Identifying Restoration Areas Priority based on the Integration of Past Pattern and Future Trends of Ecosystem Services in Coastal City

Identifying Restoration Areas Priority based on the Integration of Past Pattern and Future Trends of Ecosystem Services in Coastal City | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Identifying Restoration Areas Priority based on the Integration of Past Pattern and Future Trends of Ecosystem Services in Coastal City Jingyao Hao, Yifan Feng, Xuyang Li, Na Qian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9402363/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Coastal cities are highly urbanized regions where ecological sensitivity and the continuous expansion of urban space pose significant challenges to the sustainable development of ecosystems. This study assessed ecosystem services (ESs) from an integrated past–future perspective and elucidated spatiotemporal evolution patterns, as well as trade-offs and synergies among multiple ESs in Tianjin over the past two decades and under three future scenarios. The results indicated that, from 2000 to 2020, ESs showed a declining trend, although localized improvements were observed in the peripheral zones of ring-shaped built-up areas and in coastal wetland regions. Spatial trade-offs remained the dominant pattern among ESs, although their influence was gradually diminishing. In contrast, spatial synergies increased significantly. By integrating ESs supply, spatial distribution patterns, and future trends, we identified and delineated ecological restoration priority areas (RAPs), which were classified into three sequential categories: Core Conservation Zones, Synergistic Optimization Zones, and Ecological Improvement Zones. Based on a comprehensive analysis of land-use composition and its spatial distribution within RAPs, we proposed targeted ecological restoration strategies and spatial planning recommendations. This study provides scientific spatial guidance for ecological restoration and sustainable land management in coastal cities by identifying and prioritizing RAPs. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Social science/Environmental studies Scientific community and society/Geography Social science/Geography Restoration Areas Priority (RAP) ecosystem services scenario simulation PLUS model coastal city Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1 Introduction The accelerating pace of global urbanization is exerting mounting pressure on natural ecosystems. As the primary centers of human settlement, cities are driving profound changes in land use and land cover (LULC). This has led to a range of ecological consequences, including habitat fragmentation, soil and water pollution, and resource scarcity. This has severely compromised the provision of ecosystem services (ESs) and constrained the sustainable development of regional ecological environments 1 – 2 . This tension is particularly acute in coastal cities, where rapid economic growth has driven the overexploitation of natural resources, thereby intensifying the conflict between ecological conservation and social development 3 – 4 . Globally, roughly 60% of coastal cities are experiencing ecological space compression and degradation of ecosystem service functions 5 . These pressures are particularly evident in the shrinkage of coastal wetlands and the decline of carbon sequestration capacity 6 – 7 . Given the escalating degradation of ecological functions in both urban and rural settings, it is imperative to prioritize the protection and restoration of ecological spaces 8 . Ecological Restoration Areas (ERAs) are spatially explicit zones designated for implementing targeted restoration measures in ecosystems degraded, damaged, or destroyed by natural disturbances or human activities 9 – 10 . The Society for Ecological Restoration emphasizes that ecological restoration sites are not merely degraded areas 11 – 12 ; rather, they represent spatial units with inherent restoration and recovery potential 13 . The overarching goal is to re-establish structural integrity, functional stability, and the capacity to deliver ESs. Prioritizing ecological restoration zones is fundamental to achieving targeted and cost-effective interventions 14 . From an ecological security perspective, this prioritization enables the identification of critical nodes that underpin regional ecological barrier functions. Prioritizing the restoration of such critical areas can preserve the overall connectivity and resilience of ecosystems at minimal cost 15 – 16 . Given the inherent constraints on funding and technical capacity for ecological restoration, prioritization assessments are essential to avoid the inefficiencies of a blanket approach and to maximize the return on investment in restoration efforts 17 . Priority zoning provides a quantitative foundation for land use regulation, which effectively resolves spatial conflicts between conservation and development objectives 18 – 19 . Existing research has established three major methodological frameworks for identifying ecological restoration areas. The first is the ecosystem service valuation approach 20 . This approach quantifies the current supply levels of key ESs—such as water conservation, soil retention, and biodiversity maintenance—using tools like the InVEST model and the equivalent factor method. Areas exhibiting low service functionality or supply–demand imbalances are subsequently identified as potential restoration sites. The second framework is the ecological sensitivity and risk assessment method 21 . This approach establishes an indicator system encompassing soil erosion, rocky desertification, and habitat fragmentation, thereby identifying highly sensitive areas and high-risk ecological zones as core restoration spaces. The third framework involves landscape pattern analysis and the ecological security pattern method 22 . This approach employs techniques such as Morphological Spatial Pattern Analysis (MSPA) and the Minimum Cumulative Resistance (MCR) model to identify key landscape units, including ecological source areas and ecological corridors. This approach delineates restoration areas by identifying fragmentation points and vulnerable zones within ecological networks 23 . Although the methods described above have formed a mature technical framework, existing research generally shares a key limitation: it prioritizes static assessments over dynamic analyses, lacking a systematic perspective that integrates multiple spatiotemporal dimensions. Recently, some scholars have attempted to delineate ecological management zones by integrating historical evolution patterns with future development trends, offering a novel approach to formulating sustainable ecological management strategies 24 – 25 . As a national central city and a key node in the coordinated development of the Beijing-Tianjin-Hebei region 26 – 27 , Tianjin has undergone rapid urbanization, resulting in a highly complex and heterogeneous internal ecological spatial pattern 28 . This pronounced spatial heterogeneity renders a one-size-fits-all approach to ecological management ineffective 29 . Therefore, scientifically identifying these heterogeneous characteristics and implementing refined ecological spatial zoning based on them is essential for balancing urban development with ecological conservation in Tianjin, and for enhancing the region's ecological resilience and long-term sustainability 30 2 Study area and data 2.1 Study area Tianjin (116°43′E–118°04′E, 38°34′N–40°15′N) is situated on the western shore of Bohai Bay, in the northeastern part of China's North China Plain. As the largest coastal open city in northern China, it serves as a national central city and the economic hub of the Bohai Rim region (Fig. 1 ). As a typical estuarine coastal city, Tianjin exhibits a unique composite ecosystem pattern encompassing mountains, rivers, lakes, seas, and farmlands, with its topography characterized by low-lying terrain. Since the beginning of the 21st century, Tianjin has established a core ecological security framework through the construction of the dual-city green ecological barrier. However, the region continues to face pressures from coastal zone encroachment, ecological corridor fragmentation, and climate change risks. The urgent need to reconcile the development of an international shipping hub with the enhancement of ecological resilience 31 – 32 makes Tianjin a compelling case study for exploring sustainable spatial management in coastal cities. 2.2 Data sources This study primarily uses the following datasets: LULC data (2000, 2010, 2020), basic geographic vector data, digital elevation model (DEM) data, socioeconomic data, and meteorological data (Table 1 ). Based on the study objectives and local conditions, LULC was reclassified into six categories: cultivated land, forest land, grassland, water areas, construction land, and unutilized land. All downloaded datasets were clipped to the extent of Tianjin's administrative boundary, projected to the WGS_1984_Albers coordinate system, and uniformly resampled to a 1000 m resolution. Table 1 Data Source Data Name Data Type Data Source Spatial resolution Land use Raster Resource and Environmental Science and Data Center, Chinese Academy of Sciences ( https://www.resdc.cn/ ) 1km Precipitation Raster National Earth System Science Data Center Shared Service Platform ( http://www.geodata.cn/ ) 1km Evaporation Raster National Earth System Science Data Center Shared Service Platform ( http://www.geodata.cn/ ) 1km Temperature Raster Resource and Environmental Science and Data Center, Chinese Academy of Sciences ( https://www.resdc.cn/ ) 1km Root Restriction Layer Depth Raster ( https://doi.org/10.1038/s41597-019-0345-6 ) 1km Plant Available Water Content Raster FAO, IIASA World Soil Database 1km Major rivers Shp Open Street Map ( https://www.openstreetmap.org ) DEM Raster Geospatial Data Cloud ( http:/www.gscloud.cn ) 1km NDVI Raster Resource and Environmental Science and Data Center, Chinese Academy of Sciences ( https://www.resdc.cn/ ) 1km Soil type Raster World Soil Information Database ( http://westdc.westgis.ac.cn/da ) 1km 3 Methods In this study, we developed a novel framework that integrates ecosystem evolution patterns with development trends to identify restoration priority areas (RAPs). The framework consists of four technical steps. Firstly, we quantified four key ESs in three historical time points (2000, 2010, and 2020). Secondly, we quantified the trade-offs and synergies among the four ESs. Thirdly, we simulated and predicted the spatial distribution of ESs under three future scenarios. Finally, by integrating the spatial distribution patterns of ESs, balancing their synergistic relationships, and incorporating future development trends, we identified three categories of ecological restoration areas with distinct priority levels and proposed corresponding optimization strategies for each (Fig. 2 ). 3.1 Ecosystem services (ESs) assessment This study employed the InVEST model as the core quantitative tool, utilizing its four key modules to systematically evaluate the spatiotemporal characteristics of ESs in Tianjin, which are Water Yield (WY), Carbon Storage and Sequestration (CS), Sediment Delivery Ratio (SR), and Habitat Quality (HQ) 33 – 34 . The InVEST model, with its moderate data requirements and capacity for spatially explicit representation, has been extensively validated in ecological assessments across multiple scales worldwide. Its outputs provide a critical quantitative foundation for ecological zoning management 35 – 36 . Detailed calculation methods are presented in Appendix A. 3.2 Spatial statistics Hotspot and coldspot analysis is a spatial data analysis technique used to identify statistically significant clusters of high and low values within a dataset 37 – 38 . This study employed hotspot and coldspot analysis to identify the concentration levels and spatial distribution patterns of ESs in the study area under different development scenarios from 2000 to 2020. This method calculates the Getis-Ord Gi∗ statistic for each spatial unit; the resulting z-scores and p-values are then used to identify statistically significant hotspots and coldspots. The Getis-Ord local statistic is expressed as: Here, x j denotes the attribute value of spatial element j , w i,j represents the spatial weight between elements i and j , and n is the total number of elements. The Gi∗ statistic for each element is returned as a z-score. A positive and statistically significant z-score indicates a hotspot, with higher values corresponding to more intense clustering of high attribute values. 3.3 Correlation analysis between ESs Pearson correlation coefficient analysis is a commonly used method for identifying trade-off and synergy relationships among ESs 39 . We performed a Pearson correlation analysis using the “corrplot” package in R version 4.5.1 to calculate correlation coefficients among ESs in Tianjin for 2000, 2010, and 2020. A positive correlation indicates a synergistic relationship between two ESs, while a negative correlation indicates a trade-off. The absolute value of the correlation coefficient reflects the strength of the relationship, with larger absolute values indicating stronger associations. 3.4 Multi-scenario Simulation Prediction 3.4.1 Multi-future scenarios The Coupled Model Intercomparison Project Phase 6 (CMIP6) integrates shared socioeconomic pathways (SSPs) with representative concentration pathways (RCPs) to construct future development scenarios under varying global climate change contexts 40 . Using SSP-RCP scenarios from CMIP6, we simulated the spatiotemporal distribution of ESs in Tianjin across multiple future scenarios. In this study, the PLUS model was employed to project land-use demand and spatial distribution in Tianjin for 2030 across three SSP-RCP scenarios (SSP126, SSP245, and SSP585). 1. SSP245: Based on China's land-use data from 2000 to 2020, this scenario assumes that future land-use changes will follow historical trends, with no major policy interventions or new planning initiatives. Under this scenario, the expansion and conversion of land use types retain the driving mechanisms and transition probabilities observed over the past two decades, reflecting spontaneous evolution shaped by the combined effects of socioeconomic development and natural conditions. 2. SSP126: This scenario prioritizes economic growth as the primary development objective. This scenario was designed to assess the impacts of economically driven land use pattern evolution on ESs, thereby providing data support and a scientific basis for achieving a dynamic balance between economic development and ecological conservation. Under this scenario, the transition probabilities among land use types were adjusted as follows: the probability of conversion from construction land to cultivated land, forest, grassland, water areas, and other land categories was reduced by 30%, while the probability of conversion from cultivated land, forest land, grassland, and unutilized land to construction land was increased by 20%. 3. SSP585: This scenario draws on key policy frameworks and emphasizes the strict implementation of the “three control lines” system, which are ecological protection red lines, permanently protected farmland, and urban development boundaries. In this scenario, nature reserves at all levels within Tianjin Municipality, as well as its water areas, were designated as development-restricted zones. The transition probabilities among land use types were adjusted accordingly: the probability of converting forest and grassland to construction land was reduced by 70%; the probability of converting farmland to construction land was reduced by 30%; and the probability of converting grassland to forest land was increased by 20%. Based on these simulation results, land-use transition matrices for each development scenario were calculated in ArcGIS, and the resulting matrices are shown in Fig. 3 . 3.4.2 LULC simulation based on PLUS model The PLUS (Patch-generating Land Use Simulation) model is a patch-based land use change simulation tool developed by the High-Performance Spatial Computational Intelligence Laboratory at China University of Geosciences (Wuhan). It is grounded in spatial self-organization and cellular automata theory for simulating land use dynamics 41 – 42 . Using the PLUS model, the LEAS module was first applied to estimate the development probabilities of various land use types from 2010 to 2020, as well as the contribution rates of driving factors to land use conversions during this period. Subsequently, land demand for each category in 2030 was derived through Markov chain calculations. Based on observed land-use changes from 2010 to 2020 and relevant policies and regulations, the transition cost matrix and neighborhood weights were adjusted accordingly. Finally, the CARS module was used to simulate and project land-use changes for 2030 under each scenario 43 . 4 Results 4.1 Spatiotemporal evolution characteristics of ESs 4.1.1 Spatial patterns of ESs Overall, ESs in Tianjin exhibited a declining trend. SC increased by 41.38%, while the magnitude of change in ESs was smaller during 2000–2010 than in 2010–2020. Over the two decades, HQ declined by 12.28%. CS initially increased and then decreased, with an overall change of 0.41% over the entire period. Spatially, CS, SC, and HQ all exhibited a distinct pattern of lower values in the south and higher values in the north (Fig. 4 ). In contrast, WY showed higher values in the southeast and lower values in the northwest, with a distribution pattern resembling a coastal river belt. High WY values were concentrated in the six central urban districts, Beidagang Reservoir, and the Binhai New Area. CS and HQ peaked in the northern mountainous areas and the six urban districts, while low values were observed along the southwestern coast. SC also reached its maximum in the northern mountainous areas. Driven by rapid urbanization, changes in water yield were primarily concentrated in the six urban districts and coastal areas, while alterations in SC, CS, and HQ were mainly observed in Xiqing District and Jinghai District. 4.1.2 Evolution of ESs From 2000 to 2020, ESs in Tianjin exhibited pronounced spatiotemporal differentiation. WY, CS, SC, and HQ followed divergent evolutionary trajectories across different periods, reflecting the interactive effects of natural environmental changes and urbanization processes (Fig. 5 ). Overall, ESs displayed a pattern of “two increases and two decreases”: WY showed continuous improvement, and CS remained broadly stable, while SC and HG experienced significant degradation. As a coastal city, Tianjin's ecosystem service evolution was substantially influenced by the protection of coastal wetlands and the implementation of the dual-city development strategy. Between 2000 and 2020, WY initially contracted, then expanded. From 2010 to 2020, approximately 20% of the city's area experienced a significant increase in WY capacity, with notable gains in the southern Binhai New Area and the periphery of the central urban district. Despite this localized improvement, the overall downward trend persisted. CS was the most significantly growing ecosystem service, displaying a spatial pattern characterized by a “central zone of marked growth flanked by stable areas to the north and south,” while the coastal belt emerged as the core area of degradation. SC remained broadly stable over the two decades, with some growth observed in the six urban districts and coastal areas. HQ showed a gradual improvement, with areas showing enhancement accounting for 18% of the region during 2000–2010. Degraded areas closely overlapped with the extent of urban expansion. 4.1.3 Analysis of spatial autocorrelation in ESs Based on the ESs of Tianjin from 2000 to 2020, we employed the Getis-Ord Gi* hotspot analysis tool in ArcGIS to identify the spatial distribution of ESs hotspots and coldspots for the years 2000, 2010, and 2020. As shown in Fig. 7 , WY hotspots were concentrated around the central urban area and radiated outward. Higher hotspot values were observed in the Tianjin Beidagang Wetland Nature Reserve in the south and the Tianjin Binhai National Marine Park in the east, with these values increasing over time. This trend may be attributed to the significant expansion of green space in central urban areas under ecological conservation scenarios, as well as broader urban greening and densification efforts. CS showed an increasing pattern of hotspots in central regions and a decreasing pattern in coastal areas. In the north, relatively high hotspot values were found in the Ji County National Geological Nature Park, the Panshan Scenic Area, and the Middle–Upper Proterozoic National Nature Reserve in Tianjin. Along the eastern coast, cold spot values decreased progressively over time. For SC and HQ, the spatial patterns of ESs hotspots and coldspots remained largely unchanged from 2000 to 2020, both exhibiting clustered distributions. SC hotspots were concentrated in the northern region, primarily around the Jizhou National Geological Nature Park, the Xiaying Huanshou Lake National Wetland Park, and the Panshan Scenic Area. HQ hotspots were mainly located in the northern and central regions—including the Tianjin Tuanbo Bird Nature Reserve and the Tianjin Ancient Coast and Wetland National Nature Reserve—as well as along the eastern coast, encompassing the Tianjin Binhai National Marine Park. These hotspots were predominantly found in areas with favorable ecological conditions, although hotspot intensity along the eastern coast decreased over time. 4.2 Trade-offs and synergies of ESs 4.2.1 Correlation analysis Six pairs of correlations were identified among the four ESs. With the exception of the HQ–SC pair in 2020, all correlations between ESs were statistically significant (p < 0.05) (Fig. 8 ). The correlations among the six ES pairs exhibited consistent patterns across the three study years (2000, 2010, and 2020). First, a significant trade-off relationship was observed only between WY and HQ, with the strength of this negative correlation slightly weakening over the 20-year period. Second, strong synergistic relationships emerged between SC and WY and between CS and HQ, both of which intensified over time. Third, synergistic relationships between CS and WY, HQ and SC, and CS and SC gradually weakened. 4.2.2 Spatial-temporal patterns of trade-offs/synergies between ES pairs Pronounced spatiotemporal heterogeneity was observed in the interactions among ESs in the study area (Fig. 9 ). Spatial trade-offs remained the dominant pattern, although their intensity gradually diminished, while spatial synergies strengthened over time. Specifically, high trade-off zones for WY–SC were primarily distributed around Taiping Town in the southern Binhai New Area. The construction of the Tianjin Binhai Douzhuang General Aviation Airport, which began in 2014, significantly disturbed the ecological functions of the surrounding areas. Over time, high-synergy zones between WY and SC expanded markedly. By 2020, contiguous areas of strong WY–SC synergy were distributed across the western ecological barrier region and the eastern coastal areas. Meanwhile, although the overall spatial synergy relationship for WY–HQ improved, trade-offs intensified in the Beidagang Wetland, along the Haihe River, and in localized areas along the eastern coast. In contrast, the spatial trade-off effect for WY–CS intensified significantly. Within Ji Prefecture, Wuqing District, Xiqing District, Jinnan District, and Jinghai District, construction land was concentrated in high spatial trade-off zones for WY–CS, with both the extent and intensity of these trade-offs increasing substantially over time. Although the SC–HQ spatial synergy remained dominant, trade-off relationships strengthened in the areas surrounding Dahuangbao Wetland, Tuanbo Wetland, and Beidagang Reservoir. Furthermore, the spatial patterns of SC–CS interactions exhibited a trend toward greater polarization: the trade-off relationship deteriorated notably in the southern part of Tianjin's main urban district, while the synergy improved in the Panshan region. Spatial heterogeneity was also pronounced for HQ–CS interactions. During the initial study period, strong spatial synergy was observed in the northern mountainous areas and along the eastern coast, but this synergy gradually diminished over time. By the end of the study period, HQ–CS trade-off zones were primarily distributed on the outskirts of urban built-up areas. 4.3 Multi‑scenario Land Use Simulation and Ecosystem Services Projection 4.3.1 Multi-scenario LULC simulation In this study, we put the 2000, 2020 LULC data and various driving factors into the PLUS model to simulate the LULC in 2020. Compared with the simulation results and actual 2020 LULC, the Kappa coefficient is 0.88, indicating the high accuracy. So, the model prediction results are reasonable and the same parameters could be employed to simulate and forecast LULC in 2030. Compared with the 2020 LULC in Tianjin, cultivated land and unused land will decrease to varying degrees under the three different development scenarios in 2030, while construction land will increase in all scenarios (Fig. 10 ). Under the SSP585 scenario, cultivated land will decline most sharply by 542 km², and construction land will expand greatly by 735 km². Forest and grassland will increase under SSP126 and SSP245 scenarios, whereas water area will only rise under SSP126. Under all three scenarios, construction land will mainly expand along urban fringes and coastal zones, primarily converted from cultivated land. In the SSP126 and SSP245 scenarios, the increased forest and grassland will be concentrated in the northern mountainous areas. 4.3.2 Ecosystem service values under different scenarios As shown in Fig. 11 , under the SSP245 scenario, WY declined in urban areas, with a clustered, large-scale pattern, while reductions in other regions followed a scattered, localized pattern. CS showed relatively uniform spatial distributions of both decreasing and increasing trends under the SSP245 and SSP126 scenarios, whereas the declining trend was more pronounced under SSP585. Across all three scenarios, areas surrounding the Beidagang Wetland Nature Reserve in southern Tianjin exhibited a shrinking encircling aggregation pattern, with the central and eastern regions experiencing a more pronounced overall decline under SSP585. SDR displayed a widespread and clustered decline under SSP245. Under SSP585, localized reductions occurred in the Panshan Scenic Area in northern Tianjin, the Yuqiao Reservoir region, and the urban areas of Tianjin. Under SSP126, both increasing and decreasing trends coexisted, with large-scale reductions concentrated in the northern and southwestern regions. Under the SSP585 scenario, HQ showed a more pronounced point-based decline. Notably, this scenario was associated with global warming, which in turn leads to glacial melt and increased precipitation. Given that WY and SDR are highly sensitive to precipitation, both services showed marked increases under SSP585. 5 Discussion 5.1 Identification of RAPs By integrating the spatial patterns of ecosystem service supply, the trade-offs and synergies among ecological functions, and future development trends, we identified and prioritized ecological restoration areas in Tianjin. These areas were classified into three categories: core conservation zones, synergistic optimization zones, and ecological improvement zones (Fig. 12 ). Core Protection Zones (CPZs) covered an area of 1,377.5 km² and were primarily distributed across three main subregions of Tianjin: the northern region (including Jizhou National Geological Nature Park, Panshan Scenic Area, and Yuqiao Reservoir); the central region (Tianjin Ancient Coast and Wetland National Nature Reserve and Tianjin Dahuangbao Wetland Nature Reserve); and the southern region (Tianjin Tuanbo Bird Nature Reserve, Beidagang Wetland, and the coastal strip). Land use in these zones was dominated by forest, water areas, wetlands, and unutilized land. CS and HQ exhibited high-value clusters, while SC demonstrated outstanding capacity. WY formed a continuous, high-value belt along waterways. The synergistic relationships between WY and SC and between CS and HQ were most pronounced in this area, establishing it as the region's core ecological security barrier. The ecological restoration strategy for Core Protection Zones followed a principle of “strict protection + functional enhancement.” This involved three key actions. Firstly, establishing ecological protection red lines to prohibit any development activities unrelated to ecological conservation. Secondly, restoring wetland connectivity by constructing ecological corridors to link fragmented wetland patches. Thirdly, implementing the Mountain Forest Enhancement Project to strengthen the synergistic effects of carbon sequestration and soil and water conservation. Synergistic Optimization Zones (SOZs) covered 54.5 km 2 and were relatively scattered, with higher concentrations along the eastern edge of Lutai Farm and in the western canal area. Land use in these zones consisted primarily of cultivated land and grassland, interspersed with small amounts of construction land. Representing an urban–rural transitional ecological space, SOZs exhibited pronounced trade-offs between WY and CS, particularly around construction land. However, they also held significant potential for the synergistic optimization of both ecological and productive functions. The ecological restoration strategy for Synergistic Optimization Zones followed the principle of “functional coordination with quality and efficiency enhancement.” This involved three key actions: First, advancing the ecological transformation of farmland to enhance soil fertility and carbon sequestration capacity. Second, restoring the ecological corridor along the canal to build a composite ecosystem. Third, optimizing land use patterns by converting scattered parcels of developed land back to grassland. Ecological Improvement Zones (EIZs) covered 207.28 km² and were primarily distributed along the northern shores of Yuqiao Reservoir, with additional small clusters scattered across the central-eastern regions. Land use in these zones consisted mainly of cultivated land and water areas, interspersed with fragmented forests. HQ remained at a medium-to-low level, while CS and SC capacities required enhancement. Additionally, these areas exhibited a certain degree of habitat fragmentation. The ecological restoration strategy for Ecological Improvement Zones centered on the principle of “vegetation reconstruction + patch integration.” This involved three key actions: First, implementing vegetation restoration projects to enhance regional carbon sequestration capacity and habitat suitability. Second, carrying out ecological patch integration to connect fragmented ecological spaces into a functional network. Third, strengthening water conservation functions to mitigate the impact of agricultural nonpoint source pollution on aquatic ecosystems. 5.2 Comparison with other studies of coastal cities and regions Tianjin is a typical coastal city characterized by an overall urban spatial pattern of “mountains–city–water.” The coastal areas and the transitional zones between Tianjin and Binhai serve as critical ecological transition zones, playing a vital role in safeguarding the ecological security of Tianjin and the broader Beijing–Tianjin–Hebei region. However, these areas are facing increasingly severe ecological pressures from urban expansion. Comparison of our findings with those from other coastal cities and regions revealed that: The spatial distribution of areas with high ESs provision levels and strong synergistic relationships was strongly correlated with topography, hydrological systems, and vegetation cover 44 – 46 . The northern Panshan area and the Haihe River basin emerged as key regions for providing ecological services. The trade-offs and synergies among ESs were closely linked to the urban–rural gradient 47 – 48 . In Tianjin, areas with high ES trade-offs were primarily distributed around the periphery of built-up zones and were significantly influenced by urban expansion. The coastal areas of Tianjin encompass diverse ecosystems—including tidal flats, wetlands, forests, and farmlands—that serve as vital corridors for biological migration and function as critical ecological security barriers 49 – 50 . These areas are important distribution zones for key ESs such as WY and HQ. However, due to the prevalence of saline-alkali soils, plant growth faces numerous challenges and is less resilient 51 . Therefore, efforts should be intensified to remediate and restore coastal soils, integrate fragmented ecological patches, and enhance the stability of wetland spaces to build more resilient coastal ecosystems 52 . Therefore, based on the historical evolution patterns and future development trends of ESs in this coastal city, we identified three types of ecologically important zones (EIZs) and proposed corresponding spatial optimization strategies and management recommendations for each. Given the similarities in the distribution of ESs across coastal cities and regions, these ecological spatial planning methodologies and management strategies possess a degree of universality, offering valuable references for the sustainable management of coastal urban ecosystems worldwide. 5.3 Limitations and future research This study provides important insights for the zoning and hierarchical management of ecological spaces in coastal cities. However, several limitations should be acknowledged. First, only four representative ESs were selected for quantitative assessment, potentially overlooking the influence of other ESs—such as crop production and cultural services—on ecological spatial patterns. Additionally, despite employing the widely used InVEST model, the findings remain subject to its inherent uncertainties and to the lack of field-based validation. Second, this study revealed that the diversity of water network patterns in coastal cities significantly influences ES supply levels. Future research could further explore ES heterogeneity by integrating the urban–rural gradient with the spatial characteristics of water networks, thereby enabling more accurate identification of restoration priority areas (RAPs) in coastal urban ecosystems. Third, the supply levels, trade-offs, and synergies of ES are influenced by multidimensional factors, including natural environmental conditions and socioeconomic drivers. Subsequent studies should investigate the mechanisms driving the spatial evolution of ecological restoration areas with different priority levels, with the aim of developing more practical, context-specific spatial management strategies. 6 Conclusion In this study, we proposed a framework that integrates ecosystem service quantification with multi-scenario simulation. Based on the historical evolution patterns of ESs in Tianjin and projections from multi-scenario simulations, we develop a method for identifying and prioritizing restoration areas (RAPs). The distribution of ESs in Tianjin generally showed lower values in the south and higher values in the north. Over the past two decades, overall ESs in Tianjin have declined, reflecting significant pressures on ecological conservation in the city. Among individual services, WY showed a significant overall increase, with high values distributed along water systems and marked improvement in areas surrounding the built-up zone. CS exhibited relatively little variation, with high values concentrated in the northern mountainous regions and low-value areas distributed in contiguous zones along the coast. SC continued to increase, with improvements observed in both the six central urban districts and coastal wetland areas. In contrast, HQ declined significantly, with degraded areas closely overlapping with urban expansion zones. Among the four ESs, significant trade-offs were observed between WY and HQ, with high trade-off areas primarily distributed around wetlands and coastal regions. In contrast, the relationships between WY and SC and between CS and HQ exhibited increasingly strong synergies, with high-synergy areas concentrated in the northern mountainous regions and the transitional zone between the twin cities. Comparison of projected ecosystem service levels for 2035 under three scenarios with the 2020 baseline revealed distinct scenario-dependent patterns: under SSP245, the SDR experienced a significant decline; under SSP585, CS and HQ showed more pronounced decreases; and under SSP126, the areas of improvement for WY, CS, and HQ expanded, with ESs overall exhibiting a positive trend. Based on the supply levels, spatial distribution, and future trajectories of ESs, we categorized ecological spaces into three tiers according to ecological restoration priority—from highest to lowest: Core Protection Zones, Collaborative Optimization Zones, and Ecological Improvement Zones—and proposed corresponding ecological restoration strategies and spatial planning recommendations for each. We hope that this research will contribute to the development of more sustainable land use patterns in Tianjin, alleviate the pressures of urban growth on ecosystems, and provide valuable case studies and scientific support for ecological space management in other coastal cities and regions. Declarations Competing interests The authors declare no competing interests. Funding Hebei Provincial Natural Science Foundation project under Grant (E2025202167) and Scientific Research Project for Institutions of Higher Education in Hebei Province (QN2026320). Author Contribution All authors contributed to the study conception and design. Conceptualization, visualization, software, data curation, and resources were performed by J.H.; Methodology, writing—review & editing, software, and supervision were performed by Y.F.; Validation, software, and project administration were performed by X.L.; Visualization and formal analysis were performed by N. Q. All authors contributed to the interpretation of the data and narrative, and participated in revising the manuscript through multiple versions. All authors read and approved the final manuscript. Data Availability All data generated or analysed during this study are included in this published article. The data supporting the conclusions of this article are available from the corresponding author upon reasonable request. References Chen, X. et al. Identification of priority areas for territorial ecological conservation and restoration based on ecological networks: a case study of Tianjin City, China. Ecol. Ind. 146 , 109809 (2023). Xu, C., Jiang, W. Y., Huang, Q. Y. & Wang, Y. T. Ecosystem services response to rural-urban transitions in coastal and island cities: a comparison between Shenzhen and Hong Kong, China. J. Clean. Prod. 260 , 121033 (2020). Yu, H., Zhang, F., Yu, H. & Li, Y. 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Spatio-temporal evolution and multi-scenario prediction of ecosystem service value of resource-based cities in Henan Province based on PLUS model. J. Environ. Sci. 46 , 7878–7888 (2025). Xia, H. et al. Spatial-temporal heterogeneity of ecosystem service interactions and their social-ecological drivers: implications for spatial planning and management. Resour. Conserv. RECYCLING . 189 , 106767 (2023). Cai, G. et al. Predicting the ecosystem service values and constructing ecological security patterns in future changing land use patterns. Ecol. Ind. 154 , 110787 (2023). Liang, X. et al. Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: a case study in Wuhan, China. Computers Environ. Urban Syst. 85 , 101569 (2021). Wang, Z. Y. et al. A new approach to land use optimization and simulation considering urban development sustainability: a case study of Bortala, China. SUSTAINABLE CITIES Soc. 87 , 104135 (2022). Zhang, X. et al. Simulation of multi-scenario land use change and its impact on ecosystem services in China based on PLUS-InVEST model. Acta Ecol. Sin. 45 , 9577–9593 (2025). Ren, Q. R. et al. Spatio-temporal dynamics and socio-ecological determinants of ecosystem service interplays in Shandong Province's coastal region (2000–2020): implications for environmental protection and sustainable ecosystem management. Environ. Res. 243 , 117824 (2024). Wang, P. J. et al. Spatial-temporal changes in ecosystem services and social-ecological drivers in a typical coastal tourism city: a case study of Sanya, China. Ecol. Ind. 145 , 109607 (2022). Liu, C. et al. Ecosystem service multifunctionality assessment and coupling coordination analysis with land use and land cover change in China's coastal zones. Sci. Total Environ. 797 , 149033 (2021). Nardella, L. et al. Modeling regulating ecosystem services along the urban–rural gradient: a comprehensive analysis in seven Italian coastal cities. Ecol. Ind. 165 , 112161 (2024). Kim, I., Kwon, H., Kim, S., Son, Y. & Jun, B. Identification of landscape multifunctionality along urban-rural gradient of coastal cities in South Korea. URBAN Ecosyst. 23 , 1153–1163 (2020). Ke, L., Jiang, Q., Lu, Y., Wang, X. & Liu, Y. Coupling analysis of resident well-being and ecosystem services under sustainable development goals: a case study of China’s coastal regions. Ecol. Ind. 179 , 114234 (2025). de Andrés, M. et al. Mapping services for an ecosystem based management along the Andalusian coastal zone (Spain). Ocean. Coastal. Manage. 231 , 106402 (2023). Li, Z. et al. Ecological risk assessment of the wetlands in Beijing-Tianjin-Hebei urban agglomeration. Ecol. Ind. 117 , 106677 (2020). Ni, J. Y. & Xu, F. Remote sensing-based analysis of the coupled impacts of climate and land use changes on future ecosystem resilience: a case study of the Beijing-Tianjin-Hebei region. Remote Sens. 17 , 2546 (2025). Additional Declarations No competing interests reported. Supplementary Files IdentifyingRestorationAreasPrioritybasedontheIntegrationofPastPatternandFutureTrendsofEcosystemServicesinCoastalCity.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 15 May, 2026 Reviewers agreed at journal 15 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviewers invited by journal 06 May, 2026 Editor assigned by journal 06 May, 2026 Editor invited by journal 04 May, 2026 Submission checks completed at journal 29 Apr, 2026 First submitted to journal 29 Apr, 2026 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. 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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-9402363","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":640134151,"identity":"8d68bfd8-8dc5-4761-8185-1d993f31ae8d","order_by":0,"name":"Jingyao Hao","email":"","orcid":"","institution":"Hebei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jingyao","middleName":"","lastName":"Hao","suffix":""},{"id":640134152,"identity":"54f89ba6-7a73-40d4-b546-ed1e2c56dcfe","order_by":1,"name":"Yifan Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACfmbmAwYJPBJyBgw8YAHGBkJaJNvbEgo+yFgYg7UcIEaLwZkzBh9n2FQkbiBaC8ONBMPNPDkS6dvZe49Jf2Cwkd1wgPnZA3w6GGckJBvznJHI3dlzLk3iAEOa8YYDbOYG+LQwSyQcM+btkcjdcCPHDKjlcOKGAzxsEvi0sEkktv/m/SeRbnD/DUjLf8JaeHgOMxjO4JFIMLjBA9JygLAWCfY2BoMPPBKGG87kJVucMUg2nnmYzQyvFvvD/B+AUVknb3D87MEbFRV2sn3Hm5/h1YIGQEHFTIL6UTAKRsEoGAXYAQC9HEwH9ldMzQAAAABJRU5ErkJggg==","orcid":"","institution":"Hebei University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Yifan","middleName":"","lastName":"Feng","suffix":""},{"id":640134153,"identity":"27540586-9d97-4827-8c46-5201a961b42b","order_by":2,"name":"Xuyang Li","email":"","orcid":"","institution":"Hebei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xuyang","middleName":"","lastName":"Li","suffix":""},{"id":640134154,"identity":"037ea66a-8467-4641-82a7-9df444e6fb50","order_by":3,"name":"Na Qian","email":"","orcid":"","institution":"Hebei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Qian","suffix":""}],"badges":[],"createdAt":"2026-04-13 10:10:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9402363/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9402363/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109300921,"identity":"1d3a067a-9742-4a8b-815c-b7757a6b9027","added_by":"auto","created_at":"2026-05-15 09:24:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":404980,"visible":true,"origin":"","legend":"\u003cp\u003e(a) location and elevation; (b) LULC in 2000; (c) LULC in 2010; (d) LULC in 2020.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9402363/v1/a6762f9b8ae191f587adf10d.png"},{"id":109301050,"identity":"583e3e19-43f5-4bdf-a176-a3b5d22f0ed5","added_by":"auto","created_at":"2026-05-15 09:25:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":506455,"visible":true,"origin":"","legend":"\u003cp\u003eResearch Framework\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9402363/v1/16bd0a06494f61347a2bca84.png"},{"id":109301788,"identity":"c52251b9-b344-4151-a05e-3a1283abb999","added_by":"auto","created_at":"2026-05-15 09:27:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":98686,"visible":true,"origin":"","legend":"\u003cp\u003eTransition Cost Matrix Across Different Scenarios\u003c/p\u003e\n\u003cp\u003e(Note: a—cultivated land; b—forest; c—grassland; d—water; e—construction land; f—unused land. Values for SSP245 are shown in black, SSP126 in green, and SSP585 in red. A matrix value of 0 indicates that conversion between the two land use types is not permitted, while a value of 1 indicates that conversion is allowed.)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9402363/v1/24472d6bfb36729d502adf82.png"},{"id":109302239,"identity":"4ace96e6-3118-482b-bc3a-487895f4d5ef","added_by":"auto","created_at":"2026-05-15 09:28:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":352388,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial change of ESs from 2000 to 2020.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9402363/v1/7a4b71e3c32c5c651f8d51f6.png"},{"id":109301094,"identity":"e8e9b8f3-886b-451d-ab58-6161aa9ab192","added_by":"auto","created_at":"2026-05-15 09:25:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":283970,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial change pattern of ESs in Tianjin from 2000 to 2020.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9402363/v1/80d5fd8eee17f234937d879e.png"},{"id":109301310,"identity":"68620fc4-e4a9-4f51-9d2d-5569ccc85361","added_by":"auto","created_at":"2026-05-15 09:25:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":93576,"visible":true,"origin":"","legend":"\u003cp\u003eCange rate of ESs from 2000 to 2020.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9402363/v1/d14a50e79a95962cce321b95.png"},{"id":109301146,"identity":"c99c483c-9a63-4d50-9668-cf2f95ead010","added_by":"auto","created_at":"2026-05-15 09:25:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":242225,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of ESs hot and cold spots in the study area under multiple scenarios.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9402363/v1/c07ddebc734cc3b46f759a42.png"},{"id":109300904,"identity":"ca46a899-66a9-41c3-b626-c1ac3bf79fc3","added_by":"auto","created_at":"2026-05-15 09:24:36","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":191791,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations among ESs in 2000, 2010, 2020, and correlation variation.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9402363/v1/330fb23e6fcb7e9963f2feba.png"},{"id":109301254,"identity":"ab80af7a-b42a-41bc-9f35-045c7ee75e55","added_by":"auto","created_at":"2026-05-15 09:25:49","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":196847,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial trade-offs and synergies patterns and area ratio for four types of ESs.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-9402363/v1/1b72488ce577fb819378a306.png"},{"id":109301341,"identity":"2fb87395-4368-4229-8bde-6b19777e059c","added_by":"auto","created_at":"2026-05-15 09:26:04","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":317486,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial Distribution of LULC in 2020 and 2030 Under Three Different Scenarios.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-9402363/v1/f1f7811a41d58aa2ca69002a.png"},{"id":109301212,"identity":"2f3b2136-19c1-48d4-b2f4-8ee1ed2cb4b6","added_by":"auto","created_at":"2026-05-15 09:25:39","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":769724,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in each type of ESs in 2000-2020 under different scenarios\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-9402363/v1/063d972525cee1126393bba3.png"},{"id":109300951,"identity":"2454d13c-88d5-4fcd-a6b7-e23283807120","added_by":"auto","created_at":"2026-05-15 09:24:51","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":239533,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial-temporal heterogeneity of ecosystem service interactions and their social-ecological drivers: Implications for spatial planning and management.\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-9402363/v1/2f7d4b092831d9ec452949a0.png"},{"id":109302306,"identity":"209ce147-14c2-4432-90d6-0a2235b350f1","added_by":"auto","created_at":"2026-05-15 09:28:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3858849,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9402363/v1/ac53e8c6-2392-4b61-8ede-6ca2705cdac6.pdf"},{"id":109300906,"identity":"ea63d06e-80a4-49bc-9398-63e320e45af3","added_by":"auto","created_at":"2026-05-15 09:24:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23587,"visible":true,"origin":"","legend":"","description":"","filename":"IdentifyingRestorationAreasPrioritybasedontheIntegrationofPastPatternandFutureTrendsofEcosystemServicesinCoastalCity.docx","url":"https://assets-eu.researchsquare.com/files/rs-9402363/v1/fe406513a45fe700ce6c4898.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identifying Restoration Areas Priority based on the Integration of Past Pattern and Future Trends of Ecosystem Services in Coastal City","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe accelerating pace of global urbanization is exerting mounting pressure on natural ecosystems. As the primary centers of human settlement, cities are driving profound changes in land use and land cover (LULC). This has led to a range of ecological consequences, including habitat fragmentation, soil and water pollution, and resource scarcity. This has severely compromised the provision of ecosystem services (ESs) and constrained the sustainable development of regional ecological environments\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This tension is particularly acute in coastal cities, where rapid economic growth has driven the overexploitation of natural resources, thereby intensifying the conflict between ecological conservation and social development\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Globally, roughly 60% of coastal cities are experiencing ecological space compression and degradation of ecosystem service functions\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. These pressures are particularly evident in the shrinkage of coastal wetlands and the decline of carbon sequestration capacity\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Given the escalating degradation of ecological functions in both urban and rural settings, it is imperative to prioritize the protection and restoration of ecological spaces\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEcological Restoration Areas (ERAs) are spatially explicit zones designated for implementing targeted restoration measures in ecosystems degraded, damaged, or destroyed by natural disturbances or human activities\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The Society for Ecological Restoration emphasizes that ecological restoration sites are not merely degraded areas\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e; rather, they represent spatial units with inherent restoration and recovery potential\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The overarching goal is to re-establish structural integrity, functional stability, and the capacity to deliver ESs. Prioritizing ecological restoration zones is fundamental to achieving targeted and cost-effective interventions\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. From an ecological security perspective, this prioritization enables the identification of critical nodes that underpin regional ecological barrier functions. Prioritizing the restoration of such critical areas can preserve the overall connectivity and resilience of ecosystems at minimal cost\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Given the inherent constraints on funding and technical capacity for ecological restoration, prioritization assessments are essential to avoid the inefficiencies of a blanket approach and to maximize the return on investment in restoration efforts\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Priority zoning provides a quantitative foundation for land use regulation, which effectively resolves spatial conflicts between conservation and development objectives\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eExisting research has established three major methodological frameworks for identifying ecological restoration areas. The first is the ecosystem service valuation approach\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. This approach quantifies the current supply levels of key ESs\u0026mdash;such as water conservation, soil retention, and biodiversity maintenance\u0026mdash;using tools like the InVEST model and the equivalent factor method. Areas exhibiting low service functionality or supply\u0026ndash;demand imbalances are subsequently identified as potential restoration sites. The second framework is the ecological sensitivity and risk assessment method\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This approach establishes an indicator system encompassing soil erosion, rocky desertification, and habitat fragmentation, thereby identifying highly sensitive areas and high-risk ecological zones as core restoration spaces. The third framework involves landscape pattern analysis and the ecological security pattern method\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This approach employs techniques such as Morphological Spatial Pattern Analysis (MSPA) and the Minimum Cumulative Resistance (MCR) model to identify key landscape units, including ecological source areas and ecological corridors. This approach delineates restoration areas by identifying fragmentation points and vulnerable zones within ecological networks\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Although the methods described above have formed a mature technical framework, existing research generally shares a key limitation: it prioritizes static assessments over dynamic analyses, lacking a systematic perspective that integrates multiple spatiotemporal dimensions. Recently, some scholars have attempted to delineate ecological management zones by integrating historical evolution patterns with future development trends, offering a novel approach to formulating sustainable ecological management strategies\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs a national central city and a key node in the coordinated development of the Beijing-Tianjin-Hebei region\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, Tianjin has undergone rapid urbanization, resulting in a highly complex and heterogeneous internal ecological spatial pattern\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. This pronounced spatial heterogeneity renders a one-size-fits-all approach to ecological management ineffective\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Therefore, scientifically identifying these heterogeneous characteristics and implementing refined ecological spatial zoning based on them is essential for balancing urban development with ecological conservation in Tianjin, and for enhancing the region's ecological resilience and long-term sustainability\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"2 Study area and data","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eTianjin (116\u0026deg;43\u0026prime;E\u0026ndash;118\u0026deg;04\u0026prime;E, 38\u0026deg;34\u0026prime;N\u0026ndash;40\u0026deg;15\u0026prime;N) is situated on the western shore of Bohai Bay, in the northeastern part of China's North China Plain. As the largest coastal open city in northern China, it serves as a national central city and the economic hub of the Bohai Rim region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As a typical estuarine coastal city, Tianjin exhibits a unique composite ecosystem pattern encompassing mountains, rivers, lakes, seas, and farmlands, with its topography characterized by low-lying terrain. Since the beginning of the 21st century, Tianjin has established a core ecological security framework through the construction of the dual-city green ecological barrier. However, the region continues to face pressures from coastal zone encroachment, ecological corridor fragmentation, and climate change risks. The urgent need to reconcile the development of an international shipping hub with the enhancement of ecological resilience\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e makes Tianjin a compelling case study for exploring sustainable spatial management in coastal cities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data sources\u003c/h2\u003e \u003cp\u003eThis study primarily uses the following datasets: LULC data (2000, 2010, 2020), basic geographic vector data, digital elevation model (DEM) data, socioeconomic data, and meteorological data (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Based on the study objectives and local conditions, LULC was reclassified into six categories: cultivated land, forest land, grassland, water areas, construction land, and unutilized land. All downloaded datasets were clipped to the extent of Tianjin's administrative boundary, projected to the WGS_1984_Albers coordinate system, and uniformly resampled to a 1000 m resolution.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData Source\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpatial resolution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResource and Environmental Science and Data Center, Chinese Academy of Sciences\u003c/p\u003e \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.resdc.cn/\u003c/span\u003e\u003cspan address=\"https://www.resdc.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNational Earth System Science Data Center Shared Service Platform\u003c/p\u003e \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.geodata.cn/\u003c/span\u003e\u003cspan address=\"http://www.geodata.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvaporation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNational Earth System Science Data Center Shared Service Platform\u003c/p\u003e \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.geodata.cn/\u003c/span\u003e\u003cspan address=\"http://www.geodata.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResource and Environmental Science and Data Center, Chinese Academy of Sciences\u003c/p\u003e \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.resdc.cn/\u003c/span\u003e\u003cspan address=\"https://www.resdc.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoot Restriction Layer Depth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41597-019-0345-6\u003c/span\u003e\u003cspan address=\"10.1038/s41597-019-0345-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant Available Water Content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFAO, IIASA World Soil Database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor rivers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOpen Street Map\u003c/p\u003e \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.openstreetmap.org\u003c/span\u003e\u003cspan address=\"https://www.openstreetmap.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeospatial Data Cloud\u003c/p\u003e \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp:/www.gscloud.cn\u003c/span\u003e\u003cspan address=\"http://www.gscloud.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResource and Environmental Science and Data Center, Chinese Academy of Sciences\u003c/p\u003e \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.resdc.cn/\u003c/span\u003e\u003cspan address=\"https://www.resdc.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWorld Soil Information Database\u003c/p\u003e \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://westdc.westgis.ac.cn/da\u003c/span\u003e\u003cspan address=\"http://westdc.westgis.ac.cn/da\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Methods","content":"\u003cp\u003eIn this study, we developed a novel framework that integrates ecosystem evolution patterns with development trends to identify restoration priority areas (RAPs). The framework consists of four technical steps. Firstly, we quantified four key ESs in three historical time points (2000, 2010, and 2020). Secondly, we quantified the trade-offs and synergies among the four ESs. Thirdly, we simulated and predicted the spatial distribution of ESs under three future scenarios. Finally, by integrating the spatial distribution patterns of ESs, balancing their synergistic relationships, and incorporating future development trends, we identified three categories of ecological restoration areas with distinct priority levels and proposed corresponding optimization strategies for each (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Ecosystem services (ESs) assessment\u003c/h2\u003e\n \u003cp\u003eThis study employed the InVEST model as the core quantitative tool, utilizing its four key modules to systematically evaluate the spatiotemporal characteristics of ESs in Tianjin, which are Water Yield (WY), Carbon Storage and Sequestration (CS), Sediment Delivery Ratio (SR), and Habitat Quality (HQ) \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The InVEST model, with its moderate data requirements and capacity for spatially explicit representation, has been extensively validated in ecological assessments across multiple scales worldwide. Its outputs provide a critical quantitative foundation for ecological zoning management\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Detailed calculation methods are presented in Appendix A.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Spatial statistics\u003c/h2\u003e\n \u003cp\u003eHotspot and coldspot analysis is a spatial data analysis technique used to identify statistically significant clusters of high and low values within a dataset\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. This study employed hotspot and coldspot analysis to identify the concentration levels and spatial distribution patterns of ESs in the study area under different development scenarios from 2000 to 2020. This method calculates the Getis-Ord Gi\u0026lowast; statistic for each spatial unit; the resulting z-scores and p-values are then used to identify statistically significant hotspots and coldspots. The Getis-Ord local statistic is expressed as:\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cimg src=\"https://myfiles.space/user_files/58893_b39df98f09c4a4bb/58893_custom_files/img1778834777.png\" width=\"472\" height=\"306\"\u003e\u003c/span\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eHere, \u003cem\u003ex\u003c/em\u003e\u003csub\u003ej\u003c/sub\u003e denotes the attribute value of spatial element \u003cem\u003ej\u003c/em\u003e, \u003cem\u003ew\u003c/em\u003e\u003csub\u003ei,j\u003c/sub\u003e represents the spatial weight between elements \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e, and \u003cem\u003en\u003c/em\u003e is the total number of elements. The \u003cem\u003eGi\u0026lowast;\u003c/em\u003e statistic for each element is returned as a z-score. A positive and statistically significant z-score indicates a hotspot, with higher values corresponding to more intense clustering of high attribute values.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Correlation analysis between ESs\u003c/h2\u003e\n \u003cp\u003ePearson correlation coefficient analysis is a commonly used method for identifying trade-off and synergy relationships among ESs\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. We performed a Pearson correlation analysis using the \u0026ldquo;corrplot\u0026rdquo; package in R version 4.5.1 to calculate correlation coefficients among ESs in Tianjin for 2000, 2010, and 2020. A positive correlation indicates a synergistic relationship between two ESs, while a negative correlation indicates a trade-off. The absolute value of the correlation coefficient reflects the strength of the relationship, with larger absolute values indicating stronger associations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Multi-scenario Simulation Prediction\u003c/h2\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.1 Multi-future scenarios\u003c/h2\u003e\n \u003cp\u003eThe Coupled Model Intercomparison Project Phase 6 (CMIP6) integrates shared socioeconomic pathways (SSPs) with representative concentration pathways (RCPs) to construct future development scenarios under varying global climate change contexts\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Using SSP-RCP scenarios from CMIP6, we simulated the spatiotemporal distribution of ESs in Tianjin across multiple future scenarios. In this study, the PLUS model was employed to project land-use demand and spatial distribution in Tianjin for 2030 across three SSP-RCP scenarios (SSP126, SSP245, and SSP585).\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e1. SSP245: Based on China\u0026apos;s land-use data from 2000 to 2020, this scenario assumes that future land-use changes will follow historical trends, with no major policy interventions or new planning initiatives. Under this scenario, the expansion and conversion of land use types retain the driving mechanisms and transition probabilities observed over the past two decades, reflecting spontaneous evolution shaped by the combined effects of socioeconomic development and natural conditions.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e2. SSP126: This scenario prioritizes economic growth as the primary development objective. This scenario was designed to assess the impacts of economically driven land use pattern evolution on ESs, thereby providing data support and a scientific basis for achieving a dynamic balance between economic development and ecological conservation. Under this scenario, the transition probabilities among land use types were adjusted as follows: the probability of conversion from construction land to cultivated land, forest, grassland, water areas, and other land categories was reduced by 30%, while the probability of conversion from cultivated land, forest land, grassland, and unutilized land to construction land was increased by 20%.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e3. SSP585: This scenario draws on key policy frameworks and emphasizes the strict implementation of the \u0026ldquo;three control lines\u0026rdquo; system, which are ecological protection red lines, permanently protected farmland, and urban development boundaries. In this scenario, nature reserves at all levels within Tianjin Municipality, as well as its water areas, were designated as development-restricted zones. The transition probabilities among land use types were adjusted accordingly: the probability of converting forest and grassland to construction land was reduced by 70%; the probability of converting farmland to construction land was reduced by 30%; and the probability of converting grassland to forest land was increased by 20%.\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eBased on these simulation results, land-use transition matrices for each development scenario were calculated in ArcGIS, and the resulting matrices are shown in Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.2 LULC simulation based on PLUS model\u003c/h2\u003e\n \u003cp\u003eThe PLUS (Patch-generating Land Use Simulation) model is a patch-based land use change simulation tool developed by the High-Performance Spatial Computational Intelligence Laboratory at China University of Geosciences (Wuhan). It is grounded in spatial self-organization and cellular automata theory for simulating land use dynamics\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eUsing the PLUS model, the LEAS module was first applied to estimate the development probabilities of various land use types from 2010 to 2020, as well as the contribution rates of driving factors to land use conversions during this period. Subsequently, land demand for each category in 2030 was derived through Markov chain calculations. Based on observed land-use changes from 2010 to 2020 and relevant policies and regulations, the transition cost matrix and neighborhood weights were adjusted accordingly. Finally, the CARS module was used to simulate and project land-use changes for 2030 under each scenario\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Spatiotemporal evolution characteristics of ESs\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Spatial patterns of ESs\u003c/h2\u003e \u003cp\u003eOverall, ESs in Tianjin exhibited a declining trend. SC increased by 41.38%, while the magnitude of change in ESs was smaller during 2000\u0026ndash;2010 than in 2010\u0026ndash;2020. Over the two decades, HQ declined by 12.28%. CS initially increased and then decreased, with an overall change of 0.41% over the entire period.\u003c/p\u003e \u003cp\u003eSpatially, CS, SC, and HQ all exhibited a distinct pattern of lower values in the south and higher values in the north (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In contrast, WY showed higher values in the southeast and lower values in the northwest, with a distribution pattern resembling a coastal river belt. High WY values were concentrated in the six central urban districts, Beidagang Reservoir, and the Binhai New Area. CS and HQ peaked in the northern mountainous areas and the six urban districts, while low values were observed along the southwestern coast. SC also reached its maximum in the northern mountainous areas. Driven by rapid urbanization, changes in water yield were primarily concentrated in the six urban districts and coastal areas, while alterations in SC, CS, and HQ were mainly observed in Xiqing District and Jinghai District.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Evolution of ESs\u003c/h2\u003e \u003cp\u003eFrom 2000 to 2020, ESs in Tianjin exhibited pronounced spatiotemporal differentiation. WY, CS, SC, and HQ followed divergent evolutionary trajectories across different periods, reflecting the interactive effects of natural environmental changes and urbanization processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Overall, ESs displayed a pattern of \u0026ldquo;two increases and two decreases\u0026rdquo;: WY showed continuous improvement, and CS remained broadly stable, while SC and HG experienced significant degradation. As a coastal city, Tianjin's ecosystem service evolution was substantially influenced by the protection of coastal wetlands and the implementation of the dual-city development strategy.\u003c/p\u003e \u003cp\u003eBetween 2000 and 2020, WY initially contracted, then expanded. From 2010 to 2020, approximately 20% of the city's area experienced a significant increase in WY capacity, with notable gains in the southern Binhai New Area and the periphery of the central urban district. Despite this localized improvement, the overall downward trend persisted. CS was the most significantly growing ecosystem service, displaying a spatial pattern characterized by a \u0026ldquo;central zone of marked growth flanked by stable areas to the north and south,\u0026rdquo; while the coastal belt emerged as the core area of degradation. SC remained broadly stable over the two decades, with some growth observed in the six urban districts and coastal areas. HQ showed a gradual improvement, with areas showing enhancement accounting for 18% of the region during 2000\u0026ndash;2010. Degraded areas closely overlapped with the extent of urban expansion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3 Analysis of spatial autocorrelation in ESs\u003c/h2\u003e \u003cp\u003eBased on the ESs of Tianjin from 2000 to 2020, we employed the Getis-Ord Gi* hotspot analysis tool in ArcGIS to identify the spatial distribution of ESs hotspots and coldspots for the years 2000, 2010, and 2020.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, WY hotspots were concentrated around the central urban area and radiated outward. Higher hotspot values were observed in the Tianjin Beidagang Wetland Nature Reserve in the south and the Tianjin Binhai National Marine Park in the east, with these values increasing over time. This trend may be attributed to the significant expansion of green space in central urban areas under ecological conservation scenarios, as well as broader urban greening and densification efforts. CS showed an increasing pattern of hotspots in central regions and a decreasing pattern in coastal areas. In the north, relatively high hotspot values were found in the Ji County National Geological Nature Park, the Panshan Scenic Area, and the Middle\u0026ndash;Upper Proterozoic National Nature Reserve in Tianjin. Along the eastern coast, cold spot values decreased progressively over time. For SC and HQ, the spatial patterns of ESs hotspots and coldspots remained largely unchanged from 2000 to 2020, both exhibiting clustered distributions. SC hotspots were concentrated in the northern region, primarily around the Jizhou National Geological Nature Park, the Xiaying Huanshou Lake National Wetland Park, and the Panshan Scenic Area. HQ hotspots were mainly located in the northern and central regions\u0026mdash;including the Tianjin Tuanbo Bird Nature Reserve and the Tianjin Ancient Coast and Wetland National Nature Reserve\u0026mdash;as well as along the eastern coast, encompassing the Tianjin Binhai National Marine Park. These hotspots were predominantly found in areas with favorable ecological conditions, although hotspot intensity along the eastern coast decreased over time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Trade-offs and synergies of ESs\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Correlation analysis\u003c/h2\u003e \u003cp\u003eSix pairs of correlations were identified among the four ESs. With the exception of the HQ\u0026ndash;SC pair in 2020, all correlations between ESs were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The correlations among the six ES pairs exhibited consistent patterns across the three study years (2000, 2010, and 2020). First, a significant trade-off relationship was observed only between WY and HQ, with the strength of this negative correlation slightly weakening over the 20-year period. Second, strong synergistic relationships emerged between SC and WY and between CS and HQ, both of which intensified over time. Third, synergistic relationships between CS and WY, HQ and SC, and CS and SC gradually weakened.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Spatial-temporal patterns of trade-offs/synergies between ES pairs\u003c/h2\u003e \u003cp\u003ePronounced spatiotemporal heterogeneity was observed in the interactions among ESs in the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Spatial trade-offs remained the dominant pattern, although their intensity gradually diminished, while spatial synergies strengthened over time.\u003c/p\u003e \u003cp\u003eSpecifically, high trade-off zones for WY\u0026ndash;SC were primarily distributed around Taiping Town in the southern Binhai New Area. The construction of the Tianjin Binhai Douzhuang General Aviation Airport, which began in 2014, significantly disturbed the ecological functions of the surrounding areas. Over time, high-synergy zones between WY and SC expanded markedly. By 2020, contiguous areas of strong WY\u0026ndash;SC synergy were distributed across the western ecological barrier region and the eastern coastal areas. Meanwhile, although the overall spatial synergy relationship for WY\u0026ndash;HQ improved, trade-offs intensified in the Beidagang Wetland, along the Haihe River, and in localized areas along the eastern coast. In contrast, the spatial trade-off effect for WY\u0026ndash;CS intensified significantly. Within Ji Prefecture, Wuqing District, Xiqing District, Jinnan District, and Jinghai District, construction land was concentrated in high spatial trade-off zones for WY\u0026ndash;CS, with both the extent and intensity of these trade-offs increasing substantially over time. Although the SC\u0026ndash;HQ spatial synergy remained dominant, trade-off relationships strengthened in the areas surrounding Dahuangbao Wetland, Tuanbo Wetland, and Beidagang Reservoir. Furthermore, the spatial patterns of SC\u0026ndash;CS interactions exhibited a trend toward greater polarization: the trade-off relationship deteriorated notably in the southern part of Tianjin's main urban district, while the synergy improved in the Panshan region. Spatial heterogeneity was also pronounced for HQ\u0026ndash;CS interactions. During the initial study period, strong spatial synergy was observed in the northern mountainous areas and along the eastern coast, but this synergy gradually diminished over time. By the end of the study period, HQ\u0026ndash;CS trade-off zones were primarily distributed on the outskirts of urban built-up areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Multi‑scenario Land Use Simulation and Ecosystem Services Projection\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Multi-scenario LULC simulation\u003c/h2\u003e \u003cp\u003eIn this study, we put the 2000, 2020 LULC data and various driving factors into the PLUS model to simulate the LULC in 2020. Compared with the simulation results and actual 2020 LULC, the Kappa coefficient is 0.88, indicating the high accuracy. So, the model prediction results are reasonable and the same parameters could be employed to simulate and forecast LULC in 2030.\u003c/p\u003e \u003cp\u003eCompared with the 2020 LULC in Tianjin, cultivated land and unused land will decrease to varying degrees under the three different development scenarios in 2030, while construction land will increase in all scenarios (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Under the SSP585 scenario, cultivated land will decline most sharply by 542 km\u0026sup2;, and construction land will expand greatly by 735 km\u0026sup2;. Forest and grassland will increase under SSP126 and SSP245 scenarios, whereas water area will only rise under SSP126. Under all three scenarios, construction land will mainly expand along urban fringes and coastal zones, primarily converted from cultivated land. In the SSP126 and SSP245 scenarios, the increased forest and grassland will be concentrated in the northern mountainous areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Ecosystem service values under different scenarios\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, under the SSP245 scenario, WY declined in urban areas, with a clustered, large-scale pattern, while reductions in other regions followed a scattered, localized pattern. CS showed relatively uniform spatial distributions of both decreasing and increasing trends under the SSP245 and SSP126 scenarios, whereas the declining trend was more pronounced under SSP585. Across all three scenarios, areas surrounding the Beidagang Wetland Nature Reserve in southern Tianjin exhibited a shrinking encircling aggregation pattern, with the central and eastern regions experiencing a more pronounced overall decline under SSP585. SDR displayed a widespread and clustered decline under SSP245. Under SSP585, localized reductions occurred in the Panshan Scenic Area in northern Tianjin, the Yuqiao Reservoir region, and the urban areas of Tianjin. Under SSP126, both increasing and decreasing trends coexisted, with large-scale reductions concentrated in the northern and southwestern regions. Under the SSP585 scenario, HQ showed a more pronounced point-based decline. Notably, this scenario was associated with global warming, which in turn leads to glacial melt and increased precipitation. Given that WY and SDR are highly sensitive to precipitation, both services showed marked increases under SSP585.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5 Discussion","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Identification of RAPs\u003c/h2\u003e \u003cp\u003eBy integrating the spatial patterns of ecosystem service supply, the trade-offs and synergies among ecological functions, and future development trends, we identified and prioritized ecological restoration areas in Tianjin. These areas were classified into three categories: core conservation zones, synergistic optimization zones, and ecological improvement zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCore Protection Zones (CPZs) covered an area of 1,377.5 km\u0026sup2; and were primarily distributed across three main subregions of Tianjin: the northern region (including Jizhou National Geological Nature Park, Panshan Scenic Area, and Yuqiao Reservoir); the central region (Tianjin Ancient Coast and Wetland National Nature Reserve and Tianjin Dahuangbao Wetland Nature Reserve); and the southern region (Tianjin Tuanbo Bird Nature Reserve, Beidagang Wetland, and the coastal strip). Land use in these zones was dominated by forest, water areas, wetlands, and unutilized land. CS and HQ exhibited high-value clusters, while SC demonstrated outstanding capacity. WY formed a continuous, high-value belt along waterways. The synergistic relationships between WY and SC and between CS and HQ were most pronounced in this area, establishing it as the region's core ecological security barrier.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe ecological restoration strategy for Core Protection Zones followed a principle of \u0026ldquo;strict protection\u0026thinsp;+\u0026thinsp;functional enhancement.\u0026rdquo; This involved three key actions. Firstly, establishing ecological protection red lines to prohibit any development activities unrelated to ecological conservation. Secondly, restoring wetland connectivity by constructing ecological corridors to link fragmented wetland patches. Thirdly, implementing the Mountain Forest Enhancement Project to strengthen the synergistic effects of carbon sequestration and soil and water conservation.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSynergistic Optimization Zones (SOZs) covered 54.5 km\u003csup\u003e2\u003c/sup\u003e and were relatively scattered, with higher concentrations along the eastern edge of Lutai Farm and in the western canal area. Land use in these zones consisted primarily of cultivated land and grassland, interspersed with small amounts of construction land. Representing an urban\u0026ndash;rural transitional ecological space, SOZs exhibited pronounced trade-offs between WY and CS, particularly around construction land. However, they also held significant potential for the synergistic optimization of both ecological and productive functions.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe ecological restoration strategy for Synergistic Optimization Zones followed the principle of \u0026ldquo;functional coordination with quality and efficiency enhancement.\u0026rdquo; This involved three key actions: First, advancing the ecological transformation of farmland to enhance soil fertility and carbon sequestration capacity. Second, restoring the ecological corridor along the canal to build a composite ecosystem. Third, optimizing land use patterns by converting scattered parcels of developed land back to grassland.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEcological Improvement Zones (EIZs) covered 207.28 km\u0026sup2; and were primarily distributed along the northern shores of Yuqiao Reservoir, with additional small clusters scattered across the central-eastern regions. Land use in these zones consisted mainly of cultivated land and water areas, interspersed with fragmented forests. HQ remained at a medium-to-low level, while CS and SC capacities required enhancement. Additionally, these areas exhibited a certain degree of habitat fragmentation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe ecological restoration strategy for Ecological Improvement Zones centered on the principle of \u0026ldquo;vegetation reconstruction\u0026thinsp;+\u0026thinsp;patch integration.\u0026rdquo; This involved three key actions: First, implementing vegetation restoration projects to enhance regional carbon sequestration capacity and habitat suitability. Second, carrying out ecological patch integration to connect fragmented ecological spaces into a functional network. Third, strengthening water conservation functions to mitigate the impact of agricultural nonpoint source pollution on aquatic ecosystems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Comparison with other studies of coastal cities and regions\u003c/h2\u003e \u003cp\u003eTianjin is a typical coastal city characterized by an overall urban spatial pattern of \u0026ldquo;mountains\u0026ndash;city\u0026ndash;water.\u0026rdquo; The coastal areas and the transitional zones between Tianjin and Binhai serve as critical ecological transition zones, playing a vital role in safeguarding the ecological security of Tianjin and the broader Beijing\u0026ndash;Tianjin\u0026ndash;Hebei region. However, these areas are facing increasingly severe ecological pressures from urban expansion.\u003c/p\u003e \u003cp\u003eComparison of our findings with those from other coastal cities and regions revealed that:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe spatial distribution of areas with high ESs provision levels and strong synergistic relationships was strongly correlated with topography, hydrological systems, and vegetation cover\u003csup\u003e\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The northern Panshan area and the Haihe River basin emerged as key regions for providing ecological services.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe trade-offs and synergies among ESs were closely linked to the urban\u0026ndash;rural gradient\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. In Tianjin, areas with high ES trade-offs were primarily distributed around the periphery of built-up zones and were significantly influenced by urban expansion.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe coastal areas of Tianjin encompass diverse ecosystems\u0026mdash;including tidal flats, wetlands, forests, and farmlands\u0026mdash;that serve as vital corridors for biological migration and function as critical ecological security barriers\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. These areas are important distribution zones for key ESs such as WY and HQ. However, due to the prevalence of saline-alkali soils, plant growth faces numerous challenges and is less resilient\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Therefore, efforts should be intensified to remediate and restore coastal soils, integrate fragmented ecological patches, and enhance the stability of wetland spaces to build more resilient coastal ecosystems\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eTherefore, based on the historical evolution patterns and future development trends of ESs in this coastal city, we identified three types of ecologically important zones (EIZs) and proposed corresponding spatial optimization strategies and management recommendations for each. Given the similarities in the distribution of ESs across coastal cities and regions, these ecological spatial planning methodologies and management strategies possess a degree of universality, offering valuable references for the sustainable management of coastal urban ecosystems worldwide.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Limitations and future research\u003c/h2\u003e \u003cp\u003eThis study provides important insights for the zoning and hierarchical management of ecological spaces in coastal cities. However, several limitations should be acknowledged. First, only four representative ESs were selected for quantitative assessment, potentially overlooking the influence of other ESs\u0026mdash;such as crop production and cultural services\u0026mdash;on ecological spatial patterns. Additionally, despite employing the widely used InVEST model, the findings remain subject to its inherent uncertainties and to the lack of field-based validation. Second, this study revealed that the diversity of water network patterns in coastal cities significantly influences ES supply levels. Future research could further explore ES heterogeneity by integrating the urban\u0026ndash;rural gradient with the spatial characteristics of water networks, thereby enabling more accurate identification of restoration priority areas (RAPs) in coastal urban ecosystems. Third, the supply levels, trade-offs, and synergies of ES are influenced by multidimensional factors, including natural environmental conditions and socioeconomic drivers. Subsequent studies should investigate the mechanisms driving the spatial evolution of ecological restoration areas with different priority levels, with the aim of developing more practical, context-specific spatial management strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eIn this study, we proposed a framework that integrates ecosystem service quantification with multi-scenario simulation. Based on the historical evolution patterns of ESs in Tianjin and projections from multi-scenario simulations, we develop a method for identifying and prioritizing restoration areas (RAPs).\u003c/p\u003e \u003cp\u003eThe distribution of ESs in Tianjin generally showed lower values in the south and higher values in the north. Over the past two decades, overall ESs in Tianjin have declined, reflecting significant pressures on ecological conservation in the city. Among individual services, WY showed a significant overall increase, with high values distributed along water systems and marked improvement in areas surrounding the built-up zone. CS exhibited relatively little variation, with high values concentrated in the northern mountainous regions and low-value areas distributed in contiguous zones along the coast. SC continued to increase, with improvements observed in both the six central urban districts and coastal wetland areas. In contrast, HQ declined significantly, with degraded areas closely overlapping with urban expansion zones.\u003c/p\u003e \u003cp\u003eAmong the four ESs, significant trade-offs were observed between WY and HQ, with high trade-off areas primarily distributed around wetlands and coastal regions. In contrast, the relationships between WY and SC and between CS and HQ exhibited increasingly strong synergies, with high-synergy areas concentrated in the northern mountainous regions and the transitional zone between the twin cities.\u003c/p\u003e \u003cp\u003eComparison of projected ecosystem service levels for 2035 under three scenarios with the 2020 baseline revealed distinct scenario-dependent patterns: under SSP245, the SDR experienced a significant decline; under SSP585, CS and HQ showed more pronounced decreases; and under SSP126, the areas of improvement for WY, CS, and HQ expanded, with ESs overall exhibiting a positive trend. Based on the supply levels, spatial distribution, and future trajectories of ESs, we categorized ecological spaces into three tiers according to ecological restoration priority\u0026mdash;from highest to lowest: Core Protection Zones, Collaborative Optimization Zones, and Ecological Improvement Zones\u0026mdash;and proposed corresponding ecological restoration strategies and spatial planning recommendations for each.\u003c/p\u003e \u003cp\u003eWe hope that this research will contribute to the development of more sustainable land use patterns in Tianjin, alleviate the pressures of urban growth on ecosystems, and provide valuable case studies and scientific support for ecological space management in other coastal cities and regions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eHebei Provincial Natural Science Foundation project under Grant (E2025202167) and Scientific Research Project for Institutions of Higher Education in Hebei Province (QN2026320).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Conceptualization, visualization, software, data curation, and resources were performed by J.H.; Methodology, writing\u0026mdash;review \u0026amp; editing, software, and supervision were performed by Y.F.; Validation, software, and project administration were performed by X.L.; Visualization and formal analysis were performed by N. Q. All authors contributed to the interpretation of the data and narrative, and participated in revising the manuscript through multiple versions. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analysed during this study are included in this published article. The data supporting the conclusions of this article are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen, X. et al. 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Manage.\u003c/em\u003e \u003cb\u003e231\u003c/b\u003e, 106402 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Z. et al. Ecological risk assessment of the wetlands in Beijing-Tianjin-Hebei urban agglomeration. \u003cem\u003eEcol. Ind.\u003c/em\u003e \u003cb\u003e117\u003c/b\u003e, 106677 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNi, J. Y. \u0026amp; Xu, F. Remote sensing-based analysis of the coupled impacts of climate and land use changes on future ecosystem resilience: a case study of the Beijing-Tianjin-Hebei region. \u003cem\u003eRemote Sens.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 2546 (2025).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Restoration Areas Priority (RAP), ecosystem services, scenario simulation, PLUS model, coastal city","lastPublishedDoi":"10.21203/rs.3.rs-9402363/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9402363/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoastal cities are highly urbanized regions where ecological sensitivity and the continuous expansion of urban space pose significant challenges to the sustainable development of ecosystems. This study assessed ecosystem services (ESs) from an integrated past\u0026ndash;future perspective and elucidated spatiotemporal evolution patterns, as well as trade-offs and synergies among multiple ESs in Tianjin over the past two decades and under three future scenarios. The results indicated that, from 2000 to 2020, ESs showed a declining trend, although localized improvements were observed in the peripheral zones of ring-shaped built-up areas and in coastal wetland regions. Spatial trade-offs remained the dominant pattern among ESs, although their influence was gradually diminishing. In contrast, spatial synergies increased significantly. By integrating ESs supply, spatial distribution patterns, and future trends, we identified and delineated ecological restoration priority areas (RAPs), which were classified into three sequential categories: Core Conservation Zones, Synergistic Optimization Zones, and Ecological Improvement Zones. Based on a comprehensive analysis of land-use composition and its spatial distribution within RAPs, we proposed targeted ecological restoration strategies and spatial planning recommendations. 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