribution of migratory waterbirds in wetland habitats such as coastlines, estuaries, and Nature Reserve. Analysis of the contribution rates of various environmental factors in waterbird distribution models indicates that the main influences on waterbird distribution include factors such as \u003cem\u003eSpartina alterniflora\u003c/em\u003e, fisheries production, chemical plants, estuaries, and wind farms. The habitat characteristics of waterbirds differ from those of other bird species, being more significantly affected by land use(Sun et al., 2023). The Jiangsu coastal region features diverse wetland types and a rich variety of waterbird species, and it occupies an important geographical position along migratory routes. During the autumn and winter seasons, a large number of wintering waterbirds inhabit this area. Climate change has become a significant threat to biodiversity conservation, and waterbirds, as sensitive species of wetlands, respond particularly strongly to habitat changes(Ma et al., 2010; Wang et al., 2021). Studies have shown that changes in habitat can alter the migratory routes and wintering ranges of waterbirds(Wang et al., 2021). Given the important geographical position of the Jiangsu coastal region in the migration of waterbirds, we need to pay more attention to the impact of land use and landscape changes on waterbirds, in order to provide better stopover and wintering sites for migratory waterbirds.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnvironmental Factors Affecting the Suitability of Waterbird Habitats\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe population size and distribution of waterbirds are important indicators for assessing the ecological environment of wetlands(Qiu et al., 2024). Factors such as body size, bill length, and leg length influence the foraging habitats of different types of waterbirds, which are determined by water area and water depth conditions. Coastal waterbirds primarily rely on natural intertidal wetlands for foraging, but they also depend on artificial supratidal wetlands. During high tide, seawater inundates the intertidal mudflats, forcing geese, ducks, and shorebirds to seek refuge in nearby supratidal habitats (such as artificial wetlands)(Ge et al., 2006; Lilleyman et al., 2016). Each year, hundreds of bird species, totaling over ten million individuals, migrate through this area during the spring and autumn migration seasons(Melville et al., 2016). Due to economic development and population pressure, land use in the coastal areas of Jiangsu has changed due to activities such as land reclamation, the invasion of \u003cem\u003eSpartina alterniflora\u003c/em\u003e, and the construction of wind farms, solar power facilities, and fishing ports(Xu et al., 2011). Existing wetlands are still facing varying degrees of loss and degradation, which will impact the community structure and habitat conditions of wintering bird populations.\u003c/p\u003e\n\u003cp\u003eResearch indicates that the invasion of \u003cem\u003eSpartina alterniflora\u003c/em\u003e has complex ecological impacts on wintering waterbirds in southern Jiangsu. The vegetation in Spartina wetlands is relatively dense, with low food diversity and abundance, making it unsuitable for shorebirds and other waterfowl(Ma et al., 2011; Okoye et al., 2020). Additionally, the rapid spread of Spartina populations has led to the fragmentation of local reed community habitats. If not controlled, Spartina habitats will replace reed habitats, significantly affecting populations of waterfowl, reed warblers, and other species such as the Black-faced Bunting(Gan et al., 2009; Qu et al., 2023). Surveys show that the bird diversity index in Spartina habitats is the lowest, and Spartina mudflats lack the capacity to provide habitat for various bird species. The rapid expansion of Spartina has altered the structure of local plant communities, significantly reducing habitat quality for coastal birds, and the negative impacts on coastal bird populations are gradually becoming apparent(Li et al., 2009; Ma et al., 2011).\u003c/p\u003e\n\u003cp\u003eCoastal migratory waterbirds primarily inhabit natural intertidal zones, where mudflats exposed during ebb tides and early flood phases provide critical foraging grounds for species such as shorebirds (Charadriiformes) and waterfowl (Anatidae) by sustaining abundant benthic macroinvertebrate prey(Ma et al., 2010). During high tides, these waterbirds predominantly utilize anthropogenic wetlands (e.g., reclaimed aquaculture ponds) behind seawalls as temporary roosting habitats(Lilleyman et al., 2016). However, large-scale coastal development (e.g., wind farms, photovoltaic installations, and aquaculture operations) has induced significant habitat alterations, adversely affecting waterbird habitat selection(Ma et al., 2019). Rapidly rotating wind turbines now pose a barrier effect along migratory flyways. Excessive reclamation has precipitated population declines in intertidal-dependent species due to the progressive loss of suitable foraging and roosting habitats. Consequently, within coastal development frameworks, it is imperative to establish protected areas or conservation zones to maintain critical stopover sites and wintering grounds for waterbirds along the East Asian-Australasian Flyway.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eManagement Suggestions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on species distribution modeling analyses, waterbird conservation in Jiangsu\u0026apos;s coastal zone necessitates region-specific strategies: Within existing protected areas including the Yellow-Bohai Sea World Heritage Site, regulatory frameworks should be enhanced to preserve critical habitats, implement science-based restoration (e.g., eradicating invasive \u003cem\u003eSpartina alterniflora\u003c/em\u003e with sustained monitoring), and increase ecological carrying capacity; in economically developed regions supporting high waterbird densities, spatial planning must reconcile industrial activities (offshore wind, solar, aquaculture) with habitat integrity; for areas with fragmented suitable habitat, establishing localized wetland parks or conservation zones is advised to maintain high-tide roosting areas during reclamation, alongside optimized water-level management in engineered wetlands (particularly reclamation impoundments) to provide roosting and wintering refugia; ultimately, ecological corridors should integrate nature reserves, wetland parks, and community conservation areas to establish a cohesive protected area network aligned with waterbird movement ecology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis paper is financially supported by following projects: Youth Fund Project of Jiangsu Academy of Forestry (JAF-2022-01), and Jiangsu Forestry Science Technology Innovation and Extension Project (LYKJ[2023]08, LYKJ[2020]21).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXuan Wang : Conceptualisation, Formal analysis, Investigation, Writing\u0026mdash;Original draft, Writing\u0026mdash;Review \u0026amp; editing, Visualisation.Lei Wang: Conceptualisation, Methodology, Validation, Writing\u0026mdash;Original draft, Xue Wang: Investigation, Writing\u0026mdash;Review \u0026amp; editing. Qing Chang: Conceptualisation, Writing\u0026mdash;Review \u0026amp; editing, Supervision, Funding acquisition.Jingjing Ding: Conceptualisation, Funding acquisition, Supervision, Writing\u0026mdash;Review \u0026amp; editing,All authors reviewed and edited the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Weiming Zou, Ping Yuan, and Mingzhi Zhang for their assistance during the field surveys. Lastly, many thanks to Youth Fund Project of Jiangsu Academy of Forestry and Jiangsu Forestry Science Technology Innovation and Extension Project for funding this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAnderson, R.P., Raza, A., 2010. The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela. 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Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Science of the total environment 634, 1326-1334.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"wetlands-ecology-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wetl","sideBox":"Learn more about [Wetlands Ecology and Management](https://www.springer.com/journal/11273)","snPcode":"11273","submissionUrl":"https://submission.nature.com/new-submission/11273/3","title":"Wetlands Ecology and Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Jiangsu coast, migratory waterbirds, habitat, distribution model, suitability","lastPublishedDoi":"10.21203/rs.3.rs-7202578/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7202578/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe coastal area of Jiangsu is located in the middle section of the migratory route for birds between \"East Asia and Australia.\" It features vast natural mudflats, serving as an important \"rest stop\" for migratory birds, with hundreds of species and over ten million migratory birds stopping or wintering here each year. The more than 900 kilometers of coastline presents a diverse landscape, including wind farms, photovoltaic installations, fishing ports, and areas invaded by \u003cem\u003eSpartina alterniflora\u003c/em\u003e. This diverse land use creates potential conflicts between bird habitat selection and human activities. This study integrates 11 environmental factors, including estuaries, fishing ports, wind power, photovoltaics, \u003cem\u003eSpartina alterniflora\u003c/em\u003e, climatic conditions, and vegetation normalized difference indices, to comprehensively analyze the suitability of habitats for migratory waterbirds along the Jiangsu coast. According to species distribution models, the environmental factor that most significantly affects the habitat suitability for migratory waterbirds is \u003cem\u003eSpartina alterniflora\u003c/em\u003e, followed by the distribution of fisheries, chemical plants, and estuaries. The main suitable distribution areas for migratory waterbirds in Jiangsu's coastal region are located in the Yancheng Rare Bird Protection Zone, Dongtai Tiaozi Mud Wetland, Lianyungang Linhong Estuary, Rudong Xiaoyangkou, and Dongling. In the context of the ongoing reduction and degradation of natural wetlands, the findings provide a scientific basis for the creation, restoration, and management of artificial habitats for coastal waterbirds.\u003c/p\u003e","manuscriptTitle":"Study on suitability assessment of waterbird habitats along Jiangsu coastal wetland of China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-08 09:01:35","doi":"10.21203/rs.3.rs-7202578/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-27T00:43:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-26T13:57:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335485307852438455307427741217524061597","date":"2025-08-26T13:48:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-08T10:28:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153677444023768185657607777098754644251","date":"2025-08-03T05:14:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-02T14:44:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-25T15:43:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-24T13:15:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Wetlands Ecology and Management","date":"2025-07-24T07:10:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"wetlands-ecology-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wetl","sideBox":"Learn more about [Wetlands Ecology and Management](https://www.springer.com/journal/11273)","snPcode":"11273","submissionUrl":"https://submission.nature.com/new-submission/11273/3","title":"Wetlands Ecology and Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b07641c9-4734-4cf1-978e-1082c9e2625d","owner":[],"postedDate":"August 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:09:31+00:00","versionOfRecord":{"articleIdentity":"rs-7202578","link":"https://doi.org/10.1007/s11273-025-10096-7","journal":{"identity":"wetlands-ecology-and-management","isVorOnly":false,"title":"Wetlands Ecology and Management"},"publishedOn":"2025-10-09 15:57:53","publishedOnDateReadable":"October 9th, 2025"},"versionCreatedAt":"2025-08-08 09:01:35","video":"","vorDoi":"10.1007/s11273-025-10096-7","vorDoiUrl":"https://doi.org/10.1007/s11273-025-10096-7","workflowStages":[]},"version":"v1","identity":"rs-7202578","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7202578","identity":"rs-7202578","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}