Climate connectivity network optimization method based on temperature constraint and storage medium

Abstract

The invention discloses a climate connectivity network optimization method based on temperature constraint and a storage medium. The method comprises the following steps: identifying different habitat biodiversity hotspots based on a pre-constructed species distribution model; taking the existing protected area in the research area as an initial node, and combining a comprehensive resistance surface and a diffusion distance threshold which are obtained in advance to construct a basic ecological network; a temperature difference constraint rule is introduced into the basic ecological network, all minimum cost paths of initial nodes meeting the temperature difference constraint rule are calculated, and a current climate connectivity network is formed; for the climate connectivity network, taking different habitat biodiversity hotspots as supplementary nodes to obtain the climate connectivity network after first optimization; and performing screening and data cleaning on the climate connectivity network after the first optimization based on the path weight value to obtain a climate connectivity network after the second optimization. According to the method, the climate connectivity of a protected area ecological system in a multi-climate scene can be improved.

Description

Technical Field

[0001] This invention relates to the field of ecological network optimization and climate connectivity modeling technology, and in particular to a temperature-constrained climate connectivity network optimization method and storage medium. Background Technology

[0002] Under the dual pressures of global climate change and human activities, the spatial distribution patterns of species are undergoing significant changes, posing unprecedented challenges to the stability of ecosystems and the effectiveness of protected area connectivity. In this context, climate connectivity, as a measure of whether an ecological landscape supports species migration along temperature gradients, has become a key indicator of biodiversity's long-term adaptation to climate change. However, climate connectivity is severely disrupted by human activities, such as habitat fragmentation and increased landscape resistance caused by land-use change and infrastructure expansion, significantly reducing the connectivity of protected area networks and the adaptive capacity of species. Existing methods for constructing climate connectivity networks typically employ the least-cost path (LCP) approach.

[0003] However, the direct impact of temperature gradients on species migration is often overlooked in the construction of resistance surfaces for climate connectivity networks, and research on explicitly incorporating climate information into resistance surfaces to assess climate connectivity networks in nature reserves remains limited. In particular, the impact of species heat tolerance and temperature gradients has not been fully considered when constructing minimum cost paths (LCPs), resulting in a mismatch between the structure of climate connectivity networks and actual migration capacity. Summary of the Invention

[0004] The purpose of this invention is to provide a temperature-constrained climate connectivity network optimization method and storage medium. By introducing temperature difference constraint rules into the basic ecological network, a climate connectivity network is obtained. Then, supplementary node optimization and path weight value selection optimization are performed on the climate connectivity network to obtain an optimized climate connectivity network. This optimized network can reflect the migration potential of species under future climate scenarios and identify key ecological nodes and corridors, providing a scientific basis for regional conservation planning. This invention is achieved through the following technical solutions.

[0005] In a first aspect, the present invention provides a method for optimizing climate connectivity networks based on temperature constraints, comprising:

[0006] A species distribution model was constructed based on environmental factors, habitat area, and speciation records of the study area.

[0007] Based on the species distribution model, biodiversity hotspots in different habitats were identified;

[0008] A comprehensive resistance surface was constructed using average annual temperature data and human footprint index data.

[0009] Using existing protected areas within the study area as initial nodes, and combining the comprehensive resistance surface and the pre-acquired diffusion distance threshold, a first minimum cost path is generated, and a basic ecological network is constructed through the first minimum cost path;

[0010] By introducing temperature difference constraint rules into the basic ecological network, the minimum cost path for generating initial nodes that satisfy the temperature difference constraint rules is calculated, thus forming the current climate connectivity network.

[0011] Based on the current climate connectivity network, the biodiversity hotspots of different habitats are used as supplementary nodes to generate a second minimum cost path. Based on the first minimum cost path and the second minimum cost path, the climate connectivity network after the first optimization is obtained.

[0012] The climate connectivity network after the first optimization is filtered and optimized based on the path weight values ​​to obtain the climate connectivity network after the second optimization.

[0013] Based on the second optimized climate connectivity network, a second temperature difference constraint screening is performed. The minimum cost path generated by all nodes that satisfy the temperature difference constraint rules is calculated to obtain the final optimized climate connectivity network.

[0014] In this invention, the filtered paths undergo a second calculation based on temperature difference constraint rules, which can eliminate redundant and invalid supplementary nodes and paths.

[0015] Optionally, the protected areas within the study area are divided into three habitat types: forest, wetland, and grassland.

[0016] Optionally, identifying biodiversity hotspots in different habitats based on the species distribution model includes: obtaining a future species distribution map based on the species distribution model; obtaining a species weighted richness map based on the future species distribution map and the endangerment level; and identifying biodiversity hotspots in different habitats based on the species weighted richness map and land use data.

[0017] Optionally, the construction of the comprehensive resistance surface using annual average temperature data and human footprint index data includes: constructing a temperature difference resistance surface using annual average temperature data, constructing a human disturbance resistance surface using human footprint index data, and generating the comprehensive resistance surface by equally weighting the temperature difference resistance surface and the human disturbance resistance surface.

[0018] Optionally, constructing a temperature difference resistance surface using annual average temperature data includes performing logarithmic compression, inverse standardization, and normalization on the temperature difference resistance values ​​of the surface; wherein the calculation formula for the temperature difference resistance values ​​is as follows:

[0019] ,

[0020] In the formula, The temperature of any central grid cell in the annual average temperature data. The grid number is the grid number adjacent to the center grid, and its value ranges from 1 to... , This represents the total number of grid cells adjacent to the center grid cell. For the first The temperature of the grid cells adjacent to the central grid cell.

[0021] Optionally, the expression for the temperature difference constraint rule is as follows:

[0022] ,

[0023] In the formula, The temperature of the starting node for each path. The temperature of any node on the path connecting the starting node.

[0024] Optionally, the formula for calculating the path weight value is as follows:

[0025] ,

[0026] In the formula, and These are the first and last nodes in any minimum-cost path within the first optimized climate connectivity network. For nodes and nodes Path weight values ​​between and They are nodes and nodes area, For nodes and nodes The cost value of the minimum cost path between them. and They are nodes Inner and Node The average resistance value within, The maximum cost value of all minimum-cost paths between nodes in the current climate connectivity network. This is the temperature difference adjustment factor.

[0027] Optionally, the formula for calculating the temperature difference adjustment factor Tdiff_ab is as follows:

[0028] ,

[0029] In the formula, For nodes Minimum temperature inside, For nodes The lowest temperature inside.

[0030] Optionally, the climate connectivity network after the first optimization is filtered and optimized based on the path weight value to obtain the climate connectivity network after the second optimization. This includes extracting paths with path weight values ​​greater than or equal to a specific value from the climate connectivity network after the first optimization as effective key ecological corridors, and obtaining the climate connectivity network after the second optimization through the effective key ecological corridors.

[0031] In a second aspect, the present invention provides a computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, when the computer program / instructions are executed by a processor, they implement the steps of the temperature-constrained climate connectivity network optimization method described in the first aspect.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] The proposed temperature-constrained climate connectivity network optimization method constructs a climate connectivity network by considering species' heat tolerance, thereby improving the climate connectivity of protected ecosystems under multiple climate scenarios. Further optimization of the constructed climate connectivity network by introducing temperature difference constraint rules and path weight values ​​reduces redundant paths and enhances the accessibility of species migrating to suitable cold habitats. Simultaneously, it identifies key ecological nodes and key climate corridors, providing scientific and operational technical support for optimizing the spatial pattern of protected areas at the regional scale, delineating priority zones for biodiversity conservation, and managing climate adaptation. Attached Figure Description

[0035] Figure 1 The diagram shown is a schematic flowchart of a climate connectivity network optimization method based on temperature constraints in one embodiment of the present invention.

[0036] Figure 2 The diagram shown is a schematic representation of the optimization and evaluation of climate connectivity network based on temperature constraints in one embodiment of the present invention.

[0037] Figure 3 The diagram shown is a comparative illustration of ECC assessment of climate connectivity in forest climate in one embodiment of the present invention;

[0038] Figure 4 The diagram shown is a comparative illustration of the probability of climate connectivity (PCC) assessment in forest climate in one embodiment of the present invention.

[0039] Figure 5 The diagram shown is a comparative illustration of the ECC assessment of climate connectivity in grassland climate in one embodiment of the present invention.

[0040] Figure 6 The diagram shown is a comparative illustration of the probability of climate connectivity (PCC) assessment in grassland climate according to one embodiment of the present invention.

[0041] Figure 7 The diagram shown is a comparative illustration of the ECC assessment of climate connectivity in wetland climate in one embodiment of the present invention.

[0042] Figure 8 The diagram shown is a comparative illustration of the probability of climate connectivity (PCC) assessment in wetland climate according to one embodiment of the present invention. Detailed Implementation

[0044] The following description, in conjunction with the accompanying drawings and specific embodiments, provides further details. In this description, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature.

[0045] Example 1 This embodiment introduces a climate connectivity network optimization method based on temperature constraints, including the following steps:

[0046] A species distribution model was constructed based on environmental factors, habitat area, and speciation records of the study area.

[0047] Based on the species distribution model, biodiversity hotspots in different habitats were identified;

[0048] A comprehensive resistance surface was constructed using average annual temperature data and human footprint index data.

[0049] Using existing protected areas within the study area as initial nodes, and combining the comprehensive resistance surface and the pre-acquired diffusion distance threshold, a first minimum cost path is generated, and a basic ecological network is constructed through the first minimum cost path;

[0050] By introducing temperature difference constraint rules into the basic ecological network, the minimum cost path for generating initial nodes that satisfy the temperature difference constraint rules is calculated, thus forming the current climate connectivity network.

[0051] Based on the current climate connectivity network, the biodiversity hotspots of different habitats are used as supplementary nodes to generate a second minimum cost path. Based on the first minimum cost path and the second minimum cost path, the climate connectivity network after the first optimization is obtained.

[0052] The climate connectivity network after the first optimization is filtered and optimized based on the path weight values ​​to obtain the climate connectivity network after the second optimization.

[0053] Based on the second optimized climate connectivity network, a second temperature difference constraint screening is performed. The minimum cost path generated by all nodes that satisfy the temperature difference constraint rules is calculated to obtain the final optimized climate connectivity network.

[0054] Example 2 Based on Example 1, this example introduces a specific implementation process of a temperature-constrained climate connectivity network optimization method, such as... Figure 1 As shown, it specifically includes the following:

[0055] I. Constructing a species distribution model

[0056] In one specific embodiment of the present invention, a species distribution model is constructed based on environmental factors, habitat area, and speciation records of the study area. Specifically, this includes the following:

[0057] First, based on the spatial distribution data of endangered species in the study area, combined with land use data and elevation data, the potential habitat ranges of the species were screened, and the habitat ranges were converted into point data for subsequent use in building species distribution models. Speciation records from the GBIF and IDIGBIO databases were used as supplementary data.

[0058] Regarding the selection of environmental variables, 25 candidate environmental factors were initially selected. Variance inflation factor (VIF) test and Pearson correlation analysis were performed using the usdm toolkit in R language (Naimi, 2014) to eliminate multicollinear variables. Finally, 10 variables were retained for use in constructing the species distribution model.

[0059] In one specific embodiment of the present invention, identifying biodiversity hotspots in different habitats based on a species distribution model includes: obtaining a future species distribution map based on the species distribution model; obtaining a species weighted richness map based on the future species distribution map and its endangerment level; and identifying biodiversity hotspots in different habitats based on the species weighted richness map and land use data. Specifically, this includes the following:

[0060] We used a pre-constructed species distribution model to simulate the habitat suitability distribution of 92 target species in the current and future periods. Based on the species' endangerment level (Critically Endangered CR=8, Endangered EN=4, Vulnerable VU=2), we assigned weights and superimposed them to obtain a weighted species richness map of terrestrial vertebrates (including mammals, birds, amphibians, and reptiles).

[0061] Based on land use / cover data, the species-weighted richness map was divided into three habitat types: forest, wetland, and grassland. Within each habitat type, the top 25% of areas by species-weighted richness were identified as biodiversity hotspots and used as supplementary nodes in the climate connectivity network for subsequent optimization.

[0062] II. Constructing and Optimizing Climate Connectivity Networks

[0063] In one specific embodiment of the present invention, constructing a comprehensive resistance surface using annual average temperature data and human footprint index data includes: constructing a temperature difference resistance surface using annual average temperature data, constructing a human disturbance resistance surface using human footprint index data, and generating the comprehensive resistance surface by equally weighting the temperature difference resistance surface and the human disturbance resistance surface.

[0064] The temperature difference resistance surface is constructed using annual average temperature data. This involves logarithmic compression, inverse standardization, and normalization of the temperature difference resistance values. The formula for calculating the temperature difference resistance value is as follows:

[0065] ,

[0066] In the formula, The average annual temperature of the data center grid. The grid number is the grid number adjacent to the center grid, and its value ranges from 1 to... , This represents the total number of grid cells adjacent to the center grid cell. For the first The temperature of the grid cells adjacent to the central grid cell.

[0067] 2.1 Constructing a basic ecological network

[0068] In one specific embodiment of the present invention, existing protected areas within the study area are used as initial nodes. A first minimum cost path is generated by combining the comprehensive resistance surface and a pre-acquired diffusion distance threshold. A basic ecological network is then constructed through this first minimum cost path. Specifically, this includes the following:

[0069] Based on existing allometric growth models, the maximum dispersal distances of mammals and birds were calculated. Combined with the target species' weight and ecological characteristics, their potential migration capacity was determined. Using the quantile threshold method, 20 km (short distance), 70 km (medium distance), and 110 km (long distance) were set as multi-scale dispersal distance thresholds in each habitat type, covering the migration needs of more than 90% of the target species.

[0070] Using existing protected areas within the study area as initial nodes, and the comprehensive resistance surface as input to Graphab v3.0 (a landscape connectivity analysis software based on graph theory), a first minimum cost path is generated by combining pre-acquired diffusion distance thresholds. A basic ecological network is then constructed based on this first minimum cost path. In one specific embodiment of this invention, since there are three habitat types—forest, wetland, and grassland—the constructed basic ecological network comprises three categories. Each habitat type includes the three multi-scale diffusion distance thresholds set above.

[0071] 2.2 Constructing the current climate connectivity network

[0072] In one specific embodiment of the present invention, by introducing temperature difference constraint rules into the basic ecological network, the paths generated by all nodes that satisfy the temperature difference constraint rules are calculated to form the current climate connectivity network. The expression for the temperature difference constraint rules is as follows:

[0073] ,

[0074] In the formula, The temperature of the starting node for each path. The temperature of any node on the path connecting the starting node.

[0075] To reflect the sensitivity of species to temperature changes, this invention introduces temperature difference constraint rules in the construction of climate connectivity networks. Specifically, for each starting node temperature in a path within the climate connectivity network... Temperature of any node on its connection path Must meet The threshold Reference heat resistance studies Based on temperature and niche analysis results, covering the distribution range of over 90% of the target species, a custom Dijkstra algorithm (the Dijkstra algorithm in NetworkX, the most commonly used graph theory analysis library in Python, a core tool for extracting shortest paths and identifying key ecological corridors) was used to calculate the paths generated by all nodes that satisfy the constraint, thus enabling the screening of migration paths from warmer to colder regions.

[0076] 2.3 Obtaining the Climate Connectivity Network After the First Optimization

[0077] In one specific embodiment of the present invention, based on the current climate connectivity network, different habitat biodiversity hotspots are used as supplementary nodes to generate a second minimum cost path, and the first optimized climate connectivity network is obtained based on the first minimum cost path and the second minimum cost path.

[0078] 2.4 Obtaining the second optimized climate connectivity network

[0079] In one specific embodiment of the present invention, the climate connectivity network after the first optimization is filtered and optimized based on path weight values ​​to obtain a second optimized climate connectivity network. Specifically, this includes: extracting paths with path weight values ​​greater than or equal to a specific value from the first optimized climate connectivity network as effective key ecological corridors; and obtaining the second optimized climate connectivity network through these effective key ecological corridors. The formula for calculating the path weight value is as follows:

[0080] ,

[0081] In the formula, and For any minimum cost path in the climate connectivity network after the first optimization, these are the first and last nodes. For nodes and nodes Path weight values ​​between and They are nodes and nodes area, For nodes and nodes The cost value of the minimum cost path between them. and They are nodes Inner and Node The average resistance value within, The maximum cost value of all minimum-cost paths between nodes in the current climate connectivity network. This is the temperature difference adjustment factor.

[0082] In one specific embodiment of the present invention, paths with a path weight value greater than or equal to 5 between any nodes are selected as effective key ecological corridors, and the climate connectivity network after the second optimization is obtained through the effective key ecological corridors.

[0083] Temperature difference adjustment factor The calculation formula is as follows:

[0084] ,

[0085] In the formula, node Minimum temperature inside, For nodes The lowest temperature inside.

[0086] III. Assessment

[0087] In one specific embodiment of the present invention, the current climate connectivity network obtained in the above steps and the final optimized climate connectivity network are evaluated based on two indicators: Climate Connectivity Composition Criteria (ECC) and Climate Connectivity Probability (PCC). A schematic diagram of the optimization and evaluation process is shown below. Figure 2 As shown. Figure 2 The directed network in this context refers to the current climate connectivity network. The obtained current climate connectivity network also needs to undergo a path weight value filtering step. Similarly, the optimized climate connectivity network... It adopted the corresponding current climate connectivity network. This ensures consistency in dimensions, allowing for direct comparison of network evaluation results before and after optimization.

[0088] Climate Connectivity (ECC) represents the temperature difference between the current temperature of the starting node and the future temperature of the target node (the protected area is the starting node; as long as a path that meets the conditions (diffusion distance threshold and temperature constraint) exists, other nodes that meet the temperature constraint rules can be the terminating node, i.e., the target node). The calculation formula is as follows:

[0089] ,

[0090] In the formula, Starting node The current temperature, For the target node The future temperature.

[0091] ECC ≥ 0 indicates a successful migration to cold, while ECC < 0 indicates a connection failure.

[0092] Climate connectivity probability (PCC) represents the probability of a successful migration from the current node to the target node, and is calculated using a negative exponential function.

[0093] ,

[0094] In the formula, and Let be the starting and ending nodes of any minimum cost path. For nodes and The distance between them, d max This represents the maximum diffusion distance threshold for the corresponding class. PCC ≥ 0.5 indicates a high probability of successful migration, while PCC = 1 indicates direct structural connectivity.

[0095] Figures 3-8 These are schematic diagrams comparing the climate connectivity (ECC) and climate connectivity probability (PCC) assessments of the network before and after optimization in different habitats in this embodiment. Figures 2-7The optimized network in this embodiment refers to the climate connectivity network after the second optimization, while the baseline network refers to the current climate connectivity network after filtering in this embodiment.

[0096] like Figure 3 As shown, in habitats such as forest climates, with diffusion distance thresholds of 110km and 20km, the ECC value of the optimized network is significantly higher than that of the baseline network. Figure 4 In the case of diffusion distance thresholds of 110km, 70km and 20km, the PCC value of the optimized network is significantly higher than that of the baseline network.

[0097] like Figure 5 As shown, in habitats such as grassland climates, with diffusion distance thresholds of 110km, 70km, and 20km, the ECC value of the optimized network is slightly higher than that of the baseline network. Meanwhile... Figure 6 In the study, the PCC value of the optimized network was significantly higher than that of the baseline network.

[0098] like Figure 7 As shown, in wetland climates, with diffusion distance thresholds of 110km, 70km, and 20km, the ECC values ​​of the optimized network are basically the same as those of the baseline network. However, due to... Figure 8 It can be seen that the PCC value of the optimized network is significantly higher than that of the baseline network.

[0099] The comparison of the above indicators shows that the optimized network has better climate connectivity. In practical applications, using the optimized ecological network in a specific ecological region increases the probability of species successfully migrating along temperature gradients. Furthermore, in forest and wetland habitats, the optimized network is particularly effective for species with short dispersal distances.

[0100] Example 3 This embodiment describes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the temperature-constrained climate connectivity network optimization method as described in Embodiment 1 or 2.

[0101] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0102] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0103] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0104] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0105] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A temperature constraint based climate connectivity network optimization method, characterized in that, The method comprises the following steps: constructing a species distribution model based on environmental factors, habitat area and species occurrence records in a study area; identifying different habitat biodiversity hotspots based on the species distribution model; constructing a comprehensive resistance surface based on annual mean temperature data and human footprint index data; taking existing protected areas in the study area as initial nodes, combining the comprehensive resistance surface and a pre-obtained diffusion distance threshold to generate a first minimum cost path, and constructing a basic ecological network through the first minimum cost path; performing a first temperature difference constraint screening by introducing a temperature difference constraint rule to the basic ecological network, calculating paths generated by all nodes satisfying the temperature difference constraint rule to form a current climate connectivity network; based on the current climate connectivity network, taking the different habitat biodiversity hotspots as supplementary nodes to generate a second minimum cost path, and obtaining a first optimized climate connectivity network based on the first minimum cost path and the second minimum cost path; performing screening optimization on the first optimized climate connectivity network based on path weight values to obtain a second optimized climate connectivity network; based on the second optimized climate connectivity network, performing a second temperature difference constraint screening to calculate paths generated by all nodes satisfying the temperature difference constraint rule to obtain a final optimized climate connectivity network.

2. The temperature constraint based climate connectivity network optimization method of claim 1, wherein, The existing protected areas in the study area are divided into three habitat types: forest, wetland and grassland.

3. The temperature constraint based climate connectivity network optimization method of claim 1, wherein, The identification of different habitat biodiversity hotspots based on the species distribution model comprises the following steps: obtaining a species future distribution map based on the species distribution model, obtaining a species weighted richness map according to the species future distribution map combined with endangered grades, and identifying different habitat biodiversity hotspots according to the species weighted richness map combined with land use data.

4. The temperature constraint based climate connectivity network optimization method of claim 1, wherein, The construction of the comprehensive resistance surface based on the annual mean temperature data and the human footprint index data comprises the following steps: constructing a temperature difference resistance surface based on the annual mean temperature data, constructing a human disturbance resistance surface based on the human footprint index data, and generating the comprehensive resistance surface by equal-weight superposition of the temperature difference resistance surface and the human disturbance resistance surface.

5. The temperature constraint based climate connectivity network optimization method of claim 4, wherein, The temperature difference resistance surface is constructed by logarithmic compression processing, reverse standardization and normalization of temperature difference resistance values of the temperature difference resistance surface; wherein, the calculation formula of the temperature difference resistance values is as follows: ， wherein T is the temperature of the center grid, i is the serial number of the grid adjacent to the center grid, and the value range is 1- , N is the total number of grids adjacent to the center grid, T is the temperature of the center grid, the center grid.

6. The temperature constraint based climate connectivity network optimization method of claim 1, wherein, The expression of the temperature difference constraint rule is as follows: ， wherein is the initial node temperature for each path, is the temperature of any node on the initial node connection path.

7. The temperature constraint based climate connectivity network optimization method of claim 1, wherein, The calculation formula of the path weight value is as follows: ， wherein, and are the first and last nodes of any path in the first optimized climate connectivity network, is the path weight value between nodes and nodes , and are the areas of nodes and nodes , is the cost value of the minimum cost path between nodes and nodes , and are the average resistance values within nodes and nodes , is the maximum cost value of all minimum cost paths between nodes in the current climate connectivity network, is the temperature difference adjustment factor.

8. The temperature constraint based climate connectivity network optimization method of claim 7, wherein, The temperature difference adjustment factor The calculation formula is as follows: ， wherein is the minimum temperature in the node is the minimum temperature in the node is the minimum temperature in the node is the minimum temperature in the node 9. The temperature constraint based climate connectivity network optimization method of claim 1, wherein, The screening optimization of the first optimized climate connectivity network based on the path weight value to obtain the second optimized climate connectivity network comprises the following steps: extracting paths with path weight values greater than or equal to a specific value from the first optimized climate connectivity network as effective key ecological corridors, and obtaining the second optimized climate connectivity network through the effective key ecological corridors.

10. A computer readable storage medium having stored thereon computer programs / instructions, characterized in that, The computer program / instructions are executed by the processor to realize the steps of the temperature constraint-based climate connectivity network optimization method of any one of claims 1-9.

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