Fish potential spawning site identification and positioning method based on habitat similarity and spatial autocorrelation

Abstract

The invention discloses a fish potential spawning site identification and positioning method based on habitat similarity and spatial autocorrelation, and belongs to the technical field of river ecological protection and habitat evaluation. The method comprises the following steps: step 1, obtaining state values of hydrodynamic force, water quality and substrate indexes of a typical target fish spawning site and target fish distribution information; 2, determining egg laying habitat requirements of target fishes; 3, performing comprehensive habitat suitability evaluation on the typical target fish spawning site; 4, constructing a target fish potential spawning site identification model combining habitat similarity and spatial autocorrelation; and 5, evaluating and verifying the feasibility and accuracy of the method. According to the method, habitat similarity and spatial autocorrelation are combined, a potential spawning site of target fishes in a drainage basin is identified and positioned, and feasibility and accuracy of the method are verified by combining habitat suitability evaluation and spatial position coincidence.

Description

Technical Field

[0001] The invention relates to a method for identifying and locating potential fish spawning grounds based on habitat similarity and spatial autocorrelation, and belongs to the technical field of river ecological protection and habitat assessment. Background Art

[0002] Fish spawning grounds, the waters where fish mate, lay eggs, hatch, and raise their young, are crucial and sensitive habitats. With the development and construction of mainstream hydropower projects, natural river flow patterns have been altered, leading to fragmented fish habitats. Despite extensive habitat restoration efforts, it remains difficult to compensate for the damage caused by hydropower projects. Therefore, protecting fish habitats with naturally occurring properties has become a trend in river conservation. Plateau schizothorax fish inhabit high-altitude, alpine canyon reaches and lay sticky, sinking eggs. Low temperatures and low nutritional status result in low growth rates, making population recovery even more difficult once their spawning grounds are destroyed. Therefore, accurately identifying the natural spawning grounds of plateau schizothorax fish and implementing in situ conservation efforts are crucial strategies for maintaining the stability of plateau schizothorax fish resources. Fish spawning grounds are typically determined by the spatial extent of surveys of eggs, juveniles, or mature fish. However, this method relies on manual on-site monitoring and sampling, which is inefficient and time-consuming, making it unsuitable for identifying and locating spawning grounds at large scales, such as watersheds. Therefore, finding more efficient methods for locating spawning grounds is an urgent challenge.

[0003] Numerous studies have shown that fish preferences for exogenous environmental cues play a crucial role in selecting suitable spawning grounds. Therefore, based on the principle of habitat similarity—that is, habitats that are more similar to fish spawning grounds are more likely to serve as spawning grounds—assessing the similarity of water environmental factors to identify potential fish spawning grounds may be a more efficient and comprehensive approach, particularly in plateau, alpine, and canyon regions where information on fish distribution is scarce. Furthermore, the advent of geographic information systems (GIS) technology and the increasing use of low-cost, easily accessible satellite data have enabled the acquisition of highly accurate spatial distribution data on similar traits within a region, facilitating the precise spatial identification of potential fish spawning grounds.

[0004] However, existing spatial prediction methods suffer from insufficient utilization of available information. Specifically, spatial prediction methods based on spatial autocorrelation only utilize the spatial autocorrelation of sample points and lack consideration of environmental similarity information. Spatial prediction based on environmental variables simply establishes a linear relationship between the environmental variable and the target variable, ignoring the spatial autocorrelation of the target variable. To address this issue, the present invention proposes a method for identifying and locating potential fish spawning sites based on habitat similarity and spatial autocorrelation. This method not only considers the spatial autocorrelation of the target variable but also fully leverages the control effect of environmental factors on the target variable, thereby enabling more realistic and accurate predictions of the spatial distribution of the target variable. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a method for identifying and locating potential spawning grounds of fish based on habitat similarity and spatial autocorrelation.

[0006] The present invention is achieved through the following technical solutions:

[0007] A method for identifying and locating potential spawning grounds of fish based on habitat similarity and spatial autocorrelation comprises the following steps:

[0008] Step 1: Select a typical target fish spawning ground and obtain the status values of the hydrodynamics, water quality and bottom quality indicators of the spawning ground as well as the distribution information of the target fish;

[0009] Step 2: Based on the habitat characteristics of typical target fish spawning grounds, clarify the spawning habitat requirements of target fish;

[0010] Step 3: Conduct comprehensive habitat suitability assessment on typical target fish spawning grounds;

[0011] Step 4: Construct a potential spawning ground identification model for target fish species that combines habitat similarity and spatial autocorrelation;

[0012] Step 5: Based on the habitat quality and spatial location overlap of the target fish potential spawning grounds output by the model, evaluate and verify the feasibility and accuracy of the method for identifying and locating fish potential spawning grounds.

[0013] In step 1, the state values of the hydrodynamics, water quality and bottom quality indicators of typical target fish spawning grounds and the distribution information of target fish are obtained through on-site monitoring, numerical simulation and remote sensing inversion.

[0014] In step 2, the frequency data of each habitat factor after normalization are fitted using a Gaussian function to draw the spawning suitability curve of the target fish, and then the spawning habitat requirements of the target fish are clarified.

[0015]

[0016] Where: HSI(i) is the spawning site suitability of the Gaussian function at location i; A is the normalization constant; μ is the mean of the Gaussian distribution; σ is the standard deviation.

[0017] The value range of HSI(i) is from 0 to 1, indicating that the suitability of the spawning ground ranges from the worst to the best; when 0.6<HSI(i)<0.8, the index range is determined to be the suitable range for spawning of the target fish; when HSI(i)≥0.8, the index range is determined to be the preferred range for spawning of the target fish.

[0018] In step 3, a comprehensive habitat suitability evaluation is conducted on the flow rate, water depth, water temperature, chlorophyll a and bottom quality indicators of typical target fish spawning grounds according to the spawning requirements of target fish.

[0019]

[0020] Where: CHSI is the comprehensive habitat suitability index of each grid; HSI is the i is the habitat suitability index of the i-th habitat factor; W i The weights determined by the entropy weight method.

[0021] The method for constructing the target fish potential spawning ground identification model combining habitat similarity and spatial autocorrelation in step 4 comprises the following steps:

[0022] Step 401: Use kernel density estimation to express the relationship between the similarity between environmental factors and the probability of potential spawning grounds; normalize the calculated probability density function to obtain the similarity between a single environmental factor in the potential spawning ground and the corresponding environmental factor in the typical spawning ground of the target fish; and identify whether the potential spawning ground is a suitable spawning ground for the target fish by comprehensively analyzing the similarities between different environmental factors in the potential spawning ground and the corresponding environmental factors in the typical spawning ground of the target fish.

[0023]

[0024] Where: f(x) is the probability density function of the environmental factor x and the potential spawning ground; k(·) is the kernel function; h is the bandwidth; x is the environmental factor value of the potential spawning ground; x o is the ideal value of the environmental factor x; T x represents the similarity between the environmental factor x in the potential spawning ground and the environmental factor x in the typical target fish spawning ground; T represents the comprehensive similarity between the potential spawning ground and the typical target fish spawning ground; w x is the weight;

[0025] Step 402: Using global autocorrelation and local spatial autocorrelation analysis functions in mapping software to reveal the spatial distribution of potential spawning grounds of target fish; global autocorrelation uses Moran's I index to analyze the clustering distribution pattern of potential spawning grounds, and local spatial autocorrelation is used to identify local hot spots and cold spots, thereby revealing the distribution characteristics of spawning grounds;

[0026]

[0027] Where: I is Moran's I index; n is the number of potential spawning grounds; T i 、T j are the similarities between potential spawning grounds i and j and the typical spawning grounds of target fish; W ij is the adjacency relationship between potential spawning site i and potential spawning site j; is the average value of all habitat similarities; S is the standard deviation of all habitat similarities.

[0028] In step 401, when 0.6≤T≤1, the potential spawning ground is identified as a suitable spawning ground for the target fish.

[0029] In step 402, G i Potential spawning grounds with a ZScore > 1.96 were identified as suitable spawning grounds for target fish.

[0030] The method for evaluating and verifying the feasibility and accuracy of the method for identifying and locating potential spawning grounds of fish in step 5 is as follows: first, randomly sampling from the target fish suitable spawning grounds output by the model to conduct a comprehensive habitat suitability evaluation; the hot spots of the target fish suitable spawning grounds output by the model are spatially compared with the target fish spawning grounds that have been historically determined in the study area, and the accuracy of the model output results is tested in terms of spatial overlap rate.

[0031] The beneficial effects of this method lie in its ability to identify and locate potential spawning grounds for target fish species within a watershed by combining habitat similarity and spatial autocorrelation. The feasibility and accuracy of this method are verified through comprehensive habitat suitability evaluation and spatial location overlap. This method facilitates the rapid and precise location of suitable spawning grounds for target fish species within potential spawning grounds, assisting decision-makers in developing more refined strategies for protecting fish spawning grounds. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a flow chart of the method of the present invention;

[0033] Figure 2 This is a schematic diagram of a three-dimensional modeling of a typical target fish spawning ground of the present invention;

[0034] Figure 3A comprehensive habitat suitability distribution map of typical target fish spawning grounds;

[0035] Figure 4 This is a distribution map of hot and cold spots in potential spawning grounds of representative schizothorax in a river basin on the Tibetan Plateau. DETAILED DESCRIPTION

[0036] The technical solution of the present invention is further described below, but the scope of protection claimed is not limited to the description.

[0037] like Figure 1 As shown, this embodiment takes a representative rare and protected schizothorax distributed in a river basin of the Tibetan Plateau as the target fish, and uses a specific calculation example to illustrate the method and calculation process involved in the present invention. It should be noted that the indicators involved in the method can be adjusted considering the actual situation of the river, as follows:

[0038] 1. Obtain habitat information for typical target fish spawning grounds. Through field monitoring, numerical simulation, and remote sensing inversion, obtain hydrodynamic parameters (flow velocity and water depth), water quality indicators (water temperature and chlorophyll a), bottom type, and target fish distribution data for typical target fish spawning grounds in KS. The details are as follows:

[0039] (1) Numerical simulation of hydrodynamic parameters of target fish spawning grounds

[0040] The core of numerical simulation encompasses three aspects: first, selecting the appropriate governing hydrodynamic equations; second, constructing a detailed digital model and performing meshing; and finally, setting appropriate boundary conditions. These steps together form the complete framework of the numerical simulation presented in this paper.

[0041] First, the governing equations involved in this invention are mainly composed of the continuity equation and the momentum equation. The RNG k-ε turbulence model is used to solve the governing equations, and the VOF method is used to achieve accurate tracking of the free liquid surface. The governing equations of the three-dimensional hydrodynamic model used are:

[0042]

[0043] Where: u i is the average speed (m / s); u′ i is the pulsation velocity (m / s); P is the time-averaged pressure (pa); k is the turbulent energy (m 2 / s 2 ); ε is the turbulent energy dissipation rate; ρ is the water density (kg / m 3 );υ is the molecular viscosity coefficient;υ t is the eddy viscosity coefficient; C u =0.0845; C1 = 1.42; C2 = 1.68; α k=αε=1.39; β=0.012; η is the ratio of the turbulent time scale to the mean flow time scale, η0 is the typical value of η in uniform shear flow, which is 4.38, and F is the water-gas volume ratio function.

[0044] Secondly, a multi-beam echo sounder (R2Snoic 2022) was used to measure the underwater topography of the typical target fish spawning grounds in KS to obtain underwater topographic data. The three-dimensional model of the typical target fish spawning grounds in KS is as follows: Figure 2 shown.

[0045] Finally, based on multi-year hydrological data from a typical target fish spawning ground in KS, four scenarios were set under the multi-year average monthly flow conditions from April to July. For each specific simulation scenario, flow boundaries were used at the computational inlets (bd34 and bd32), and the inflow water level was input. Atmospheric pressure was used as the water surface pressure at the outlet (bd30), and the water surface elevation was specified. A no-slip setting was used for the walls, and the Manning roughness coefficient was set to 0.05. The following table shows the calculation combinations for the hydrodynamic conditions at the typical target fish spawning ground in KS.

[0046] Table 1 Calculation combination of hydrodynamic conditions of typical KS spawning grounds

[0047]

[0048]

[0049] (2) Satellite remote sensing inversion of water temperature and chlorophyll a data

[0050] ① The water temperature data source comes from Landsat Collection 2 Level-2, which provides multispectral band surface reflectance and thermal infrared band surface temperature products. The Collection 2 Level-2 pixel value is converted into surface temperature using formula (6).

[0051] ST=DN×0.00341802+149-273 (6)

[0052] Where: ST is the surface temperature (degrees Celsius); DN is the brightness value in the original thermal infrared data.

[0053] ② Select Sentinel-2 series satellites as the chlorophyll a data source and use the MERIS two-band algorithm to invert Chla concentration. The formula is as follows:

[0054]

[0055] Where: R rs (709) and R rs(665) represent the remote sensing reflectance values of the water surface at wavelengths of 709 nm and 665 nm respectively.

[0056] (3) On-site measurement of riverbed sediments in KS typical target fish spawning sites

[0057] Five measurement sections were selected in typical target fish spawning grounds. The size and quantity of the bottom sediment were counted and analyzed based on the photographs with rulers taken at each monitoring point. The results were classified according to particle size. The results are shown in Table 2.

[0058] Table 2 Results of bottom sediment particle size distribution in typical KS spawning grounds

[0059]

[0060] 2. Spawning habitat requirements of a representative schizothorax species

[0061] Frequency distributions of environmental factors in the spawning grounds of typical KS target fish species were calculated and normalized. A Gaussian function was used to fit the normalized frequency data of each environmental factor to plot habitat suitability curves. The spawning habitat suitability index (HSI) ranges from 0 to 1, representing suitability from worst to best. Based on historical surveys and literature research, the range of 0.6 < HSI < 0.8 was defined as the spawning suitability interval for target fish species, and the range of HSI > 0.8 was defined as the preferred spawning interval for target fish species.

[0062]

[0063] Where: HSI i is the spawning site suitability of the Gaussian function at location i; A is the normalization constant, usually in the probability density function The function integral is 1; μ is the mean of the Gaussian distribution; σ is the standard deviation.

[0064] 3. Comprehensive habitat suitability evaluation of typical target fish spawning grounds

[0065] In this study, a comprehensive habitat suitability index (CHSI) was developed using hydrodynamic, water quality, and bottom sediment datasets to assess the suitability of spawning sites. We integrated the CHSI with geographic analysis tools (ArcGIS 10.3) to generate a CHSI distribution map of spawning sites from April to July (e.g., Figure 3 To improve visualization, the final evaluation model was reclassified into five categories, ranging from 0 (unsuitable habitat) to 1 (optimal habitat), with intervals of 0–0.2, 0.2–0.4, 0.4–0.6, 0.6–0.8, and 0.8–1.0, respectively.

[0066]

[0067] Where CHSI is the comprehensive habitat suitability index of each grid; HSI is the i is the habitat suitability index of the i-th habitat factor (such as flow velocity, water depth, water temperature, Chla and bottom type); W i The weights determined by the entropy weight method.

[0068] (1) Normalization of evaluation indicators

[0069]

[0070] Where: p(x ij ) are the standardized values of each indicator, i = 1, 2, 3…m, j = 1, 2,…n.

[0071] (2) Calculation of the entropy value of the jth indicator

[0072]

[0073] Where: E j is the information entropy, k>0, E j >0;p(x ij ) are all equal for a given j, then p(x ij )=1 / m, then E j Take the maximum value, that is, E j =klnm; if k=lnm, then E j =1, so 0≤E j ≤1.

[0074] (3) Difference coefficient d of the jth indicator j Calculation

[0075] d j =1-E j (12)

[0076] Determination of the weight of the jth indicator

[0077]

[0078] The weights of key environmental factors for fish spawning in typical target fish spawning grounds of KS are shown in Table 3.

[0079] Table 3 Weights of key environmental factors for fish spawning

[0080]

[0081] 4. Construction of a potential spawning site identification model for target fish species based on a combination of habitat similarity and spatial autocorrelation

[0082] In actual monitoring, it is often impossible to obtain all the environmental indicators of concern to evaluate the comprehensive suitability of potential spawning grounds, resulting in the inability to obtain the true habitat status of potential spawning grounds. However, the similarity between several key environmental factors and the habitat of typical target fish spawning grounds can approximately reflect the true habitat status of potential spawning grounds, thereby finding suitable spawning grounds for target fish in the region based on the spawning needs of target fish. Therefore, this method selects two key environmental factors for fish spawning, water temperature and chlorophyll a, and spatially identifies suitable spawning grounds for plateau schizothorax by evaluating their similarity and spatial autocorrelation with the habitat indicators of typical target fish spawning grounds. The specific method is as follows:

[0083] ① The kernel density estimation method is used to express the relationship between the similarity between environmental factors and the probability of potential spawning grounds. The calculated probability density function is normalized to obtain the similarity between a single environmental factor in the potential spawning ground and the corresponding environmental factor in the typical spawning ground of the target fish. By comprehensively analyzing the similarities between different environmental factors in the potential spawning ground and the corresponding environmental factors in the typical spawning ground of the target fish, it is possible to identify whether the potential spawning ground is a suitable spawning ground for the target fish.

[0084]

[0085] Where: f(x) is the probability density function of the environmental factor x and the potential spawning ground; k(·) is the kernel function; h is the bandwidth; x is the environmental factor value of the potential spawning ground; x o is the ideal value of the environmental factor x (the environmental factor value corresponding to the maximum suitability of target fish for spawning); T x represents the similarity between the environmental factor x in the potential spawning ground and the environmental factor x in the typical target fish spawning ground; T represents the comprehensive similarity between the potential spawning ground and the typical target fish spawning ground; w x is the weight, which is determined by fitting the CPUE and environmental factors of the known target fish spawning grounds.

[0086] The similarity between the normalized catch per unit fishing effort (CPUE) of known target fish spawning grounds in the study area is used to characterize the comprehensive similarity with the typical target fish spawning grounds. The corresponding water temperature and chlorophyll a dataset are then introduced, and the similarity weights of water temperature and chlorophyll a are solved using formula (17). Finally, a potential spawning ground identification model for a representative schizothorax based on the similarity of water temperature and chlorophyll a is derived:

[0087] T=0.72T t +0.28T Chla (19)

[0088] T represents the comprehensive similarity between the potential spawning grounds and the typical spawning grounds of target fish; T t Indicates the similarity of water temperature between the potential spawning ground and the typical spawning ground of target fish; TChla It indicates the chlorophyll a similarity between the potential spawning ground and the typical spawning ground of target fish. Studies have shown that when the comprehensive similarity between the potential spawning ground and the typical spawning ground is between [0.6 and 1.0], the potential spawning ground is considered to be a suitable spawning ground for target fish.

[0089] ② The global autocorrelation and local spatial autocorrelation analysis in Arcgis10.3 software was used to reveal the spatial distribution of potential spawning grounds of plateau schizothoracic fish; the global autocorrelation used the Moran's I index to analyze the aggregation distribution pattern of unknown spawning grounds, and the local spatial autocorrelation was used to identify local aggregation hot spots and cold spots, revealing the distribution characteristics of spawning grounds.

[0090]

[0091] Where, I is Moran's I index; n is the number of potential spawning grounds; T i 、T j are the similarities between potential spawning grounds i and j and the typical spawning grounds of target fish; W ij is the adjacency relationship between potential spawning site i and potential spawning site j. When potential spawning site i and potential spawning site j are adjacent, W ij =1, when not adjacent, W ij =0; is the average value of all habitat similarities; S is the standard deviation of all habitat similarities. i Potential spawning grounds with a ZScore > 1.96 were identified as suitable spawning grounds for target fish.

[0092] 5. Comprehensive suitability evaluation and testing of potential spawning grounds

[0093] In order to evaluate the quality and effectiveness of the fish spawning ground identification model, the present invention adopted two evaluation methods. First, we randomly selected two potential spawning grounds (SQ and MQ) from the hotspots for comprehensive suitability evaluation. The methods and calculation formulas for obtaining flow velocity, water depth, water temperature, chlorophyll a and bottom quality indicators in the potential spawning grounds were the same as those in step 1. The CHSI values of SQ and MQ were 0.746 and 0.793, respectively. The results showed that the two potential spawning grounds were suitable spawning grounds for a certain plateau schizothorax. Secondly, we performed a spatial position comparison based on the locations of 28 known plateau schizothorax spawning grounds determined by the historical fish resource survey in the basin, such as Figure 4 As shown, 20 known spawning sites for plateau schizothorax within the study area are located within the model's output of suitable spawning sites for the target species, with a spatial overlap rate of 71%. Therefore, the feasibility and accuracy of this method were verified in terms of the quality and spatial distribution of potential spawning sites.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for identifying and locating potential spawning grounds of fish based on habitat similarity and spatial autocorrelation, characterized by: The following steps are involved: Step 1: Select a typical target fish spawning ground and obtain the status values of the hydrodynamics, water quality and bottom quality indicators of the spawning ground as well as the distribution information of the target fish; Step 2: Based on the habitat characteristics of typical target fish spawning grounds, clarify the spawning habitat requirements of target fish; Step 3: Conduct comprehensive habitat suitability assessment on typical target fish spawning grounds; Step 4: Construct a potential spawning ground identification model for target fish species that combines habitat similarity and spatial autocorrelation; Step 5: Based on the habitat quality and spatial location overlap of the target fish potential spawning grounds output by the model, evaluate and verify the feasibility and accuracy of the method for identifying and locating fish potential spawning grounds.

2. The method for identifying and locating potential spawning grounds of fish based on habitat similarity and spatial autocorrelation according to claim 1, wherein: In step 1, the state values of the hydrodynamics, water quality and bottom quality indicators of typical target fish spawning grounds and the distribution information of target fish are obtained through on-site monitoring, numerical simulation and remote sensing inversion.

3. The method for identifying and locating potential spawning grounds of fish based on habitat similarity and spatial autocorrelation according to claim 1, wherein: In step 2, the frequency data of each habitat factor after normalization are fitted using a Gaussian function to draw the spawning suitability curve of the target fish, and then the spawning habitat requirements of the target fish are clarified. Where: HSI(i) is the spawning site suitability of the Gaussian function at location i; A is the normalization constant; μ is the mean of the Gaussian distribution; σ is the standard deviation.

4. The method for identifying and locating potential spawning grounds of fish based on habitat similarity and spatial autocorrelation according to claim 3, wherein: The value range of HSI(i) is from 0 to 1, indicating that the suitability of the spawning ground ranges from the worst to the best; when 0.6<HSI(i)<0.8, the index range is determined to be the suitable range for spawning of the target fish; when HSI(i)≥0.8, the index range is determined to be the preferred range for spawning of the target fish.

5. The method for identifying and locating potential spawning grounds of fish based on habitat similarity and spatial autocorrelation according to claim 1, wherein: In step 3, a comprehensive habitat suitability evaluation is conducted on the flow rate, water depth, water temperature, chlorophyll a and bottom quality indicators of typical target fish spawning grounds according to the spawning requirements of target fish. Where: CHSI is the comprehensive habitat suitability index of each grid; HSI is the i is the habitat suitability index of the i-th habitat factor; W i The weights determined by the entropy weight method.

6. The method for identifying and locating potential spawning grounds of fish based on habitat similarity and spatial autocorrelation according to claim 1, wherein: The method for constructing the target fish potential spawning ground identification model combining habitat similarity and spatial autocorrelation in step 4 comprises the following steps: Step 401: Use kernel density estimation to express the relationship between the similarity between environmental factors and the probability of potential spawning grounds; normalize the calculated probability density function to obtain the similarity between a single environmental factor in the potential spawning ground and the corresponding environmental factor in the typical spawning ground of the target fish; and identify whether the potential spawning ground is a suitable spawning ground for the target fish by comprehensively analyzing the similarities between different environmental factors in the potential spawning ground and the corresponding environmental factors in the typical spawning ground of the target fish. Where: f(x) is the probability density function of the environmental factor x and the potential spawning ground; k(·) is the kernel function; h is the bandwidth; x is the environmental factor value of the potential spawning ground; x o is the ideal value of the environmental factor x; T x represents the similarity between the environmental factor x in the potential spawning ground and the environmental factor x in the typical target fish spawning ground; T represents the comprehensive similarity between the potential spawning ground and the typical target fish spawning ground; w x is the weight; Step 402: Using global autocorrelation and local spatial autocorrelation analysis functions in mapping software to reveal the spatial distribution of potential spawning grounds of target fish; global autocorrelation uses Moran's I index to analyze the clustering distribution pattern of potential spawning grounds, and local spatial autocorrelation is used to identify local hot spots and cold spots, thereby revealing the distribution characteristics of spawning grounds; Where: I is Moran's I index; n is the number of potential spawning grounds; T i 、T j are the similarities between potential spawning grounds i and j and the typical spawning grounds of target fish; W ij is the adjacency relationship between potential spawning site i and potential spawning site j; is the average value of all habitat similarities; S is the standard deviation of all habitat similarities.

7. The method for identifying and locating potential spawning grounds of fish based on habitat similarity and spatial autocorrelation according to claim 6, wherein: In step 401, when 0.6≤T≤1, the potential spawning ground is identified as a suitable spawning ground for the target fish.

8. The method for identifying and locating potential spawning grounds of fish based on habitat similarity and spatial autocorrelation according to claim 6, wherein: In step 402, G i Potential spawning grounds with a ZScore > 1.96 were identified as suitable spawning grounds for target fish.

9. The method for identifying and locating potential spawning grounds of fish based on habitat similarity and spatial autocorrelation according to claim 1, wherein: The method for evaluating and verifying the feasibility and accuracy of the method for identifying and locating potential spawning grounds of fish in step 5 is as follows: first, randomly sampling from the target fish suitable spawning grounds output by the model to conduct a comprehensive habitat suitability evaluation; the hot spots of the target fish suitable spawning grounds output by the model are spatially compared with the target fish spawning grounds that have been historically determined in the study area, and the accuracy of the model output results is tested in terms of spatial overlap rate.

Images