A methodological framework for ecological network optimization integrating circuit theory and scenario simulation：Application in the Liangzi Lake Basin, China

A methodological framework for ecological network optimization integrating circuit theory and scenario simulation：Application in the Liangzi Lake Basin, China | 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 Research Article A methodological framework for ecological network optimization integrating circuit theory and scenario simulation：Application in the Liangzi Lake Basin, China Yan Zhou, Mengyao Liu, Lina Wang, Yawen Luo, Qiaoling Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4142154/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract As an approach to manage ecological security patterns and construct ecological spaces, the ecological network can identify sources, corridors, and nodes of landscape, improve landscape connectivity and biodiversity. A basic working framework for ecological network construction already existed though, it’s necessary to constantly optimize the network when facing rapid land use/cover changes. This study aims to explore a systematic framework for ecological network optimization, the Liangzi Lake Basin was chosen as the sample area. By considering ecosystem services and landscape connectivity, key ecological sources can be identified. Resistance surfaces were constructed based on the natural and anthropogenic factors. Ecological corridors and nodes were extracted with the Minimum Cumulative Resistance model and circuit theory, and Future Land Use Simulation Model was used to simulate the land use changes over time. Three scenarios: increasing stepping stones, removing obstacle points, and protecting key pinch points were set up to perform simulation and assess the connectivity to compare the effects of optimization. The results showed that the ecological network in the Liangzi Lake Basin consisted of 20 sources, 56 corridors, 64 pinch points, and 25 obstacle points, and the spatial distribution of these elements was relatively homogeneous. By comparing the indicators under three scenarios, it was revealed that removing obstacle points had the most significant effects on the network optimization, which deserved the most concerns in the network construction and optimization. A comprehensive optimization scheme was formed and the order of ecological restoration to different was determined. This methodological framework provides a systematic tool and theoretical basis for constructing ecological networks and determining the restoration order of various ecological elements. It can be applied to various ecological restoration scenarios and be referred to when planning ecological spaces and reserves. ecological network circuit theory scenario simulation landscape connectivity ecological strategic nodes ecological restoration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Highlights Based on the scenario simulation, the ecological strategic points that have important influences on the ecological network were identified and the ecological network was optimized. Using MSPA-InVEST model and MCR model, 20 ecological sources and 56 ecological corridors were identified. Using circuit theory to identify the obstacle points in the network. Three optimization scenarios: “increasing ecological stepping stones, removing obstacle points, and protecting key pinch points" were set up to compare network connectivity and cost. The optimization scheme that has the greatest effect on the optimization of ecological network connectivity was proposed and the optimal strategies of ecological network were formulated. 1 Introduction The rapid urbanization and land use expansion have led to the encroachment of natural spaces with important ecosystem services (ESs) (He and Zhang, 2022 ; Wang et al., 2020 ), this has brought about a variety of problems to ecosystem such as landscape fragmentation, loss of biodiversity and degradation of ES, posing a serious threat to regional ecological security and sustainable development (Tang et al., 2018 ; Xia et al., 2020 ). By integrating the ideas from different disciplines, the scopes of ecological security management and environmental restoration have gradually been transformed from restoring points to comprehensive restoration in networks (Kukkala and Moilanen, 2017 ). In this background, constructing and optimizing ecological networks (EN) has become one of the major concerns of ecological security pattern research (Li et al., 2010 ; Zhang et al., 2022 ). EN is based on the "patch-corridor-matrix" model (Jongman, 2002 ), and has been widely used in ecological security pattern construction (Yuyang et al., 2022 ), biodiversity conservation (Clauzel et al., 2018 ), green infrastructure construction (Benedict and McMahon, 2002 ), watershed ecological restoration (Kang et al., 2022 ), and coordinated development of urban clusters (Zhou et al., 2021 ). It can benefit the reasonable distribution of land resources and plays an important role in constructing ecological reserves, balancing ecological preservation and economic development, and maximizing ecological benefits (De Montis et al., 2016 ). Constructing EN can integrate scattered patches, corridors, and other important land resources to improve the connectivity and mobility of landscape elements, and maintain the health and stability of the ecosystem. EN is now one of the widely-used approaches for regional ecological spaces construction (Peng et al., 2020 ), with its advantages in ecosystem management and decision making. Establishing EN can also help to protect the ecological security, benefit the sustainable use of the ecological resources, and provide a theoretical basis for planners and policy makers (Cunha and Magalhães, 2019 ).The previous research on EN mainly focus on network identification and construction, and the paradigm of "sources identification, resistance surfaces construction and corridors extraction, and ecological nodes identification" has been formed (Aminzadeh and Khansefid, 2010 ; Peng et al., 2018 ). Ecological sources are patches with important ecological functions, which are important for maintaining regional ecological security and stability. The identification of ecological sources used to be direct judgement, now it has been evolved to comprehensive assessment based on various elements (Vergnes et al., 2013 ; Zhao and Xu, 2015 ). Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) and Morphological Spatial Pattern Analysis (MSPA) are widely used to identify ecological sources by taking ecological functions and landscape connectivity into consideration, to make the identification more objective (Wang et al., 2021 ; Zhang et al., 2015 ). Ecological corridors are bridges connecting ecological sources and are widely used in the simulation of ecological flows and species flows (Huang et al., 2021 ). To extract corridors, the resistance surfaces should be established first, and further simulation can be carried out based on the Minimum Cumulative Resistance (MCR) model (Peng et al., 2018 ). In terms of resistance surfaces establishment, many studies only used one land use pattern for resistance distribution analysis, which cannot comprehensively reflect the impacts of habitat, human activities, and other factors (Li et al., 2015 ). Therefore, it is necessary to use multi-source data and information to correct the resistance value of surfaces and improve the accuracy and objectivity of corridor identification. Ecological nodes are often neglected in the EN construction because many studies did not perform node extraction, or simply used the intersection of corridors as nodes. By doing these, the important functions of nodes in EN structure and connectivity cannot be well reflected (Kong et al., 2010 ). Using circuit theory can effectively assist the identification of ecological strategic nodes. By discriminating high current intensity areas in the network, the strategic nodes that are vital for network connectivity can be precisely extracted (Gong et al., 2021 ). The aims of EN construction are to systematically present the spatial distribution characteristics of regional ecological resources and give specific recommendations on ecological restoration (Xiao et al., 2020 ). However, with the improvement of the methodological framework of construction, EN tends to be more patterned and simplified now (Wang et al., 2022 ), so the networks are not easily to be implemented in the planning practices. Besides, with extracted sources and corridors only is not enough for the decision making of ecological preservation and restoration (Jing et al., 2023 ), the corresponding changes of EN with the constant expansion of land use should be taken into account (Ding et al., 2022 ). Previous research have already confirmed that various urban construction and ecological preservation projects have impacts on the corridors and nodes (Fu et al., 2020 ), therefore, constructing EN with focus on the current ecological structure only is not enough, quantitative analysis of the changes in EN connectivity after restoration over periods cannot be neglected (Li et al., 2022 ). How to effectively use EN to achieve the overall monitoring and rational planning of regional ecological resources? The answer to this question will be the key to maintain regional ecological security and achieve sustainable development goals (Schröder and Seppelt, 2006 ). In this study, the ES was innovatively combined with MSPA to identify sources, so the network can more accurately reflect the ecosystem functions and connectivity status in the area. As an area with massive water area and wetlands, two significant and typical ES of LLB–habitat quality and water connotation, were used in the source identification and network construction. Located in the southeastern part of Hubei Province, China, the Liangzi Lake Basin (LLB) is a pivotal target area of watershed preservation and ecological restoration in Hubei province. In the middle reaches of Yangtze River, LLB has the typical wetland landscape and biodiversity patterns. It is of great significance in the provincial water resources conservation and watershed restoration projects. At the same time, LLB is neighbor to the metropolis of central China, Wuhan, the rapid changes in land use around the basin have had great impacts on its ecological stability. According to the requirements of the overall plan for the protection and restoration of national ecosystems in China, when carrying out ecological restoration planning, it is necessary to accurately locate the ecological space that needs urgent restoration, and to rank the restoration priorities of different spaces (National Development and Reform Commission and China, 2020). It is important for policy makers and planners to figure out how to reasonably construct an EN in LLB to intercede the conflicts between economic development and ecological security, and to achieve economic benefits and sustainable development for LLB and its nearby cities. Toward the targets, this study takes LLB as the sample area to explore the methodological framework for the construction and optimization of EN, with the trials to answer the following questions: 1) How to precisely locate the important ecological patches in the network that need urgent protection and restoration; 2) How to figure out the appropriate network optimization scheme towards different restoration objectives; 3) How to evaluate the effect of EN under different restoration scenarios? To answer the above questions, sources, corridors and nodes were identified and extracted to construct the EN in LLB. The land use patterns in LLB from 2000 to 2020 were analyzed to find out potential stepping stones, obstacles and pinch points in the EN, so the different restoration scenarios can be set up. After simulating the connectivity of the network under different scenarios, the restoration priorities of each type of landscape elements can be determined, and the specific optimization scheme can be decided for the EN in LLB. Through the study, it is expected to ensure the reasonable deployment of natural ecological resources and the steady improvement of the network connectivity in LLB, to achieve the maximum benefits of EN construction and provide a quantitative basis for future optimization and improvement of EN constructions. 2 Materials and methods 2.1 Study area Liangzi Lake is one of the "Top Ten Famous Lakes" in China, and the second largest lake in Hubei Province, with a water area of 370 km 2 . The range of its watershed reaches six administrative districts in five cities, with a total area of 6236.44 km 2 (Fig. 1 ). LLB has relatively low elevation, except for its southern side which is dominated by mountains. The rest of LLB is flat and open and covered by hills and plains. The elevation of LLB decreases from its south to north. The vast water area and the abundant vegetation on land provide livable habitats for wildlife. LLB is in the north subtropical monsoon climate zone, with moderate sunlight, mild climate, and sufficient rainfall. The annual average temperature there is 17.3°C and the annual average precipitation is between 1350 to 1415 mm, the rainfall decreases from its south to north. The natural background conditions of the study area are superior and the habitat quality there is high. At the same time, the large area of wetland lakes provides sufficient water resources and has high water conservation capacity. The natural background conditions of the study area are superior, the habitat quality is high. The large area of wetlands and lakes provides sufficient water resources, which possess strong water conservation ability (Zhou et al., 2019 ). The GDP of LLB has been steadily growing ever since 2000, while the problems of water pollution and ecological fragmentation have been increasingly serious. The inflow of pollutants from external sources and the surface water pollution caused by domestic production led to the deterioration of the self-purification capacity of the water. The long-term aquaculture, lake reclamation, and tourism development have led to the decline of the ecological functions of LLB. How to balance the ecological preservation and economic development is a great challenge in the sustainable development of LLB. In this study, the ES and land use/cover changes of LLB were used to analyze the changes caused by urban construction and to identify important elements in the EN. The period from 2000 to 2020 was selected to illustrate the changes over time because the pace of modernization of the districts around LLB had been obviously speeding up since 2000. 10-year interval was used so that the changes of land use patterns can be more obvious. To evaluate the ecosystem services and analyze the changes of land use/cover changes of LLB, the following data sources were used: The land use data of LLB from 2000 to 2020 were obtained from the Land Satellite Remote Sensing Application Center of the Ministry of Natural Resources of China ( https://www.webmap.cn/ ) with a resolution of 30 × 30 m. The vector boundary data in the study area were obtained from OpenStreetMap ( http://www.openstreetmap.org/ ). The water connotation function and the quality of habitat are the major significant ESs in LLB. To evaluate the functions, the data collected in the study included precipitation, evapotranspiration, and land use changes. The value of resistance was indicated by the elevation, slope, average annual temperature, average annual precipitation, distances to the nearest railroad, road, and water body. The data of elevation and water were from the Geospatial Data Cloud ( http://www.gscloud.cn/search ). The slope was calculated based on the elevation. Average annual temperature, average annual precipitation, and evapotranspiration were obtained from the National Earth System Science Data Center of China ( http://www.geodata.cn/ ). The information of railroads and roads distribution can be found in the Global Road Open Access Data Set (gROADS). 2.2 The process of EN construction and optimization The methodological framework of EN construction and optimization established in this study can be divided into the following three steps (Fig. 2 ): (1) EN construction based on MSPA-MCR and ES. Assessing the ecosystem service functions and analyze the landscape patterns of LLB, so the ecologically important areas and high connectivity areas can be identified. The overlapping parts of the two types of areas are the ecological sources. Then we constructed and correct of resistance surfaces by using natural and social indicators, so the ecological corridor could be identified using MCR model. The ecological strategic nodes (obstacle points and pinch points) were extracted based on the circuit theory. With these elements, the EN based on the conditions in 2020 can be constructed. (2) Land use simulation for steppingstones. In this step, the land use data in 2000 and FLUS model were used to simulate the land use patterns in 2010 and 2020, the simulation results were compared with the actual data in the two years to verify the accuracy of simulation results. After the verification, further simulation of the land use/cover changes in 2030 was conducted to extract the ecological steppingstones, which are the ecological spaces that will be encroached by urban construction land in 2030. (3) Scenario simulation and optimization evaluation. Based on the results acquired from the first two steps, three EN optimization scenarios of "increasing ecological steppingstones, removing obstacle points, and protecting key pinch points" were set up. The connectivity and cost ratio of the EN under the three scenarios were simulated and calculated to figure out the one with the highest network connectivity and lowest optimization cost. 2.3 EN construction based on MSPA-MCR and ES 2.3.1 Identification of ecological sources Ecological sources are the core of EN construction, the accurate identification of sources is the key to establish regional ecological security patterns and sustain the ecological security (Peng et al., 2018 ). Ecological sources are mostly natural patches with sufficient material exchanges and energy flows inside. Zones like water areas, forests, and large wildlife habitats are mostly ecological sources with strong ES functions and high aesthetic values. Meanwhile, as habitats for species’ survival, reproduction, and migration, ought to have certain degrees of landscape connectivity and extensibility. Two typical and important ESs of LLB–habitat quality, and water connotation, were selected for ES evaluation, the Habitat Quality and Water Yield modules in the InVEST were applied to calculate the level of the ES functions with the following equations (Sun et al., 2019 ; Zhou et al., 2021 ): $${Q}_{xj}={H}_{j}\left(1-\frac{{{D}_{xj}}^{2}}{{{D}_{xj}}^{2}+{k}^{2}}\right)$$ 1 $${Y}_{\left(x\right)}=\left[1-\frac{AE{T}_{\left(x\right)}}{{P}_{\left(x\right)}}\right]\times {P}_{\left(x\right)}$$ 2 In Eq. 1 , \(\left({Q}_{xj}\right)\) is the habitat quality index, \({H}_{j}\) is the habitat suitability of landscape pattern j, k is the half-saturation constant and \({D}_{xj}\) is the habitat stress level of raster cell x of landscape type j. In Eq. 2 , \({Y}_{\left(x\right)}\) is the annual water supply (mm) on raster cell x in the study area, \(AE{T}_{\left(x\right)}\) denotes the annual actual evapotranspiration (mm) from raster cell x, and \({P}_{\left(x\right)}\) denotes the annual precipitation (mm) from raster cell x. Water connotation capacity \({Y}_{\left(x\right)}\) can be obtained by correcting water production through topographic index, soil saturated hydraulic conductivity and flow velocity coefficient. Both functions were given the same weight and superimposed, and then divided into five levels according to the Jenks natural breaks, with higher level representing more significance. The zones of level 4 and 5 were selected as the ES-significant areas. MSPA was widely used to obtain the ranges of cores and bridges in EN. Differs from the traditional landscape connectivity analysis methods, MSPA identifies the high-connectivity zones with digital images (Soille and Vogt, 2009 ). In this study, forest, water, and grassland were used as foreground, and the other land use patterns were used as background for MSPA analysis. Seven basic classes of landscape: cores, islets, bridges, edges, branches, loops, and perforations were obtained regarding to their connectivity. The cores and bridges with high connectivity were selected for source identification (Wickham et al., 2010 ). The zones with strong ecological functions and the areas with high connectivity were superimposed, the overlapping zones, together with important nature reserves and water bodies, composed the ecological sources for this study. 2.3.2 Resistance surface construction and corridor identification The flow of ecological elements among different sources are influenced by natural and human factors (Loro et al., 2015 ), the resistance surface can reflect the degree of difficulty of cross-source flows, therefore, can be used for modelling corridors (Fan et al., 2021 ). Regarding to the natural and socioeconomic characteristics in LLB, five factors were selected to establish the resistance surface: land use pattern, elevation, slope, distance to the nearest road, and distance to the nearest railroad. The resistance values were assigned to different land use patterns, the higher the degree of human disturbance, the higher the resistance value of the land use pattern. The land use data of LLB in 2020 was used for value assignment. The elevation and slope factors were classified to five levels according to the natural breakpoint method. The distances of buffer zones to their nearest roads and railroads were classified and assigned resistance values using the multi-loop buffer analysis of GIS. Weights were given to each factor referring to the previous studies (Jing et al., 2023 ), to limit the value of resistance in the range of 1-100. The lower the resistance value of the surface, the stronger the capacity of migration and ecological flow between sources. The resistance values and weights of different land use patterns were listed in Table 1 . Table 1 Resistance coefficients and weights of different factors Resistance value Land use pattern Elevation Slope Distance to the nearest road(km) Distance to the nearest railway(km) 100 Construction land >1200 >35° <500 <500 50 Waters 800–1200 25°-35° 500–1000 500–1000 30 Others 400–800 15°-20° 1000–1500 1000–1500 10 Cultivated land 200–400 8°-15° 1500–2000 1500–2000 1 Forest <200 5000 >5000 Weight 0.40 0.12 0.18 0.15 0.15 Based on the constructed resistance surface, the corridors with minimum resistance value can be modelled using the MCR with the following equation (Knaapen et al., 1992 ): MCR= \(f{\int }_{j=n}^{i=m}（{D}_{ij}\times {W}_{i}）\) (3) In Eq. 3, MCR represents the cumulative resistance value, \(f\) represents the ecological process function positively correlated with MCR, \({D}_{ij}\) represents the distance between source i and source j, and \({W}_{i}\) represents the resistance to biological migration and ecological flows generated by source i. 2.3.3 Identification of ecological strategic nodes Ecological nodes are essential for promoting or inhibiting the functional integrity of ecological sources, they also influence the connectivity of ecological corridors, and the stability of ecological safety patterns (Yu et al., 2018 ). These nodes are often small and relatively easy to be protected and restored, specific practices in nodes protection and restoration can be cost effective in EN optimization. In the circuit theory, the ecological landscape is recognized as a conductive surface, the ecological flow is considered as electric current, the current density can reflect the resistance encountered by species when migrating (Yuyang et al., 2022 ). Linkage Mapper is a toolkit with 6 tools to analyze regional wildlife habitats’ connectivity, it was used in this study to identify the strategic nodes like obstacle points and pinch points. Obstacle points are the areas where species are easily obstructed during migration. Eliminating obstacle points can effectively improve a corridor’s connectivity. Barrier Mapper was used to identify the significant barrier areas in the EN as obstacle points, the minimum and maximum search radius were set to 500 m and 1500 m respectively, to search obstacle points by the moving window method. Pinch points, also known as bottlenecks, are high current intensity areas in the EN. They are areas where are very likely to be passed by during migrations and there is no other alternative pathway. The degradation of the pinch points will have a serious impact on the function of ecological sources and ecosystem stability, so they should be well protected. If a pinch point happens to be in an area with high resistance value, it is easily to be disturbed and degraded by external factors. Pinchpoint Mapper was used to identify pinch points. 2.4 Land use simulation for steppingstones FLUS model can simulate the influences of human activities and natural environment on land use and predict the future development of different land use patterns (Liu et al., 2017 ). In this study, the land use data of LLB in 2000, 2010, and 2020 were used. To verify the simulation results and calibrate model parameters, the land use data of LLB in 2000 was imported to simulate the land use conditions in 2010 and 2020. Later, a Kappa consistency test was conducted to compare the simulated land use conditions with the actual data in the corresponding year, strong consistency could be indicated if the kappa coefficient was between 0.8 and 1.0. After validation and correction on the parameters, FLUS was able to simulate the future land use in 2023, the data of 2020 was imported to conduct the simulation. By comparing the land use conditions in 2020 and 2030, the ecological spaces that would be encroached by the construction land in 2030 were recognized as ecological "stepping stones". Protecting such sensitive and vulnerable spaces can preserve the species migrations and energy flows between distant patches, benefit the ecosystem functioning and connectivity. After the first two steps of EN construction and steppingstone identification, three crucial elements for the regional ecological security pattern were acquired. They are relatively small, so protections and restoration in these elements will cost less but the effects can be remarkable. By paying more attention to steppingstones, protecting pinch points with high current intensity, and eliminating obstacle points in migrations, we can improve the smoothness of ecological corridors and the overall connectivity of the region. 2.5 Scenario simulation and optimization evaluation Target on improving the connectivity and stability of the EN in LLB, three scenarios– increasing ecological stepping stones, removing obstacle points, protecting key pinch points, have been set up to optimize the current EN (based on the 2020 land use). Problems such as fragile network structure, unstable and incomplete ecological functions caused by insufficient flow and obstructed flow of ecological flows in the current network were expected to be solved through the EN optimization (Table 2 ). Table 2 EN optimization scenario settings and operations Scenario Setting Targeted problems Operation 1) Increasing ecological stepping stones Reduced network connectivity due to functional degradation and insufficient ecological sources On the basis of identifying stepping stones, simulate the impacts of stepping stone degradation on the ecological network to verify the necessity of stepping stone protection. 2) Removing obstacle points Inaccessibility of corridors and blocked ecological flow caused by external human interference such as urban construction and land expansion Identify obstacle points, remove or restore them through urban construction projects by reducing the resistance value. 3) Protecting key pinch points Impaired network function and reduced stability due to the degradation of important pinch points Identify pinch points and simulating the impact of pinch point degradation on habitat network to verify the necessity of pinch points’ protection. To compare the EN optimization effects and cost effectiveness of the three scenarios, all the results in the three scenarios were analyzed based on the current EN, ensuring their area and number of ecological sources remain unchanged. EN connectivity indexes, cost ratio (c) and structural indicators of EN were selected and calculated to assess each solution, see the following equations (Cook, 2002 ): $$\alpha =\frac{L-V+1}{2V-5}$$ 4 $$\beta =\frac{L}{V}$$ 5 $$\gamma =\frac{L}{3（\text{v}-2）}$$ 6 c = 1-( \(\frac{L}{l}\) ) ༈7༉ The network connectivity indexes include network closure (α), line point rate (β), and network connectivity (γ), which are widely used in the calculation of the overall EN connectivity. α represents the degree of existence of loops between ecological sources and ecological nodes in the network, and the value ranges from 0 to 1, with larger values indicating smoother ecological flow; β represents the degree of difficulty in connecting ecological sources and nodes, and the value ranges from 0 to 3. It is usually considered that the network connection is more complex when β > 1. γ represents the ratio of the number of existing corridors to the maximum number of possible corridors in the ecological network, and the value ranges from 0 to 1. The larger the value, the higher the degree of node connectivity. In the equations, L is the number of corridors, V is the number of nodes (corridor intersections), and l is the total length of the corridors. c reflects the effectiveness and feasibility of the EN, with lower values indicating lower EN construction costs (Kong et al., 2010 ). 3 Results 3.1 Comprehensive Ecological sources identification To acquire the sources in the EN of LLB from more objective angle, the identification was conducted from ecosystem services and morphological spatial pattern. By this way, the identified sources are compatible of both strong ES functions and high landscape connectivity. 3.1.1 Ecological identification regarding to ES From the ES perspective, the regional habitat quality and water connotation service function were selected as the typical representatives of the entire regional ecosystem service function. The InVEST modeling results were classified into five levels based on natural breaks, indicating low to high importance of ES. Zones of level 4 and 5 were defined as ecological sources with strong ecosystem service functions. The results showed that the ES distribution was highly spatial heterogenous (Fig. 3 a). The high habitat quality areas cover 1590.92 km 2 , accounting for 25.51% of the total area. Most of the areas distributed in the southern forests and lake. Waters and forests have stronger ability to provide adequate resources and conditions for wildlife’s survival and development compared with other land patterns, showing the obvious importance in the maintenance of biodiversity. The overall area of strong water connotation function zones is 2161.55 km 2 , accounting for 34.66% of the total area, which is mainly distributed in the eastern part of the study area with low slope, abundant rainfall, and high vegetation coverage. In addition, the water connotation ability of LLB is significantly weaker than its surrounding areas, which indicates that LLB has relatively weaker ability to intercept, infiltrate and accumulate precipitation, so the ecological spaces there may have been seriously disturbed by human activities. The areas that have both ESs of level 4 to 5 were identified to be the ecological sources regarding to ES. The total area of ecological sources regarding to strong ES functions was 2195.23 km 2 , accounting for 35.20% of the total area (Fig. 3 b). They mainly were distributed in the southern high-elevation forest area, with a few scattered locations in the northeast, and the land use patterns were mainly forests, cultivated lands and waters. 3.1.2 Ecological identification based on morphological spatial patterns From the aspect of spatial connectivity, MSPA was applied to analyze forest, grassland, and water as foreground. The outputs of foreground were divided into six classes and visualized in different colors (Fig. 4 a). The cores accounted for 75.85% of the foreground, and the cores with relatively large aera were mainly in the central water area and southern forest. The bridges accounted for about 2.81% of the foreground and were mainly in the center of LLB. The dominant land use pattern in the bridges is water. The edges accounted for 13.95% of the imported foreground. Due to the edge effect, the edges were mainly composed of forests and waters, indicating that the regional landscape connectivity needs to be further improved. The cores and the bridges with high spatial connectivity were selected as the candidate ecological sources (Fig. 4 b). 3.1.3 Comprehensive ecological source identification The intersections of strong ES function areas and high spatial connectivity cores and bridges were considered as the candidates of ecological sources. Nature reserves, important wetlands in LLB were further superimposed with the source candidates to form the compatible ecological sources for this study. Since ecological sources must have a certain area to keep the core free from external interference and the ecological radiation capacity of small, fragmented patches is weak, the minimum threshold determination method was used to screen ecological sources by scale. The screening results showed that the number of ecological sources decreased rapidly at beginning as the threshold increased. When the threshold increased to over 1.5 km 2 , the number of ecological sources begun to decrease slowly. When the threshold was set to 10 km² and more, the decline tended to level off. Therefore, the area threshold was set 10 km², 20 ecological sources were screened out after excluding fragmented patches. The total area of sources at last was 1269.98 km 2 , accounting for 20.36% of the study area (Fig. 5 a). These comprehensive ecological sources were mainly distributed in the central and southern parts with high altitudes and steep slopes, and the land use patterns there were mainly water, forest, and cultivated land, accounting for 49.13%, 46.13%, and 2.18%, respectively. Seeing from administrative aspect, the ecological sources in Liangzihu District, Xianan District, and Daye District were more abundant and less subject to the human interference. 3.2 Results of resistance surface construction and corridors identification Resistance surfaces can be influenced by various factors. In this study, five factors were selected to evaluate the resistance value: land use pattern, elevation, slope, distance to the nearest road, and distance to the nearest railroad. Weights were assigned to each factor according to regional habitat characteristics, and finally, the cumulative ecological resistance surface was constructed and weighted (Fig. 5 b). The average resistance value in LLB was 14.18, and the high-valued resistance zones were mainly distributed in the areas with high urbanization levels and dense construction. The low-valued resistance zones mainly stretched from the southern forests to the central water area along the forests and rivers. MCR can simulate the least-cost path from one source to another, these paths were the corridors in this study. After merging and deleting duplicate corridors, 56 ecological corridors were obtained with their lengths ranging from 1.178 to 35.501 km and a total length of 482.15 km. The land patterns of most corridors were forest and cultivated land (Fig. 5 c). The central part of LLB had many sources with large area which distributed at small distances to each other. Therefore, the corridors there were densely distributed and short in length, which can be high-quality corridors for species migration. They were conducive to the connectivity of forest, grassland and lake at the same time. Corridors longer than 10 km were mostly distributed between the central water area and the southern forests, where there were 10 long corridors with poor landscape connectivity. The southern part of LLB was dominated by forests, with sources densely distributed, so the corridors there were mostly less than 10km in length with high quality, they were the connection between forests and grasslands. The southwestern part was significantly affected by the expansion of urban construction, there was a potential of interconnection between some sources though, none complete ecological corridor could be simulated. 3.3 Ecological strategic nodes identification results In the process of ecological preservation and restoration, key nodes and obstacle areas are the essential component that worth focus and consideration. The circuit theory was applied in the identification regional obstacle points and pinch points with Linkage Mapper toolkit. The all-to-one mode was used to identify the high current areas and superimpose them with the ecological resistance surface. The areas were divided into different classes with the natural breaks method and the high current density area of the first class was taken as the pinch points. The Barrier Mapper tool was used to identify the obstacle points by clicking the “calculate percent improvement scores relative to corridor LCD” option. The higher the improvement score relative to the LCD, the more demands in this area to be restored and improved landscape connectivity. The areas of high improvement scores were identified as ecological obstacle points that were in urgent need of improvement connectivity (Fig. 5 d). 64 ecological pinch points with an area of 19.16 km² and 25 ecological obstacle points with an area of 20.88 km² were identified in the study (Fig. 5 e). Seeing from the spatial distribution aspect, the high current areas were mostly concentrated in the high resistance zones, which had more overlapping parts with ecological corridors and rivers. The land use patterns of pinch points were mostly cultivated land and water, the planting and breeding activities in and around the waters should be noticed to prevent the function degradation of the pinches. Obstacle points were mostly found at the intersection of roads within the ecological corridors. There was a large obstacle point in the east, land use patterns there were mainly cultivated land and construction land, these two patterns both had the hard sub-bedding surfaces, which could reduce the landscape connectivity to some extents. The identified 20 ecological sources, 56 ecological corridors, and 89 ecological nodes together form the regional EN (Fig. 5 f). The connectivity indexes and cost ratio mentioned in part 2.5 were used to assess the structure and connectivity of the EN in LLB, 2020. The results revealed that the, β, and γ were respectively 0.23, 1.27 and 0.44, and c was 0.88, indicating that the regional EN connectivity in 2020 was relatively complex, the ecological flows were strongly obstructed, and the nodes within the area were relatively weakly connected. The practical network construction can be difficult, and the protection and optimization of key ecological spaces and the entire EN were urgently needed. 3.4 Land use simulation and steppingstone identification Using the FLUS model, we used 2000 and 2010 as the base years to create suitability probability atlases, the land use of LLB in 2010 and 2020 was simulated compared with the actual data. The Kappa coefficients were 0.91 and 0.87 respectively, showing a high accuracy in the prediction results and the model could be applied to the simulation of further land use distribution in the study area. The land use simulation showed that from 2000 to 2020, the speed of urban expansion of LLB was rapid and the cultivated land has been decreasing. Most lost cultivated land was converted into forest and grassland, indicating that the policy of returning farmland to forest has achieved certain results. While the growth of construction land remained fast, the centers of the administrative districts around LLB gradually expand outwards, the problem of insularization and fragmentation of ecological patches has been serious. Further prediction of 2030 land use in LLB was conducted based on 2020 land use data. The results showed that the further expansion of construction land would encroach much more on the surrounding ecological spaces such as forest and water (Fig. 6 a). Comparing the land use/cover changes from 2020 to 2030, 24.52 km² of forest, grassland, shrubs, and waters that would be encroached by construction land in 2030 were extracted as ecological steppingstones that need urgent protection (Fig. 6 b). Among the encroached land, forests and waters account for the first two largest proportion, with 55.18% and 35.74% respectively of the total. Therefore, it was important to ensure the ecological quality and connectivity of the steppingstones in the process of urban construction and development for the stability of the regional EN. 3.5 Scenario setup and simulation towards EN optimization Based on the results of EN construction and land use simulation, three types of landscape elements that are crucial for the ecological security pattern were obtained: obstacle points, pinch points and stepping stones. These elements are significant for the EN, and they are relatively small in scale, so the preservation and restoration practices on such elements can be easy to be carried out, and the outcome should be cost effective. Three EN optimization scenarios: "protecting ecological steppingstones, removing obstacle points, protecting key pinch points" were set up and the EN under different scenarios was simulated with FLUS (Fig. 7 ). To better assess the significance of the three elements, we simulated the EN connectivity and cost ratio when ecological stepping stones are not well protected and degraded, obstacle points are removed, and the key pinch points are degraded and reduced. The indicators used to evaluate the effects of optimization were compared and listed in Table 3 . In Scenario 1, in 2030, 24.52 km² of ecologically sensitive areas that would be converted into construction land due to urban expansion in 2030 were identified. If such sensitive areas cannot receive indeed preservation, the number of ecological nodes would increase. The total length of the corridors would increase to 490.12 km due to the bypassing of degraded steppingstones. The average resistance value would increase by 2.71, and the network closure index, line point rate index, and network connectivity would all decrease significantly. The stability and connectivity of the EN would be greatly affected compared with it in 2020. In Scenario 2, we simulated the restoration on the high resistance value areas to reduce the obstacle points and increase the landscape connectivity. In 2020, the area of the obstacle points was 20.88 km², and most of them were located within the habitats of corridors. In the simulation of removing ecological obstacle points, the number of corridors would increase by 4 and the average value of resistance surfaces would decrease to 13.22. The network closure index, line point rate index, and network connectivity would be significantly improved. In Scenario 3, the total area of the pinch points in 2020 was 19.16 km², and they were mainly located at the edges and connections of the waters, indicating that the ecological corridors should be particularly well protected in the water areas. Therefore, it was necessary to pay attention to the protection of ecological pinch points in such areas to prevent the degradation caused by human activities. Under the scenario of pinch point degradation, the number and length of corridors would increase, the network closure index would decrease significantly, and the network structure would become more complex. Table 3 Ecological networks optimization comparison Indicators EN in 2020 Scenario 1 Scenario 2 Scenario 3 Number of sources 20 20 20 20 Number of corridors 56 61 60 67 The total length of the corridor/km 482.15 490.12 489.60 509.19 Average resistance value 14.18 16.89 13.22 15.89 Number of nodes 44 48 38 52 closure indexα 0.23 0.15 0.32 0.16 line point rate indexβ 1.27 1.27 1.58 1.29 network connectivity γ 0.44 0.44 0.56 0.45 Cost Ratio (c) 0.88 0.88 0.88 0.87 3.6 Ecological networks optimization scheme After comparing the structure and connectivity of the EN under the three scenarios with it in 2020, it can be found that the β and γ indexes in Scenario 1 and Scenario 3 fluctuated less while the network closure index dropped significantly, indicating that the ecological flows in the EN would be significantly impeded under the condition of stepping stones and pinch points degradation, and the network structure would be more complex. The α, βand γ indices in Scenario 2 all showed significant increases, meant that the connectivity of the network would be significantly improved, and the corridors would be more evenly distributed. The cost ratio in the three scenarios were close. Therefore, when constructing an EN in LLB and carrying out network optimization, priority should be given to removing ecological obstacle points in the existing network, followed by strengthening the protection of ecological pinch points. According to the improvement of the EN under each scenario, the ecological stepping stones were determined as the secondary ecological sources, the obstacle points were determined as the prior ecological restoration targets, the pinch points were recognized as ecological protection areas, and the ecological corridors that would appear in Scenario 2 were recognized as secondary corridors. These strategies forming an EN optimization scheme for LLB (Fig. 8 ). When optimizing the EN, priority should be firstly given to removing obstacle points and constructing secondary ecological corridors based on protecting the current sources and corridors. Protecting nature should be the principle in EN construction, and the exploitation of ecological land should be strictly prohibited. For ecological reserves and secondary sources, ecological construction should be gradually strengthened towards the target of ecological restoration, combined with optimization of ecological substrates to improve the ecosystem stability and functions. The optimizing approaches of zoning and grading can be important for improving the structure and functional connectivity of EN, enhancing the stability and connectivity of ecosystems, and realizing the dynamic and sustainable development of the EN in LLB. 4 Discussion The increasing population and rapid expansion of urban construction land have led to great threats to the ecological land, and the implementation of sustainable development goals to protect ecological security has become a global consensus (Schindler, 2017 ). As the construction of ecological civilization in China continues to advance, identifying and protecting key ecological spaces have become a top priority for ecological restoration and planning. At the same time, the land use is changing faster in developing countries, the ecological security pattern changes brought by the land use changes are more susceptible, too. A single, static network construction method can hardly meet the current needs for ecological restoration. Therefore, we applied various methods such as ES evaluation, MSPA, and circuit theory to accurately identify various ecological elements and further optimize the network. The scenario simulation was used to explore the most suitable network optimization scheme with the best restoration effect and the lowest cost. EN is composed of various ecological elements such as sources, corridors, and nodes, and the quality of these elements can be important for the stability of the entire EN (Li et al., 2019 ). In the context of the continuous and rapid development of China's economy and community, the ecological restoration needs precise positioning, step-by-step implementation, and continuous construction. EN construction, as a nature-based ecological restoration solution, also requires continuous optimization and adjustment to achieve sustainable development (Zhang et al., 2017 ). LLB has a good ecological matrix, with large wetlands and forests to ensure biodiversity, the spatial distribution of ecological corridors is relatively homogeneous. The EN of LLB includes 20 ecological sources, 56 corridors, 64 pinch points, and 25 obstacle points. Still, we need identify and monitor the important zones and elements in the EN to adjust and optimize the network according to the development and changes. Nevertheless, the ecological strategic nodes and resistance surfaces in the current EN revealed that the resistance values of the pinch points were mostly high, and their functions were vulnerable to human activities. Besides, there were many obstacle points in the network, which could impede the ecological flows. The simulation on the land use in 2030 revealed that more ecological patches would be encroached on by construction land in the future, the ecological quality would degrade, and the connectivity of the EN would be greatly influenced. By setting up three scenarios "adding ecological stepping stones, removing ecological obstacles, and protecting key ecological pinch points" for simulation, we could compare and select the best EN optimization scheme. The priority of ecological importance in different areas could also help the EN restoration to be carried out in an orderly manner. Specifically, in the EN of LLB, obstacle points had the greatest impact on network connectivity and should be removed firstly. This is because the obstacle points were concentrated in the high resistance value area and were small in size, and the ecological restoration works on them were relatively easy, and the comparison of different scenarios showed the removal of obstacle points had the most significant effects. Secondly, the pinch points provide important ESs, the degradation of them will have serious impacts on the network structure. And last, since urban expansion is inevitable, the protection and construction of ecological stepping stones should be emphasized, and urban construction should be carried out moderately and selectively to avoid large-scale disorderly land expansion. The obstacle points, pinch points, and stepping stones in LLB were planned as prior ecological restoration targets, ecological protection areas, and secondary ecological sources respectively. And the newly-appear corridors under Scenario 2 were extracted as secondary corridors. The overall scheme of network optimization can provide a guidance and reference for the subsequent network construction and optimization. It can effectively guarantee the construction and optimization of the regional network and benefit the efficient allocation and management of land, human and financial resources. 5 Conclusion With the rapid expansion of urbanization and intensifying of human activities, there is an urgent need to identify important ecological spaces and determine priorities for ecological restoration planning (Peng et al., 2018 ). The findings from this study are expected to benefit the future construction and development of the regional EN and provide spatial guidance for ecological restoration planning. The network construction and optimization methods formed and refined in this study further emphasize the relevance and sustainability of the EN. The study optimized the basic framework of EN construction and provided a feasible way to conduct network optimization and compare the effects. A complete workflow and methodological framework of "network construction-network optimization" was proposed in this study, which enriched the theoretical basis of EN construction at the level and emphasized the EN dynamic development from practical perspective. Meanwhile, the framework has its limitations. To ensure the accuracy of the assessment results, it is suggested using multiple sources of data (e.g., nighttime light and field survey results) to calibrate the models. The evaluation methods used in the study should be compared and selected based on the current characteristics of the EN to find out the model that can highlight the regional natural, geographical, and social characteristics for the comprehensive evaluation. In addition, after the modeling and evaluating the EN under different scenarios, there was still some inevitable subjectivities in the analysis of indicators, such as the priority determination of indices like α, β, and γ. The solutions for these issues should be further considered in the future research to make reasonable adjustments on the optimization scheme according to the reality of network construction. The results of the study not only have importance for instructing the ecological restoration projects in LLB, but can also effectively help decision-makers and planners to formulate corresponding policies and strategies in urban planning and social developing. By quantitatively analyzing the network structure and evaluating the effects of restoring various ecological elements, urban planners and decision-makers can better formulate regional ecological restoration strategies and natural resources allocation policies, so that the EN can be constructed with the greatest ecological benefits and the lowest economic costs. What’s more, the "network construction-network optimization" framework can be the reference for ecological restoration planning in other regions. In summary, the methodological framework proposed in this study provides a scientific and quantitative basis for the construction and optimization of EN and is adaptable in planning. It can be a reference for the ecological civilization construction and sustainable development goals implementation. This issue still needs further exploration in future studies to gradually improve the methodological framework. The rationality and scale suitability of the framework should be verified through the successive practice in different sites at different scales. Future exploration of α, β and γ indices in terms of network connectivity to find out the laws of their changes can be necessary to promote the effective integration of EN construction and ecological restoration of national territory. Declarations Ethical Approval Our study did not require further ethics committee approval as it did not involve animal or human clinical trials and was not unethical. In accordance with the ethical principles outlined in the Declaration of Helsinki, all participants provided informed consent before participating in the study. The anonymity and confidentiality of the participant were guaranteed, and participation was completely voluntary. Consent to Participate This paper mainly explores an ecological network construction method based on scenario simulation and circuit theory, in order to explore the scientific work path of ecological space protection planning. All researchers participate in the study voluntarily and have the right to withdraw from the study at any time. The research content does not involve the researchers, and there is no risk of privacy disclosure. Consent to Publish The publication of this study and all accompanying images was agreed upon by all researchers. CRediT authorship contribution statement Yan Zhou (Investigation; Conceptualization; Formal analysis; Writing – Review & Editing;Project administration); Menegyao Liu(Formal analysis; Data extraction and analysis tools; Writing - Original Draft༛Writing – Review & Editing); Lina Wang (Data extraction༛Writing – Review & Editing); Jianing Yu (Formal analysis; Writing – Review & Editing); Yawen Luo (Formal analysis; Writing – Review & Editing); Qiaoling Luo (Writing - Review & Editing; Funding acquisition; Project administration). Competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data will be made available on request. Declaration of Generative AI and AI-assisted technologies in the writing process During the preparation of this work the author(s) didn’t used any AI or AI-assisted technologies. 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Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBAC+2bmgw8keP7L2R9vIFKLATtbsoGFDLMxw5kDxGrh51GTqLBhTmS4kUCkFnNmHgaJGzlsCYwzH2+8wVBjE01Qi2Uz7wHDGWd48pil04otGI6l5TYQ1HOYLyFZskeimE06x0yCseEwMVp4DA7//WeQ2CN5hkgtBod5DBskeBISZ0jwEKlFspktmUGC54CxAQ/QLwnE+IWf//DxH0Atcgbshzfe+FBjQ4RfkB0pkUCKcogWUnWMglEwCkbByAAAYTU7WPjd4m8AAAAASUVORK5CYII=","orcid":"","institution":"Wuhan University","correspondingAuthor":true,"prefix":"","firstName":"Mengyao","middleName":"","lastName":"Liu","suffix":""},{"id":346500427,"identity":"818314c5-a611-4d58-9fca-902b0b13881b","order_by":2,"name":"Lina Wang","email":"","orcid":"","institution":"Hubei Provincial planning and design institute","correspondingAuthor":false,"prefix":"","firstName":"Lina","middleName":"","lastName":"Wang","suffix":""},{"id":346500428,"identity":"9cfd507a-f62d-417a-ac11-f39ef282cc47","order_by":3,"name":"Yawen Luo","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Yawen","middleName":"","lastName":"Luo","suffix":""},{"id":346500429,"identity":"ee2bccc4-e643-4f3f-8951-0e39472040f6","order_by":4,"name":"Qiaoling Luo","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Qiaoling","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2024-03-21 09:06:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4142154/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4142154/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65434791,"identity":"b1781f7b-0646-4080-8444-19f18897b0f4","added_by":"auto","created_at":"2024-09-27 12:11:33","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":255938,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the Liangzi Lake Basin\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4142154/v1/e7019d71aa4854a195b45c93.jpeg"},{"id":65434738,"identity":"b6dec0ab-b3f6-4093-be8f-4e31d5b983a0","added_by":"auto","created_at":"2024-09-27 12:11:22","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":279517,"visible":true,"origin":"","legend":"\u003cp\u003eThe workflow of EN construction and optimization\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4142154/v1/5103877fec53c63572e569d3.jpeg"},{"id":65434789,"identity":"ca5375e8-d47a-4df3-ab79-1986e87c9165","added_by":"auto","created_at":"2024-09-27 12:11:33","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1262021,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distributions of ecological sources based on dominant ESs\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4142154/v1/16c456c330055be1a422d9dd.jpeg"},{"id":65434800,"identity":"5ffc930d-4e1b-481a-832f-0c73b105fd53","added_by":"auto","created_at":"2024-09-27 12:11:37","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":919351,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distributions of ecological sources based on landscape connectivity.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4142154/v1/30bddde485c72ce5c6eaa0de.jpeg"},{"id":65434790,"identity":"6f3ad2a8-ea73-4a0d-931b-b2a978d6b250","added_by":"auto","created_at":"2024-09-27 12:11:33","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1237158,"visible":true,"origin":"","legend":"\u003cp\u003eThe constructing process of EN in LLB, 2020\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4142154/v1/22385ef170b77c1824bee66c.jpeg"},{"id":65434761,"identity":"e57fc27b-1cda-4b04-99bf-20c27df9428f","added_by":"auto","created_at":"2024-09-27 12:11:27","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1480744,"visible":true,"origin":"","legend":"\u003cp\u003eEcological steppingstones identification\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4142154/v1/c949a990f1d29c8b234e290c.jpeg"},{"id":65434722,"identity":"db85eac6-88c8-4e9c-b573-64a66ed18713","added_by":"auto","created_at":"2024-09-27 12:11:17","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":776302,"visible":true,"origin":"","legend":"\u003cp\u003eEcological corridors optimization scenario simulation\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4142154/v1/8887c159a8f17ab6092571ac.jpeg"},{"id":65434854,"identity":"181ac24c-81a9-4ef0-8b1d-8eb21d79a3be","added_by":"auto","created_at":"2024-09-27 12:11:40","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1137208,"visible":true,"origin":"","legend":"\u003cp\u003eEcological network optimization scheme for LLB\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4142154/v1/d921e4352b108e29c85745d7.jpeg"},{"id":65438050,"identity":"ddfde13a-f09b-4af9-9fd3-9428dd555a85","added_by":"auto","created_at":"2024-09-27 12:21:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8201076,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4142154/v1/8f25cb57-c842-433b-a9dc-55c8904fe1ed.pdf"},{"id":65434715,"identity":"bf866486-4f01-48cb-a332-07844ae61de8","added_by":"auto","created_at":"2024-09-27 12:11:16","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":632188,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical Abstract\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4142154/v1/b9174ae3e19ca79b53bb598d.jpeg"}],"financialInterests":"","formattedTitle":"A methodological framework for ecological network optimization integrating circuit theory and scenario simulation：Application in the Liangzi Lake Basin, China","fulltext":[{"header":"Highlights","content":"\u003cul start=\"12\"\u003e\n \u003cli\u003eBased on the scenario simulation, the ecological strategic points that have important influences on the ecological network were identified and the ecological network was optimized.\u003c/li\u003e\n \u003cli\u003eUsing MSPA-InVEST model and MCR model, 20 ecological sources and 56 ecological corridors were identified.\u003c/li\u003e\n \u003cli\u003eUsing circuit theory to identify the obstacle points in the network.\u003c/li\u003e\n \u003cli\u003eThree optimization scenarios: \u0026ldquo;increasing ecological stepping stones, removing obstacle points, and protecting key pinch points\u0026quot; were set up to compare network connectivity and cost.\u003c/li\u003e\n \u003cli\u003eThe optimization scheme that has the greatest effect on the optimization of ecological network connectivity was proposed and the optimal strategies of ecological network were formulated.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1 Introduction","content":"\u003cp\u003eThe rapid urbanization and land use expansion have led to the encroachment of natural spaces with important ecosystem services (ESs) (He and Zhang, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), this has brought about a variety of problems to ecosystem such as landscape fragmentation, loss of biodiversity and degradation of ES, posing a serious threat to regional ecological security and sustainable development (Tang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Xia et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). By integrating the ideas from different disciplines, the scopes of ecological security management and environmental restoration have gradually been transformed from restoring points to comprehensive restoration in networks (Kukkala and Moilanen, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this background, constructing and optimizing ecological networks (EN) has become one of the major concerns of ecological security pattern research (Li et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEN is based on the \"patch-corridor-matrix\" model (Jongman, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), and has been widely used in ecological security pattern construction (Yuyang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), biodiversity conservation (Clauzel et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), green infrastructure construction (Benedict and McMahon, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), watershed ecological restoration (Kang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and coordinated development of urban clusters (Zhou et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It can benefit the reasonable distribution of land resources and plays an important role in constructing ecological reserves, balancing ecological preservation and economic development, and maximizing ecological benefits (De Montis et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Constructing EN can integrate scattered patches, corridors, and other important land resources to improve the connectivity and mobility of landscape elements, and maintain the health and stability of the ecosystem. EN is now one of the widely-used approaches for regional ecological spaces construction (Peng et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with its advantages in ecosystem management and decision making. Establishing EN can also help to protect the ecological security, benefit the sustainable use of the ecological resources, and provide a theoretical basis for planners and policy makers (Cunha and Magalh\u0026atilde;es, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).The previous research on EN mainly focus on network identification and construction, and the paradigm of \"sources identification, resistance surfaces construction and corridors extraction, and ecological nodes identification\" has been formed (Aminzadeh and Khansefid, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEcological sources are patches with important ecological functions, which are important for maintaining regional ecological security and stability. The identification of ecological sources used to be direct judgement, now it has been evolved to comprehensive assessment based on various elements (Vergnes et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zhao and Xu, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) and Morphological Spatial Pattern Analysis (MSPA) are widely used to identify ecological sources by taking ecological functions and landscape connectivity into consideration, to make the identification more objective (Wang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEcological corridors are bridges connecting ecological sources and are widely used in the simulation of ecological flows and species flows (Huang et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To extract corridors, the resistance surfaces should be established first, and further simulation can be carried out based on the Minimum Cumulative Resistance (MCR) model (Peng et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In terms of resistance surfaces establishment, many studies only used one land use pattern for resistance distribution analysis, which cannot comprehensively reflect the impacts of habitat, human activities, and other factors (Li et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Therefore, it is necessary to use multi-source data and information to correct the resistance value of surfaces and improve the accuracy and objectivity of corridor identification.\u003c/p\u003e \u003cp\u003eEcological nodes are often neglected in the EN construction because many studies did not perform node extraction, or simply used the intersection of corridors as nodes. By doing these, the important functions of nodes in EN structure and connectivity cannot be well reflected (Kong et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Using circuit theory can effectively assist the identification of ecological strategic nodes. By discriminating high current intensity areas in the network, the strategic nodes that are vital for network connectivity can be precisely extracted (Gong et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe aims of EN construction are to systematically present the spatial distribution characteristics of regional ecological resources and give specific recommendations on ecological restoration (Xiao et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, with the improvement of the methodological framework of construction, EN tends to be more patterned and simplified now (Wang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), so the networks are not easily to be implemented in the planning practices. Besides, with extracted sources and corridors only is not enough for the decision making of ecological preservation and restoration (Jing et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the corresponding changes of EN with the constant expansion of land use should be taken into account (Ding et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Previous research have already confirmed that various urban construction and ecological preservation projects have impacts on the corridors and nodes (Fu et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), therefore, constructing EN with focus on the current ecological structure only is not enough, quantitative analysis of the changes in EN connectivity after restoration over periods cannot be neglected (Li et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). How to effectively use EN to achieve the overall monitoring and rational planning of regional ecological resources? The answer to this question will be the key to maintain regional ecological security and achieve sustainable development goals (Schr\u0026ouml;der and Seppelt, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In this study, the ES was innovatively combined with MSPA to identify sources, so the network can more accurately reflect the ecosystem functions and connectivity status in the area. As an area with massive water area and wetlands, two significant and typical ES of LLB\u0026ndash;habitat quality and water connotation, were used in the source identification and network construction.\u003c/p\u003e \u003cp\u003eLocated in the southeastern part of Hubei Province, China, the Liangzi Lake Basin (LLB) is a pivotal target area of watershed preservation and ecological restoration in Hubei province. In the middle reaches of Yangtze River, LLB has the typical wetland landscape and biodiversity patterns. It is of great significance in the provincial water resources conservation and watershed restoration projects. At the same time, LLB is neighbor to the metropolis of central China, Wuhan, the rapid changes in land use around the basin have had great impacts on its ecological stability. According to the requirements of the overall plan for the protection and restoration of national ecosystems in China, when carrying out ecological restoration planning, it is necessary to accurately locate the ecological space that needs urgent restoration, and to rank the restoration priorities of different spaces (National Development and Reform Commission and China, 2020). It is important for policy makers and planners to figure out how to reasonably construct an EN in LLB to intercede the conflicts between economic development and ecological security, and to achieve economic benefits and sustainable development for LLB and its nearby cities.\u003c/p\u003e \u003cp\u003eToward the targets, this study takes LLB as the sample area to explore the methodological framework for the construction and optimization of EN, with the trials to answer the following questions: 1) How to precisely locate the important ecological patches in the network that need urgent protection and restoration; 2) How to figure out the appropriate network optimization scheme towards different restoration objectives; 3) How to evaluate the effect of EN under different restoration scenarios? To answer the above questions, sources, corridors and nodes were identified and extracted to construct the EN in LLB. The land use patterns in LLB from 2000 to 2020 were analyzed to find out potential stepping stones, obstacles and pinch points in the EN, so the different restoration scenarios can be set up. After simulating the connectivity of the network under different scenarios, the restoration priorities of each type of landscape elements can be determined, and the specific optimization scheme can be decided for the EN in LLB. Through the study, it is expected to ensure the reasonable deployment of natural ecological resources and the steady improvement of the network connectivity in LLB, to achieve the maximum benefits of EN construction and provide a quantitative basis for future optimization and improvement of EN constructions.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eLiangzi Lake is one of the \"Top Ten Famous Lakes\" in China, and the second largest lake in Hubei Province, with a water area of 370 km\u003csup\u003e2\u003c/sup\u003e. The range of its watershed reaches six administrative districts in five cities, with a total area of 6236.44 km\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). LLB has relatively low elevation, except for its southern side which is dominated by mountains. The rest of LLB is flat and open and covered by hills and plains. The elevation of LLB decreases from its south to north. The vast water area and the abundant vegetation on land provide livable habitats for wildlife. LLB is in the north subtropical monsoon climate zone, with moderate sunlight, mild climate, and sufficient rainfall. The annual average temperature there is 17.3\u0026deg;C and the annual average precipitation is between 1350 to 1415 mm, the rainfall decreases from its south to north. The natural background conditions of the study area are superior and the habitat quality there is high. At the same time, the large area of wetland lakes provides sufficient water resources and has high water conservation capacity. The natural background conditions of the study area are superior, the habitat quality is high. The large area of wetlands and lakes provides sufficient water resources, which possess strong water conservation ability (Zhou et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The GDP of LLB has been steadily growing ever since 2000, while the problems of water pollution and ecological fragmentation have been increasingly serious. The inflow of pollutants from external sources and the surface water pollution caused by domestic production led to the deterioration of the self-purification capacity of the water. The long-term aquaculture, lake reclamation, and tourism development have led to the decline of the ecological functions of LLB. How to balance the ecological preservation and economic development is a great challenge in the sustainable development of LLB.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this study, the ES and land use/cover changes of LLB were used to analyze the changes caused by urban construction and to identify important elements in the EN. The period from 2000 to 2020 was selected to illustrate the changes over time because the pace of modernization of the districts around LLB had been obviously speeding up since 2000. 10-year interval was used so that the changes of land use patterns can be more obvious.\u003c/p\u003e \u003cp\u003eTo evaluate the ecosystem services and analyze the changes of land use/cover changes of LLB, the following data sources were used:\u003c/p\u003e \u003cp\u003eThe land use data of LLB from 2000 to 2020 were obtained from the Land Satellite Remote Sensing Application Center of the Ministry of Natural Resources of China (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.webmap.cn/\u003c/span\u003e\u003cspan address=\"https://www.webmap.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a resolution of 30 \u0026times; 30 m. The vector boundary data in the study area were obtained from OpenStreetMap (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.openstreetmap.org/\u003c/span\u003e\u003cspan address=\"http://www.openstreetmap.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe water connotation function and the quality of habitat are the major significant ESs in LLB. To evaluate the functions, the data collected in the study included precipitation, evapotranspiration, and land use changes. The value of resistance was indicated by the elevation, slope, average annual temperature, average annual precipitation, distances to the nearest railroad, road, and water body. The data of elevation and water were from the Geospatial Data Cloud (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gscloud.cn/search\u003c/span\u003e\u003cspan address=\"http://www.gscloud.cn/search\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The slope was calculated based on the elevation. Average annual temperature, average annual precipitation, and evapotranspiration were obtained from the National Earth System Science Data Center of China (\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). The information of railroads and roads distribution can be found in the Global Road Open Access Data Set (gROADS).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 The process of EN construction and optimization\u003c/h2\u003e \u003cp\u003eThe methodological framework of EN construction and optimization established in this study can be divided into the following three steps (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e(1) \u003cb\u003eEN construction based on MSPA-MCR and ES.\u003c/b\u003e Assessing the ecosystem service functions and analyze the landscape patterns of LLB, so the ecologically important areas and high connectivity areas can be identified. The overlapping parts of the two types of areas are the ecological sources. Then we constructed and correct of resistance surfaces by using natural and social indicators, so the ecological corridor could be identified using MCR model. The ecological strategic nodes (obstacle points and pinch points) were extracted based on the circuit theory. With these elements, the EN based on the conditions in 2020 can be constructed.\u003c/p\u003e \u003cp\u003e(2) \u003cb\u003eLand use simulation for steppingstones.\u003c/b\u003e In this step, the land use data in 2000 and FLUS model were used to simulate the land use patterns in 2010 and 2020, the simulation results were compared with the actual data in the two years to verify the accuracy of simulation results. After the verification, further simulation of the land use/cover changes in 2030 was conducted to extract the ecological steppingstones, which are the ecological spaces that will be encroached by urban construction land in 2030.\u003c/p\u003e \u003cp\u003e(3) \u003cb\u003eScenario simulation and optimization evaluation.\u003c/b\u003e Based on the results acquired from the first two steps, three EN optimization scenarios of \"increasing ecological steppingstones, removing obstacle points, and protecting key pinch points\" were set up. The connectivity and cost ratio of the EN under the three scenarios were simulated and calculated to figure out the one with the highest network connectivity and lowest optimization cost.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 EN construction based on MSPA-MCR and ES\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Identification of ecological sources\u003c/h2\u003e \u003cp\u003eEcological sources are the core of EN construction, the accurate identification of sources is the key to establish regional ecological security patterns and sustain the ecological security (Peng et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Ecological sources are mostly natural patches with sufficient material exchanges and energy flows inside. Zones like water areas, forests, and large wildlife habitats are mostly ecological sources with strong ES functions and high aesthetic values. Meanwhile, as habitats for species\u0026rsquo; survival, reproduction, and migration, ought to have certain degrees of landscape connectivity and extensibility. Two typical and important ESs of LLB\u0026ndash;habitat quality, and water connotation, were selected for ES evaluation, the Habitat Quality and Water Yield modules in the InVEST were applied to calculate the level of the ES functions with the following equations (Sun et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${Q}_{xj}={H}_{j}\\left(1-\\frac{{{D}_{xj}}^{2}}{{{D}_{xj}}^{2}+{k}^{2}}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${Y}_{\\left(x\\right)}=\\left[1-\\frac{AE{T}_{\\left(x\\right)}}{{P}_{\\left(x\\right)}}\\right]\\times {P}_{\\left(x\\right)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left({Q}_{xj}\\right)\\)\u003c/span\u003e\u003c/span\u003eis the habitat quality index, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({H}_{j}\\)\u003c/span\u003e\u003c/span\u003eis the habitat suitability of landscape pattern j, k is the half-saturation constant and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({D}_{xj}\\)\u003c/span\u003e\u003c/span\u003e is the habitat stress level of raster cell x of landscape type j. In Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{\\left(x\\right)}\\)\u003c/span\u003e\u003c/span\u003e is the annual water supply (mm) on raster cell x in the study area, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(AE{T}_{\\left(x\\right)}\\)\u003c/span\u003e\u003c/span\u003edenotes the annual actual evapotranspiration (mm) from raster cell x, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{\\left(x\\right)}\\)\u003c/span\u003e\u003c/span\u003e denotes the annual precipitation (mm) from raster cell x. Water connotation capacity \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{\\left(x\\right)}\\)\u003c/span\u003e\u003c/span\u003e can be obtained by correcting water production through topographic index, soil saturated hydraulic conductivity and flow velocity coefficient. Both functions were given the same weight and superimposed, and then divided into five levels according to the Jenks natural breaks, with higher level representing more significance. The zones of level 4 and 5 were selected as the ES-significant areas.\u003c/p\u003e \u003cp\u003eMSPA was widely used to obtain the ranges of cores and bridges in EN. Differs from the traditional landscape connectivity analysis methods, MSPA identifies the high-connectivity zones with digital images (Soille and Vogt, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In this study, forest, water, and grassland were used as foreground, and the other land use patterns were used as background for MSPA analysis. Seven basic classes of landscape: cores, islets, bridges, edges, branches, loops, and perforations were obtained regarding to their connectivity. The cores and bridges with high connectivity were selected for source identification (Wickham et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe zones with strong ecological functions and the areas with high connectivity were superimposed, the overlapping zones, together with important nature reserves and water bodies, composed the ecological sources for this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Resistance surface construction and corridor identification\u003c/h2\u003e \u003cp\u003eThe flow of ecological elements among different sources are influenced by natural and human factors (Loro et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), the resistance surface can reflect the degree of difficulty of cross-source flows, therefore, can be used for modelling corridors (Fan et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Regarding to the natural and socioeconomic characteristics in LLB, five factors were selected to establish the resistance surface: land use pattern, elevation, slope, distance to the nearest road, and distance to the nearest railroad. The resistance values were assigned to different land use patterns, the higher the degree of human disturbance, the higher the resistance value of the land use pattern. The land use data of LLB in 2020 was used for value assignment. The elevation and slope factors were classified to five levels according to the natural breakpoint method. The distances of buffer zones to their nearest roads and railroads were classified and assigned resistance values using the multi-loop buffer analysis of GIS. Weights were given to each factor referring to the previous studies (Jing et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), to limit the value of resistance in the range of 1-100. The lower the resistance value of the surface, the stronger the capacity of migration and ecological flow between sources. The resistance values and weights of different land use patterns were listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eResistance coefficients and weights of different factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResistance\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand use pattern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDistance to the nearest road(km)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDistance to the nearest railway(km)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstruction\u003c/p\u003e \u003cp\u003eland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;35\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWaters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e800\u0026ndash;1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u0026deg;-35\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e500\u0026ndash;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e500\u0026ndash;1000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e400\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u0026deg;-20\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1000\u0026ndash;1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1000\u0026ndash;1500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCultivated land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200\u0026ndash;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u0026deg;-15\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1500\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1500\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;8\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;5000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the constructed resistance surface, the corridors with minimum resistance value can be modelled using the MCR with the following equation (Knaapen et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1992\u003c/span\u003e):\u003c/p\u003e \u003cp\u003eMCR=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(f{\\int }_{j=n}^{i=m}（{D}_{ij}\\times {W}_{i}）\\)\u003c/span\u003e\u003c/span\u003e (3)\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;3, MCR represents the cumulative resistance value, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(f\\)\u003c/span\u003e\u003c/span\u003e represents the ecological process function positively correlated with MCR, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({D}_{ij}\\)\u003c/span\u003e\u003c/span\u003e represents the distance between source i and source j, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the resistance to biological migration and ecological flows generated by source i.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Identification of ecological strategic nodes\u003c/h2\u003e \u003cp\u003eEcological nodes are essential for promoting or inhibiting the functional integrity of ecological sources, they also influence the connectivity of ecological corridors, and the stability of ecological safety patterns (Yu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These nodes are often small and relatively easy to be protected and restored, specific practices in nodes protection and restoration can be cost effective in EN optimization. In the circuit theory, the ecological landscape is recognized as a conductive surface, the ecological flow is considered as electric current, the current density can reflect the resistance encountered by species when migrating (Yuyang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Linkage Mapper is a toolkit with 6 tools to analyze regional wildlife habitats\u0026rsquo; connectivity, it was used in this study to identify the strategic nodes like obstacle points and pinch points.\u003c/p\u003e \u003cp\u003eObstacle points are the areas where species are easily obstructed during migration. Eliminating obstacle points can effectively improve a corridor\u0026rsquo;s connectivity. Barrier Mapper was used to identify the significant barrier areas in the EN as obstacle points, the minimum and maximum search radius were set to 500 m and 1500 m respectively, to search obstacle points by the moving window method.\u003c/p\u003e \u003cp\u003ePinch points, also known as bottlenecks, are high current intensity areas in the EN. They are areas where are very likely to be passed by during migrations and there is no other alternative pathway. The degradation of the pinch points will have a serious impact on the function of ecological sources and ecosystem stability, so they should be well protected. If a pinch point happens to be in an area with high resistance value, it is easily to be disturbed and degraded by external factors. Pinchpoint Mapper was used to identify pinch points.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Land use simulation for steppingstones\u003c/h2\u003e \u003cp\u003eFLUS model can simulate the influences of human activities and natural environment on land use and predict the future development of different land use patterns (Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this study, the land use data of LLB in 2000, 2010, and 2020 were used. To verify the simulation results and calibrate model parameters, the land use data of LLB in 2000 was imported to simulate the land use conditions in 2010 and 2020. Later, a Kappa consistency test was conducted to compare the simulated land use conditions with the actual data in the corresponding year, strong consistency could be indicated if the kappa coefficient was between 0.8 and 1.0.\u003c/p\u003e \u003cp\u003eAfter validation and correction on the parameters, FLUS was able to simulate the future land use in 2023, the data of 2020 was imported to conduct the simulation. By comparing the land use conditions in 2020 and 2030, the ecological spaces that would be encroached by the construction land in 2030 were recognized as ecological \"stepping stones\". Protecting such sensitive and vulnerable spaces can preserve the species migrations and energy flows between distant patches, benefit the ecosystem functioning and connectivity.\u003c/p\u003e \u003cp\u003eAfter the first two steps of EN construction and steppingstone identification, three crucial elements for the regional ecological security pattern were acquired. They are relatively small, so protections and restoration in these elements will cost less but the effects can be remarkable. By paying more attention to steppingstones, protecting pinch points with high current intensity, and eliminating obstacle points in migrations, we can improve the smoothness of ecological corridors and the overall connectivity of the region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Scenario simulation and optimization evaluation\u003c/h2\u003e \u003cp\u003eTarget on improving the connectivity and stability of the EN in LLB, three scenarios\u0026ndash; increasing ecological stepping stones, removing obstacle points, protecting key pinch points, have been set up to optimize the current EN (based on the 2020 land use). Problems such as fragile network structure, unstable and incomplete ecological functions caused by insufficient flow and obstructed flow of ecological flows in the current network were expected to be solved through the EN optimization (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEN optimization scenario settings and operations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenario Setting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted problems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOperation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1) Increasing ecological stepping stones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduced network connectivity due to functional degradation and insufficient ecological sources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOn the basis of identifying stepping stones, simulate the impacts of stepping stone degradation on the ecological network to verify the necessity of stepping stone protection.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2) Removing obstacle points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInaccessibility of corridors and blocked ecological flow caused by external human interference such as urban construction and land expansion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIdentify obstacle points, remove or restore them through urban construction projects by reducing the resistance value.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3) Protecting key pinch points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpaired network function and reduced stability due to the degradation of important pinch points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIdentify pinch points and simulating the impact of pinch point degradation on habitat network to verify the necessity of pinch points\u0026rsquo; protection.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eTo compare the EN optimization effects and cost effectiveness of the three scenarios, all the results in the three scenarios were analyzed based on the current EN, ensuring their area and number of ecological sources remain unchanged. EN connectivity indexes, cost ratio (c) and structural indicators of EN were selected and calculated to assess each solution, see the following equations (Cook, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e):\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\alpha =\\frac{L-V+1}{2V-5}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e \u003cdiv id=\"Equ4\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\beta =\\frac{L}{V}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e \u003cdiv id=\"Equ5\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\gamma =\\frac{L}{3（\\text{v}-2）}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ec\u0026thinsp;=\u0026thinsp;1-(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{L}{l}\\)\u003c/span\u003e\u003c/span\u003e) ༈7༉\u003c/p\u003e \u003cp\u003eThe network connectivity indexes include network closure (α), line point rate (β), and network connectivity (γ), which are widely used in the calculation of the overall EN connectivity. α represents the degree of existence of loops between ecological sources and ecological nodes in the network, and the value ranges from 0 to 1, with larger values indicating smoother ecological flow; β represents the degree of difficulty in connecting ecological sources and nodes, and the value ranges from 0 to 3. It is usually considered that the network connection is more complex when β\u0026thinsp;\u0026gt;\u0026thinsp;1. γ represents the ratio of the number of existing corridors to the maximum number of possible corridors in the ecological network, and the value ranges from 0 to 1. The larger the value, the higher the degree of node connectivity. In the equations, L is the number of corridors, V is the number of nodes (corridor intersections), and l is the total length of the corridors. c reflects the effectiveness and feasibility of the EN, with lower values indicating lower EN construction costs (Kong et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Comprehensive Ecological sources identification\u003c/h2\u003e \u003cp\u003eTo acquire the sources in the EN of LLB from more objective angle, the identification was conducted from ecosystem services and morphological spatial pattern. By this way, the identified sources are compatible of both strong ES functions and high landscape connectivity.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Ecological identification regarding to ES\u003c/h2\u003e \u003cp\u003eFrom the ES perspective, the regional habitat quality and water connotation service function were selected as the typical representatives of the entire regional ecosystem service function. The InVEST modeling results were classified into five levels based on natural breaks, indicating low to high importance of ES. Zones of level 4 and 5 were defined as ecological sources with strong ecosystem service functions. The results showed that the ES distribution was highly spatial heterogenous (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The high habitat quality areas cover 1590.92 km\u003csup\u003e2\u003c/sup\u003e, accounting for 25.51% of the total area. Most of the areas distributed in the southern forests and lake. Waters and forests have stronger ability to provide adequate resources and conditions for wildlife\u0026rsquo;s survival and development compared with other land patterns, showing the obvious importance in the maintenance of biodiversity.\u003c/p\u003e \u003cp\u003eThe overall area of strong water connotation function zones is 2161.55 km\u003csup\u003e2\u003c/sup\u003e, accounting for 34.66% of the total area, which is mainly distributed in the eastern part of the study area with low slope, abundant rainfall, and high vegetation coverage. In addition, the water connotation ability of LLB is significantly weaker than its surrounding areas, which indicates that LLB has relatively weaker ability to intercept, infiltrate and accumulate precipitation, so the ecological spaces there may have been seriously disturbed by human activities.\u003c/p\u003e \u003cp\u003eThe areas that have both ESs of level 4 to 5 were identified to be the ecological sources regarding to ES. The total area of ecological sources regarding to strong ES functions was 2195.23 km\u003csup\u003e2\u003c/sup\u003e, accounting for 35.20% of the total area (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). They mainly were distributed in the southern high-elevation forest area, with a few scattered locations in the northeast, and the land use patterns were mainly forests, cultivated lands and waters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Ecological identification based on morphological spatial patterns\u003c/h2\u003e \u003cp\u003eFrom the aspect of spatial connectivity, MSPA was applied to analyze forest, grassland, and water as foreground. The outputs of foreground were divided into six classes and visualized in different colors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The cores accounted for 75.85% of the foreground, and the cores with relatively large aera were mainly in the central water area and southern forest. The bridges accounted for about 2.81% of the foreground and were mainly in the center of LLB. The dominant land use pattern in the bridges is water. The edges accounted for 13.95% of the imported foreground. Due to the edge effect, the edges were mainly composed of forests and waters, indicating that the regional landscape connectivity needs to be further improved. The cores and the bridges with high spatial connectivity were selected as the candidate ecological sources (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Comprehensive ecological source identification\u003c/h2\u003e \u003cp\u003eThe intersections of strong ES function areas and high spatial connectivity cores and bridges were considered as the candidates of ecological sources. Nature reserves, important wetlands in LLB were further superimposed with the source candidates to form the compatible ecological sources for this study. Since ecological sources must have a certain area to keep the core free from external interference and the ecological radiation capacity of small, fragmented patches is weak, the minimum threshold determination method was used to screen ecological sources by scale. The screening results showed that the number of ecological sources decreased rapidly at beginning as the threshold increased. When the threshold increased to over 1.5 km\u003csup\u003e2\u003c/sup\u003e, the number of ecological sources begun to decrease slowly. When the threshold was set to 10 km\u0026sup2; and more, the decline tended to level off. Therefore, the area threshold was set 10 km\u0026sup2;, 20 ecological sources were screened out after excluding fragmented patches. The total area of sources at last was 1269.98 km\u003csup\u003e2\u003c/sup\u003e, accounting for 20.36% of the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). These comprehensive ecological sources were mainly distributed in the central and southern parts with high altitudes and steep slopes, and the land use patterns there were mainly water, forest, and cultivated land, accounting for 49.13%, 46.13%, and 2.18%, respectively. Seeing from administrative aspect, the ecological sources in Liangzihu District, Xianan District, and Daye District were more abundant and less subject to the human interference.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Results of resistance surface construction and corridors identification\u003c/h2\u003e \u003cp\u003eResistance surfaces can be influenced by various factors. In this study, five factors were selected to evaluate the resistance value: land use pattern, elevation, slope, distance to the nearest road, and distance to the nearest railroad. Weights were assigned to each factor according to regional habitat characteristics, and finally, the cumulative ecological resistance surface was constructed and weighted (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The average resistance value in LLB was 14.18, and the high-valued resistance zones were mainly distributed in the areas with high urbanization levels and dense construction. The low-valued resistance zones mainly stretched from the southern forests to the central water area along the forests and rivers.\u003c/p\u003e \u003cp\u003eMCR can simulate the least-cost path from one source to another, these paths were the corridors in this study. After merging and deleting duplicate corridors, 56 ecological corridors were obtained with their lengths ranging from 1.178 to 35.501 km and a total length of 482.15 km. The land patterns of most corridors were forest and cultivated land (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). The central part of LLB had many sources with large area which distributed at small distances to each other. Therefore, the corridors there were densely distributed and short in length, which can be high-quality corridors for species migration. They were conducive to the connectivity of forest, grassland and lake at the same time. Corridors longer than 10 km were mostly distributed between the central water area and the southern forests, where there were 10 long corridors with poor landscape connectivity. The southern part of LLB was dominated by forests, with sources densely distributed, so the corridors there were mostly less than 10km in length with high quality, they were the connection between forests and grasslands. The southwestern part was significantly affected by the expansion of urban construction, there was a potential of interconnection between some sources though, none complete ecological corridor could be simulated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Ecological strategic nodes identification results\u003c/h2\u003e \u003cp\u003eIn the process of ecological preservation and restoration, key nodes and obstacle areas are the essential component that worth focus and consideration. The circuit theory was applied in the identification regional obstacle points and pinch points with Linkage Mapper toolkit. The all-to-one mode was used to identify the high current areas and superimpose them with the ecological resistance surface. The areas were divided into different classes with the natural breaks method and the high current density area of the first class was taken as the pinch points. The Barrier Mapper tool was used to identify the obstacle points by clicking the \u0026ldquo;calculate percent improvement scores relative to corridor LCD\u0026rdquo; option. The higher the improvement score relative to the LCD, the more demands in this area to be restored and improved landscape connectivity. The areas of high improvement scores were identified as ecological obstacle points that were in urgent need of improvement connectivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e64 ecological pinch points with an area of 19.16 km\u0026sup2; and 25 ecological obstacle points with an area of 20.88 km\u0026sup2; were identified in the study (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). Seeing from the spatial distribution aspect, the high current areas were mostly concentrated in the high resistance zones, which had more overlapping parts with ecological corridors and rivers. The land use patterns of pinch points were mostly cultivated land and water, the planting and breeding activities in and around the waters should be noticed to prevent the function degradation of the pinches. Obstacle points were mostly found at the intersection of roads within the ecological corridors. There was a large obstacle point in the east, land use patterns there were mainly cultivated land and construction land, these two patterns both had the hard sub-bedding surfaces, which could reduce the landscape connectivity to some extents.\u003c/p\u003e \u003cp\u003eThe identified 20 ecological sources, 56 ecological corridors, and 89 ecological nodes together form the regional EN (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef). The connectivity indexes and cost ratio mentioned in part 2.5 were used to assess the structure and connectivity of the EN in LLB, 2020. The results revealed that the, β, and γ were respectively 0.23, 1.27 and 0.44, and c was 0.88, indicating that the regional EN connectivity in 2020 was relatively complex, the ecological flows were strongly obstructed, and the nodes within the area were relatively weakly connected. The practical network construction can be difficult, and the protection and optimization of key ecological spaces and the entire EN were urgently needed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Land use simulation and steppingstone identification\u003c/h2\u003e \u003cp\u003eUsing the FLUS model, we used 2000 and 2010 as the base years to create suitability probability atlases, the land use of LLB in 2010 and 2020 was simulated compared with the actual data. The Kappa coefficients were 0.91 and 0.87 respectively, showing a high accuracy in the prediction results and the model could be applied to the simulation of further land use distribution in the study area.\u003c/p\u003e \u003cp\u003eThe land use simulation showed that from 2000 to 2020, the speed of urban expansion of LLB was rapid and the cultivated land has been decreasing. Most lost cultivated land was converted into forest and grassland, indicating that the policy of returning farmland to forest has achieved certain results. While the growth of construction land remained fast, the centers of the administrative districts around LLB gradually expand outwards, the problem of insularization and fragmentation of ecological patches has been serious. Further prediction of 2030 land use in LLB was conducted based on 2020 land use data. The results showed that the further expansion of construction land would encroach much more on the surrounding ecological spaces such as forest and water (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eComparing the land use/cover changes from 2020 to 2030, 24.52 km\u0026sup2; of forest, grassland, shrubs, and waters that would be encroached by construction land in 2030 were extracted as ecological steppingstones that need urgent protection (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Among the encroached land, forests and waters account for the first two largest proportion, with 55.18% and 35.74% respectively of the total. Therefore, it was important to ensure the ecological quality and connectivity of the steppingstones in the process of urban construction and development for the stability of the regional EN.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Scenario setup and simulation towards EN optimization\u003c/h2\u003e \u003cp\u003eBased on the results of EN construction and land use simulation, three types of landscape elements that are crucial for the ecological security pattern were obtained: obstacle points, pinch points and stepping stones. These elements are significant for the EN, and they are relatively small in scale, so the preservation and restoration practices on such elements can be easy to be carried out, and the outcome should be cost effective. Three EN optimization scenarios: \"protecting ecological steppingstones, removing obstacle points, protecting key pinch points\" were set up and the EN under different scenarios was simulated with FLUS (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). To better assess the significance of the three elements, we simulated the EN connectivity and cost ratio when ecological stepping stones are not well protected and degraded, obstacle points are removed, and the key pinch points are degraded and reduced. The indicators used to evaluate the effects of optimization were compared and listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn Scenario 1, in 2030, 24.52 km\u0026sup2; of ecologically sensitive areas that would be converted into construction land due to urban expansion in 2030 were identified. If such sensitive areas cannot receive indeed preservation, the number of ecological nodes would increase. The total length of the corridors would increase to 490.12 km due to the bypassing of degraded steppingstones. The average resistance value would increase by 2.71, and the network closure index, line point rate index, and network connectivity would all decrease significantly. The stability and connectivity of the EN would be greatly affected compared with it in 2020.\u003c/p\u003e \u003cp\u003eIn Scenario 2, we simulated the restoration on the high resistance value areas to reduce the obstacle points and increase the landscape connectivity. In 2020, the area of the obstacle points was 20.88 km\u0026sup2;, and most of them were located within the habitats of corridors. In the simulation of removing ecological obstacle points, the number of corridors would increase by 4 and the average value of resistance surfaces would decrease to 13.22. The network closure index, line point rate index, and network connectivity would be significantly improved.\u003c/p\u003e \u003cp\u003eIn Scenario 3, the total area of the pinch points in 2020 was 19.16 km\u0026sup2;, and they were mainly located at the edges and connections of the waters, indicating that the ecological corridors should be particularly well protected in the water areas. Therefore, it was necessary to pay attention to the protection of ecological pinch points in such areas to prevent the degradation caused by human activities. Under the scenario of pinch point degradation, the number and length of corridors would increase, the network closure index would decrease significantly, and the network structure would become more complex.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEcological networks optimization comparison\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEN in 2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScenario 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScenario 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eScenario 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of sources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of corridors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe total length of the corridor/km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e482.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e490.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e489.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e509.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage resistance value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of nodes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eclosure indexα\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eline point rate indexβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enetwork connectivity γ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost Ratio (c)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\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 \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Ecological networks optimization scheme\u003c/h2\u003e \u003cp\u003eAfter comparing the structure and connectivity of the EN under the three scenarios with it in 2020, it can be found that the β and γ indexes in Scenario 1 and Scenario 3 fluctuated less while the network closure index dropped significantly, indicating that the ecological flows in the EN would be significantly impeded under the condition of stepping stones and pinch points degradation, and the network structure would be more complex. The α, βand γ indices in Scenario 2 all showed significant increases, meant that the connectivity of the network would be significantly improved, and the corridors would be more evenly distributed. The cost ratio in the three scenarios were close. Therefore, when constructing an EN in LLB and carrying out network optimization, priority should be given to removing ecological obstacle points in the existing network, followed by strengthening the protection of ecological pinch points.\u003c/p\u003e \u003cp\u003eAccording to the improvement of the EN under each scenario, the ecological stepping stones were determined as the secondary ecological sources, the obstacle points were determined as the prior ecological restoration targets, the pinch points were recognized as ecological protection areas, and the ecological corridors that would appear in Scenario 2 were recognized as secondary corridors. These strategies forming an EN optimization scheme for LLB (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen optimizing the EN, priority should be firstly given to removing obstacle points and constructing secondary ecological corridors based on protecting the current sources and corridors. Protecting nature should be the principle in EN construction, and the exploitation of ecological land should be strictly prohibited. For ecological reserves and secondary sources, ecological construction should be gradually strengthened towards the target of ecological restoration, combined with optimization of ecological substrates to improve the ecosystem stability and functions. The optimizing approaches of zoning and grading can be important for improving the structure and functional connectivity of EN, enhancing the stability and connectivity of ecosystems, and realizing the dynamic and sustainable development of the EN in LLB.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe increasing population and rapid expansion of urban construction land have led to great threats to the ecological land, and the implementation of sustainable development goals to protect ecological security has become a global consensus (Schindler, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). As the construction of ecological civilization in China continues to advance, identifying and protecting key ecological spaces have become a top priority for ecological restoration and planning. At the same time, the land use is changing faster in developing countries, the ecological security pattern changes brought by the land use changes are more susceptible, too. A single, static network construction method can hardly meet the current needs for ecological restoration. Therefore, we applied various methods such as ES evaluation, MSPA, and circuit theory to accurately identify various ecological elements and further optimize the network. The scenario simulation was used to explore the most suitable network optimization scheme with the best restoration effect and the lowest cost.\u003c/p\u003e \u003cp\u003eEN is composed of various ecological elements such as sources, corridors, and nodes, and the quality of these elements can be important for the stability of the entire EN (Li et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the context of the continuous and rapid development of China's economy and community, the ecological restoration needs precise positioning, step-by-step implementation, and continuous construction. EN construction, as a nature-based ecological restoration solution, also requires continuous optimization and adjustment to achieve sustainable development (Zhang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLLB has a good ecological matrix, with large wetlands and forests to ensure biodiversity, the spatial distribution of ecological corridors is relatively homogeneous. The EN of LLB includes 20 ecological sources, 56 corridors, 64 pinch points, and 25 obstacle points. Still, we need identify and monitor the important zones and elements in the EN to adjust and optimize the network according to the development and changes. Nevertheless, the ecological strategic nodes and resistance surfaces in the current EN revealed that the resistance values of the pinch points were mostly high, and their functions were vulnerable to human activities. Besides, there were many obstacle points in the network, which could impede the ecological flows. The simulation on the land use in 2030 revealed that more ecological patches would be encroached on by construction land in the future, the ecological quality would degrade, and the connectivity of the EN would be greatly influenced. By setting up three scenarios \"adding ecological stepping stones, removing ecological obstacles, and protecting key ecological pinch points\" for simulation, we could compare and select the best EN optimization scheme. The priority of ecological importance in different areas could also help the EN restoration to be carried out in an orderly manner.\u003c/p\u003e \u003cp\u003eSpecifically, in the EN of LLB, obstacle points had the greatest impact on network connectivity and should be removed firstly. This is because the obstacle points were concentrated in the high resistance value area and were small in size, and the ecological restoration works on them were relatively easy, and the comparison of different scenarios showed the removal of obstacle points had the most significant effects. Secondly, the pinch points provide important ESs, the degradation of them will have serious impacts on the network structure. And last, since urban expansion is inevitable, the protection and construction of ecological stepping stones should be emphasized, and urban construction should be carried out moderately and selectively to avoid large-scale disorderly land expansion. The obstacle points, pinch points, and stepping stones in LLB were planned as prior ecological restoration targets, ecological protection areas, and secondary ecological sources respectively. And the newly-appear corridors under Scenario 2 were extracted as secondary corridors. The overall scheme of network optimization can provide a guidance and reference for the subsequent network construction and optimization. It can effectively guarantee the construction and optimization of the regional network and benefit the efficient allocation and management of land, human and financial resources.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eWith the rapid expansion of urbanization and intensifying of human activities, there is an urgent need to identify important ecological spaces and determine priorities for ecological restoration planning (Peng et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The findings from this study are expected to benefit the future construction and development of the regional EN and provide spatial guidance for ecological restoration planning. The network construction and optimization methods formed and refined in this study further emphasize the relevance and sustainability of the EN. The study optimized the basic framework of EN construction and provided a feasible way to conduct network optimization and compare the effects.\u003c/p\u003e \u003cp\u003eA complete workflow and methodological framework of \"network construction-network optimization\" was proposed in this study, which enriched the theoretical basis of EN construction at the level and emphasized the EN dynamic development from practical perspective. Meanwhile, the framework has its limitations. To ensure the accuracy of the assessment results, it is suggested using multiple sources of data (e.g., nighttime light and field survey results) to calibrate the models. The evaluation methods used in the study should be compared and selected based on the current characteristics of the EN to find out the model that can highlight the regional natural, geographical, and social characteristics for the comprehensive evaluation. In addition, after the modeling and evaluating the EN under different scenarios, there was still some inevitable subjectivities in the analysis of indicators, such as the priority determination of indices like α, β, and γ. The solutions for these issues should be further considered in the future research to make reasonable adjustments on the optimization scheme according to the reality of network construction.\u003c/p\u003e \u003cp\u003eThe results of the study not only have importance for instructing the ecological restoration projects in LLB, but can also effectively help decision-makers and planners to formulate corresponding policies and strategies in urban planning and social developing. By quantitatively analyzing the network structure and evaluating the effects of restoring various ecological elements, urban planners and decision-makers can better formulate regional ecological restoration strategies and natural resources allocation policies, so that the EN can be constructed with the greatest ecological benefits and the lowest economic costs. What\u0026rsquo;s more, the \"network construction-network optimization\" framework can be the reference for ecological restoration planning in other regions.\u003c/p\u003e \u003cp\u003eIn summary, the methodological framework proposed in this study provides a scientific and quantitative basis for the construction and optimization of EN and is adaptable in planning. It can be a reference for the ecological civilization construction and sustainable development goals implementation. This issue still needs further exploration in future studies to gradually improve the methodological framework. The rationality and scale suitability of the framework should be verified through the successive practice in different sites at different scales. Future exploration of α, β and γ indices in terms of network connectivity to find out the laws of their changes can be necessary to promote the effective integration of EN construction and ecological restoration of national territory.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical Approval\u003c/h2\u003e \u003cp\u003eOur study did not require further ethics committee approval as it did not involve animal or human clinical trials and was not unethical. In accordance with the ethical principles outlined in the Declaration of Helsinki, all participants provided informed consent before participating in the study. The anonymity and confidentiality of the participant were guaranteed, and participation was completely voluntary.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Participate\u003c/strong\u003e \u003cp\u003eThis paper mainly explores an ecological network construction method based on scenario simulation and circuit theory, in order to explore the scientific work path of ecological space protection planning. All researchers participate in the study voluntarily and have the right to withdraw from the study at any time. The research content does not involve the researchers, and there is no risk of privacy disclosure.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Publish\u003c/strong\u003e \u003cp\u003eThe publication of this study and all accompanying images was agreed upon by all researchers.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCRediT authorship contribution statement\u003c/h2\u003e \u003cp\u003eYan Zhou (Investigation; Conceptualization; Formal analysis; Writing \u0026ndash; Review \u0026amp; Editing;Project administration); Menegyao Liu(Formal analysis; Data extraction and analysis tools; Writing - Original Draft༛Writing \u0026ndash; Review \u0026amp; Editing); Lina Wang (Data extraction༛Writing \u0026ndash; Review \u0026amp; Editing); Jianing Yu (Formal analysis; Writing \u0026ndash; Review \u0026amp; Editing); Yawen Luo (Formal analysis; Writing \u0026ndash; Review \u0026amp; Editing); Qiaoling Luo (Writing - Review \u0026amp; Editing; Funding acquisition; Project administration).\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data will be made available on request.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eDeclaration of Generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the author(s) didn\u0026rsquo;t used any AI or AI-assisted technologies.\u0026nbsp;\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant No. 71774214 \u0026amp; 72174158).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAminzadeh B, Khansefid M. A case study of urban ecological networks and a sustainable city: Tehran\u0026rsquo;s metropolitan area. Urban Ecosystems 2010; 13: 23-36.http://doi.org/10.1007/s11252-009-0101-3.\u003c/li\u003e\n\u003cli\u003eBenedict MA, McMahon ET. Green infrastructure: smart conservation for the 21st century. 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Planners (in Chinese) 2019; 39: 125-131.\u003c/li\u003e\n\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":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"ecological network, circuit theory, scenario simulation, landscape connectivity, ecological strategic nodes, ecological restoration","lastPublishedDoi":"10.21203/rs.3.rs-4142154/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4142154/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"As an approach to manage ecological security patterns and construct ecological spaces, the ecological network can identify sources, corridors, and nodes of landscape, improve landscape connectivity and biodiversity. A basic working framework for ecological network construction already existed though, it’s necessary to constantly optimize the network when facing rapid land use/cover changes. This study aims to explore a systematic framework for ecological network optimization, the Liangzi Lake Basin was chosen as the sample area. By considering ecosystem services and landscape connectivity, key ecological sources can be identified. Resistance surfaces were constructed based on the natural and anthropogenic factors. Ecological corridors and nodes were extracted with the Minimum Cumulative Resistance model and circuit theory, and Future Land Use Simulation Model was used to simulate the land use changes over time. Three scenarios: increasing stepping stones, removing obstacle points, and protecting key pinch points were set up to perform simulation and assess the connectivity to compare the effects of optimization. The results showed that the ecological network in the Liangzi Lake Basin consisted of 20 sources, 56 corridors, 64 pinch points, and 25 obstacle points, and the spatial distribution of these elements was relatively homogeneous. By comparing the indicators under three scenarios, it was revealed that removing obstacle points had the most significant effects on the network optimization, which deserved the most concerns in the network construction and optimization. A comprehensive optimization scheme was formed and the order of ecological restoration to different was determined. This methodological framework provides a systematic tool and theoretical basis for constructing ecological networks and determining the restoration order of various ecological elements. It can be applied to various ecological restoration scenarios and be referred to when planning ecological spaces and reserves.","manuscriptTitle":"A methodological framework for ecological network optimization integrating circuit theory and scenario simulation：Application in the Liangzi Lake Basin, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-27 11:29:38","doi":"10.21203/rs.3.rs-4142154/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2024-11-26T01:42:09+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-11-06T04:02:23+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-28T20:07:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2024-05-16T17:49:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-27T05:12:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2024-03-25T03:59:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c95b2b30-ebc1-44a4-829a-11c28b312286","owner":[],"postedDate":"September 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-12-25T03:13:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-27 11:29:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4142154","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4142154","identity":"rs-4142154","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}