Enhancing Ecological Network Connectivity Through Urban–Rural Gradient Zoning Optimization of Ecological Process Flow

Abstract

Urbanization has significantly impacted ecological connectivity, making the optimization of ecological networks (ENs) crucial. However, many existing strategies focus on overall network structure and overlook the spatial concentration of local ecological processes flow (EPF), limiting the effectiveness of ecological planning. This study proposes a novel EN optimization framework based on urban–rural gradient spatial zoning to enhance connectivity from the perspective of EPF. The framework divides areas outside the core urban zone (CUZ) into the urban fringe zone (UFZ), urban–rural interface zone (UIZ), and natural rural zone (NRZ), applying tailored optimization strategies in each zone. These strategies include increasing corridor redundancy, reducing corridor resistance, and expanding corridor width to alleviate EPF concentration. Using Jinan, a mega-city in China’s Yellow River Basin, as a case study, this study simulated EN changes over 20 years and validated the framework’s effectiveness. Optimization validation showed that increasing ecological land in low-flow corridors to 65% in the UIZ and expanding NRZ corridors to 5 km improved connectivity by 6.3%, addressing seven pinch points and three barrier points. This study highlights the importance of optimizing ENs via urban–rural zoning to support sustainable development and ecological protection policies.

1. Introduction

Rapid urbanization has led to the degradation of over 80% of natural habitats in some regions to varying degrees [1], disrupting ecological connectivity and subsequently affecting regional sustainable development [2,3]. Constructing ecological networks (ENs) can effectively improve ecosystem connectivity and is considered one of the most effective measures for balancing the conflicts between urban development and environmental protection [4]. Currently, EN planning has been widely adopted by governments at all levels globally to guide land use/land cover (LULC) planning, ecological restoration [5,6], and the delineation of ecological red lines [7]. Therefore, the scientific construction and optimization of ENs are crucial for achieving sustainable urban development and protecting biodiversity.

Studies on ENs have established a standardized paradigm of ecological source identification, ecological corridor extraction, ecological network evaluation, and ecological network optimization [8,9]. The identification of ecological sources and the extraction of ecological corridors aim to determine the fundamental ecological elements of the network while constructing a complex, interconnected network structure. Based on this, the evaluation of EN could further reveal how issues in the spatial distribution and structure of network elements affect ecological connectivity [10]. The goal of EN optimization is to address these issues, making it the most critical task for scientifically guiding ecological practices. Many scholars have explored optimization strategies for the spatial structure of ENs from different perspectives, focusing primarily on the improvement and layout optimization of ecological sources and key ecological nodes. Some studies [11,12] advocate prioritizing degraded or eroded ecological sources and integrating urbanization processes and future expansion trends to implement corresponding ecological protection and restoration measures, thereby indirectly improving EN. However, other studies [13,14] found that this optimization approach or framework overlooks the issue of missing key ecological nodes within the network structure. Consequently, they adopted the method of adding stepping-stone patches to optimize the network structure, with results showing significant improvements in network structure connectivity. Additionally, some studies [15,16] built on these optimization approaches using dynamic spatiotemporal analysis of ENs to identify key ecological node areas, leading to more targeted optimization and improvement. Meanwhile, other studies [17,18] have attempted to improve ecological connectivity by addressing barrier points, reducing their obstructive effects, and thereby increasing corridors or stepping-stone patches. The commonality among these approaches lies in their aim to enhance ecological connectivity by improving the overall spatial structure of EN, either directly or indirectly. However, these methods overlook the issue of local connectivity within ecological networks, specifically the balance of ecological processes flow such as ecosystem services circulation, material and energy exchange, and species migration [18]. Imbalanced ecological processes flow may manifest as an overdependence on certain corridors or local key nodes, leading to phenomena of overload. This oversight may lead to an optimized EN that shows improved overall structural connectivity without optimizing the connectivity of the ecological processes within the network. In some cases, this could even result in the emergence of pinch points and barrier points, worsening the connectivity of ecological processes. Consequently, the practical effectiveness of ecological conservation planning may fall far below expectations. Therefore, addressing how to optimize ENs from the perspective of EPF to provide deeper and more accurate guidance for conservation planning is a critical issue that needs to be resolved.

Additionally, to effectively address the aforementioned issues, it is essential to consider the differences in development levels and ecological resource endowments along the urban–rural gradient [11]. In summary, the connectivity, flow characteristics, and resistance of ecological processes vary across different gradient zones. The urban–rural interface zone, in particular, lies at the forefront of urban expansion [19] and is rich in ecological resources. Consequently, it serves as both a hotspot for ecological degradation and a critical area for ecological conservation, playing a vital role in connecting urban to rural ecological processes circulation [7]. However, current studies and policymakers often overlook the perspective of urban–rural gradient differences when planning and constructing ENs. This oversight may lead to conservation and restoration measures lacking regional adaptability and sustainability. Therefore, studies on constructing and optimizing ENs within urban–rural gradient zones can provide more scientific guidance for ‘location-specific’ conservation practices.

To address the aforementioned issues, we selected Jinan, a mega-city in the Yellow River Basin of China, as a case study to simulate the spatio-temporal evolution of its ENs over nearly 20 years. This study aims to reveal the impact of urban expansion on both overall and internal ecological connectivity and to explore and validate an EN optimization method that adapts to urban–rural gradient zones from the perspective of EPF. The specific objectives of this study are as follows: (1) to analyze the spatio-temporal changes and urban–rural gradient characteristics of EN elements; (2) to reveal the impact of urbanization on EN connectivity and its urban–rural gradient differences; and (3) to propose an urban–rural gradient zoning optimization framework for EN from the perspective of EPF and to verify its effectiveness in enhancing ecological connectivity. The results of this study will provide theoretical references and a scientific basis for urban landscape ecology optimization and restoration and are of significant importance for advancing sustainable development goals.

2. Materials and Methods

2.1. Study Area

Jinan, located in the central North China Plain, is the capital city of Shandong Province and a major metropolis in the downstream of the Yellow River Basin. It also encompasses significant core ecological sources, such as Mount Tai and the Yellow River. The city covers a vast area of 10,244.5 km², with more than 50% of its land area covered by green space. Because the Yellow River is in the northern part of the study area and Mount Tai is in the southern part, the southern region features steeper terrain and higher elevation, while the northern region has flatter terrain and lower elevation (Figure 1a). Agricultural land accounts for the majority of the LULC in Jinan, with ecological land primarily concentrated in the central and southern parts (Figure 1b). The city’s advantageous geographical location and rich natural resources have made Jinan a key regional city in northern China. At the end of 2022, Jinan’s population reached 9.415 million, reflecting a nearly 60% increase from 2000, while GDP surged from 104.885 billion yuan in 2000 to 1202.75 billion yuan. Rapid population growth and economic development have driven a swift urbanization process [20], particularly along the urban-to-rural vertical gradient. In this context, varying human activities and urbanization processes across different urban–rural gradients have had unique and significant impacts on regional ecological processes and the achievement of sustainable development goals.

2.2. Data Sources

In this study, seven types of data (

Table 1. Data sources and profiles.

Data Name	Data Sources
Landsat remote sensing image dataset	America EROS (https://www.usgs.gov/centers/eros, accessed on 5 August 2024); spatial resolution: 30 m
Terrain data	Geospatial Data Cloud (https://www.gscloud.cn/, accessed on 2 August 2024); spatial resolution: 30 m
Precipitation and evapotranspiration	Resource and Environmental Science Data Platform, IGSNRR, CAS (http://www.resdc.cn/, accessed on 10 August 2024); spatial resolution: 1000 m
Net primary productivity	America LAADS DAAC (https://ladsweb.modaps.eosdis.nasa.gov, accessed on 5 August 2024); spatial resolution: 1000 m
Soil type data	National Tibetan Plateau/Third Pole Environment Data Center. (https://data.tpdc.ac.cn/, accessed on 7 August 2024); spatial resolution: 1000 m
Global population density distribution data	America LandScan Population (https://landscan.ornl.gov, accessed on 4 August 2024); spatial resolution: 1000 m
Electricity consumption and water consumption	Statistics Bureau of Jinan City (http://jntj.jinan.gov.cn/, accessed on 8 August 2024)

) were used. We interpreted the land use of the study area using a Landsat remote sensing imagery dataset. We utilized ENVI 5.6 software, ArcGIS 10.8 software, and eCognition Developer 8.9.0 to process the data based on object-oriented classification methods. Based on the actual conditions of the study area, LULC was classified into six categories: grassland, woodland, water body, cropland, unutilized land, and construction land. To ensure the accuracy of the interpreted results, we selected ground verification points to validate the interpreted LULC data, achieving an overall accuracy of over 85%, which meets the requirements of this study. LULC data were primarily used for ecological source identification, ecological resistance surface construction, and EN optimization. To ensure consistency in spatial resolution and comparability of results, all data were standardized to the WGS_1984_UTM_50N projected coordinate system, with the spatial resolution resampled to 30 m.

2.3. Methods

The framework for optimizing EN based on urban–rural gradient spatial zoning is illustrated in Figure 2. Step 1: urban–rural gradient zones were defined using a concentric circle method based on LULC composition. Step 2: the ENs of the study area were simulated by integrating multiple methods following the traditional EN construction paradigm. Step 3: building upon the EN simulation in step 2, EN connectivity of the study area was evaluated from two perspectives: spatial structure and internal flow. Step 4: based on the urban–rural gradient zoning from step 1 and integrating the network construction results from step 2 and the connectivity evaluation outcomes from step 3, the network’s spatial structure and connectivity were enhanced through optimization strategies tailored to urban–rural gradient zones, and the optimization results were validated.

2.3.1. Urban–Rural Gradient Zoning

Based on the characteristics of LULC composition changes along the urban–rural gradient, we employed a concentric circle-based gradient analysis method [21,22] to spatially divide the study area.

First, we identified the core urban zone (CUZ) of the study area based on nighttime light data, population distribution data, and LULC data in conjunction with the 2000–2020 LULC planning map for Jinan’s central urban area (Jinan People’s Government, http://www.jinan.gov.cn/, accessed on 2 August 2024). Then, using the buffer tool in ArcGIS 10.8, we created a gradient curve showing changes in the proportion of LULC types from the CUZ to the rural at 500-m intervals (Figure 3). Based on previous studies [19,23] and the major land use proportion change curves observed in this research, we divided the urban–rural gradient into three segments: urban fringe zone (UFZ), urban–rural interface zone (UIZ), and natural rural zone (NRZ). UFZ, located at the outer edge of CUZ, represents the frontier of urban expansion. As a result, this area has a higher proportion of cropland and construction land, with fewer ecological land. UIZ, a transitional zone from urban to rural, is the zone where human agricultural activities are most widespread. In this zone, cropland still occupies a significantly high proportion, while construction land is much smaller than ecological land. NRZ, situated in the most remote rural and suburban zones, has a balanced mix of natural ecological land and cropland.

2.3.2. Identification of Ecological Sources

Ecological sources have relatively complete ecosystem services (ESs) functions and high landscape connectivity [24]. Ecological sources not only participate in natural ecological processes circulation but also provide corresponding ESs to human society, fulfilling human ecological demands [25]. Therefore, we first used Guidos Toolbox 2.8 software (https://forest.jrc.ec.europa.eu/en/, accessed on 9 August 2024) to perform morphological spatial pattern analysis (MSPA) classification. Core patches are recognized as the landscape patch type with the most structurally intact edges and the highest stability, playing a critical role in sustaining ecological processes. For these reasons, we designated core patches as the primary ecological source areas in this study. Subsequently, the primary ecological sources were filtered based on the area, landscape connectivity index, and ecological supply–demand ratio [26]. The relevant indicators are detailed in Appendix A 

Table A1. Indicators of ecological sources identification.

Indicators		Formulas and Meanings
Water Conservation	Supply function	$WR=\mathit{min}(1,{\displaystyle \frac{249}{\mathit{Velocity}}})\times \mathit{min}\left(\mathrm{1,0.3}TI\right)\times \mathit{min}(1,{\displaystyle \frac{K_{sat}}{300}})\times {WY}_x$	(A1)
		where $WR$ represents the annual average water retention capacity, which signifies water conservation supply. $Velocity$ represents the flow velocity coefficient, $TI$ represents the terrain index, $K_{sat}$ represents the soil saturated hydraulic conductivity, and ${WY}_x$ represents the water yield per unit grid (calculated by the water yield module of the Invest model),
	Demand function	$D_{WY}=D_{\mathit{perwater}}\times P_{\mathit{xpop}}$	(A2)
		$D_{\mathit{perwater}}=D_{\mathit{water}}÷P$	(A3)
		where $D_{WY}$ represents the water resource usage demand per unit grid, which indicates the demand capacity for water resource services. $D_{perwater}$ represents the per capita water consumption, $P_{xpop}$ represents the population within a unit grid, $D_{water}$ represents the total annual water consumption, and $P$ represents the total population.
Carbon Sequestration	Supply function	$S_{\mathit{xcarbon}}=1.63\times NPPx$	(A4)
		where $S_{xcarbon}$ represents the total carbon supply in a unit grid, which is carbon sequestration supply. $NPPx$ represents the total net primary productivity in a unit grid.
	Demand function	$D_{\mathit{xcarbon}}=D_{\mathit{percarbon}}\times P_{\mathit{xpop}}$	(A5)
		$D_{\mathit{percarbon}}=D_{\mathit{carbon}}÷P$	(A6)
		$D_{\mathit{carbon}}=E_{all}\times C_t$	(A7)
		where $D_{carbon}$ represents the total carbon emissions. $E_{all}$l represents the total electricity consumption, including residential, industrial, and agricultural electricity data for the entire society. $C_t$ represents the carbon emission coefficient. $P$ represents the total population. $D_{percarbon}$ represents the per capita carbon emissions. $P_{xpop}$ represents the population count in a unit grid. $D_{xcarbon}$ represents the carbon emissions in a unit grid, which represents carbon sequestration demand services.
Soil Conservation	Supply function	$A=R\times K\times LS\times (1-C\times P)$	(A8)
		where $A$ represents the annual average soil conservation amount, $R$ represents the rainfall erosivity factor, $K$ represents the soil erodibility factor (calculated based on the percentage of silt, sand, clay, and organic matter in the soil), $LS$ represents the topographic factor, $C$ represents the crop management factor, and $P$ represents the conservation practice factor.
	Demand function	InVEST model SDR module
Habitat Quality	Supply function	InVEST model habitat quality module
	Demand function	$HQD=\left\{{\displaystyle \genfrac{}{}{0pt}{}{{HQD}_{st}-Q_x,Q_x<{HQD}_{st}}{0,Q_x\ge {HQD}_{st}}}\right\}$	(A9)
		where $HQD$ represents the habitat quality demand, $Q_x$ represents the habitat quality supply index, which is the habitat quality supply capacity calculated based on Invest, and ${HQD}_{st}$ represents the habitat quality demand standard.
Landscape connectivity		$PC={\displaystyle \frac{{\sum }_{i=1}^n{\sum }_{j=1}^n{P}_{ij}\times a_i\times a_j}{A_L^2}}$	(A10)
		$dPC={\displaystyle \frac{PC-{PC}^{\prime }}{PC}}\times 100\mathrm{\%}$	(A11)
		where $n$ represents the total number of landscape patches, and $a_i$ and $a_j$ represent the areas of patches $i$ and $j$, respectively. $A_L$ is the total landscape area of the study region. $P_{ij}$ represents the maximum product of probabilities for all paths between patch $i$ and $j$. $PC$ represents the potential connectivity of landscape patches, and ${PC}^{\prime }$ represents the potential connectivity of the remaining patches after removing a certain.
Ecological supply–demand ratio		$SDR={\displaystyle \frac{{ES}_s-{ES}_d}{{ES}_s+{ES}_d}}$.	(A12)
		In this equation, $SDR$ represents the supply–demand ratio within a single grid, ${ES}_s$ represents the supply function of ${ES}_s$ per grid, and ${ES}_d$ represents the demand function of ${ES}_s$ per grid. When $SDR$ > 0, it indicates that the supply of ${ES}_s$ within the unit grid exceeds the demand, suggesting a surplus in $ES$ supply and the absence of supply–demand imbalance. When $SDR$ = 0, it indicates that the $ES$ supply within the unit grid exactly meets the demand, resulting in a supply–demand balance. When $SDR$ < 0, it indicates that the $ES$ supply within the unit grid is insufficient to meet the demand, indicating a supply–demand imbalance.

[27,28]. Considering that patches with a high ecological supply–demand ratio are in a state of supply exceeding demand, they contribute more significantly to the circulation of ecological processes. Therefore, we classified the ecological supply–demand ratio results using a combination of the natural breaks method and manual categorization. Patches with the highest ecological supply–demand ratio values (top 20%, ranging from 0.557 to 1) were selected as ecological sources. Additionally, ecological sources with small areas and low landscape connectivity played a weaker role in ecological processes. Therefore, we selected only ecological sources with an area greater than 5 km² and a landscape connectivity index higher than 1.

2.3.3. Construction of Ecological Resistance Surface

The ecological resistance surface is used to describe the extent of obstacles encountered by ecological processes as they flow through heterogeneous landscapes [29]. We integrated human activity and natural environmental factors by selecting five resistance factors: LULC, population intensity, DEM, slope, and NDVI. Firstly, LULC serves as the most direct indicator reflecting human activities and natural environmental conditions. Its spatial heterogeneity in geographic distribution directly influences the pathway selection of EPF, with different LULC types corresponding to distinct ecological resistance values. Secondly, densely populated areas significantly hinder ecological process cycles due to intense human activities, though they lack the pronounced spatial heterogeneity characteristic of LULC. In contrast, regions with favorable natural environmental conditions facilitate EPF circulation, while disadvantaged areas primarily exhibit poor foundational support for EPF rather than actively disrupting it. This implies that natural environmental factors exert weaker interference on EPF compared to the aforementioned factors. Regarding species migration through EPF cycles, areas with high vegetation coverage provide essential shelter. Additionally, regions with steep terrain (high slope gradients) and high elevations present geological challenges that impede species movement, with topographic complexity being more prevalent. In conclusion, the hierarchy of influencing factors on EPF is determined as LULC > population density > vegetation coverage > slope > elevation. Following established methodologies from previous studies [9,11,24,28], we have assigned weights and resistance values to these factors (

Table 2. Weight settings for the resistance factors.

Resistance Factors	Criteria for Grading	Resistance Value	Weight
LULC	Grassland	10	0.31
	Woodland	15
	Waterbody	40
	Cropland	60
	Unutilized land	80
	Construction land	100
Population intensity	0–908	20	0.26
	908–3290	40
	3290–7300	60
	7300–15,940	80
	≥15,940	100
Slope	0.000–1.071°	20	0.14
	1.071–3.013°	40
	3.013–5.289°	60
	5.289–8.235°	80
	≥8.235°	100
DEM	10–99 m	20	0.11
	99–225 m	40
	225–350 m	60
	350–500 m	80
	≥500 m	100
NDVI	0–0.437	20	0.18
	0.437–0.590	40
	0.590–0.682	60
	0.682–0.757	80
	≥0.757	100

). Notably, the natural breaks classification method was applied to grade resistance values for the four non-LULC factors, effectively reducing subjective bias in manual classification. Finally, using the raster calculator tool in ArcGIS 10.8, these factors were weighted and overlaid (Table 2) to generate a comprehensive ecological resistance surface.

2.3.4. Simulation of Ecological Corridors

Ecological corridors, as key pathways connecting ecological sources, are crucial for ecological processes such as the circulation of ecological materials, energy, and species migration [30]. Species migration is an ecological process characterized by significant random flow, where species do not always move along the path of the least cumulative resistance. Circuit theory offers a way to model this random flow by treating the ecological resistance surface as a resistance grid, thereby simulating potential paths for random migration of species between sources [31].

Therefore, we adopted this method to simulate the ecological corridors of the study area using the Circuitscape tool and the Linkage Mapper plugin based on ArcGIS 10.8, with the ecological resistance surface as the foundation. Using the pinch point tool in the Linkage Mapper plugin, we simulated the cumulative current thresholds for different corridor widths (1000–9000 m) at 1000 m intervals (Figure 4). We selected the corridor width (4000 m) with the smallest rate of change in the cumulative current threshold [18] as the optimal ecological corridor width for this study.

2.3.5. Evaluation of Ecological Network Connectivity

This study aimed to evaluate EN connectivity from two perspectives. First, the quality of the EN structure directly reflects the overall network connectivity. Therefore, we employed a graph-theory-based network structure evaluation index system to reveal changes in the spatial structure of the EN. This index system includes three indicators (α, β, γ, Formulas (1)–(3)) that reflect the degree of circuit occurrence, node connectivity, and structural types within the network, based on the count of nodes and corridors [32]. Second, the spatial distribution of the current intensity within the EN is the best representation of local network connectivity. We assessed the internal connectivity of the EN by analyzing the count and spatial distribution of ecological pinch points and barrier points [30]. Ecological pinch points are considered key nodes within ecological corridors that experience high flow. Due to their high flow load, these nodes are at greater risk of being disrupted [33], and once damaged, there are no alternative regions nearby. In contrast, ecological barrier points are areas with high resistance within corridors, which directly reflect the permeability of ecological processes flow in those regions [34]. Therefore, we believe that ecological pinch points and ecological barrier points are important indicators for assessing local connectivity within ecological networks. Both were calculated and quantified using the Linkage Mapper plugin in the ArcGIS 10.8. These methods allow us to comprehensively evaluate the connectivity of the EN, providing scientific support for ecological conservation and management.

$$\alpha ={\displaystyle \frac{l-v+1}{2v-5}}$$

(1)

$$\beta ={\displaystyle \frac{l}{v}}$$

(2)

$$\gamma ={\displaystyle \frac{l}{3(v-2)}}$$

(3)

where $l$ represents the number of ecological corridors, $v$ represents the number of ecological nodes (intersections of ecological corridors).

2.3.6. Ecological Networks Optimization Framework

Considering the differences in urban development levels and natural endowments across urban–rural gradients, and to enhance the regional adaptability of ecological optimization strategies, we proposed an urban–rural gradient zoning optimization framework for EN (Figure 5). This framework adopts three different optimization strategies for ENs, based on the perspective of EPF, across the three defined zones. It aims to provide valuable insights for real-world ecological conservation practices.

➢

UFZ: Increasing corridor redundancy

As UFZ is located at the forefront of urban edge expansion, human activities have significantly encroached upon natural or semi-natural lands, making ecological processes particularly prone to disruption. Given the difficulty in adjusting the land-use structure, we recommend increasing the redundancy of ecological corridors by adding ecological stepping stones, thereby adjusting the structural layout of EN. This approach not only disperses EPF but also helps prevent the fragmentation of ecological processes, thereby enhancing the resilience of EN.

➢

UIZ: Reducing corridor resistance

In the UIZ, where ecological land is the most concentrated, and ecological processes are the most active, there are numerous ecological corridors with a complex network structure. We recommend increasing the proportion of ecological land covered by corridors with high ecological resistance and a low EPF. The goal is to reduce the ecological resistance of these corridors (implementation corridors) to alleviate the higher EPF in other corridors (target corridors). Specifically, this involves establishing a relationship between the proportion of ecological land and EPF in both the acting and target corridors. The optimization threshold (Formula (4)) was determined by finding the minimum proportion of ecological land that resulted in the smallest change rate in the EPF.

$$P_{min}=min\left\{P:\right.\left. \left|{f\left(P\right)}^{\prime }\right|<\epsilon \cap \left|{g\left(P\right)}^{\prime }\right|<\epsilon \right\}$$

(4)

where $P$ represents the proportion of ecological land covered by the implementation corridors, $f\left(P\right)$ and $g\left(P\right)$ are the functions of EPF for the implementation corridors and target corridors, respectively, in terms of $P$, $\epsilon$ denotes a very small positive number, and $P_{min}$ indicates the optimal proportion threshold.

➢

NRZ: Expanding corridor width

Unlike the previous two zones, the NRZ has a higher proportion of ecological land with ample ecological space, making the addition of ecological nodes or stepping-stone patches less effective in improving connectivity. Therefore, we recommend increasing the width of the ecological corridors to reduce the issue of overly concentrated EPF. The optimal width threshold (Formula (5)) with the lowest cost is determined based on the relationship between corridor width and EPF. The best ecological corridor width is the one that results in the smallest change rate in the EPF.

$$W_{min}=min\left\{W:\right.\left.\left|{f\left(W\right)}^{\prime }\right|<\epsilon \right\}$$

(5)

where $W$ represents the width of the corridors, $f\left(W\right)$ is the function of EPF for the corridors in terms of $W$, $\epsilon$ denotes a very small positive number, and $W_{min}$ indicates the optimal width threshold.

3. Results

3.1. Spatio-Temporal Pattern Evolution of Ecological Networks

The spatial distribution of ecological sources (Figure 6a) shows that, in Jinan, ecological sources are primarily concentrated in the UIZ, followed by the NRZ and UFZ, whereas they are scarce in the CUZ. Spatially, these sources are mainly located in southern Mount Tai, with the central and northern regions relying only on the Yellow River and its tributaries as ecological sources. Over the past 20 years, the area of ecological sources has experienced fluctuating changes (Figure 7a), first increasing and then decreasing, but overall, it has expanded by 43.09 km². This increase was mainly observed in the NRZ, whereas the proportion of the final area in the other zones decreased.

As urbanization continues to advance, the high-value areas of the ecological resistance surface in Jinan (Figure 6b) show a pattern of multiple points emerging and gradually expanding, particularly in the central and southeastern main urban areas. Statistical results (Figure 7b) indicate that the average ecological resistance has increased year by year, with the most significant increases observed in CUZ and UFZ, where CUZ saw the largest increase of 12.5%. In contrast, the average resistance values in the UIZ and NRZ gradually decrease.

The distribution of ecological corridors (Figure 6c) aligns with the location of ecological sources and is concentrated in the southern and central parts of the study area within Mount Tai. These corridors are numerous and short, and they exhibit a high degree of structural compactness. Conversely, in the outer areas, the count of ecological corridors decreased significantly, their length increased notably, and their structural compactness declined. The statistical results for ecological corridor lengths (Figure 7c) show a slight downward trend in total length, with a reduction of 46.50 km compared with 2000. This reduction was primarily observed in the CUZ and UFZ, whereas an upward trend was noted in the UIZ and NRZ.

3.2. Spatio-Temporal Pattern Evolution of EN Connectivity

Based on the evaluation results of the network structure (Figure 8a), the complexity of the network structure in the study area continuously weakened, with an overall decline of approximately 9% in the ecological connectivity. Additionally, the counts of ecological pinch points (Figure 8b) and barrier points (Figure 8c) showed a gradual increase, with the growth rate of barriers being particularly significant. These changes collectively indicate a persistent decline in both overall and local ecological connectivity within Jinan City EN.

The spatial analysis of ecological pinch points (Figure 9) and barrier points (Figure 10) further revealed urban–rural gradient differences in the deterioration of internal connectivity within the EN. These pinch points and barrier points are primarily concentrated in the UFZ and UIZ of Jinan, accounting for approximately 70% of the total. In contrast, although the count of ecological pinch points and barrier points in the NRZ has been relatively low in recent years, there has been a growing trend. Notably, the count of pinch points in the UFZ decreased as a result of the disappearance of a few corridors, whereas in UIZ, it has increased along with the lengthening of corridors. In addition, most pinch points experienced spatial shifts and flow changes. On the other hand, the trend in the count of ecological barrier points is the opposite, with most barrier points showing stable growth in both extent and resistance values. Especially in the CUZ, the number of barrier points is not only increasing, but their extent is also significantly expanding, indicating that the obstructive effects on ecological corridors during urban development are intensifying, with a higher risk of fragmentation or erosion. A special case occurs within region 7 (Figure 10), where the disappearance of ecological barrier points does not indicate successful ecological restoration, but rather the disappearance or rerouting of ecological corridors due to the excessive obstruction of ecological processes. These findings suggest that urban expansion has led to spatial expansion and intensification of ecological barrier points in the UFZ, exacerbating the spatial clustering effects of EPF in the UIZ, thereby increasing the risk of ecological processes fragmentation along the urban–rural gradient.

3.3. EN Optimization and Validation

Based on the optimization framework we proposed, and incorporating field remote imagery, we selected areas with potential ecological restoration conditions or adjustable features within the study area (Figure 11a,b). First, we identified potential areas within the UFZ where “stepping stone” patches could be constructed, aiming to connect these areas with other ecological sources via ecological corridors to increase the redundancy of the network structure. These “stepping stone” patches are mostly non-permanent cropland, offering a degree of adjustability. Next, we adjusted the proportion of ecological land in high-resistance, low-flow ecological corridor areas within the UIZ. These areas are typically located near roads or villages, where the proportion of ecological land is adjustable. Based on the relationship of change, we found that when the proportion of ecological land in these areas reached 65%, the change in flow was minimized (Figure 11c), which we considered the optimal adjustment threshold. Finally, for high-flow corridor areas with pinch points in the NRZ, we expanded the width of the ecological corridors. We found through imagery that this area consists of natural land, with no human activity interference, making it highly adaptable for corridor width expansion. The flow threshold variation results (Figure 11d) showed that when the ecological corridor width reached 5000 m, the flow change was minimized, establishing it as the optimal expansion threshold.

Based on the above analysis, we optimized the EN in Jinan and compared the results with those of the pre-optimization scenario, as shown in Figure 12. The spatial analysis revealed significant changes during the optimization process: five ecological pinch points completely disappeared, and the flow at two other pinch points significantly decreased. One barrier point nearly vanished, and the resistance values at the other two barrier points decreased significantly. These changes directly reflect the effects of EN optimization. Additionally, the newly added ecological nodes in the UFZ not only significantly improved the stability and resilience of the EN structure but also adjusted the locations of some ecological corridors to avoid passing through the CUZ, thereby reducing the formation of ecological barrier points and mitigating the negative impacts of human activities on ecological processes. Further comparative evaluation showed a 6.3% improvement in network structure connectivity after optimization. This result confirms the effectiveness of our proposed EN optimization framework in addressing the uneven distribution of EPF and enhancing the overall ecological connectivity.

4. Discussion

4.1. Impact of Urbanization on Ecological Connectivity and Its Countermeasures

Our findings indicate that urbanization over the past 20 years has led to a reduction in ecological corridors and increased barriers to ecological processes in the study area (Figure 10), resulting in an overall 9% decrease in ecological connectivity (Figure 8). Although significant improvements have been made to ecological sources through protection and restoration measures, these efforts have not substantially enhanced the ecological connectivity. Moreover, the decline in ecological connectivity exhibits a notable urban–rural gradient difference. Specifically, the UFZ is the area most severely affected by urban expansion, where ecological sources have suffered significant erosion and degradation. Areas with high ecological resistance continue to expand or intensify, leading to shorter corridor lengths and reduced structural redundancy. UFZ has also become the most susceptible area for the emergence of ecological barrier points, with some corridors disappearing owing to the rapid increase in ecological resistance. Although UFZ possesses relatively abundant ecological resources, urban expansion has increased the resistance of certain ecological corridors and eroded some stepping-stone patches, resulting in decreased ecological connectivity. Although ecological protection measures have provided some improvements, they have not been sufficient to counteract the negative impacts. In this context, ecological processes have been squeezed into very limited, long-distance corridors between two sources, causing a significant increase in flow within these corridors, which makes them highly prone to the formation of barrier points and pinch points. Unlike the previous two areas, the NRZ benefited from concentrated ecological protection policies, leading to continuous increases in both the ecological source area and corridor length, along with a decrease in ecological resistance and noticeable improvements in ecological connectivity. These changes indicate that although ecological protection measures have achieved some success, there are still limitations in improving the overall and internal ecological connectivity in the region. Specifically, the spatially uneven distribution and continuous increase of ecological pinch points and barrier points in our study (Figure 8) further support this notion. Existing studies [11,23,35] have also confirmed our concerns regarding these issues. In other words, current ecological protection measures may exhibit a pseudo-improvement effect, where the ecological sources or environment appear to be enhanced on the surface but overlook the issue of EPF within the corridors. Insufficient attention to the planning, protection, and restoration of ecological corridors, particularly the neglect of region-specific ecological issues, could undermine years of ecological protection and restoration efforts. This view is consistent with the findings of previous studies [36,37]. To address these issues, we propose an urban–rural gradient-based optimization framework for ENs from the perspective of EPF and validate its effectiveness through simulation results. This means that future ecological policies and measures need to place greater emphasis on the restoration of internal ecological connectivity within regions [38] and consider developing ecological corridor optimization measures that are adaptable to urban–rural gradient zones.

4.2. Advantages of the Proposed EN Optimization Framework

In previous studies [14,39,40,41,42] on EN optimization, scholars have primarily focused on improving the network structure. While such improvements may enhance overall ecological connectivity, they may not achieve the desired effects on local connectivity. Moreover, optimization strategies in prior studies often emphasize singular interventions—such as adding stepping stones or modifying corridor widths—rather than adopting integrated, multifactorial solutions. A notable limitation lies in the frequent oversight of heterogeneous conditions across urban–rural gradients during optimization processes. This neglect risks generating context-specific effectiveness, where optimizations yield tangible benefits only in certain areas while failing to achieve substantive improvements in others, thereby compromising the spatial equity and adaptability of ecological network planning. Hence, this study introduces a new perspective by considering the spatial unevenness of EPF. Although some studies [18,42,43] have used ecological processes flow parameters as key threshold indicators in the construction or optimization of ENs, their general view is that ecological pinch points cannot be mitigated or moved. They have suggested that increasing the corridor width is the only way to disperse EPF and reduce the impact of pinch points on ecological processes. However, this perspective may lack consideration of the potential impact of dynamic changes in local elements within ENs on the overall spatial interaction patterns of the ecosystem. Our study overcomes this traditional understanding by suggesting that widening ecological corridors is not the only way to improve the EPF. Our proposed optimization framework enhances the connectivity of ecological processes in the network through a coordinated strategy of increasing corridor redundancy, reducing corridor resistance, and expanding corridor width, thereby alleviating the local spatial clustering pressure of a high EPF. The study results (Figure 12) indicate that our optimization framework significantly improves both overall and local ecological connectivity and also demonstrates the potential disappearance or mobility of ecological pinch points. Additionally, the advantage of the framework lies in its regional adaptability. Considering the differences in development levels and natural endowments across urban–rural gradients, our framework enhances the feasibility and implementation of ecological planning, protection, and restoration practices.

4.3. Limitations and Future Work

Despite the significant progress made in optimizing the EN connectivity in this study, there are still some limitations. First, although our proposed gradient-based EN optimization framework has achieved some success in improving regional EN connectivity, it cannot fully address all pinch points or barrier points. In particular, pinch points and barriers located in core urban areas or industrial zones are challenging to address because of constraints in urban planning and LULC, making effective ecological restoration measures difficult to implement. Second, ecological processes encompass not only natural material and energy circulation and species migration on the supply side but also demand-side ecological processes oriented towards human demands [44]. The current simulation of EN has not fully considered the flow relationships between supply- and demand-side ecological processes, which may limit our understanding of the network’s integrity [45]. To address these shortcomings, future studies will aim to integrate human ecological needs into a gradient-based EN construction and optimization framework. This approach will help us better reveal ecological supply–demand relationships and adjust the existing framework to propose a more suitable optimization strategy for ENs with supply and demand characteristics. Through this comprehensive approach, we hope to provide more in-depth and practical guidance for the planning and management of EN.

5. Conclusions

Urbanization significantly impacts ecological connectivity, affecting both the overall circulation of regional ecological processes and the uneven distribution of the local EPF. Although previous research and ecological practices have focused extensively on ecological network sources and overall connectivity, there has been insufficient attention paid to internal ecological connectivity, which may result in actual ecological protection measures failing to meet expectations. Moreover, as urbanization and natural endowment differences increase along the urban–rural gradient, the development of regionally adaptive ecological protection policies and plans has become more complex. To address this challenge, this study proposes a gradient-based EN optimization framework from the perspective of EPF. The innovation of this framework lies in its comprehensive consideration of the characteristics of land use structure across urban–rural gradients. It alleviates the spatial clustering pressure of the local EPF through a coordinated strategy of increasing corridor redundancy, reducing corridor resistance, and expanding corridor width, thereby enhancing the connectivity of ecological processes. The effectiveness of this optimization framework was validated through a case study in Jinan, a mega-city in China’s Yellow River Basin. The results show that despite significant improvements in ecological sources due to protection and restoration efforts, urbanization has continued to damage ecological elements and increase ecological resistance, leading to a persistent decline in both overall and local ecological connectivity. Urban expansion has exacerbated the upgrading and expansion of ecological barrier points in the UFZ, further promoting the spatial clustering of EPF in the UIZ and resulting in the formation of numerous ecological pinch points. These coupled processes increase the risk of ecological process fragmentation along the urban–rural gradient. The simulation results confirmed the modifiability of the ecological pinch points and barrier points. Our recommendations for a 65% ecological land proportion in low-flow ecological corridors in the UFZ and a 5 km width for ecological corridors in the NRZ have significantly improved both overall and local connectivity in the study area. However, future research should incorporate strategies to address human ecological demands into the existing optimization framework to promote the achievement of sustainable urban development goals that harmonize human and natural systems.