Efficient urban ecological network element boundary range identification method based on function connectivity efficiency

Abstract

The invention relates to the technical field of urban ecological networks, in particular to an efficient urban ecological network element boundary range identification method based on functional connectivity efficiency, and the method specifically comprises the following steps: constructing a cost weighted distance surface capable of reflecting landscape heterogeneity on the basis of a known current habitat plaque and a resistance surface of a research area; simulating to obtain a minimum cost path between adjacent habitat plaques; calculating the overall function connectivity of the urban ecological network by using the possible connectivity index PC; quantifying the contribution degree of the function connectivity of each habitat plaque in the urban ecological network; identifying efficient habitat plaques, and determining a boundary range of the efficient habitat plaques; identifying the boundary range of the urban ecological corridor under different cost weighted distance widths; calculating the function connectivity of the urban ecological corridor; the efficient urban ecological corridor boundary based on functional connectivity efficiency is identified. The invention provides an accurate and efficient urban ecological network element boundary range identification method.

Description

Technical Field

[0001] The present invention relates to the field of urban ecological network technology, and in particular to a method for efficiently identifying the boundary range of urban ecological network elements based on functional connectivity efficiency. Background Art

[0002] The construction of ecological networks within cities plays an important role in promoting the connection between discrete habitat patches and maintaining biodiversity. Functional connectivity mainly refers to the degree to which the landscape promotes or hinders the movement of biological entities or certain ecological processes between habitat patches. It is a key factor in protecting biodiversity and maintaining the stability and integrity of ecosystems. It has important guiding significance for the planning and construction of urban ecological networks.

[0003] The elements of urban ecological networks mainly include habitat patches and ecological corridors.

[0004] At present, scholars have comprehensively considered various influencing factors such as area and habitat quality in the identification and selection of urban habitat patches, and the technical methods are quite mature. However, for high-density urbanized areas, the matrix environment is complex, the land used to build ecological space is severely fragmented, and land is scarce. It is often difficult to establish large habitat patches. In view of the current situation of urban land shortage, when the current habitat patches are known, there is still a lack of methods for identifying habitat patches that can effectively exert functional connectivity.

[0005] For the identification of ecological corridor boundaries between adjacent habitat patches in cities, the minimum cost model is often used to simulate the minimum cost path between adjacent habitat patches. However, the minimum cost path only represents the direction of the ecological corridor and cannot clearly define the boundary and scope of the corridor. In addition, the environment around the minimum cost path between each adjacent habitat patch is different. The suitable spatial width of the ecological corridor based on the minimum cost path for different species is different. In addition, the landscape heterogeneity of urbanized areas leads to different variations in the width of urban ecological corridors, and their boundaries will also change with the matrix environment. At present, some scholars have made buffer zones of the same spatial width on both sides of the minimum cost path to obtain the corridor range, but have not considered the impact of the complex environment of the urban matrix on ecological resistance, and have failed to reflect the heterogeneity of urban landscapes. Some scholars have also used ant colony algorithms or spatial continuous wavelet transforms (SCWT) to identify the mutation points of the ecological resistance sample belts on both sides of the minimum cost path to obtain the boundaries of the corridor, but they have not considered the scarcity of urban land and the functional connectivity efficiency of ecological corridors.

[0006] In summary, at present, the boundaries of urban ecological network elements that effectively play a connecting function are still unclear and ambiguous. Identifying the location and boundary range of efficient urban ecological network elements based on functional connectivity in a complex matrix environment can be used to grasp the characteristics of efficient urban ecological network elements and guide the effective protection of urban ecological networks. It is a necessary measure for efficient urban ecological protection and high-quality urban development and refined construction. Summary of the invention

[0007] The purpose of the present invention is to provide an efficient urban ecological network element boundary range identification method based on functional connectivity efficiency in view of the shortcomings of the current efficient urban ecological network element boundary range determination method, so as to solve the technical problems existing in the above-mentioned background technology.

[0008] To achieve the above object, the technical solution of the present invention is as follows:

[0009] An efficient urban ecological network element boundary range identification method based on functional connectivity efficiency specifically includes the following steps:

[0010] Step 1: Based on the known existing habitat patches and resistance surfaces in the study area, a cost-weighted distance surface that can reflect landscape heterogeneity was constructed to simulate and obtain the minimum cost path between adjacent habitat patches;

[0011] Step 2: Use the possible connectivity index PC to calculate the overall functional connectivity of the urban ecological network;

[0012] Step 3: Quantify the contribution of functional connectivity of each habitat patch in the urban ecological network;

[0013] Step 4: Identify efficient habitat patches and determine their boundaries;

[0014] Step 5: Identify the boundary range of urban ecological corridors under different cost-weighted distance widths;

[0015] Step 6: Calculate the functional connectivity of urban ecological corridors;

[0016] Step 7: Identify the boundaries of efficient urban ecological corridors based on functional connectivity efficiency.

[0017] Furthermore, the step one is specifically as follows: according to the selection of habitat patches in the study area and the evaluation results of the resistance surface, the Linkage Mapper2.0 toolbox is loaded in ArcMap, and the BuildNetwork and Map Linkages tool of the LinkagePathways Tool tool in the Linkage Mapper 2.0 toolbox is called to create a cost-weighted distance surface, and simulate the minimum cost path between adjacent habitat patches.

[0018] Furthermore, the step 2 is specifically as follows: the possible connectivity index PC refers to the probability that two animals randomly placed in the landscape enter the habitat patch area that can be reached by each other, given n habitat patches and the connectivity between them; its calculation formula is as follows:

[0019]

[0020] Where: A L is the total area of ​​the study area, n is the total number of habitat patches in the study area, a i 、a j is the area of ​​the i-th and j-th habitat patches, k is a constant, d ij It represents the resistance value of the ecological corridor between the i-th habitat patch and the j-th habitat patch; represents the possibility of species diffusion between the i-th habitat patch and the j-th habitat patch; for each pair of patches, the path used is such that The maximized path is the path that minimizes the cost between patches i and j.

[0021] Furthermore, the step three is specifically as follows: using the possible connectivity index dPC of the target element at the individual scale to quantify the contribution of each habitat patch to the functional connectivity of the overall urban ecological network, and the calculation formula is as follows:

[0022]

[0023] Where: The possible connectivity index dPC of the target element i represents the connectivity importance index corresponding to habitat patch i, reflecting the importance of individual elements in the entire network. PC is the possible connectivity index of the overall urban ecological network. ’ i It is the possible connectivity index of the overall urban ecological network after removing element i, where i represents the i-th habitat patch.

[0024] Furthermore, the step 4 is specifically as follows: on the basis of obtaining the contribution of each habitat patch to the functional connectivity of the overall urban ecological network, the area occupied by each habitat patch is counted at the same time, the ratio of the contribution of each habitat patch to the functional connectivity of the overall urban ecological network to its area is calculated, and the functional connectivity contribution per unit area of ​​each habitat patch is obtained, which is used as the standard for determining the functional connectivity efficiency of urban habitat patches; the calculation formula for the functional connectivity efficiency of habitat patches is as follows:

[0025]

[0026] Where: p i is the functional connectivity efficiency of habitat patch i, dPC iis the contribution of each habitat patch to the functional connectivity of the overall urban ecological network, a i is the area of ​​habitat patch i;

[0027] By calculating the functional connectivity efficiency of habitat patches, the functional connectivity efficiency of habitat patches in the study area was ranked and graded according to 20%, 30%, and 50% of the total number, among which the first 20% were the most efficient habitat patches, the middle 30% were efficient habitat patches, and the last 50% were habitat patches with average efficiency; the locations of the most efficient habitat patches and efficient habitat patches were determined in GIS, and their boundary ranges were extracted, that is, the boundary ranges of the efficient habitat patches in the study area were obtained.

[0028] Furthermore, the step five is specifically as follows: setting the cost-weighted distance width of the urban ecological corridor, and obtaining corridors with different cost-weighted distance widths between adjacent habitat patches.

[0029] Furthermore, the cost-weighted distance width of the urban ecological corridor is specifically set as follows: based on the consideration of urban landscape heterogeneity, the cost-weighted distance that can reflect landscape heterogeneity is used as the unit of measurement for width; on the basis of the habitat patches and resistance surfaces that have been obtained, combined with the species types, urbanized regional characteristics and accuracy of the study area, in order to identify the distribution of urban ecological corridors under different cost-weighted distance costs based on the minimum cost path, the cost-weighted distance is used as the unit of measurement, and multiple corridor cost-weighted distance widths are set according to the corridor width required for the movement of target species in the study area; the acquisition of corridors with different cost-weighted distance widths between adjacent habitat patches is specifically as follows: combined with the setting of different cost-weighted distance widths of the corridors in the study area and the minimum cost path that has been obtained, the Clip Corridors to CutoffWidth tool in the Linkage Mapper2.0 toolbox is used to clip the cost-weighted distance surface, that is, to obtain the corridor boundaries with different cost-weighted distance widths based on the minimum cost path between adjacent habitat patches.

[0030] Furthermore, the step six is ​​specifically as follows:

[0031] Quantification of the effective resistance of each urban ecological corridor: The circuit theory was used to calculate the effective resistance of the urban ecological corridor under different cost-weighted distance widths between adjacent habitat patches. Specifically, the Pinchpoint Mapper tool in the Linkage Mapper 2.0 toolbox was used to obtain the effective resistance corresponding to the boundaries of the urban ecological corridor under different cost-weighted distance widths;

[0032] Calculation of effective conductance of each urban ecological corridor: Effective conductance is introduced as an indicator to quantify the functional connectivity of the urban ecological corridor under different cost-weighted distance widths. The effective conductance of urban ecological corridors with different cost-weighted distance widths connecting adjacent habitat patches can be calculated by taking the inverse of the effective resistance, that is, the functional connectivity of the urban ecological corridor.

[0033] Furthermore, the step seven is specifically as follows: calculating the area of ​​each urban ecological corridor and identifying efficient urban ecological corridors and determining their boundary ranges.

[0034] Furthermore, the calculation of the area of ​​each urban ecological corridor is specifically as follows: statistics are performed on the area within the boundary of the urban ecological corridor under different cost-weighted distance widths, so as to obtain the area of ​​each urban ecological corridor under different cost-weighted distance widths; the identification of efficient urban ecological corridors and determination of their boundaries is specifically as follows: the inflection point of the corridor area and the functional connectivity data of the ecological corridor under multiple different cost-weighted distance widths between each habitat patch is calculated; the inflection point of the functional connectivity efficiency is obtained by running Python to call the knee toolkit, and the corridor cost-weighted distance width corresponding to the inflection point is found, so as to obtain the corridor boundary that effectively exerts the functional connectivity efficiency.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. Based on the known existing habitat patches and resistance surfaces in the study area, the present invention constructs a cost-weighted distance surface that can reflect the heterogeneity of the landscape, and uses the Fractions of delta Probability of Connectivity (dPC) of the target elements at the individual scale to measure the contribution of each habitat patch to the functional connectivity of the overall urban ecological network. Combined with the area of ​​the habitat patches, the functional connectivity efficiency is innovatively introduced to obtain the location and boundary range of efficient urban habitat patches; the circuit theory is used to identify the corridor boundaries under different "cost-weighted distance widths", and the functional connectivity and corridor area corresponding to the corridors under different "cost-weighted distance widths" are calculated. The corridor functional connectivity efficiency is innovatively introduced as a method for determining the efficient corridor boundary, and the corridor boundary range with higher corridor efficiency between adjacent habitat patches is identified.

[0037] 2. The present invention takes into account the key functional role of the urban ecological network - functional connectivity, and at the same time considers the current situation of land shortage within the city, and innovatively uses the concept of functional connectivity efficiency to provide a method for quantitatively identifying the boundary range of efficient urban ecological network elements. At the same time, the present invention fully considers the heterogeneity of urban landscapes, and identifies corridor boundaries under different "cost-weighted distance widths" on the basis of creating a cost-weighted distance surface. This is in line with the characteristics that the boundaries of urban ecological corridors are constantly changing and the spatial widths vary due to the influence of the complex matrix of the city, and provides an innovative method for accurate and efficient corridor boundary identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of the present invention;

[0039] Figure 2 is a schematic diagram of habitat patches and resistance surfaces in the present invention;

[0040] Figure 3 It is a schematic diagram of minimum cost path extraction in the present invention;

[0041] Figure 4 is a schematic diagram of the corridor boundary with a cost-weighted distance width of 10 in the present invention;

[0042] Figure 5 It is a schematic diagram of the corridor boundary with a cost-weighted distance width of 30 in the present invention;

[0043] Figure 6 It is a schematic diagram of the corridor boundary with a cost-weighted distance width of 60 in the present invention;

[0044] Figure 7 It is a schematic diagram of the corridor boundary with a cost-weighted distance width of 90 in the present invention;

[0045] Figure 8 It is a schematic diagram of the corridor boundary with a cost-weighted distance width of 120 in the present invention;

[0046] Fig. 9 It is a schematic diagram of the corridor boundary with a cost-weighted distance width of 200 in the present invention;

[0047] Fig.10 It is a schematic diagram of the corridor boundary with a cost-weighted distance width of 400 in the present invention;

[0048] Fig.11 It is a schematic diagram of the corridor boundary with a cost-weighted distance width of 600 in the present invention;

[0049] Fig.12 It is a schematic diagram of the corridor boundary with a cost-weighted distance width of 800 in the present invention;

[0050] Fig.13 is a schematic diagram of the corridor boundary with a cost-weighted distance width of 1000 in the present invention;

[0051] Fig.14 It is the inflection point diagram of corridor functional connectivity efficiency in the case of the present invention. DETAILED DESCRIPTION

[0052] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0053] See also Figures 1 to 14 As shown in FIG. 1 , an efficient urban ecological network element boundary range identification method based on functional connectivity efficiency includes the following steps:

[0054] Step 1: Based on the known existing habitat patches and resistance surfaces in the study area, a cost-weighted distance surface that can reflect landscape heterogeneity was constructed to simulate and obtain the minimum cost path between adjacent habitat patches;

[0055] According to the selection of habitat patches in the study area (vector file) and the evaluation results of the resistance surface (raster file), the Linkage Mapper2.0 toolbox was loaded in ArcMap, and the “Build Network and Map Linkages” tool of the LinkagePathways Tool in the Linkage Mapper 2.0 toolbox was called to create a cost-weighted distance surface to simulate and obtain the minimum cost path between adjacent habitat patches;

[0056] Step 2: Use the possible connectivity index PC to calculate the overall functional connectivity of the urban ecological network;

[0057] The probability of connectivity (PC) refers to the probability that two animals randomly placed in the landscape will enter a habitat patch area that is reachable to each other, given n habitat patches and the connectivity between them. The calculation formula is as follows:

[0058]

[0059] Where: A L is the total area of ​​the study area, n is the total number of habitat patches in the study area, a i 、a j is the area of ​​the i-th and j-th habitat patches, k is a constant, d ij It represents the resistance value of the ecological corridor between the i-th habitat patch and the j-th habitat patch; represents the possibility of species diffusion between the i-th habitat patch and the j-th habitat patch; for each pair of patches, the path used is such that The maximized path is the path that minimizes the distance (or cost) between patches i and j. The present invention uses the cost distance of the minimum cost path;

[0060] Step 3: Quantify the contribution of functional connectivity of each habitat patch in the urban ecological network;

[0061] In order to understand the contribution of each habitat patch in the urban ecological network to the overall functional connectivity of the network, the possible connectivity index (Fractions of delta Probability of Connectivity, dPC) of the target element at the individual scale was selected. The possible connectivity index dPC of the target element in this invention is i It represents the connectivity importance index corresponding to habitat patch i, reflecting the importance of individual elements in the overall urban ecological network;

[0062]

[0063] PC is the possible connectivity index of the overall urban ecological network. ’ i is the possible connectivity index of the overall urban ecological network after removing element i, i represents the i-th habitat patch. The present invention selects dPC to measure the contribution of each habitat patch to the functional connectivity of the overall urban ecological network;

[0064] Step 4: Identify efficient habitat patches and determine their boundaries;

[0065] On the basis of obtaining the contribution of each habitat patch to the functional connectivity of the overall urban ecological network, the area occupied by each habitat patch is counted, and the ratio of the contribution of each habitat patch to the functional connectivity of the overall urban ecological network to its area is calculated to obtain the functional connectivity contribution per unit area of ​​each habitat patch, which is used as the standard for determining the functional connectivity efficiency of urban habitat patches. The calculation formula for the functional connectivity efficiency of habitat patches is as follows:

[0066]

[0067] Where: p i is the functional connectivity efficiency of habitat patch i, dPC i is the contribution of each habitat patch to the functional connectivity of the overall urban ecological network, a i is the area of ​​habitat patch i;

[0068] Through the calculation of the functional connectivity efficiency of habitat patches, the functional connectivity efficiency of habitat patches in the study area was ranked and graded according to the number of 20%, 30%, and 50% of the total number, among which the first 20% were the most efficient habitat patches, the middle 30% were efficient habitat patches, and the last 50% were habitat patches with average efficiency; the locations of the most efficient habitat patches and efficient habitat patches were determined in GIS, and their boundary ranges were extracted, that is, the boundary range of the efficient habitat patches in the study area was obtained (the specific percentage of the number of efficient habitat patches can be set according to the protection needs of different study areas);

[0069] Step 5: Identify the boundary range of urban ecological corridors under different cost-weighted distance widths;

[0070] (1) Set the “cost-weighted distance width” of the urban ecological corridor.

[0071] Based on the consideration of urban landscape heterogeneity, this paper uses the "cost-weighted distance" that can reflect landscape heterogeneity as the unit of measurement width. Based on the habitat patches and resistance surfaces that have been obtained, combined with the species types, urbanized regional characteristics and accuracy of the study area, in order to identify the distribution of urban ecological corridors under different cost-weighted distance costs based on the minimum cost path, the cost-weighted distance is used as the unit of measurement, and multiple corridor "cost-weighted distance widths" are set according to the corridor width required for the movement of target species in the study area.

[0072] (2) Obtain corridors with different “cost-weighted distance widths” between adjacent habitat patches.

[0073] Combined with the different "cost-weighted distance width" settings of the corridors in the study area and the minimum cost path obtained, the "Clip Corridors to Cutoff Width" tool in the Linkage Mapper2.0 toolbox was used to clip the cost-weighted distance surface, that is, to obtain the corridor boundaries of different "cost-weighted distance widths" based on the minimum cost path between each adjacent habitat patch;

[0074] Step 6: Calculate the functional connectivity of urban ecological corridors;

[0075] (1) Quantification of the effective resistance of ecological corridors in each city.

[0076] In ecology, effective resistance (also known as resistance distance) is considered to be an indicator of the degree of spatial isolation between nodes. It is similar to the ecological concept of effective distance. As the connection path and corridor width between habitat patches increase, the effective resistance decreases. Therefore, effective resistance is more suitable for urbanized areas with complex and diverse elements, and can more comprehensively reflect the isolation between patches.

[0077] The present invention uses circuit theory to calculate the effective resistance of the urban ecological corridor under different “cost-weighted distance widths” between adjacent habitat patches. Specifically, the Pinchpoint Mapper tool in the Linkage Mapper 2.0 toolbox is used to obtain the effective resistance corresponding to the boundary of the urban ecological corridor under different “cost-weighted distance widths”.

[0078] (2) Calculation of effective conductance of ecological corridors in each city.

[0079] Effective conductance is the reciprocal of effective resistance. In ecology, effective conductance is a measure of the functional connectivity between node pairs or habitat patches, which increases with the increase of available paths. The present invention introduces effective conductance as an indicator to quantify the functional connectivity of urban ecological corridors under different "cost-weighted distance widths". By taking the reciprocal of effective resistance, the effective conductance of urban ecological corridors with different "cost-weighted distance widths" connecting adjacent habitat patches can be calculated, that is, the functional connectivity of urban ecological corridors.

[0080] Step 7: Identify the boundaries of efficient urban ecological corridors based on functional connectivity efficiency;

[0081] (1) Calculation of ecological corridor areas in each city.

[0082] Statistics are conducted on the areas within the boundaries of urban ecological corridors under different “cost-weighted distance widths” to obtain the areas of each urban ecological corridor under different “cost-weighted distance widths”.

[0083] (2) Identify efficient urban ecological corridors and determine their boundaries.

[0084] For corridors between adjacent habitat patches obtained based on the minimum cost path, the larger the spatial width, the larger the urban land area occupied. Based on the shortage of urban land, it is necessary to consider the changing relationship between the land input area and the obtained corridor functional connectivity, that is, to consider the degree of improvement of the urban ecological corridor functional connectivity by increasing the corridor area, and then find the range of the boundary of the efficient corridor between adjacent habitat patches in the urbanized area. Therefore, for the identification of the boundary of the efficient urban ecological corridor between adjacent habitat patches, it is mainly based on the comprehensive judgment of the corridor functional connectivity and corridor area under different "cost-weighted distance widths".

[0085] Through the above steps, the functional connectivity of the corridor and the area of ​​the corridor under different "cost-weighted distance widths" were obtained, and the functional relationship between the two was constructed to find the "cost-weighted distance width" where the functional connectivity efficiency inflection point is located, that is, the boundary range of the efficient urban ecological corridor is obtained.

[0086] Since the present invention identifies ecological corridors with multiple different "cost-weighted distance widths" based on the minimum cost path between adjacent habitat patches, the inflection point of the corridor area and the functional connectivity data of the ecological corridor under multiple different "cost-weighted distance widths" between each habitat patch is obtained. By running Python to call the Kneed toolkit to obtain the inflection point of the functional connectivity efficiency, the "cost-weighted distance width" of the corridor corresponding to the inflection point is found, and the corridor boundary that effectively exerts the functional connectivity efficiency can be obtained.

[0087] Case: Based on the habitat patches and resistance surfaces in the study area, the minimum cost path between adjacent habitat patches is simulated (taking habitat patches A and B in the study area as an example, Figure 2 , Figure 3 ), according to the species type and data accuracy of the study area, 10 “cost-weighted distance widths” were set, including 10, 30, 60, 90, 120, 200, 400, 600, 800, and 1000, and the ecological corridor boundary between habitat patches A and B was obtained through simulation calculation ( Figure 4-Figure 13 ) and the functional connectivity and area of ​​each ecological corridor (Table 1).

[0088] Table 1 is as follows:

[0089] Serial number Cost-weighted distance width Corridor functional connectivity Corridor area (square meters) 1 10 0.0000518 34398.21 2 30 0.0000545 37098.07 3 60 0.000075 42097.81 4 90 0.0000771 45297.65 5 120 0.0000822 49197.44 6 200 0.000104235 57597.01 7 400 0.000174605 85495.56 8 600 0.000203465 125993.46 9 800 0.000229859 171691.08 10 1000 0.000256761 212988.94

[0090] By running Python to obtain the inflection point of the corridor functional connectivity efficiency between habitat patches A and B in the case, the corresponding corridor area is 85495.56 square meters ( Fig.14 ), and its corresponding “cost-weighted distance width” is 400, so the boundary range of the efficient urban ecological corridor between habitat patches A and B can be obtained as Fig.10 The corresponding corridor boundary range.

[0091] Based on the case of the present invention, the specific python code for running the inflection point of obtaining the corridor functional connectivity efficiency is as follows:

[0092] from kneed import KneeLocator

[0093] import matplotlib.pyplot as plt

[0094] curve = 'concave'

[0095] direction = 'increasing'

[0096] x=[34398.21,37098.07,42097.81,45297.65,49197.44,57597.01,85495.56,125993.46,171691.08,212988.94]# Input the area of ​​each corridor with different cost-weighted distance widths based on the same minimum cost path

[0097] y=[0.0000518,0.0000545,0.000075,0.0000771,0.0000822,0.000104235,0.000174605,0.000203465,0.000229859,0.000256761]# Input the functional connectivity of each corridor with different cost-weighted distance widths based on the same minimum cost path

[0098] kneedle=KneeLocator(x,y,S=1.0,curve='concave',direction='increasing')

[0099] print(f'The x-axis where the inflection point is: {kneedle.elbow}')

[0100] kneedle.plot_knee()

[0101] plt.show()

[0102] Operation results:

[0103] The inflection point on the x-axis is: 85495.56.

[0104] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any technician familiar with the profession, without departing from the scope of the technical solution of the present invention, according to the technical essence of the present invention, any simple modification, equivalent replacement and improvement made to the above embodiment still falls within the protection scope of the technical solution of the present invention.

Claims

1. An efficient urban ecological network element boundary range identification method based on functional connectivity efficiency, It is characterized in that The specific steps include: Step 1: Based on the known existing habitat patches and resistance surfaces in the study area, a cost-weighted distance surface that can reflect landscape heterogeneity was constructed to simulate and obtain the minimum cost path between adjacent habitat patches; Step 2: Use the possible connectivity index PC to calculate the overall functional connectivity of the urban ecological network; Step 3: Quantify the contribution of functional connectivity of each habitat patch in the urban ecological network; Step 4: Identify efficient habitat patches and determine their boundaries; Step 5: Identify the boundary range of urban ecological corridors under different cost-weighted distance widths; Step 6: Calculate the functional connectivity of urban ecological corridors; Step 7: Identify the boundaries of efficient urban ecological corridors based on functional connectivity efficiency.

2. According to claim 1, a highly efficient urban ecological network element boundary range identification method based on functional connectivity efficiency, Features: The step one is specifically as follows: according to the selection of habitat patches in the study area and the evaluation results of the resistance surface, the Linkage Mapper 2.0 toolbox is loaded in ArcMap, and the Build Network and Map Linkages tool of the Linkage Pathways Tool tool in the Linkage Mapper 2.0 toolbox is called to create a cost-weighted distance surface, and simulate the minimum cost path between adjacent habitat patches.

3. According to claim 2, a method for efficiently identifying the boundary range of urban ecological network elements based on functional connectivity efficiency, Features: The step 2 is specifically as follows: the possible connectivity index PC refers to the probability that two animals randomly placed in the landscape will enter the habitat patch area that is reachable to each other, given n habitat patches and the connectivity between them; The calculation formula is as follows: Where: A L is the total area of ​​the study area, n is the total number of habitat patches in the study area, a i 、a j is the area of ​​the i-th and j-th habitat patches, k is a constant, d ij It represents the resistance value of the ecological corridor between the i-th habitat patch and the j-th habitat patch; represents the possibility of species diffusion between the i-th habitat patch and the j-th habitat patch; for each pair of patches, the path used is such that The maximized path is the path that minimizes the cost between patches i and j.

4. According to claim 3, a method for efficiently identifying the boundary range of urban ecological network elements based on functional connectivity efficiency, Features: The step three is specifically: using the possible connectivity index dPC of the target element at the individual scale to quantify the contribution of each habitat patch to the functional connectivity of the overall urban ecological network, and the calculation formula is as follows: Where: The possible connectivity index dPC of the target element i represents the connectivity importance index corresponding to habitat patch i, reflecting the importance of individual elements in the entire network. PC is the possible connectivity index of the overall urban ecological network. ’ i It is the possible connectivity index of the overall urban ecological network after removing element i, where i represents the i-th habitat patch.

5. According to claim 4, a method for efficiently identifying the boundary range of urban ecological network elements based on functional connectivity efficiency, Features: The step 4 is specifically as follows: on the basis of obtaining the contribution of each habitat patch to the functional connectivity of the overall urban ecological network, the area occupied by each habitat patch is counted at the same time, the ratio of the contribution of each habitat patch to the functional connectivity of the overall urban ecological network to its area is calculated, and the functional connectivity contribution per unit area of ​​each habitat patch is obtained, which is used as the standard for determining the functional connectivity efficiency of urban habitat patches; the calculation formula for the functional connectivity efficiency of habitat patches is as follows: Where: p i is the functional connectivity efficiency of habitat patch i, dPC i is the contribution of each habitat patch to the functional connectivity of the overall urban ecological network, a i is the area of ​​habitat patch i; By calculating the functional connectivity efficiency of habitat patches, the functional connectivity efficiency of habitat patches in the study area was ranked and graded according to 20%, 30%, and 50% of the total number, among which the first 20% were the most efficient habitat patches, the middle 30% were efficient habitat patches, and the last 50% were habitat patches with average efficiency; the locations of the most efficient habitat patches and efficient habitat patches were determined in GIS, and their boundary ranges were extracted, that is, the boundary ranges of the efficient habitat patches in the study area were obtained.

6. According to claim 5, a method for efficiently identifying the boundary range of urban ecological network elements based on functional connectivity efficiency, Features: The step five is specifically as follows: setting the cost-weighted distance width of the urban ecological corridor, and obtaining corridors with different cost-weighted distance widths between adjacent habitat patches.

7. According to claim 6, a method for efficiently identifying the boundary range of urban ecological network elements based on functional connectivity efficiency, Features: The cost-weighted distance width of the urban ecological corridor is specifically set as follows: based on the consideration of urban landscape heterogeneity, the cost-weighted distance that can reflect landscape heterogeneity is used as the unit of measurement for width; on the basis of the habitat patches and resistance surfaces that have been obtained, combined with the species types, urbanized regional characteristics and accuracy of the study area, in order to identify the distribution of urban ecological corridors under different cost-weighted distance costs based on the minimum cost path, the cost-weighted distance is used as the unit of measurement, and multiple corridor cost-weighted distance widths are set according to the corridor width required for the movement of target species in the study area; the acquisition of corridors with different cost-weighted distance widths between adjacent habitat patches is specifically as follows: combined with the setting of different cost-weighted distance widths of the corridors in the study area and the minimum cost path that has been obtained, the Clip Corridors to Cutoff Width tool in the Linkage Mapper2.0 toolbox is used to clip the cost-weighted distance surface, that is, to obtain the corridor boundaries with different cost-weighted distance widths based on the minimum cost path between adjacent habitat patches.

8. According to claim 7, a highly efficient urban ecological network element boundary range identification method based on functional connectivity efficiency, Features: The step six is ​​specifically as follows: Quantification of the effective resistance of each urban ecological corridor: The effective resistance of the urban ecological corridor under different cost-weighted distance widths between adjacent habitat patches was calculated using circuit theory. Specifically, the Pinchpoint Mapper tool in the Linkage Mapper 2.0 toolbox was used to obtain the effective resistance corresponding to the boundaries of the urban ecological corridor under different cost-weighted distance widths; Calculation of effective conductance of each urban ecological corridor: Effective conductance is introduced as an indicator to quantify the functional connectivity of the urban ecological corridor under different cost-weighted distance widths. The effective conductance of urban ecological corridors with different cost-weighted distance widths connecting adjacent habitat patches can be calculated by taking the inverse of the effective resistance, that is, the functional connectivity of the urban ecological corridor.

9. According to claim 8, a highly efficient urban ecological network element boundary range identification method based on functional connectivity efficiency, Features: The step seven specifically includes: calculating the area of ​​each urban ecological corridor, identifying efficient urban ecological corridors and determining their boundaries.

10. According to claim 9, a highly efficient urban ecological network element boundary range identification method based on functional connectivity efficiency, Features: The calculation of the area of ​​each urban ecological corridor is specifically as follows: statistics are performed on the area within the boundary of the urban ecological corridor under different cost-weighted distance widths, and the area of ​​each urban ecological corridor under different cost-weighted distance widths is obtained; the identification of efficient urban ecological corridors and determination of their boundary ranges is specifically as follows: the inflection point of the corridor area and the functional connectivity data of the ecological corridor under multiple different cost-weighted distance widths between each habitat patch is obtained; the inflection point of the functional connectivity efficiency is obtained by running Python to call the knee toolkit, and the corridor cost-weighted distance width corresponding to the inflection point is found, so as to obtain the corridor boundary that effectively exerts the functional connectivity efficiency.

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