Coupling models to assess impacts of land and carbon changes on sustainable ecological safety networks of Gui’an New Area, China

The management and assessment of territorial spatial changes and carbon storage(CS) are essential for alleviating the greenhouse effect and attaining carbon neutrality^1,2. Urbanization, an inevitable trend in societal progression³, not only transforms land use and cover types but also significantly impacts carbon reserves and the ecological safety framework^4,5. This process exacerbates landscape fragmentation, resulting in habitat area reduction, diminished landscape connectivity, lower biodiversity, and compromised ecosystem health and service functions^3,5,6,7. Land use transitions can influence ecosystem service functions, particularly key aspects such as vegetation change and carbon sequestration^8,9,10. Consequently, examining land use alterations and terrestrial ecosystem CS assessments is vital for developing ecological safety patterns that enhance regional carbon sinks, thereby positively affecting natural ecosystems.

Advanced modeling techniques such as the Future Land Use Simulation (FLUS), Patch Generating Land Use Simulation (PLUS), Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST), Circuit Theory (CT), and Minimum Cumulative Resistance (MCR) have been crucial in comprehending the intricate interactions among land use types, CS, and ecological safety across various landscapes in China^{3,11,12,13,14}. These models have not only enhanced our predictive capabilities but have also improved our understanding of ecological risks and the optimization of ecological networks. For instance, the application of ecological carrying capacity assessments in the Southwest Guangxi Karst – Beibu Gulf region has been instrumental in constructing ecological security patterns¹¹, while in Xinjiang, the InVEST-Conefor-MCR model has further strengthened these patterns with robust scientific backing¹². These studies have collectively improved our predictive capabilities and understanding of ecological risks and the optimization of ecological networks. The synergistic application of FLUS and InVEST models has been particularly effective for an in-depth evaluation of land use changes and CS dynamics, owing to their efficiency and practical utility. For example, research utilizing the Markov-PLUS-InVEST model in the Sichuan and Chongqing urban agglomeration under complex terrain conditions has illustrated how urban expansion into undeveloped mountainous areas can decelerate CS growth and enhance spatial heterogeneity³. Urban growth patterns in Hangzhou have similarly been associated with reduced habitat quality(HQ)¹⁵. The FLUS-InVEST model has been particularly enlightening in the Chengdu-Chongqing urban agglomeration, revealing the impact of land use change on CS¹³, and similar advancements have been noted in the Pearl River Delta, where ecological protection scenarios have significantly improved HQ and CS¹⁶. Even with these contributions, a cohesive framework is still needed to integrate the temporal and spatial variability of land use change and CS assessments within ecological safety networks. This gap is apparent in the use of models on complex terrains like the Hohhot-Baotou-Ordos-Yulin urban area¹⁷, in studies along China’s swiftly urbanizing coastal areas¹⁸, and in the Qinghai Lake Basin¹⁹, and Guilin²⁰, highlighting the need for a more unified approach. Efforts to optimize ecological protection in southern China’s hilly regions²¹ and to simulate future land use scenarios by coupling ecological security patterns with multiple scenarios²² have advanced the field. However, constructing a comprehensive ecological safety network, as highlighted by research focusing on Shenzhen’s urban ecological security pattern²³, which proposes various development zone patterns based on HQ assessment and the MCR model, remains a challenge. A synthesis of these studies reveals a research gap: while individual models have been applied with success, an integrated approach that harnesses their collective power to construct a predictive framework for ecological safety networks, accounting for the intricate interplay between land use changes, CS, and ecological dynamics, is still required. However, while individual models have contributed valuable insights, there is an urgent need for an integrated approach that harnesses the collective power of these models.

To address this research gap, this study proposes an integrated framework coupling the FLUS, InVEST, and MCR models. This synergistic approach leverages the predictive power of FLUS to generate future land use scenarios, providing essential inputs for InVEST to quantify the resulting spatial and temporal dynamics of CS, thereby explicitly linking land use transitions to impacts on this critical ecosystem service. Crucially, the MCR model utilizes these future-oriented land use patterns and associated ecosystem service assessments (e.g., HQ/CS from InVEST) to construct ecological safety networks that inherently account for anticipated changes²¹. Although both Li et al.¹⁶ and Zhang et al.³ have verified the efficiency and universal potential of FLUS-InVEST in land use simulation and CS assessment, their research frameworks have not been systematically coupled with the construction of ecological safety networks, leaving a gap in relevant integrated studies. Consequently, this sequential FLUS-InVEST-MCR workflow forms a holistic, closed-loop framework that predicts land use drivers, evaluates ecological service functions (e.g., CS), and designs adaptive spatial solutions (e.g., eco-network). This extends existing integrated approaches by explicitly linking projected CS to dynamic eco-network design. It uniquely enables the construction of forward-looking ecological safety networks grounded in projected ecosystem service status and responsive to anticipated land use transformations, directly addressing the identified need for a cohesive framework integrating land use change, CS, and ecological network dynamics.

Applying this integrated framework, this study focuses on the Gui’an New Area, as the eighth state-level new area approved by the State Council in 2014, assigned the important roles of economic growth pole in the western region, new inland open economy highway, and ecological civilization demonstration area²⁴. However, under the special karst natural environment of the new area, its construction and development face serious challenges in terms of ecological safety. Therefore, in the context of the “dual-carbon” goal and the strategy of ecological civilization construction, we utilize the coupled FLUS-InVEST-MCR approach to conduct a comprehensive assessment of the land use change and CS in Gui’an New Area and to construct an ecological safety network. The study addresses the following three scientific questions: (1) Analyze the trend of land use change in Gui’an New Area from 2010 to 2022 and predict the change characteristics of land use in 2030 versus 2060 under the ecological protection scenario. (2) Evaluate the spatial and temporal differentiation of CS in Gui’an New Area using the InVEST model, as well as the differences in CS among different land use types. (3) Construct and analyze the ecological safety network pattern of Gui’an New Area at different development stages.

Study area

Gui’an New Area is China’s eighth state-level new area, established in 2014, located in the combined area of Guiyang City and Anshun City (Fig. 1), Guizhou Province, with a planned area of 1,901 square kilometers and a current resident population density of about 404,000 people. Since its conception in 2011, it has developed rapidly with the support of the national and provincial governments and is positioned as an economic growth pole in the west, a new inland open economy highland, and an ecological civilization demonstration area²⁴. Meanwhile, it is strongly supported by policies, and Gui’an New Area has been approved as a number of national experimental demonstration zones, which provides a strong guarantee for development²⁵. In terms of the economy, the GDP of the direct administration area increased from 5.23 to 22.913 billion yuan between 2014 and 2023, with an annual average growth rate of 18.2%. The new zone focuses on modern industrial clusters such as big data, high-end electronic information manufacturing, high-end specialty equipment manufacturing, cultural tourism, and health and high-end service industries and has attracted large-scale data centers, including the three major carriers, Huawei, Tencent, and Apple. Since the establishment of the new area, a series of public service facilities have been built, the integration of urban and rural water supply has achieved full coverage, and the urbanization rate has reached 76%.

In terms of the ecological environment, the area is characterized by its karst landscape and exhibits a diverse vegetation cover. The predominant natural vegetation types include subtropical evergreen broad-leaved woodland, mixed evergreen and deciduous broad-leaved forests, and secondary shrubland and grassland resulting from historical land use changes. Key native tree species contributing significantly to the regional carbon pool include Cyclobalanopsis glauca, Castanopsis spp, Pinus massoniana, and Cunninghamia lanceolata, particularly in forest areas. Additionally, significant areas are covered by bamboo forests (Phyllostachys spp.) and various fruit orchards²⁶. The notable 42% forest coverage provides a substantial foundation for carbon sequestration. Therefore, in the plan, it is expected that the urban population density will reach 2 million by 2030 and the land for urban construction will be controlled at 220 square kilometers, further promoting regional economic and social development and ecological civilization.

Data sources and processing

The data used in this study include land use data, climate and environmental data, and socioeconomic data (Table 1). Among them, the land use data were obtained from Wuhan University 30-meter-resolution Chinese surface cover data²⁷. The data were merged into six primary categories: cropland, woodland, grassland, water, building land, and unused land. This establishes a unified benchmark dataset for characterizing the pattern of land cover changes and supporting CS assessment based on the InVEST model. Climate and environmental data include elevation, slope, NDVI, mean annual precipitation, mean annual temperature data, soil moisture²⁸, and rocky desertification index. Among them, we coupled the carbonate content with 30 m DEM and NDVI data, calculated the rock exposure rate, and finally obtained the pixel scale rocky desertification index. Socio-economic data included roads, water system, population density, GDP, nighttime lights index²⁹, and so on. Highway, main road, secondary road, and water system data for each period were extracted from the road network as the main research object. The annual average temperature data is based on the monthly average temperature raster data with 1 km by 1 km resolution and the annual average temperature raster obtained by calculating the average of the 12 months of the year’s month-by-month average temperature raster. The monthly average precipitation is based on the monthly average precipitation raster data at 0.1° × 0.1° resolution and is obtained by calculating the average of the 12-month average precipitation using the raster calculation tool.

Methods

The objective of this study is to investigate the impact of land-use changes at various temporal and spatial scales on landscape patterns, CS functions, and ecological safety networks in the Gui’an New Area. To this end, we analyzed land-use changes in the Gui’an New Area before and after its construction (in the years 2010, 2014, 2018, and 2022) and predicted the land-use trends for the next two periods (2030 and 2060). Concurrently, we conducted a quantitative analysis of the landscape patterns and CS during these periods. Based on these analyses, we constructed the corresponding ecological safety network patterns, aiming to provide a scientific basis for future ecological restoration and protection strategies in the Gui’an New Area. Figure 2 illustrates the specific structure of this technical framework.

FLUS model

With cellular automaton as its theoretical core, the FLUS model can capture the nonlinear coupling relationship between multi-source driving factors and land use types^2,30. The Gui’an New Area has witnessed rapid urban expansion and significant land changes, and the use of data from adjacent years can enable the model to more truly reproduce the evolution trajectory. The simulation process is divided into three steps: (1) The occurrence probability module based on Artificial Neural Network (ANN). Ten categories of driving factors such as elevation, slope, annual average precipitation, annual average temperature, distance to water, population density, GDP, distance to highway, distance to main road, and distance to secondary road were selected, and a transfer cost matrix was defined based on existing studies² (Table 2). (2) Introduce an adaptive inertia competition mechanism, coupling the conversion rules with neighborhood weights. Neighborhood weight settings: Cropland, 0.2; Woodland, 1.0; Grassland, 0.9; Water, 0.6; Other land, 0.1; Building, 0.4; so as to depict the expansion/shrinkage behaviors of different land use types¹³. (3) Use the FLUS model to predict the future land use pattern. The relevant calculation formulas are as described in the studies by Li et al.² and Zhang et al.³⁰.

This study takes the land use data of Gui’an New Area in 2018 as the benchmark to simulate the land use pattern in 2022, and verifies it by comparing with the actual data³¹. After ensuring the overall accuracy, the land demand of 2030 output by the System Dynamics (SD) model is embedded into the FLUS framework. Starting from 2022, it further predicts the changes in spatial distribution in 2030 and 2060 under the principle of ecological priority.

Landscape pattern analysis

Based on the raster data of land use types from 2010 to 2060, woodland, grassland, and water were extracted as foreground elements of the MSPA, and other land as background. Based on the Guidos Toolbox 2.8 software platform, seven landscape elements such as core area, bridge, islet, loop, edge, branch, and perforation were identified, and finally, the core area of the landscape types, which is important for maintaining connectivity, was extracted as the landscape element for the later connectivity analysis (Fig. 2).

Assessment of CS changes

The estimation of CS in the InVEST model is based on the alternative method of pools. In this paper, the average carbon densities of above-ground carbon pools, below-ground carbon pools, soil carbon pools, and dead organic matter carbon pools of various land classes were counted according to the land use situation, and then the carbon densities were multiplied by the area of each land class to obtain the CS of four major pools, and the total value of CS in the whole study area could be obtained by accumulating the CS of the four major pools³. The calculation formula is as follows:

Eq. C_total represents the total CS. C_above is the aboveground CS. C_below is the subsurface CS. C_soil is the soil CS. C_dead is the dead organic CS.

The carbon density parameters of different land covers were mainly referred to the relevant studies in Guizhou or the Southwest Karst region^3,32, which are shown in Table 3.

Ecological safety network construction

(1) Ecological source identification: In this study, important habitat patches in the study area were identified as potential ecological sources using the MSPA model and landscape connectivity analysis method^21,23. Three landscape indices of overall connectivity, possible connectivity, and patch importance were selected, and the threshold of patch connectivity distance was set to 40,000 m and the probability of connectivity was set to 0.5 to evaluate the landscape connectivity of the core area. The patches with the area of the core area larger than 2.5 km² and the value of dPC larger than 2.5 were considered ecological sources to avoid the subjectivity of ecological source selection. The specific calculation formula is as follows:

Ea. denotes the total number of patches in the landscape. (:{a}_{i:})and (:{a}_{j}) denote the area of patch i and patch j, respectively. (:{nl}_{ij}:)denotes the connection between patch i and patch j. (:{A}_{L}) is the total area of the landscape, and (:{P}_{ij}^{text{*}}) is the maximum likelihood of direct dispersal of species in patches i and j. (:I) is the value of the connectivity index for a given landscape, which in this paper refers to the index of overall connectivity (IIC) and the index of possible connectivity (PC); the (:{I}_{remove}) is the value of the connectivity index of the landscape after removing patch i from this landscape.

(2) Resistance surface construction: based on the karst characteristics of Gui’an New Area, seven factors were selected by synthesizing the natural environment, ecological resources, and economic society, as shown in Table 4. Referring to the existing research³³, the corresponding resistance values were assigned to different land use types, and the resistance coefficients of cropland, woodland, grassland, water, other land, and buildings were 30, 1, 5, 10, 25, and 100, respectively. The AHP method was then used to calculate the weights of the factors (λmax = 7.52209, CI = 0.0870153, CR = 0.0639819 < 0.1, passed the consistency test) to get the ecological resistance factor weights of Gui’an New Area (Table 4). Completed by spatial superposition analysis resistance surface construction (Fig. 3).

(3) Ecological corridor identification: this paper adopts the Minimum Cumulative Resistance (MCR) model to extract the ecological corridors in Gui’an New Area. The basic formula is as follows:

Where: MCR is the minimum cumulative resistance value;(:{D}_{ij}) is the spatial distance of the species from source j to landscape unit i; (:{R}_{i:})is the coefficient of resistance of landscape unit i to the movement of a species; f denotes the positive correlation between the minimum cumulative resistance and the ecological process. And based on the gravity model, the interaction matrix between source sites was constructed to quantitatively evaluate the interaction strength between habitat patches to scientifically determine the relative importance of potential ecological corridors. Based on the results of the matrix and combining them with the actual situation of the study area, the corridors with interaction strengths greater than 3000 were extracted as important corridors, and the others were treated as general corridors, and the ecological network map of the study area was obtained.

The gravity model can calculate the interaction matrix between ecological source sites, and the higher the interaction force between two source sites, the more important the ecological corridor between two source sites is in the ecological service system of the study area. The calculation formula is as follows:

Where, (:{F}_{ab}) is the interaction force between the source sites a & b. (:{N}_{i}), (:{N}_{j}) is the weight value of i, j; (:{D}_{ij}) is the resistance value of the potential corridor between source sites i and j; (:{L}_{max}^{:}) is the maximum resistance value of the potential corridor in the study area; (:{S}_{i}), (:{S}_{j})are the areas of i and j, respectively; (:{L}_{ij}) is the cumulative resistance value of the potential corridor between i and j; (:{P}_{i}{P}_{j}) is the average resistance value of i and j.

Land use change and landscape pattern characterization

This study reveals the land use change characteristics of Gui’an New Area from 2010 to 2020 and predicts the development characteristics of the district in 2030 and 2060 (Figs. 4 and 5a-f). Land-use simulations for Gui’an New Area in 2030 and 2060, conducted with the FLUS model, achieved an overall accuracy of 0.93 and a Kappa coefficient of 0.85, indicating strong agreement between the projected outcomes and actual land-use patterns and underscoring the model’s robust predictive reliability. Between 2010 and 2022, the area of woodland and cropland in Gui’an New Area decreased, with cropland decreasing by 5.2% to 1221.62 km². Woodland, grassland, and buildings grow by 4.3%, 40.49% and 87.36%, respectively. Cropland is projected to continue to decrease by 5.5% by 2030, while woodland, grassland and buildings will grow by 5.6%, 15.5% and 39.8% respectively. During 2010–2030, built-up areas expanded annually while cropland decreased and woodland increased post-2014. Water grew marginally. By 2060, cropland is projected to rise 14% from 2030 levels with built-up areas doubling. Woodland and grassland will decline, whereas other land use types increase.

Eight landscape types were identified in this study: core, bridging, loop, perforation, islet, branch, edge, and background. The background has the largest percentage of area, followed by the core and edge. The share of the core in the total area of the ecological landscape between 2010 and 2060 decreased with proportions of 18.35%, 16.67%, 17.70%, 19.59%, 17.78% and 10.37%, respectively (Fig. 6). It is mainly distributed in the northwestern and central parts of the study area with good spatial connectivity, while it is less distributed and poorly connected in the eastern part. The marginal zone is large and growing steadily, and the perforation and loop zones account for a relatively small proportion of the area.

Spatial and Temporal variations in CS

In this study, the CS of Gui’an New Area from 2010 to 2060 was calculated at six time points, and the results showed that the CS slightly decreased in 2014, but the overall trend of growth was observed between 2014 and 2030, reaching 28,977,800 T in 2030, with a growth rate of 4.6%. By 2060, CS had decreased by 19.7%. Among the land use types, the CS of cropland, woodland, grassland, buildings, and other land decreases in order. From 2010 to 2060, the static CS of buildings land increased by a total of 1,147,600 T, and the CS of woodland increased by a total of 1,339,200 T, with a growth rate of 10.6%. Cropland, grassland, and other land CS fluctuated, but water CS was not accounted for due to model limitations (Fig. 7). CS in Gui’an New Area show spatial heterogeneity, mainly concentrated in the woodland and grassland areas in the northwest, forming a high-density CS area (Fig. 8). In contrast, CS is lower in the northern and eastern regions, which are densely populated and have more watersheds. The CS potential of Gui’an New Area is influenced by the area of ecological space and carbon density. Future projections show that changes in land use and landscape types will maintain the “large concentration and small dispersion” pattern of CS in Gui’an New Area.

Characterization of ecological safety network patterns

The number and area of ecological source sites in the Gui’an New Area experienced fluctuations between 2010 and 2030 (Fig. 9a-e). There were 14 ecological source sites in 2010, while they decreased to 12 in 2014, 2030, and 2060, and 13 in 2018 and 2022. The total area of ecological source sites also varied from year to year, ranging from 161.94 km² to 196.44 km², and the intersection area between the periods was 114.74 km². The number and type of ecological corridors changed significantly over time, with 91 corridors in 2010, including 17 important corridors; in 2014, the number decreased to 66 and 11 important; in 2018 and 2022, there were 78 corridors each, with 11 and 19 important corridors, respectively; and in 2030, there were 63 corridors and 15 important. By 2060, the number of primary ecological corridors will decrease to 66, with no important corridors (Fig. 9f). The overall trend shows a decrease in the number of ecological corridors.

In terms of the spatial distribution of the ecological safety network, the ecological sources in Gui’an New Area are mainly concentrated in watersheds, woodland, and grassland and are denser in the northwestern area and the south-central part of the area and less in the southwestern part of the area and the eastern part of the area. The ecological corridors are densely distributed in 2010, 2018, and 2022, and the important ecological corridors in 2010 connect all the major ecological sources. The important ecological corridors in the central and western parts of the area are decreasing in 2014, and general ecological corridors are increasing. and general ecological corridors increase. 2018 important ecological corridors in the west further decrease, general ecological corridors continue to increase, and the distribution pattern is like that of 2030. By 2060, only first-class ecological corridors will remain to be interspersed. It is evident that ecological corridors in the eastern region are restricted in their formation and development due to the compression of ecological space.

Policies drive Spatial changes in the landscape of Gui’an New Area

Since the establishment of Gui’an New Area in 2014, there has been a continuous expansion of land for construction, a decrease in cropland, and a significant increase in grassland area (Fig. 5). This is a key role of policy orientation in driving spatial changes in regional landscapes. Rapid economic growth and sustained population growth, coupled with accelerated urbanization, have together led to a significant increase in the demand for construction land, which in turn has triggered a significant shift in the type of land use^3,18,34. The land use policy and urban planning of Gui’an New Area (Tables 5 and 6), similar to that of Shenzhen SAR and Xiong’an New Area, focuses on promoting regional economic development and urbanization^35,36, with special support for the development of big data and high-end manufacturing industries, which further increases the land resource demand and puts pressure on ecological space³⁷.

Urban expansion has not only reshaped the original natural landscape but also reduced the area of the core area of the landscape and its ecological connectivity in Gui’an New Area. Although some greening measures have been taken during urban development and the area of woodland has increased in 2022 and is expected to continue to grow under the impetus of ecological preservation policies, ecological preservation in the core area is still insufficient, and part of the ecological space has been replaced by economic activities³⁸. Population growth is driving the expansion of residential and commercial space, especially in the more economically developed eastern regions, which further exacerbates the pressure on ecological space³⁹. This rapid urbanization-induced ecological space compression exhibits convergent evolutionary patterns in Shenzhen³⁵ and the Yangtze River Delta (Shanghai)⁴⁰. Land use projections to 2060 indicate that planning policies will have a significant impact on long-term trends in land use types. Building land is expected to continue to grow, while cropland will undergo a process of decreasing and then increasing. This suggests that policy-oriented urban expansion is a direct reflection of the increased demand for non-agricultural land, especially the growth of building land^41,42. While policy decisions and land management may be more inclined to support economic development, the karst ecosystem of Gui’an New Area also maintains its inherent woodland and water. This change reflects a general trend in the urbanization process and highlights future challenges in land management. This study confirms that the changes in land use in Gui’an New Area are the result of changes in socio-economic conditions and natural factors brought about by policy-oriented urbanization.

Land use types dominate CS patterns

Our study shows that the type of land use types in Gui’an New Area is a key factor that dominates the regional CS pattern (Figs. 7 and 8). The study shows that despite a decline in the region’s CS in 2014, the overall trend of CS growth from 2014 to 2030 is 1,277,700 T, a growth rate of 4.6%. However, this growth trend did not continue until 2060, when the CS declined. This suggests that land use changes, especially urban expansion, have a significant impact on CS in ecosystems^3,18. Woodland and grassland, as important carbon pools in Gui’an New Area, contribute more to the total CS. In the process of urbanization, cropland and natural vegetation are converted into building land, which not only reduces carbon sinks but also increases carbon emissions. However, sound land planning and ecological restoration projects can increase carbon sequestration capacity, especially in woodland and grassland areas. From 2010 to 2060, the CS of built-up land increased by 1,147,600 T, and this increase may be related to the increase in urban greening and NDVI. The karst topography, geomorphology, climate, and soil conditions in Gui’an New Area have a special impact on carbon sequestration, making it different from other regions^9,43. For example, urbanization expansion in the Sichuan-Chongqing urban agglomeration has led to a decline in CS and HQ, which underscores the urgency of sustainable land management practices³. In addition, the land-use change of ” Grain for Green” also enhances the carbon sequestration capacity of ecosystems⁴⁴, suggesting that land-use changes, such as vegetation restoration, can increase regional CS.

CS in Gui’an New Area exhibits distinct spatial heterogeneity, predominantly concentrated in northwestern woodland and grassland forming a high-density carbon zone. From 2010 to 2030, CS is influenced by a variety of factors, including land management practices, vegetation restoration projects, and natural carbon cycle changes. Urbanization has led to localized declines in CS, especially in the northern and eastern regions with high population density and watersheds, which have been affected by reduced NDVI due to urbanization activities and watershed development. Among all land use types, cropland has the largest CS, but with a decreasing trend, while the CS of woodland has experienced a decrease and then an increase and is expected to grow to 13,919,900 T by 2030. This emphasizes the importance of protecting and restoring woodland and optimizing land use structure and habitat to maintain and enhance regional CS⁴⁵. Therefore, to protect and enhance the regional CS capacity, promote sustainable development, and conserve biodiversity, Gui’an New Area needs to adopt active ecological protection measures, strengthen ecological spatial planning and management, raise public awareness of ecological protection, and conduct in-depth studies on the impacts of changes in landscape patterns. These measures are essential to achieving long-term stability and growth in regional CS.

Ecological safety network pattern

This study reveals a significant decrease in the number of ecological source sites and ecological corridors in Gui’an New Area from 2010 to 2060, as well as the resulting decrease in ecological network connectivity. In 2010, Gui’an New Area had 14 ecological source sites, whereas by 2060, this number was reduced to 12, suggesting a clear downward trend. At the same time, the total number of ecological corridors decreases from 91 to 66, with the number of important ecological corridors decreasing from 17 to zero, a change that reflects the significant impacts of land-use changes and disturbances in landscape patterns on the ecological safety network¹⁸. The overall decline in the number of ecological corridors between 2010 and 2030 is of particular concern, especially the reduction of important ecological corridors, while the increase in the number of common ecological corridors indicates a shift from natural landscapes to built-up land in the process of urbanization, which not only reduces biodiversity but also weakens the connectivity of the ecological network⁴⁴. Although some ecological corridors have been preserved, their connectivity, function, and importance have declined, posing an impact on the overall health of the ecosystem. For example, in the coastal cities of eastern China¹⁸, urbanization has also led to the reduction of ecological source sites and fragmentation of ecological corridors, suggesting that insufficient ecological protection and irrational planning are the main reasons. The analysis shows socio-economic pressures in eastern Gui’an New Area compress ecological space, limiting the formation of ecological corridors. However, compared with the successful case of ecological conservation in Shenzhen³⁵, the changes in the ecological network of Gui’an New Area appear to be more complex. The ecological source areas in Gui’an New Area vary significantly in scale (with the largest being 85.76 km² and the smallest 2.82 km² by 2030), highlighting patch fragmentation. Scenario projections for 2060 indicate that the network is showing a degenerative trend, with the number of corridors and connectivity decreasing simultaneously, which is highly consistent with the findings of existing studies in rapidly urbanizing regions such as Shanghai⁴⁰, Nanchang³³, and the Chengdu-Chongqing¹³ area.

Despite the continuous changes over time, the ecological source areas in Gui’an New Area are still mainly concentrated in watersheds, woodland, and grassland, especially denser in the northwestern part of the area and in the south-central part of the area, and less in the southwestern part of the area and in the eastern part of the area, showing the heterogeneity of the spatial distribution. This emphasizes the need to prioritize the protection and restoration of the ecological functions of the core zone as a key part of the ecological safety network. Therefore, we emphasize that the Gui’an New Area should strictly adhere to the ecological red line to lock in core areas and corridors; adopt a compact city model to curb urban sprawl while incorporating green corridors; carry out targeted restoration in key fragile areas; and establish a collaborative assessment mechanism for ecology and development.

Limitations and perspectives

This study adopts two major models of land use prediction and CS estimation to quantitatively analyze the characteristics of land use change and CS change in Gui’an New Area from 2010 to 2060 and construct the corresponding ecological safety network pattern. This study can provide a scientific basis for ecological protection planning, environmental policymaking, and sustainable development, and at the same time improve the understanding of the value of ecosystem services and biodiversity conservation. Nevertheless, this study still has certainshortcomings and foresight for future research. The CS model only considers the static CS and ignores the carbon cycle and the dynamic transformation between different carbon pools, which does not differ from the actual situation, thus affecting the accuracy of CS calculation. Additionally, the present work relies on a single “ecological-priority” scenario; the projected 2030 peak and the 19.7% net loss of carbon stock by 2060 are therefore scenario-dependent. Whether an “economic-growth-priority” trajectory (e.g., faster urban expansion, higher industrial land demand) would erase the 2030 peak or even accelerate the decline remains untested. In future research, we will couple the FLUS model with scenario drivers such as GDP, population and policy constraints to generate a spectrum of development pathways. These steps will refine targeted strategies for green and low-carbon development as well as for the ecological security pattern of Gui’an New Area.

Gui’an New Area, as China’s 8th state-level new area’s spatial changes in land use, CS characteristics, and ecological safety network are crucial to the green and sustainable development of the region. In this study, the following conclusions are drawn by analyzing the land use changes in Gui’an New Area from 2010 to 2060, predicting the land use in 2030 and 2060 using the FLUS model, conducting the spatial-temporal dynamic analysis of CS using the InVEST model, as well as constructing the ecological safety network pattern by combining the MSPA and MCR models:

(1) Policies have driven changes in land use types and landscape spatial patterns in Gui’an New Area. For example, in 2014, the building land of Gui’an New Area promoted the reduction of woodland and cropland and the significant increase of building land. As Gui’an New Area becomes more urbanized, the landscape pattern continues to change. The area of woodland and grassland is predicted to decline by 2060, while built-up land expands year by year. The area of the landscape core area also shows a decreasing trend in terms of percentage decline between 2010 and 2060.

(2) The CS in Gui’an New Area rises to a peak in 2030, then declines toward 2060; the net change from 2010 to 2060 is 19.7%. From 2014 to 2030, the CS of cropland, woodland, and grassland remains the highest, while that of building land, though lower, increases significantly during the study period. By 2060, land use changes in Gui’an New Area exert a pronounced negative impact on CS, driving the post-2030 decline.

(3) Between 2010 and 2060, the number and area of ecological source sites in Gui’an New Area fluctuated, and the number of ecological corridors generally decreased. Ecological source areas and ecological corridors are mainly concentrated in watersheds, woodland, and grassland, especially in the northwestern area and the central and southern parts. In the eastern part, due to economic development and population growth, ecological space is compressed, and corridor formation is limited, reflecting the challenge of economic development to ecological protection.

Overall, future land use planning for Gui’an New Area should focus on ecological protection and restoration, optimizing the land use structure, and enhancing ecological service functions. In the eastern part of the area, where there are fewer ecological corridors, the construction and maintenance of ecological networks should be strengthened to enhance ecosystem connectivity and biodiversity. At the same time, the land-use change, CS capacity, and ecological safety network pattern of Gui’an New Area will be continuously monitored and assessed so that problems can be identified in a timely manner and appropriate management measures can be taken to promote sustainable development.