Spatio-Temporal Evolution and Optimization of Ecospatial Networks in County Areas Based on Ecological Risk Assessment: Taking Dalian Pulandian District as an Example

Abstract

The ecological security of a county is the basis for guaranteeing sustainable socio-economic development in the process of new urbanization, as well as the key to maintaining the rational functioning of natural ecosystems in urban and rural areas, and the primary prerequisite for satisfying the ecosystem service functions enjoyed by urban and rural residents. This study takes Pulandian, an estuary county with low mountains, hills, plains, and beach lands, as an example, and comprehensively applies various methods such as the model of driving–pressure–state–impact–response (DPSIR), the mainstream model of minimum cumulative resistance (MCR), the model of morphological spatial pattern analysis (MSPA), and the circuit theoretical model to assess the spatial and temporal evolution characteristics of the ecological spatial network of Pulandian District from 1990 to 2020 and evaluate its ecological risk from the socio-economic and environmental perspectives to provide a basis for the construction of ecological resistance surfaces. On this basis, an ecospatial network optimization model was constructed to reduce ecological risk and meet ecological security requirements. The results showed that the ecological space showed an upward trend of increasing, then decreasing, and then increasing in the area during the 30-year period, but there was serious fragmentation in the low area of the northeastern river valley, the low-hill plain area in the central part of the county, and the coastal area in the southeastern part of the county. High-resistance radiation centered on townships with high urbanization breaks the original ecological spatial network gradient, resulting in the absence of ecological corridors in large areas of the central and southeastern regions. Therefore, seven new ecological source sites were added for the central and southern portions of the study area, and the number of optimized ecological corridors increased from 47 to 66. In addition, we established an ecosystem consisting of an ecological barrier, an ecological coastal zone, multiple ecological corridors, and multiple ecological sites as an optimization system. This is of great scientific value and practical significance to provide reference for optimizing the ecological spatial network in Pulandian District of Dalian, to promote coastal ecological protection and construction, and to promote regional construction and sustainable development.

1. Introduction

As urbanization progresses, dramatic changes in urban and rural land use patterns have led to a series of problems, such as a decline in biodiversity, reductions in the scale of ecological land use, and the fragmentation of ecological landscape types [1], which have weakened the ecological substrate of the study area, blocked biological migration corridors, and posed a great challenge for the sustainable development of urban and rural environments, societies, and economies [2,3]. The ecospatial network refers to the layout, arrangement, configuration, and interrelationships of multi-type and multi-level ecological elements in space [4,5], as well as the spatial structure of different ecological function bodies such as wood land, grassland, and water bodies and their ecological processes in the region [6]. Constructing an ecological space network requires effectively connecting fragmented landscape types through ecological nodes and ecological corridors, so as to maintain ecosystem balance, reduce ecological risks, and enhance biodiversity within the ecological space [1,7]. This not only provides an effective way to promote biological flow, avoid ecological risks, and improve the realization of ecosystem service values, but also controls the uncontrolled spread of towns and cities to a certain extent, which is of great significance for the protection and development of the regional ecological space environment [5,6,7].

Current research on ecospatial networks [8,9,10,11,12] focuses on the identification and construction of ecospatial networks [8,9], analysis of the network structure [9,10], and optimization of the network structure [9,10,11]. Based on the theory of “source-sink”, the ecological network is constructed by identifying ecological sources [12]. In terms of research methodology, the indicator evaluation method [13,14], the MSPA method [11,14], and the comprehensive identification method [15] were used to identify ecological source areas. Ecological corridors are constructed using the MCR model [14,15,16], the Linkage Mapper model of circuit theory, etc. [15,16]. The landscape pattern index method [11,15,16], the network structure index method [9,15,16], landscape visualization, and other methods are often used to analyze and optimize the evaluation of the ecological spatial network structure. In conclusion, the ecological spatial network has gradually formed the research paradigm of “ecological source–resistance surface–ecological corridor”, and most of the studies on this subject adopt the MCR model to identify the least-cost paths between the sources, but they cannot reflect the real width of the ecological corridors, and ignore the characteristics of the stochastic wandering of organisms [15,16,17,18,19]. In terms of research scales, studies have mostly focused on cities above the municipal level [17,18,20], urban agglomerations and metropolitan clusters [21,22], as well as the scope of natural lakes [2,3,16,17], while fewer studies have been carried out on the ecospatial network of coastal counties. The current direction of China’s research on ecospatial networks in coastal areas is also gradually shifting from ecosystem evaluation to the development of ecospatial patterns, regionally involving the Guangdong–Hong Kong–Macao Greater Bay Area, Ningbo, and Sanya in the East China Sea region [21,22,23,24]. Since Liaoning not only has the largest per capita sea area, but also has a greater per capita coastline length [25], in which Dalian Pulandian District is close to the Yellow Sea and Bohai Sea, this paper selects Dalian Pulandian District as a coastal county study area, and identifies the ecological corridors by using circuit theory and the Linkage Mapper model in order to construct the ecological spatial pattern of the coastal counties.

There is a close relationship between ecological risk assessment and ecospatial network optimization. Ecological risk assessment can provide directions for ecological spatial network optimization and promote the optimal allocation of ecological spatial elements to enhance ecological security [22,26]. Ecological spatial network optimization is not only a proactive intervention in the ecological security situation regarding the existence of risk in order to achieve the purpose of weakening the ecological risk [26], it also plays an important role in stabilizing and maintaining the ecological environment and biodiversity, and promoting the harmonious development of human beings and nature [16]. Ecological risk assessment can, to a certain extent, change the mode and method of human development of ecological space, thus causing changes in the ecological pattern. From the perspective of ecological risk assessment methods, the commonly used landscape pattern index analysis method [17,18] cannot comprehensively consider the socio-economic and environmental impacts of human and nature; therefore, this study combines the relevant literature [2,3,4,9,10] and utilizes the DPSIR model to assess the ecological risk estimation and incorporate ecosystem services into the DPSIR model, which highlights that ecological changes affect ecological services and also considers the cost-effectiveness in relation to social well-being. From the perspective of ecospatial network construction, most studies utilize the MCR model to construct resistance surfaces, and construct ecospatial networks based on ecological security evaluation [15,16,26], ecosystem service assessment [22,23], and ecological sensitivity evaluation [22,26], while there are still relatively few studies on the construction of ecospatial networks from the ecological risk perspective. Constructing an ecospatial network from the perspective of ecological risk can better identify and assess the risks to the ecological environment. This new ecological environment management approach organically integrates the relationship between the ecological environment and various aspects of human society and economic activities, which can more comprehensively identify and assess the risks of the ecological environment and provide a basis for formulating a more scientific and rational ecological environment management strategy. For this reason, this study takes Pulandian District of Dalian on the Yellow Sea and Bohai Sea coast, which has a high level of urbanization and a typical “low mountain–hill–plain–sea” pattern, as the object, and analyzes the ecological risk, ecological resistance surface, ecological resistance surface, and ecological sensitivity of the land use in four periods between 1990 and 2020. By analyzing the evolution characteristics of land use, ecological risk, ecological resistance surface, ecological source, and ecological corridor in the four periods from 1990 to 2020, exploring the characteristics of spatial and temporal patterns of ecological spatial networks in each period, and proposing ecological spatial network optimization strategies in terms of restoration of and increase in ecological source land, the optimization of the ecological spatial network, ecological footpath planning, and the restoration of ecological obstacles, the optimization strategy of ecological spatial networks will be provided as a reference for the optimization of ecological spatial networks of coastal counties.

2. Materials and Methods

2.1. Study Area

Pulandian (121°50′33″–122°36′15″ E and 39°18′25″–39°59′00″ N) is located on the east side of the south-central Liaodong Peninsula, with a total coastline length of about 187 km, with the comprehensive Pikou Harbor in the southeast, which integrates passengers, cargo, and fishing [27]. The terrain is a low mountain–hill–plain–ocean topography with a high north, a low south, a high west, and a low east, belonging to the temperate monsoon climate zone, with an average annual temperature of 9.7 °C, four distinct seasons, and an average annual precipitation of about 635~920 mm [27]. Pulandian District has a long history of human habitation, with an economy centered around mountains, water, and forests dominating in the north, with the Anbo Hot Spring, Biliuhe Reservoir Scenic Area, and Laomao Mountain Scenic Area, agriculture dominating in the center, and aquaculture of harbor-raised shrimp, river crabs, and a variety of shellfish dominating in the south; it represents one of the most densely populated, economically developed areas of the Liaodong Peninsula, where the degree of development of towns and cities is high. The study area is divided by the administrative boundaries of the townships, covering 18 subdistricts including Fengrong, Tiexi, and Lejia (Figure 1).

The dataset created by Yang and Huang served as this study’s data source for land use information. The land use data for 1990, 2000, 2010, and 2020 (spatial resolution 30 m) [28] and their seven land types—wood land, grassland, lake, beach land, construction land, farm land, and unused land—were classified into seven groups. Grassland, wood land, lake, and beach land were defined as ecological spaces based on national standards and related studies. Elevation data were obtained from the National Geographic Information Public Service Platform (https://www.tianditu.gov.cn) URL (accessed on 16 April 2023), and the administrative division of Pulandian (1:1 million) was sourced from the Chinese Academy of Sciences Geospatial Data Cloud (https://www.gscloud.cn/) (accessed on 16 April 2023). The Pulandian Yearbook for 1990–2020 served as a source of economic and social indices. Field research was conducted in the study area to understand the landscape distribution and location of the study area.

2.2. Ecological Risk Assessment Methodology

The DPSIR model covers social, economic, and environmental elements, and can systematically analyze the interrelationships among people, the natural environment, and socio-economics [21,29,30]. In order to highlight that ecological changes affect ecological risks and ecological services, and to consider the cost-effectiveness of social well-being, this study selected the DPSIR model to evaluate ecological risks and included ecosystem services in the DPSIR model [21,30]. According to the literature [22,26,29,30] and the resource endowment of Pulandian District, this study constructed the evaluation index model based on five perspectives: driver, pressure, state, and impact and response (

Table 1. Ecological risk evaluation index system of Pulandian District based on the DPSIR model.

Standardized Layer	Indicator Layer	Weights	Positive or Negative
Socio-economic driver systems	Population density	0.0368	−
	GDP per capita	0.0812	+
	Urbanization rate	0.0424	−
Economic Environment Stress System	Percentage of gross output value of agriculture, forestry, livestock and fisheries	0.0586	+
	Fertilizer application per unit of cultivated area	0.0667	−
	Regional development index	0.0516	−
Environmental Status System	Share of blue and green space sites	0.0505	+
	wood land area per capita	0.0921	+
	Landscape uniformity index	0.0399	+
Environmental Impact System	Ecosystem services index	0.0684	+
	Landscape Ecological Risk Intensity Index	0.2321	−
	Ecological resilience index	0.0451	+
Economic Environment Response System	Environmental investment rate	0.0770	+
	Biological richness index	0.0576	+

). In the process of selecting indicators, on the one hand, it is necessary to consider whether the indicators can scientifically reflect the current situation of the ecosystem, to ensure that they are not repeated, too limited, or too simple, and to ensure that the indicators have a certain degree of representativeness and comprehensively reflect the characteristics of the ecosystem. On the other hand, the indicators of the statistical system were selected as far as possible to ensure the accessibility and authenticity of the data. This paper took the connotation of ecological risk as the starting point, combined the actual situation of the socio-economic environment and ecosystem in the study area, and established an evaluation index system by selecting 14 index factors from 3 aspects, namely natural factors, economic factors, and social factors. The weights in Table 1 were derived by means of a combination of the entropy weight method and the mean square deviation decision-making method to avoid subjectivity.

The ecosystem service index was evaluated using the ecological service value [20,22], with cropland corresponding to farmland, woodland corresponding to forest, garden land corresponding to grassland, waters corresponding to rivers and lakes, and unutilized corresponding to wasteland, and the ecological service value of the construction land was assigned as 0. Based on the ecosystem biomass factor of farmland in different provinces of China formulated by Xie Gao Di et al. [31], we determined that the unit area of each land use type had an ecological service value, i.e., CNY 40,676.4 m²/year for lakes, CNY 55,489 m²/year for beach lands, CNY 19,276 m²/year for forested land, CNY 6406.5 m²/year for grassland, CNY 0 m²/year for construction land, and CNY 6114.3 m²/year for cropland.

According to the existing studies [8,9,10,11,32,33,34], and combining with the characteristics of the landscape pattern in the study area, the landscape disturbance index S_i, landscape fragility index F_i, and landscape loss index S_i were selected to construct the landscape ecological evaluation index and landscape ecological risk model. The landscape disturbance index S_i refers to the degree of disturbance to ecosystems of different landscape types, which is obtained by accumulating the weights of landscape fragmentation C_i, landscape separation Ni, and landscape dominance D_i. Combined with the results of previous research, the importance of C_i, N_i, and D_i decreases in order, and this paper assigns values of 0.6, 0.3, and 0.1 to them, respectively; the formula is

$$C_i=\frac{n_i}{A_i{C}_i},$$

(1)

where $n_i$ is the number of patches of landscape type $i$, and $A_i$ is the area of landscape type $i$; its value is used to express the stability and degree of fragmentation of the internal units of the landscape, and the smaller the value, the higher the stability of the internal units of the landscape and the smaller the degree of fragmentation.

$$N_i=l_i\times \frac{A}{A_i},l_i=\frac{1}{2}\sqrt{\frac{n_i}{A}}$$

(2)

where $A_i$ is the total area of landscape type $i$; $l_i$ is the distance index of landscape type $i$; $n_i$ is the number of patches of landscape type $i$; and $A$ is the total area of the landscape, the value of which expresses the degree of separation of the individual distribution of different elements or patches of a certain landscape type, and the greater the degree of separation, the more complex the distribution of the landscape, and the more dispersed the landscape is in terms of geographical distribution.

$$D_i=\frac{\left(Rd+Rf\right)/2+Lp}{2}\times 100\%$$

(3)

where $Rd$ is the density, $Rf$ is the frequency, and $Lp$ is the proportion of the landscape. The dominance value plays a significant role in the analysis of the spatial structure of the landscape pattern, and can be used to judge the dominance of a certain block type in the landscape.

S_i = 0.6C_i + 0.3N_i + 0.1D_i

(4)

$$R_i=S_i\times F_i$$

(5)

$${ERI}_i={\sum }_{i=1}^N\frac{A_{ki}}{A_k}{R}_i$$

(6)

where ERI_i represents the landscape ecological risk index of risk evaluation unit i. The larger the index value, the higher the ecological risk index of the evaluation unit, and the smaller the index, the lower the evaluation ecological risk index. k in A_ki represents the ecological risk unit, i represents the area of landscape component, and A_k represents the total area of the kth risk unit.

The ecological resilience of an ecosystem can be assessed according to the condition of the different components within the system as well as between the components [2,22,23]. The ecological resilience score can be used to reflect the corresponding characteristics of the ecological components. The more diverse and complex the ecosystem’s composition, the higher the ecological risk coefficient and the higher the ecological elasticity value. Referring to the relevant literature [15,16,17,18,19] and including the environment of the study area, this study adopts the following equation:

$$E_{CORES}=(-{\sum }_{i=1}^n{S}_i{log}_2{S}_i)\times {\sum }_{i=1}^n{S}_i{P}_i$$

(7)

where E_CORES is the regional ecological resilience degree; i is the land use type, n = 6 in the text; S_i is the area of land type i as a percentage of all land use types; and P_i is the resilience score of land type i. The ecological elasticity intensity impact can be divided into three types. The first type is the important type, including lakes and wood land, and is favorable for the ecological elasticity degree, having an important impact on the ecological elasticity degree. The second type is the intermediate type, including grassland, farm land, beach land; in order to avoid the degradation of this land, which would reduce the ecological resilience degree of the study area, this type has a positive assisting role, and should be emphasized in protective activities. The third type is the threatened type, which plays a marginal role in the ecological resilience of the area and is affected by anthropogenic activities, and should be utilized with caution and properly managed, and the main land cover type is construction land.

2.3. Ecospatial Network Construction Methodology

2.3.1. MSPA-Based Screening of Ecological Source Sites

Ecological sources are important patches for maintaining regional ecological security and the smooth operation of the ecological service system. This study is of great significance to optimize the ecological spatial network pattern based on the spatial quality and spatial accessibility of the source area to maintain the ecological spatial biodiversity, landscape connectivity, and material and energy flow in the county, and the advantage of MSPA analysis is that it emphasizes the structural connectivity of the network, which can better promote the flow of material information and energy [7,8,9,22], and helps to scientifically select ecological source areas and corridors. Using the MSPA method to categorize the landscape land use [7,8,9,21], the lake and wood land were taken as the research focus, the rest of the landscape land types were taken as the research background, and the deciphered images of the land use landscape types in Pulandian District from 1990 to 2020 were converted into binary maps in TIFF format. On this basis, using the Guidos Toolbox tool, the data were analyzed via MSPA, and seven landscape types were obtained, namely the core area, island patch, pore, bridging area, edge area, ring road area, and branch line.

2.3.2. Normalization of Evaluation Indicator Values

Landscape ecological risk evaluation is a process of comprehensive evaluation of multiple indicators, and various types of indicators are involved in a wide range of process and take values with large difference intervals, while the scale between the indicators is inconsistent, and comparability is low, meaning that the comprehensive evaluation of these indicators cannot be directly performed. In this regard, this paper selected 14 indicators to grade and standardize the impact on the ecological environment of the study area, and the grading was divided into five levels (

Table 2. Pulandian District rural ecological risk state grading standard.

Corridor	Level	Degree of Risk	Risk Characteristics
[0, 0.22)	I	High risk	The ecology of the land is highly damaged and faces enormous economic, environmental, social, and other pressures
[0.22, 0.4)	II	Higher risk	The ecological environment of the land is seriously damaged, and ecological restoration and reconstruction are more difficult
[0.4, 0.58)	III	Medium risk	The ecosystem is in a largely low-risk state, but it faces a variety of problems.
[0.58, 0.76)	IV	Lower risk	Less ecological damage to land, less difficulty in ecological restoration, less pressure faced
[>0.76]	V	Low risk	Little ecological disturbance and high ecological resilience

) to eliminate the error between the data of each indicator due to the inconsistency of the unit, with the aim of better evaluating the ecological risk of Pulandian District.

In the landscape ecological risk evaluation index, the larger the value, the greater the contribution to the landscape ecological risk of the region, and the higher the level of low risk. The formula for the positive index is

$$X_i=(X_i-X_{min})/(X_{max}-X_{min})$$

(8)

The larger some of the values in the landscape ecological risk assessment metrics are, the less they contribute to the landscape ecological risk of the region. The formula for the negative indicator is

$$X_i=(X_{max}-X_i)/(X_{max}-X_{min})$$

(9)

where X_i is the normalized value, X_i is the original value of indicator i, X_max is the maximum value of the indicator, and X_min is the minimum value of the indicator.

2.3.3. Ecological Resistance Surface Construction

In terms of resistance surface construction, comprehensively analyzing the natural environment elements in Pulandian District, while combining the results of the related literature [3,9,32,33,34,35] and the frequency of their evaluation indicators, this paper selected seven factors, namely the landscape ecological risk index, land use type, distance to water, distance to impervious surfaces (mainly impervious surfaces such as construction land and roads), elevation and slope, etc., and determined their weights based on the analysis of coefficient of variation method (

Table 3. Ecological resistance surface factor weights and coefficients.

Drag Factor	Drag Coefficient					Weights
	90	70	50	30	10
Altitude/m	[0, 50)	[50, 100)	[100, 200)	[200, 300)	[300, +∞)	0.2263
Elevation/°	[0, 5)	[5, 10)	[10, 15)	[15, 25)	[25, 90)	0.2098
Ecological risk index	high risk	Higher risk	medium risk	lower risk	high risk	0.0702
Land use type	Impervious surface	Farm land	Lake	Grassland and unused land	Wood land	0.3976
Distance to water	[1500, +∞)	[1000, 1500)	[500, 1000)	[100, 500)	[0, 100)	0.0961

), so as to construct a comprehensive resistance surface.

2.3.4. Identification of Ecological Corridors and Ecological Pinch Points

Ecological corridors are direct channels connecting ecological source sites that carry energy and material flows, usually presenting a linear or belt-shaped form [22,24], and play a role in improving structural elements or connecting regions in the process of ecological restoration and protection. Securing regional ecospatial networks can be achieved by identifying and protecting key ecological corridors, an approach that is important for securing ecological security patterns. This study utilizes Linkage Pathways in Linkage Mapper 1.0, a GIS tool module developed by Mc Rae et al., to simulate least-cost pathways and extract ecological corridors. The Linkage Pathways analysis function was used to calculate the least-cost weighted distances for each source site and determine the spatial location of the least-cost pathways as ecological corridors to derive the corridor structure in the study area. According to circuit theory, to determine the ecological source and cost resistance value [20,21,22], iterative calculation was used to generate the weighted cost pathway map through the Linkage Mapper Pinchoint Mapper patches in order to select the “All to one” many to one mode, under the consideration of the overall connectivity of the regional landscape. The weighted distance of the corridor was set to 2 km, and the areas with a higher cumulative current in the corridor were extracted as ecological pinch points. Ecological barrier zones are key nodes that play an important role in connectivity between different ecological source plates. When ecological restoration measures are taken, the cumulative resistance value within the barrier area is reduced accordingly, and the area with the greatest reduction can be regarded as the ecological barrier point.

2.4. Ecospatial Network Evaluation and Optimization Ideas

The ecological spatial network evaluation can quantitatively analyze the ecology as well as the complexity of the ecological network structure [36]. The α-index (network closure), γ-index (network connectivity), and β-index (point and line rate) were used to evaluate and analyze the completeness of the ecological network. The larger the value of the result, the greater the number of ecological nodes and ecological corridors, the more complex the ecological network structure, and the better the ecology. The α index indicates the number of migratory paths available for species to choose in the ecological spatial network, the γ index is used to test the degree of connectivity of ecological nodes in the ecological network, and the β index refers to the average number of connecting corridors of each ecological node in the ecological network. Their formulas are as follows:

α = (L − V + 1)/(2V − 5)

(10)

γ = L/3(V − 2)

(11)

β = L/V

(12)

where L denotes the number of ecological corridors and V denotes the number of winning ecological nodes.

Combined with the existing related literature research [32,33,34,35], morphological spatial pattern analysis of the study area, comparing the important ecological core area in four periods at a certain threshold, screened the important ecological source land, which can be used as an important ecological source land for restoration. Therefore, in this study, ecological spatial patches with better current natural conditions and greater than 0.1 km² in area were selected as supplemental ecological source sites to enhance the coverage of ecological networks in the study area. The optimized ecological network was verified using circuit theory [36], and the spatial layout of the optimized ecological corridors at all levels was proposed as a basis for planning the ecological steppingstone nodes in conjunction with the current situation of the area, and the main obstacle points were screened out.

3. Results

3.1. Evolution of Ecospatial Networks

3.1.1. Evolution of Ecological Spatial Landscape Types

The areas of different land use landscape types in Figure 2 were statistically analyzed. It can be seen from

Table 4. Comparison of landscape type elements of ecological space in Pulandian District from 1990 to 2020.

Landscape Type		Farm Land	Wood Land	Grass Land	Construction Land	Lake	Beach Land	Unused Land
1990	Area/km2	1735.87	419.11	112.98	177.67	36.82	191.35	1.62
	Proportions/%	64.88%	15.67%	4.22%	6.64%	1.38%	7.15%	0.06%
2000	Area/km2	1671.57	455.01	86.09	234.19	33.29	194.24	1.03
	Proportions/%	62.48%	17.01%	3.22%	8.75%	1.24%	7.26%	0.04%
2010	Area/km2	1628.18	449.59	84.11	281.87	51.11	179.68	0.87
	Proportions/%	60.86%	16.80%	3.14%	10.54%	1.91%	6.72%	0.03%
2020	Area/km2	1531.33	552.87	49.43	331.51	44.93	164.74	0.59
	Proportions/%	57.24%	20.66%	1.85%	12.39%	1.68%	6.16%	0.02%

that during the 30 years, the total area of ecological space in Pulandian District showed a wave-like trend of increasing, then decreasing, then increasing again. The total amount of ecological space started to increase from 1990 and increased from the initial 760.26 km² to 811.98 km² by 2020. Single types of ecological space, such as woodland, grassland, lakes, and beachland, etc., have all changed to different degrees.

In 1990, the total area of ecological space was about 760.26 km², accounting for 28.42% of the total area of the study area, of which forest land accounted for the largest proportion, about 15.67%, with an area of 419.11 km², which was much larger than the proportion of other ecological space areas. Lakes accounted for the smallest proportion, about 1.38%, with an area of 36.82 km².

In 2000, the total area of ecological space was about 768.63 km², accounting for 28.73% of the total area of the study area, of which forested land accounted for the largest proportion of about 17.01%, with an area of 455.01 km², which was much larger than the area proportion of other ecological spaces. Lakes accounted for about 1.38% of the total area of the study area, with an area of 36.82, which is the smallest among the four types of ecological space.

In 2010, the total area of ecological space was about 764.49 km², accounting for 28.57% of the total area of the study area, of which forest land accounted for the largest share of about 16.80%, which was reduced from the previous year, with an area of 449.59 km², but was much larger than the share of other ecological space areas. Lakes accounted for the smallest proportion, about 1.91%, with an area of 51.11 km².

The total area of ecological space grows to about 811.98 km² by 2020, accounting for 30.35% of the total area of the study area, of which wood land accounts for the largest share, about 20.66%, with an area of 552.87 km², still much larger than the area share of other ecological spaces. Lakes accounted for about 1.68%, with an area of 44.93, which is the smallest among the four types of ecological space.

3.1.2. Evolution of Important Ecological Sources

Comparative analysis of the spatial patterns of landscape morphology in the coastal county Pulandian District in four periods is provided in Figure 3. The area of the ecological core area in the region showed a wave-like growth trend of increasing, then decreasing and then increasing during the 30-year period, with an increase of 104.257 km²; the area of the fringe area demonstrated a growth trend of decreasing and then increasing, with an increase of 12.141 km². Considering that the core area, as a large area of ecological patches, is constantly being encroached upon and fragmented during the process of urbanization, this will form more fringe zones, leading to an increase in the percentage of the area of fringe zones. From the change in landscape selection pattern in Figure 3 and

Table 5. Changes in landscape patterns in the study area over 4 periods.

Type	1990		2000		2010		2020
	Area/km2	Proportions/%	Area/km2	Proportions/%	Area/km2	Proportions/%	Area/km2	Proportions/%
Core region	466.767	72.12%	514.163	75.36%	485.178	71.33%	571.024	74.89%
Island plaques	10.146	1.57%	6.947	1.02%	11.540	1.70%	9.600	1.26%
Gap region	20.291	3.14%	20.400	2.99%	17.141	2.52%	17.146	2.25%
Marginal region	104.581	16.16%	102.205	14.98%	111.654	16.41%	116.722	15.31%
Bridge region	13.398	2.07%	10.724	1.57%	15.162	2.23%	12.561	1.65%
Rotary road region	9.607	1.48%	9.010	1.32%	12.131	1.78%	10.166	1.33%
Branch lines	22.411	3.46%	18.783	2.75%	27.399	4.03%	25.295	3.32%

, it can be concluded that the percentage of the ecological core area decreased by 4.03% from 2000 to 2010, while its area increased by 3.56% during the 2010–2020 period. Meanwhile, the percentage of the bridging area decreased by 0.42% and the area of the fringe area increased by 12.141 km² in 30 years.

The area of ecological source areas increased by 88.335 km² during the 30-year period, but the area of ecological source areas decreased by 18.703 km² during the period of 2000–2010, and increased by 63.79 km² during the period of 2010–2020 (

Table 6. Statistical results of changes in ecologically important source areas in the study area over four periods of time.

Type	1990	2000	2010	2020	Magnitude of Change/km2
					1990–2000	2000–2010	2010–2020	1990–2020
Total Area/km2	410.856	454.104	435.401	499.191	43.248	−18.703	63.79	88.335
Main Urban Area	31.606	28.396	19.232	18.107	−3.21	−9.164	−1.125	−13.499

). In terms of spatial distribution, in 1990, the ecological source areas were mainly distributed in the coastal areas such as Yangshufang and Daliujia in the southeast, and the woodland areas such as Tongyi and Anbo in the north, while in the central part of the county, the internal and core areas of the main water bodies and woodland patches could not play the role of ecological source areas. In 2010, with the ecological restoration and protection, the ecological source areas in the central part of the county increased, and the ecological source areas of the northern part of the county increased significantly by 2020, which shows a clear trend of an increase in ecological source areas. There is a clear trend of increasing ecological source land area. In the period of 1990–2020, the ecological source land in the southwestern part of the district showed a trend of a gradual decrease (Figure 4).

3.1.3. Ecological Risk Evolution

As can be seen from Figure 5, the overall ecological risk of Pulandian District’s landscape presents the spatial distribution characteristics of high in the central and southwestern parts and low in the northern part, i.e., the high risk is distributed in the southwestern part of Fengrong, Tiexi, etc.

The average values of the ecological risk index in 1990, 2000, 2010, and 2020 were 0.5607, 0.5437, 0.5718, and 0.5751, respectively, indicating that the ecological risk in the study area showed a trend of decreasing and then increasing during the 30-year period, with 2000 being the critical point of the ecological risk index. From 1990 to 2000, the area of high-ecological-risk areas and higher-ecological-risk areas in the study area increased by 6.51% and 4.35%, respectively, and the area of low-ecological risk-areas and lower-ecological-risk areas decreased by 4.35% and 2.42%, respectively; the area of lower-ecological-risk areas decreased by 4.09% from 2000 to 2010, while the area of high-ecological-risk areas and higher-ecological-risk areas decreased. From 2000 to 2010, the area of medium-ecological-risk areas continued to increase, accounting for 4.09%; the area of high-ecological-risk areas and higher-ecological-risk areas decreased, accounting for 6.52% and 3.38%, respectively; the area of low-ecological-risk areas continued to increase, accounting for 3.58%; from 2010 to 2020, the area of medium-ecological-risk areas continued to increase, accounting for 11.31%; the area of high- and lower-ecological-risk areas continued to increase, accounting for 1.05% and 2.01%, respectively; and the area of low-ecological-risk areas continued to increase, accounting for 1.05% and 2.01%, respectively. The area of low-ecological-risk areas declined by 4.49% (

Table 7. Area and proportion of ecological risk classes in the study area from 1990 to 2020.

Type	1990		2000		2010		2020
	Area/km2	Proportions/%	Area/km2	Proportions/%	Area/km2	Proportions/%	Area/km2	Proportions/%
High Risk	326.871	12.22%	501.157	18.73%	326.609	12.21%	354.799	13.26%
Higher Risk	760.724	28.43%	876.935	32.78%	786.477	29.40%	522.016	19.51%
Medium Risk	575.908	21.53%	466.381	17.43%	744.776	27.84%	1047.419	39.15%
Lower Risk	535.545	20.02%	419.278	15.67%	309.851	11.58%	363.529	13.59%
Low Risk	476.366	17.81%	373.159	15.39%	507.701	18.98%	387.653	14.49%

).

3.1.4. Evolution of Ecological Resistance Surface

According to the ecological resistance surface factor in

Table 8. Ecosystem service value per unit area for each land use type in the study area (CNY m²·Year).

Particular Year	Important Number of Corridors	General Number of Corridors	Total Length of Corridor/km	Number of Ecological Nodes
1990	38	17	317.44	32
2000	31	20	301.05	31
2010	35	15	297.04	30
2020	33	14	269.98	30

, the ecological resistance surface change in the study area (Figure 6) was derived, and in 1990, the ecological resistance in Pulandian District showed three gradient changes from low to high and then to low depending on the pattern of the northern mountains, central low hills, southwestern coastal plain, and southeastern coastal plain, and the ecological resistance surface change in 2000, 2010, and 2020 basically did not differ much from that in 1990. In the 30-year study period, the high-resistance surfaces occupied a larger proportion and showed a tendency for being concentrated in the central Xingtai and Lian Mountain areas, along with the southeastern Pikou, Yangshufang, Tangjiafang, and other areas. The low-resistance surfaces occupied a smaller proportion and were mainly distributed in Tongyi, Anbo, and Lejia in the north.

3.1.5. Ecological Corridor Evolution

The ecological corridors in the study area in the four periods were analyzed using circuit theory, and the results are shown in Figure 7. In terms of spatial distribution, the differences between the four periods were quite large. On the whole, important corridors are observed in the northern and southwestern regions, and general corridors are clustered in the southeastern region; shorter key ecological corridors are mainly located in the northern part of the study area, mainly connecting some closer ecological sources, and longer key ecological corridors are mainly located in the southern and western parts of the study area. The important corridors observed in 1990 basically bring together the Laomao Mountain Scenic Area, Jiguan Mountain National Forest Park, Daoshigou Forestry Scenic Area, Jiulong Mountain Scenic Area, Erlong Mountain National Forest Park, and Bihua Mountain Scenic Area, etc. The general corridors are mostly distributed in the connecting corridor with the southeastern beach lands.

The statistics for ecological corridor changes in Figure 7 are shown in Table 8, from which it can be seen that the total number of ecological corridors and the total length of ecological corridors show a continuous decreasing trend. The reduction in the number of ecological corridors from 1990 to 2000 was the largest, with a reduction magnitude of 4; the reduction in the number of ecological corridors from 2000 to 2010 was the smallest, with a magnitude of 1. The important ecological corridors in the four periods showed a trend of decreasing, then increasing, and then decreasing, with the number of important corridors decreasing the most in the period of 1990–2000, with a reduction magnitude of 7. The number of important corridors decreased the most in 1990–2000, with a magnitude of 7. The number of general corridors showed a trend of increasing and then decreasing, with the largest decrease in 2000–2010, with a magnitude of 5. Regarding the continuous change in the total length of the ecological corridors, the smallest decrease was observed in 2000–2010, with a magnitude of 1.33%, and the largest decrease was observed in 2010–2020, with a magnitude of 9.12%. Calculation of network closure, network connectivity, and the point–line ratio for the four periods using Equations (5)–(7) showed that all three values of the ecospatial network were the highest in 1990, and all three values were the smallest in 2020, with relatively weak habitat connectivity. From 1990, 2000, 2010, and 2020, the network closure, network connectivity, and point–line ratio all showed a wave-like decreasing trend of decreasing, then increasing, and then decreasing. Overall, 1990 had the highest number of ecological corridors and the best habitat connectivity. The year 2000 saw a decrease in the number of ecological corridors due to the shrinking of the core source area from the outer rim, resulting in a general increase in the length of important corridors. The year 2010 saw a decrease in the number of ecological corridors and a decrease in the total length of ecological corridors, as the erosion and disappearance of important ecological source areas led to a decrease in connectivity corridors and a decrease in the number of north–south connected corridors, with decreases in both the number and length. In 2020, the number of corridors and the total length decreased due to the increase in habitat resistance caused by the intensity of urbanization and development, and the disappearance of some corridors passing through this area.

3.2. Ecological Spatial Network Optimization Analysis

3.2.1. Supplementary Important Ecological Source

According to the previous analysis of the evolution of ecological sources and ecological corridors in Pulandian, the ecological sources in Pulandian District show a distribution trend of gathering in the north and scarcity in the central and southern parts of the district, and the problem of insufficient coherence of ecological corridors is more prominent. In the north, forested areas such as Laomao Mountain Scenic Area, Jiguan Mountain National Forest Park, and Jiulong Mountain Scenic Area are the main sources of existing ecological sources, while the lack of ecological sources in the central and southern parts of the district leads to an incomplete ecological security pattern. Ecological source areas with a higher degree of importance of ecological patches are more closely connected in the north, and the circulation of ecological corridors is also good, but there is a lack of connection with the woodlands in the west and south. At the same time, due to the relative lack of ecological source areas in the coastal zone in the southeast, the central part of the cultivated area accounts for a larger area, which is unable to form a better connection with the ecological source areas in the north and southwest.

Ecological source land plays an extremely important role in the construction of ecological spatial patterns, and from the current ecological spatial pattern and ecological risk status in the study area, it can be known that the ecological source land patches in the study area are more fragmented, unevenly distributed, uneven in size, and with a lower degree of contiguity, so it is necessary to take the identified ecological source land as a key ecological protection zone. Focusing on the restoration of the central and western areas of larger size will increase the connectivity of the ecological corridor from the south to the north of the region, and focusing on the protection of the forest land in the north and the center of the region, which has a crucial role in the protection of ecological source lands in the study area, is important. In order to enhance the connectivity between ecological source areas and to rehabilitate their ecological nodes, high resistance areas that exist between ecological source areas can be identified through ecological pinch points. The identification of ecological pinch points can connect key areas of ecological sources and restore and protect them. Thus, the connectivity of ecological source areas is maintained. Strengthening the restoration and protection of the corridors from the west to the southeast and from the center to the southeast plays an important role in strengthening the coastal ecological landscape zone as well as enhancing the landscape connectivity. To this end, seven ecological source areas were added in the central and southern regions of the study area, and the small ecological patches in the central and southern regions as well as the seasonal river corridors were also treated as ecological corridors, so that the ecological source areas, ecological corridors, ecological nodes, and ecological breaks together constitute the ecological spatial optimization pattern of the Pulandian District. After reconstructing the optimization pattern, the number of ecological corridors reached 66, the number of important corridors reached 48, the number of ecological pinch points reached 44, the number of ecological obstacles reached 43, and the number of ecological barrier points reached 53 (Figure 8).

Comparing the ecological spatial pattern before and after optimization using the network structure index (

Table 9. Comparison between before and after optimization of ecological patterns.

	Number of Corridors	Number of Patches	Circular Connectivity	Point-Line Ratio	Connectivity
Pre-optimization	47	30	0.327	1.567	0.560
Post-optimization	66	37	0.435	1.784	0.629

), the ecological source areas in the ecological security protection elements increased to 37, an increase of 7 compared to the original, with an area of 645.84 km², accounting for 10.04% of the total area, and the number of corridors increased by 19. The degree of connectivity increased by 12.34% compared to the pre-optimization period, the degree of loop-through increased by 32.85% compared to the pre-optimization period, and the rate of points and lines increased by 13.86% compared to the pre-optimization period. This is because some ecological zones in the south-central and southwestern regions are in isolation, and the new corridors integrate corridors in isolation into the ecological network by adding necessary ecological sources as stepping stones through the evolutionary characteristics of the ecological core area in the last 30 years. The organic integration of ecological reserves and ecological networks expands the distribution range of ecological networks, thus perfecting the protection elements, circumventing the problems of one-sidedness and poor connectivity of protected areas, and promoting the circulation of material energy.

3.2.2. Ecospatial Network Optimization Planning Map

According to the ecological spatial network pattern of Pulandian District in the past 30 years, the optimization system of the ecological spatial pattern of Pulandian District in this study is “an ecological barrier, an ecological coastal zone, multiple ecological corridors, and multiple ecological sites” (Figure 9), with “an ecological barrier” being the ecological barrier of the northern hilly forests, and “an ecological coastal zone” being the salt fields and coastal belt in the Yellow Sea inlet area in the southeast. The “multiple ecological corridors” consist of the Biliu River ecological corridor, Dasha River ecological corridor, Tongyi-Anbo area of the Laomao Mountain National Forest Park system, the northern part of Lian Mountain—Datan, the northern part of the Tiexi line, the Tangjiafang–Fengrong–Daliujia line, central Xingtai and Mopan, the northern part of the southwestern part of the Siping-Lejia, and the northwestern part of the Anbo ecological corridor. The ecological corridors include the central part of Siping, the north part of Lejia, and the center part of Anbo. The “multiple ecological sites” mainly refers to Pulandian National Forest Park, Laomao Mountain Scenic Area, Biliuhe Reservoir, Liuda Reservoir, Wusi Reservoir, Dalianghuahu Reservoir, and other areas. These basically cover the ecological space resources of Pulandian District, and provide good landscape access for its positive development. Comprehensive consideration is given to the construction of the northern forest ecological barrier and water conservation area, the northeastern river valley lowland ecological agriculture area, the northwestern hilly ecological agriculture area, the central low-hill plain ecological agriculture construction area, and the southeastern low-hill coastal ecological coastal construction area.

4. Discussion

4.1. Characterizing the Evolution of Ecospatial Landscape Types

From 1990 to 2020, the ecological space of Pulandian District, such as woodland, grassland, lake, beach land, etc., showed a wave-like trend in quantity, with a total increase of 51.71 km², representing an increase of 6.8%. However, the area of beach land continuously decreased, with a decrease of 13.91%, which is 15.27% higher than the decrease in the area of farmland. The largest change in the area of a land use landscape type was the expansion of construction land, with an increase of 153.84 km², or 86.59%; the land use type with the largest transfer to construction land was beach land, and the ecological space in the area near the construction land was more severely fragmented. The cultivated land area decreased the most, by 204.544 km², with a decrease of 11.78% (Figure 2). From the changes in ecological landscape types, it can be concluded that the total ecological space volume has increased over the past 30 years because the increase in woodland is much larger than the decrease in grassland, and beach land, and the lake area is generally seen to have increased, and the trend of the total ecological space volume is also related to the trend of the change in the area of woodland. Driven by factors such as the economy, population development, and social construction, the landscape type of ecological space has been increasingly disturbed by external factors, and the pattern tends to be fragmented and heterogeneous, resulting in a significant reduction and fragmentation of woodland and farmland in the northeast river valley lowlands, the central low-lying plains, and the southeast coastal area. This is strongly associated with the intensification of infrastructure development in the study area, which depletes natural resources and disregards long-term land planning.

4.2. Characterization of the Evolution of Ecological Source Areas

Comparative analysis of the spatial pattern of landscape morphology in the coastal county Pulandian District in four periods is provided in Figure 3. The area of the ecological core area in the region showed a wave-like trend of increasing, then decreasing, and then increasing during the 30-year period, with an increase of 104.257 km²; the area of the fringe area demonstrated a growth trend of decreasing and then increasing, with an increase of 12.141 km². Considering that the core area, as a large area of ecological patches, is constantly being encroached upon and fragmented during the process of urbanization, this will form more fringe zones, leading to an increase in the percentage of the area of fringe zones. This indicates that the continuous development of new urbanization, people demanding higher requirements for the living environment, and the implementation of a series of policies such as afforestation and the return of farmland to woodlands have contributed to the increase in the ecological core area in the study area. From the change in landscape selection pattern in Figure 3 and Table 5, it can be concluded that the percentage of the ecological core area decreased by 4.03% from 2000 to 2010, while its area increased by 3.56% during the 2010–2020 period. Meanwhile, the percentage of the bridging area decreased by 0.42% and the area of the fringe area increased by 12.141 km² in 30 years, indicating a decreasing trend in ecological connectivity and a more fragmented regional landscape. The results of this study show that the area of the ecological core area tended to decrease as a result of the vigorous urban construction in Pulandian District after 2000. After 2010, ecological restoration in the main urban area was mostly carried out locally, which did not fundamentally alleviate the shrinkage of the ecological source area caused by urbanization, making it more difficult to maintain the ecological function of the region.

4.3. Characterization of Spatial and Temporal Changes in Ecological Risk

From a time-dimension perspective, between 1990 and 2020, the proportion of high- and higher-risk areas first increased and then decreased, the proportion of medium- and lower-risk areas first decreased and then increased, and the proportion of low-risk areas first decreased, then increased, and then decreased again. The share of high- and higher-risk areas reached its lowest value, and the share of low-risk areas reached its highest value. Overall, during the period from 1990 to 2020, due to the acceleration of urbanization, the intensity of land use in Pulandian has been increasing, human activities have intervened to enhance land ecological risk, and the degree of land use ecological risk has been decreasing.

From the perspective of the spatial dimension, compared with 1990, the high-risk area in 2000 increased by two areas: Tiexi in the west and Daliujia in the southeast. The higher-risk areas are mainly located in Yangshufang and Tangjiafang in the southeast, Datan and Lianshan and Mopan in the center, and Siping and Shuangta in the north. The medium-risk zones are distributed in Taiping in the west and Chengzitan in the southeast, the southwest of Tongyi in the north, and Shabao in the center. The lower-risk zones are distributed in Xingtai in the center and Lejia in the north. The low-risk areas are mainly located in the coastal areas of Anbo in the north and Chengzitan in the southeast. In 2010, the high-risk areas were mainly located in Fengrong and Taiping in the west, and the higher-risk areas were located in Pikou and Yangshufang in the southeast, as well as Shuangta in the north and Mopan in the center. The medium-risk areas are mainly located in Lianshan and Datan in the center and Tongyi and Siping in the north. The lower-risk zones are distributed in Shabao in the center and Chengzitan in the southeast. The low-risk areas are located in Lejia and Anbo in the north. In 2020, the high-risk zones are mainly located in Tiexi and Fengrong in the west, and the higher-risk zones are located in Pikou, Yangshufang, and Daliujia in the southeast. The medium-risk zones are mainly located in Lianshan, Xingtai, and Datan in the center, and Siping in the north. The low-risk zones are mainly located in the north, such as Anbo and Siping.

In general, during the period from 1990 to 2020, high-risk areas are mainly distributed in the western region, such as Taiping and Fengrong, which belong to regions with rapid urbanization. Higher-risk areas are distributed in the south-east, north, and central parts of the country. Medium-risk areas are distributed in the central part of the country, and lower-risk and low-risk areas are distributed in the central and northern parts of the country. In the later stages of the development process, attention should be paid to ecological security in high-risk areas, so as to safeguard ecological security in the whole region.

4.4. Characterization of the Evolution of Ecological Resistance Surfaces

From the perspective of the time dimension, the geography of Pulandian has not fundamentally changed from 1990 to 2020, and therefore, the overall change in ecological resistance surfaces does not show a significant trend. However, this does not mean that the ecological environment of the region has not been affected in any way, but it may just have had a relatively small impact or slow change.

From the perspective of the spatial dimension, the ecological resistance surfaces in the area show obvious spatial distribution characteristics. High resistance surfaces are mainly concentrated in Xingtai and Lianshan in the center and Pikou, Yangshufang, and Tangjiafang in the southeast, which may exhibit high ecological resistance due to various factors such as topography, geomorphology, and human activities. In contrast, low resistance surfaces are mainly located in the northern areas such as Tongyi, Anbo, and Lejia, which may show lower ecological resistance due to better natural conditions and being farther away from the centers of human activities.

To sum up, areas with higher ecological resistance surfaces are more affected by economic development, increased urbanization, and agricultural activities, which may mean that these areas have more severe environmental damage and are not suitable for ecological land expansion. Conversely, areas with lower ecological resistance surfaces are mainly distributed in the northern ecological reserve, which is more suitable for ecological land expansion because of its mountainous peaks and undulating terrain. This suggests that human activities have an obvious impact on the destruction of the ecological environment, and the protection and restoration of the ecological environment need to focus on those areas with higher ecological resistance.

4.5. Characterization of the Evolution of Ecological Corridors

In the whole study area, important corridors are mainly distributed in the northern and southwestern regions, and general corridors are clustered in the southeastern region. Shorter important corridors are mainly located in the northern part of the study area, mainly connecting some ecological source sites in close proximity, and longer important corridors are mainly located in the southern and western parts of the study area. The year 1990 is a critical point when the number of important corridors is highest and habitat connectivity is best. This suggests that corridors in the region or ecosystem reached a relative peak in 1990. Starting from 2000, the number of important corridors began to decrease. This may be due to the fact that the core source areas began to shrink from the outside in, resulting in increased pressure on the corridors connecting to the source areas. This means that corridors may disconnect or lose their connectivity. By 2010, the number of important corridors had decreased but the total length had increased. This may be due to the erosion and loss of important ecological source sites, resulting in fewer connecting corridors, especially those connecting north and south. This suggests that overall connectivity is decreasing despite the existence of fewer corridors. By 2020, the number of important corridors decreases again, but the total length remains the same. This may be due to increased urbanization and development, resulting in higher habitat resistance and the subsequent loss of some corridors that pass through this area. This suggests that human activities, such as urbanization and development, have a negative impact on corridor connectivity.

4.6. Optimization of Ecological Space Network and Strategies

The ecological corridors in the northern part of Pulandian are closely connected and have good circulation, but the ecological corridors in the west and south are weakly connected. The coastal zone area in the southeast has few ecological source lands, and the cultivated land area of production land in the center accounts for a large proportion, which has not formed better connection with the ecological source lands in the north and southwest. The ecological source land in Pulandian District generally shows a state of aggregation in the north and scarcity in the south-central part, and there is the problem of insufficient ecological corridor connectivity. Based on the current situation of the study area, ecological source land, ecological corridor, ecological node, and ecological patch together constitute the ecological spatial optimization pattern of Pulandian District. We should strengthen the restoration and protection of corridors linking the western to southeastern and central to southeastern parts of the study area, in order to form a benign ecological network, and optimize Pulandian District. It can be made up of an ecological barrier, a coastal ecological zone, multiple ecological corridors, multiple ecological nodes, and patches, including the northern mountainous forest, the salt field, and coastal belt in the Yellow Sea inlet area in the southeast, the Biliu River ecological corridor, the Dasha River ecological corridor, the Laoma Mountain National Forest Park system in the Tongyi and Anbo areas, and the connecting lines consisting of Lianshan and Datan in the northwestern region and Tiesi, connecting lines consisting of Tangjiafang, Fengrong, and Daliujia in the central region and Xingtai and Mopan in the central region, and connecting lines consisting of the southwestern part of Siping and the northwestern part of Lejia, Central Anbo, and other ecological corridors Pulandian National Forest Park, Laomao Mountain Scenic Area, Biliuhe Reservoir, Liuda Reservoir, Wusi Reservoir, and Daliangho Reservoir.

In addition, the protection and restoration of medium- and high-risk areas within the Pulandian District should begin with the ecological source buffer zone. Restoring ecological source buffer zones can greatly enhance landscape connectivity and reduce the resistance of ecological processes, thereby effectively reducing the ecological risk of the area. The construction land and farmland in the high-risk area have high resistance values, and the terrain in the buffer zone with a high resistance level is flat. Therefore, the rapid expansion of construction land and the blind occupation of farmland have seriously damaged the ecological environment. The stability of the ecosystem should be strengthened by encouraging local residents to carry out the protection and restoration of ecological land, returning farmland to forests, and raising the environmental awareness of residents. Ecological corridor protection and restoration focuses on the construction of biological migration corridors, building channels for exchanges between species and ecological environments to increase the circulation of the entire ecological environment and enhance the sustainability of the ecological environment.

5. Conclusions

In this study, we employed three different evaluation methods—the indicator-based method, the MSPA method, and a comprehensive evaluation method—to identify ecological source areas. We constructed ecological corridors using the MCR model and Linkage Mapper model. The ecological risk assessment provided guidance for optimizing the ecological spatial network and promoted an optimal allocation of ecological spatial elements, enhancing the level of ecological security.

Our research results showed that:

The ecological space in the Pulandian District of Dalian City from 1990 to 2020 shows a trend of increase-decrease-increase in terms of quantity. The main reason for the first increase is that the Pulandian District of Dalian City responded positively to the policy of returning farmland to forests and converted part of its cultivated land to forests, which led to an increase in the area of ecological land. The main reason for the decrease is the continuous expansion of urban and industrial land use and the pressure on the ecological environment caused by the increase in population and the expansion of production activities, resulting in a decrease in ecological space. Among them, the area of beach land is continuously decreasing, and it is also the ecological space landscape type that has been converted to construction land the most. The main reasons for the final increase are economic development and industrial restructuring, as well as the implementation of more ecological restoration and protection projects, which reduced the expansion of industrial and construction land and reduced the loss of ecological space.

From 1990 to 2020, the spatial variability of ecological risk was high in different periods, with the proportion of high- and higher-risk areas showing a trend of increase and then decrease, the proportion of medium and lower-risk areas showing a trend of decrease and then increase, and the low-risk areas showing a trend of decrease and then increase and then decrease. The changes were most pronounced in the western and central parts of the study area.

The contraction or even disappearance of the core ecological source areas in the study area is due to disturbances in the economy, population development, and social construction during the period from 1990 to 2020. The number of corridors, the number of patches, the degree of loop-through, the point-line rate, and the degree of connectivity in the study area after restoring the ecological source area based on the ecological risk assessment results have been increased to varying degrees.

Currently, many researchers are studying cities or urban clusters on a larger scale or focusing on ecological security evaluation and ecosystem-based regional planning. We believe that the ecological spatial network optimization based on ecological risk assessment at the county level not only reflects the ecological security situation of the studied area, but also provides direction for optimizing the ecological spatial network, and furthermore, provides a theoretical basis for the construction of ecological environments in the studied area as well as for decision-making by other relevant departments within the county.