Fish habitat connectivity assessment method based on swimming ability and river environment

Abstract

The invention relates to the technical field of water conservancy ecological engineering and river ecological management, and discloses a fish habitat connectivity assessment method based on swimming ability and river environment, which comprises the following steps: calculating and storing the directivity connectivity probability between adjacent grid units in a grid topology network, constructing a connectivity probability model, and calculating the connectivity of the adjacent grid units in the grid topology network; and calculating the connectivity probability of each grid unit and other grid units in the habitat, and carrying out weighted average on the connectivity of all the grid units to obtain the overall habitat connectivity level of the target fish. According to the method, on the basis of a grid unit, a habitat river network is finely divided, the connectivity degree of fishes at any position of a habitat can be described on a finer spatial scale, the spatial resolution of connectivity analysis is improved, meanwhile, the directivity of fish movement is fully considered, forward flow and reverse flow migration modes are distinguished, and the accuracy of fish connectivity analysis is improved. When the connectivity probability between the grid units is calculated, quantification is carried out in combination with the specific river flow velocity and the fish moving direction, and a more accurate connectivity result is obtained.

Description

Technical Field

[0001] This invention relates to the fields of water conservancy ecological engineering and river ecological management technology, specifically a method for assessing fish habitat connectivity based on swimming ability and river environment. Background Technology

[0002] Fish habitat connectivity is a crucial indicator of a fish's ability to perform key life activities such as migration, foraging, and reproduction within river ecosystems. With the continuous increase in the number of man-made river obstructions, river connectivity is being affected, fish migration routes are being blocked, and the problem of fish habitat fragmentation is becoming increasingly serious. Therefore, conducting detailed assessments of habitat connectivity is of great significance for quantifying the ecological impact of water conservancy projects and developing strategies for fish habitat restoration.

[0003] Existing research mainly focuses on the physical structural characteristics of rivers, typically constructing connectivity indices based on the river network discontinuity-connectivity pattern, directly equating "river connectivity" with "fish habitat connectivity." While this method can reveal the dissection effect of obstacles on habitat river networks at a macroscopic level, it has limitations: (1) Insufficient spatial accuracy: The assessment unit is mostly watershed or river section, which makes it difficult to depict the connectivity between specific locations within the habitat; (2) Ignoring the directionality of fish movement: Existing assessments often use fish migration distance as a single indicator, without distinguishing between downstream and upstream migration patterns, and underestimate the impact of water flow direction on fish migration success rate and habitat availability; (3) Ignoring the interaction between fish physiological characteristics and hydrodynamic environment: Some improved methods consider the probability of fish crossing obstacles or their dispersal ability, but most methods fail to couple fish physiological characteristics such as swimming ability with river hydrodynamic conditions. In fact, different species may exhibit significantly different migration abilities under the same hydrodynamic environment. If this interaction mechanism is ignored, the connectivity evaluation results will be difficult to truly reflect the real state of fish in complex river networks.

[0004] In summary, existing habitat connectivity assessment methods still have certain limitations in terms of spatial resolution, simulation of fish migration directions, and coupling of physiological and hydrodynamic factors. Especially in complex river network environments, these methods often struggle to simultaneously consider the combined effects of changes in water flow velocity, individual fish swimming abilities, and obstacles, potentially leading to discrepancies between connectivity assessment results and actual ecological processes. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for assessing fish habitat connectivity based on swimming ability and river environment. This method has advantages such as spatial refinement, direction perception, and coupling of fish swimming ability characteristics with aquatic environment parameters, thus solving the aforementioned technical problems.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for assessing fish habitat connectivity based on swimming ability and river environment, comprising the following steps: S1: Obtain swimming ability data of the target fish; S2: Determine the habitat distribution range of the target fish species, extract the river network data within the range and perform rasterization processing on it to construct a habitat raster file; S3: Construct an attribute table for the river network grid, where each grid cell contains the river flow velocity, the presence of obstacles in the river section and their probability of passage; S4: Construct a grid topology network, establish adjacency relationships for each grid cell, and form a complete graph structure covering the entire habitat; S5: Calculate and store the directional connectivity probability between adjacent grid cells in the grid topology network; S6: Construct a connectivity probability model, calculate the connectivity probability between each grid cell and other grid cells in the habitat, and obtain the reachability results of each grid cell; S7: Based on the reachability results in S6, the connectivity of all grid cells is weighted and averaged to obtain the overall habitat connectivity level of the target fish.

[0007] As a preferred embodiment of the present invention, the swimming ability data of S1 is specifically the continuous swimming speed.

[0008] As a preferred embodiment of the present invention, the specific steps of S3 are as follows: S31: Establish a unique index number for each grid cell and initialize fields in the attribute table, including flow velocity, obstacle presence status, and obstacle passage probability. Set the initial value of the fields to NA. S32: Obtain obstacle data and spatially overlay the obstacle data with the grid cells. If there is an obstacle in the grid cell, set the obstacle presence flag field in the attribute table to 1; otherwise, set it to 0. S33: Obtain the flow velocity data of the river section within the target habitat area. If there is a lack of measured data for the corresponding area, calculate it using the following expression: in, This indicates the flow velocity of the river segment corresponding to the current grid. The ratio between adjacent grid cells is expressed as follows: in, This represents the absolute value of the elevation difference between adjacent grid cells. This represents the horizontal distance between adjacent grid cells, and calculates the flow velocity of the river segment corresponding to the current grid cell. Write to the raster cell attribute field.

[0009] As a preferred technical solution of the present invention, step S5 specifically includes the following steps: S51: Obtain the diffusion ability of the target fish, specifically: predict the farthest distance the target fish can diffuse based on the fishmove model. The diffusion ability of target fish species; S52: Based on the maximum dispersible distance of fish in S51, and the total length of the river network of the habitat already acquired and the total number of river network units after gridding The number of migrateable grid cells is calculated without considering flow velocity and obstacles, and the specific expression is as follows: in, This represents the number of transferable units at the raster scale. S53: Calculate and store the directional connectivity probability between adjacent grid cells. The specific expression is as follows: in, Represents the natural constant. This indicates that the swimming ability data of the target fish is obtained in S1. This indicates the flow velocity of the river segment corresponding to the current grid. If the elevation of the arriving grid is lower than that of the starting grid, it is considered as moving downstream. The above formula is taken as... Otherwise, it is considered as moving against the current, and [the following is taken]: , The adjustment factor is expressed as follows: in, Represents the logarithmic function with the natural constant as the base; S54: Calculate the connectivity probability of adjacent grid cells considering the influence of obstacles. The specific expression is as follows: in, This represents the connectivity probability of adjacent grid cells considering the influence of obstacles. The probability factor for passing an obstacle is expressed as follows: in, Indicates cumulative multiplication. The number of obstacles in the river section; Indicates the first The probability of passing through an obstacle.

[0010] As a preferred embodiment of the present invention, step S6 includes the following steps: S61: Starting from any grid cell, use the breadth-first search algorithm to traverse all possible paths between it and other grid cells in the habitat. S62: Multiply the connectivity probabilities of all adjacent grid cells on the path from the current grid cell to other grid cells, including the connectivity probabilities of adjacent grid cells considering the influence of obstacles. directional connectivity probability between adjacent grid cells in the natural state When considering the connectivity probability of adjacent grid cells affected by obstacles When the probability is not less than 0.05, the path is determined to be connectable considering obstacles, and the connectivity probability is recorded. This includes the directional connectivity probability between adjacent grid cells under natural conditions. If the probability is not less than 0.05, the path is determined to be connected under natural conditions and the connection probability is recorded; otherwise, the path is considered to have lost its effective connectivity and the search on the path is terminated. S63: Using the connectivity probability from the current grid to other grids as statistical data in S62 as input data, a heatmap of the reachable area of ​​the current grid under natural conditions and under obstacle consideration can be generated. S64: Calculate the connectivity of the current raster cell as follows: in, Indicates the connectivity of the grid within the habitat. This represents the number of grid cells that can be reached, taking obstacles into account. Indicates the number of grid cells that are reachable under natural conditions; S65: Repeat the above calculation steps for all grid cells within the habitat area to obtain the connectivity results for each grid cell.

[0011] As a preferred embodiment of the present invention, step S7 includes the following steps: S71: Using the connectivity results of each grid cell obtained in S6 as input data, generate a heat map of the overall connectivity of the habitat; S72: Sum and average all grid cell connectivity results to obtain the overall connectivity level of the target fish across its entire habitat. The specific expression is as follows: in, Indicates the total number of grid cells within the habitat; Indicates the first The overall connectivity level of each grid.

[0012] Compared with existing technologies, this invention provides a method for assessing fish habitat connectivity based on swimming ability and river environment, which has the following advantages: 1. This invention, by using grid cells as a basis, finely divides the spatial structure within a habitat, enabling the characterization of fish connectivity at any location on a microscale. This significantly improves the spatial resolution of habitat connectivity analysis. Simultaneously, it fully considers the directionality of fish movement, distinguishing between downstream and upstream migration patterns. When calculating the connectivity probability between grid cells, it quantifies the probability by combining specific flow velocity and direction of movement, obtaining more accurate connectivity results. Compared to existing methods that ignore differences in flow direction, this invention can more realistically reflect the migration process of fish under complex hydrodynamic conditions, improving the accuracy and applicability of the evaluation.

[0013] 2. This invention incorporates fish swimming ability as a core parameter into the model, taking into account fish dispersal ability and obstacle crossing ability. By integrating the fish's own movement characteristics with the hydrodynamic conditions of the river section, it achieves accurate quantification of habitat connectivity. This provides a more comprehensive and realistic reflection of fish migration ability in actual river environments, offering a scientific basis for habitat protection, restoration, and aquatic ecological management. Attached Figure Description

[0014] Figure 1 This is a schematic diagram showing the specific measurement locations of each parameter when using the R language package 'fishmove' in this invention; Figure 2 This is a schematic diagram of the overall connectivity results of the gridded habitat in this invention; Figure 3 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Please see Figure 1 - Figure 3 A method for assessing fish habitat connectivity based on swimming ability and river environment includes the following steps: S1: Obtain swimming ability data of the target fish; After identifying the target fish species, set corresponding search keywords: TS=((“collimationcapacity” OR "overcome flow" OR “swimming speed” OR “swimming ability” OR “ascending sustainability” OR “first attempt” OR “swimming time” OR “velocity*” OR “flow speed” OR “induction speed” OR “tail beat”) AND(“fish*”) AND (“target species Latin name”)). Search for swimming ability data related to the target species in literature databases (such as Web of Science, CNKI, etc.). In the search results, prioritize extracting indicators of sustained swimming ability. If such data is unavailable, use other relevant indicators provided in the literature (such as critical swimming speed) as substitutes. If swimming ability data for the target species cannot be obtained from the literature, use relevant data from species of the same genus as substitutes, and record the obtained data as... ; S2: Determine the habitat distribution range of the target fish species, extract the river network data within the range and perform rasterization processing on it to construct a habitat raster file; S21. Integrate the distribution data of the target fish species, including the research team's long-term field survey records, as well as literature, monographs, online databases (such as FishBase, GBIF, IUCN Red List) and unpublished survey reports retrieved according to the PRISMA-EcoEvo guide system; Based on the HydroBASINS framework, combined with buffer analysis and sub-basin trimming methods, determine the distribution range of the target fish species' habitats. S22. Based on the HydroSHEDS global river network database, extract the river segments that intersect with the distribution range of the target habitat; then, create a raster vector file according to the preset accuracy. This file consists of several raster units and can completely cover the target river network. S23. Spatially overlay the raster vector file generated in S22 with the extracted river segment, filter and retain the raster cells that intersect with the river segment, and thus obtain the habitat raster file for subsequent modeling. S3: Construct a river network raster file attribute table to record the flow velocity, obstacle presence, and passage probability of each raster; S31. Establish a unique index number for each grid cell and initialize fields in the attribute table, including flow velocity, obstacle presence status and obstacle passage probability. Set the initial value of the fields to NA. S32. Integrate global obstacle databases (such as Global Dam Watch, Global Dam Tracker Database, Global River Obstruction Database, etc.) to extract obstacle data that overlaps with habitat ranges; spatially overlay the obstacle data with grid cells to determine whether obstacles exist within each grid cell; if an obstacle exists within a grid cell, set the obstacle presence identifier field 'BARRIER_EXISTENCE' to 1 in the attribute table, otherwise set it to 0; simultaneously, assign a value to the field 'BARRIER_PASSABILITY' in the attribute table according to the obstacle type (such as large hydropower stations, sluice gates, weirs, etc.), with a value range of 0 to 1, to quantify the probability of target fish passing through the obstacle. For example, the passability probabilities of large hydropower stations, sluice gates, and weirs (from high to low obstruction to fish) are set to 0.3, 0.5, and 0.8, respectively. These values ​​correspond to the corresponding values. S33. Prioritize collecting flow velocity data for river sections within the target habitat area, sourced from hydrological monitoring stations, meteorological and hydrological databases, or relevant research data, and record the flow velocity in the flow velocity field of the corresponding raster cell; for areas lacking measured data, use HydroSHEDS data to extract raster elevation (DEM) values ​​and horizontal distances between adjacent rasters, and calculate the gradient between adjacent rasters using the following formula: in, This represents the absolute value of the elevation difference between adjacent grid cells. Indicates the horizontal distance between adjacent grid cells; Using the gradient data, the Manning formula can be applied: Calculate the flow velocity of the river section; where, This represents the flow velocity of the river segment corresponding to the current grid. This is the unit conversion factor (1.0 corresponds to the International System of Units). This is the roughness coefficient of the river channel; The hydraulic radius; For the gradient; considering that this scheme uses a grid as the analysis unit, the scale is small, and the changes in hydraulic radius and roughness in local river sections are limited, having little impact on relative velocity; to simplify the calculation and maintain spatial consistency, only the slope (i.e., gradient) factor is considered in the velocity estimation, which is simplified as follows: The calculated flow velocity value is written into the raster cell attribute field 'FLOW_VELOCITY' as an input parameter for subsequent habitat connectivity assessment; S4: Construct a grid topology network, establish adjacency relationships for each grid cell, and form a complete graph structure covering the entire habitat; Based on the rasterized habitat file, each raster cell is regarded as a node in the graph. This scheme adopts the 4-adjacency relationship of the raster. If two raster cells are spatially adjacent (share an edge), an edge is established between the corresponding nodes. This constructs a complete graph structure covering the entire habitat, and records the connection between each raster cell in the form of an adjacency table or adjacency matrix, providing a topological basis for subsequent connectivity probability calculation. S5: Calculate and store the directional connectivity probability between adjacent grid cells in the grid topology network; The directional connectivity probability between adjacent grid cells is calculated and stored based on the following mathematical formula: in, The swimming ability of fish, measured in units of... The swimming ability data collected in step S1 is used; The current flow velocity is the velocity of the river segment corresponding to the current grid, in units of... The flow rate data collected in step S33 is used; As a regulating factor and The calculation method is shown in step S52; symbol or This indicates whether the migration is downstream or upstream, determined by the elevation difference between adjacent grid cells: if the elevation of the arriving grid cell is lower than that of the originating grid cell, it is considered downstream movement, and the value is taken as... Otherwise, it is considered as moving against the current, and [the following is taken]: ; S51. Obtain the dispersal ability of the target fish; retrieve information from the FISHBASE database such as... Figure 1 The fish ecological parameters shown include the total length, caudal fin area, and caudal fin height of the target species. The dispersal ability of the target species is obtained using the R language package 'fishmove', as shown in the following example: fishmove(L=300, AR=2.25, SO=6, T=365) After executing this command, the predicted movement distances of the static and moving fish components will be obtained, including the maximum, minimum, and mean values. The maximum value of the moving component prediction will be recorded as the farthest distance the fish can reach. This data serves as the dispersal ability data for the target fish species. It is important to note that the "swimming ability" and "dispersal ability" mentioned in this scheme are not the same concept: the former emphasizes the active movement limit of an individual fish under specific hydrodynamic conditions, reflecting the physiological boundary of the fish overcoming water flow resistance and achieving downstream or upstream migration; while the latter focuses on the passive dispersal or random distribution trend of fish groups in space, mainly reflecting the spatial expansion characteristics at the group level; the two have significant differences in connotation and application and are not directly equivalent. S52, Confirm The specific numerical value; first, the farthest moving distance of the target fish obtained in step S51. Maximum number of mobile units converted to grid scale Specifically, first calculate the total length of the river network in the habitat. and the total number of river network units after gridding Therefore, the river network length corresponding to each grid cell is approximately Based on this, dividing the farthest movement distance of the fish by the cell length yields the number of mobile cells at the grid scale. This value characterizes the maximum migration range of fish at the grid scale; in the fishmove model, the probability of fish reaching the farthest distance is 0.05; to ensure scale consistency, this scheme maps this assumption to the grid scale, assuming that fish cross... The cumulative connectivity probability of each grid cell is also 0.05; based on this, it can be inferred that the natural connectivity probability of a single adjacent grid cell pair is... ; Further combining with equation (1), under the condition of not considering the influence of water flow (i.e. The regulation factor can be obtained by solving for: S53. Calculate the connectivity probability between adjacent grid cells under natural conditions; without considering the influence of obstacles, calculate the swimming ability of the target fish. With the speed of water flow in the river section Substitute into equation (1) and calculate the connectivity probability between all adjacent grid cells within the habitat area one by one; based on the grid adjacency table constructed in step S4, calculate the connectivity probability under natural conditions for each pair of adjacent grid cells. The results are recorded in the form of an adjacency list, with the following table structure: ,in and These are the numbers of the departure and arrival grids, respectively. The corresponding connectivity probability; S54. Calculate the connectivity probability of adjacent grid cells considering the influence of obstacles; based on the natural state connectivity probability adjacency list, introduce an obstacle passage probability factor. This factor is used to quantify the degree to which an obstacle hinders fish from passing through; its value ranges from 0 to 1. This indicates that there are no obstacles or that the obstacles do not affect the passage of fish; when The presence of an obstacle indicates that it hinders the passage of fish. If there are multiple obstacles between adjacent grid cells, assuming that the effects of each obstacle are independent, the overall probability of passage can be expressed as: In the formula, The number of obstacles in the river section; Indicates the first The probability of passing through an obstacle; Based on this, the connectivity probability of adjacent grid cells after an obstacle is considered. It can be represented as: Through the above calculations, another adjacency list will be obtained, with the following structure: Once the table is systematically stored, it can be directly used for further analysis and modeling of overall habitat connectivity. It should be noted that in the calculation of S53 and S54, the direction of movement should be determined based on the elevation difference between the starting grid and the arriving grid, i.e., in formula (1): when the elevation of the arriving grid is lower than that of the starting grid, it is determined to be downstream migration, and the following formula is used. When the arriving grid elevation is higher than the originating grid, it is determined to be reverse migration, and the following is taken: ; S6: Construct a connectivity probability model, calculate the connectivity probability between each grid cell and other grid cells in the habitat, and obtain the reachability results of each grid cell; S61. Starting from any grid cell, use the breadth-first search (BFS) algorithm to traverse all possible paths between it and other grid cells in the habitat. During the traversal, expansion is only allowed along adjacent grid cells in the established adjacency table to ensure the spatial continuity of the path. S62. Multiply the connectivity probabilities of all adjacent grid cells on the path from the current grid cell to other grid cells, including the connectivity probabilities of adjacent grid cells considering the influence of obstacles. directional connectivity probability between adjacent grid cells in the natural state When considering the connectivity probability of adjacent grid cells affected by obstacles When the probability is not less than 0.05, the path is determined to be connectable considering obstacles, and the connectivity probability is recorded. This includes the directional connectivity probability between adjacent grid cells under natural conditions. If the value is not less than 0.05, the path is determined to be connected under natural conditions and the connectivity probability is recorded, that is, the fish can reach the grid from the starting grid and can continue to move to more distant grid cells; otherwise, the path is considered to have lost effective connectivity and the search on the path is terminated. S63. Using the connectivity results from the current raster to other raster cells obtained in step S62 as input data, construct a heatmap of the reachable region of a single raster using the ggplot2 package in R language. In practice, each raster cell corresponds to a color block, and the color intensity represents the connectivity level of the raster. The color level is set by the 'scale_fill_gradient' function to intuitively reflect the connectivity distribution between the raster and the surrounding area. S64. According to the above rules, record the numbers of all target raster cells reachable from the current starting raster; simultaneously, record the number of raster cells reachable under natural conditions as... The number of grid cells that can be reached, considering obstacles, is denoted as . Therefore, the connectivity of the current raster is defined as: When unaffected by any obstacles, ,at this time As the number of obstacles increases, the number of grid cells becomes more achievable. It will decrease accordingly. It also decreased accordingly, respectively from and Calculated; Repeat the above calculation steps for all grid cells within the habitat area to obtain the connectivity results for each grid cell; S7: Based on the reachability results in S6, the connectivity of all grid cells is weighted and averaged to obtain the overall habitat connectivity level of the target fish. S71. The connectivity results of each grid cell obtained in step S6 are... As input data for the heatmap, data is generated using R language packages such as... Figure 2 The image shows a heatmap of overall habitat connectivity. In practice, the 'ggplot2' package can be used to construct a raster heatmap, where each raster cell corresponds to a color block, and the color intensity represents the connectivity level of that raster. Then, the color level can be set using 'scale_fill_gradient' or a similar function to visually reflect the high and low distribution of connectivity of each raster cell in the river network, thereby facilitating the analysis of habitat connectivity patterns and local fragmentation characteristics. S72, All grid cells The results are summed and averaged to obtain the overall connectivity level of the target fish species throughout its habitat. The calculation formula is as follows: in, Indicates the total number of grid cells within the habitat; Indicates the first The overall connectivity level of each grid; When there are no obstacles in the habitat, the connectivity probability between all grid cells is 1. This indicates that the habitat is in a fully connected state; with the introduction of obstacles, the connectivity probability between some grid cells decreases, leading to a decrease in overall connectivity. The decrease reflects the weakening of habitat connectivity and the intensification of fragmentation.

[0017] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for assessing fish habitat connectivity based on swimming ability and river environment, characterized by: Includes the following steps: S1: Obtain swimming ability data of the target fish; S2: Determine the habitat distribution range of the target fish species, extract the river network data within the range and perform rasterization processing to construct a habitat river network raster file; S3: Construct a habitat grid attribute table to record the flow velocity, obstacle presence, and passage probability of each grid cell; S4: Construct a grid topology network, establish adjacency relationships for each grid cell, and form a complete graph structure covering the entire habitat; S5: Calculate and store the directional connectivity probability between adjacent grid cells in the grid topology network; S6: Construct a connectivity probability model, calculate the connectivity probability between each grid cell and other grid cells in the habitat, and obtain the reachability results of each grid cell; S7: Based on the reachability results in S6, the connectivity of all grid cells is weighted and averaged to obtain the overall habitat connectivity level of the target fish.

2. The method for assessing fish habitat connectivity based on swimming ability and river environment according to claim 1, characterized in that: The S1 swimming ability data specifically refers to the continuous swimming speed of the target fish.

3. The method for assessing fish habitat connectivity based on swimming ability and river environment according to claim 1, characterized in that: The specific steps of S3 are as follows: S31: Establish a unique index number for each grid cell and initialize fields in the attribute table, including flow velocity, obstacle presence status, and obstacle passage probability. Set the initial value of the fields to NA. S32: Obtain obstacle data and spatially overlay the obstacle data with the grid cells. If there is an obstacle in the grid cell, set the obstacle presence flag field in the attribute table to 1; otherwise, set it to 0. S33: Obtain the flow velocity data of the river section within the target habitat area. If there is a lack of measured data for the corresponding area, calculate it using the following expression: in, This indicates the flow velocity of the river segment corresponding to the current grid. The ratio between adjacent grid cells is expressed as follows: in, This represents the absolute value of the elevation difference between adjacent grid cells. This represents the horizontal distance between adjacent grid cells, and indicates the flow velocity of the river segment corresponding to the current grid cell. Write to the raster cell attribute field.

4. The method for assessing fish habitat connectivity based on swimming ability and river environment according to claim 3, characterized in that: S5 specifically includes the following steps: S51: Obtain the diffusion ability of the target fish, specifically: predict the farthest distance the target fish can diffuse based on the fishmove model. As an indicator of the dispersal ability of the target fish species; S52: Based on the maximum dispersible distance of fish in S51, and the total length of the river network of the habitat already acquired and the total number of river network units after gridding The number of migrateable grid cells is calculated without considering flow velocity and obstacles, and the specific expression is as follows: in, This represents the number of transferable units at the raster scale. S53: Calculate and store the directional connectivity probability between adjacent grid cells. The specific expression is as follows: in, Represents the natural constant. This indicates that the swimming ability data of the target fish is obtained in S1. This indicates the flow velocity of the river segment corresponding to the current grid. If the elevation of the arriving grid is lower than that of the starting grid, it is considered as moving downstream. The above formula is taken as... Otherwise, it is considered as moving against the current, and [the following is taken]. , The adjustment factor is expressed as follows: in, Represents the logarithmic function with the natural constant as the base; S54: Calculate the connectivity probability of adjacent grid cells considering the influence of obstacles. The specific expression is as follows: in, This represents the connectivity probability of adjacent grid cells considering the influence of obstacles. The probability factor for passing an obstacle is expressed as follows: in, Indicates cumulative multiplication. The number of obstacles in the river section; Indicates the first The probability of passing through an obstacle.

5. The method for assessing fish habitat connectivity based on swimming ability and river environment according to claim 4, characterized in that: S6 includes the following steps: S61: Starting from any grid cell, use the breadth-first search algorithm to traverse all possible paths between it and other grid cells in the habitat. S62: Multiply the connectivity probabilities of all adjacent grid cells on the path from the current grid cell to other grid cells, including the connectivity probabilities of adjacent grid cells considering the influence of obstacles. directional connectivity probability between adjacent grid cells in the natural state When considering the connectivity probability of adjacent grid cells affected by obstacles When the probability is not less than 0.05, the path is determined to be connectable considering obstacles, and the connectivity probability is recorded. This includes the directional connectivity probability between adjacent grid cells under natural conditions. If the probability is not less than 0.05, the path is determined to be connected under natural conditions and the connection probability is recorded; otherwise, the path is considered to have lost its effective connectivity and the search on the path is terminated. S63: Using the connectivity probability from the current grid to other grids as statistical data in S62 as input data, a heatmap of the reachable area of ​​the current grid under natural conditions and under obstacle consideration can be generated. S64: Calculate the connectivity of the current raster cell as follows: in, Indicates the connectivity of the grid within the habitat. This represents the number of grid cells that can be reached, taking obstacles into account. Indicates the number of grid cells that are reachable under natural conditions; S65: Repeat the above calculation steps for all grid cells within the habitat area to obtain the connectivity results for each grid cell.

6. The method for assessing fish habitat connectivity based on swimming ability and river environment according to claim 5, characterized in that: S7 includes the following steps: S71: Using the connectivity results of each grid cell obtained in S6 as input data, generate a heat map of the overall connectivity of the habitat; S72: Sum and average all grid cell connectivity results to obtain the overall connectivity level of the target fish across its entire habitat. The specific expression is as follows: in, Indicates the total number of grid cells within the habitat; Indicates the first The overall connectivity level of each grid.

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