A seasonal quantitative assessment method for skipjack tuna habitat in temperate waters

Abstract

The present invention provides a seasonal quantitative assessment method for the habitat of skipjack tuna in temperate waters, which relates to the technical field of marine fishery resources research, including data selection and collection, data preprocessing, construction of suitability index (SI), and construction of habitat suitability index (HSI) model. Through the technical solution of the present invention, the patent of the present invention can carry out quantitative simulation of skipjack tuna habitat based on the reanalysis environmental forecast data of the ocean model, especially integrating the important vertical environmental variable in the process of skipjack tuna migration-17℃ isotherm depth, and provide a reference for operation plans that are more in line with actual fishing needs for skipjack tuna fishery production plans, such as the location and season of fishing hotspots in fishing grounds, so as to practically improve the efficiency of skipjack tuna fishery production in temperate waters.

Description

Technical Field

[0001] The present invention relates to the technical field of marine fishery resources research, and in particular to a seasonal quantitative assessment method for skipjack tuna habitats in temperate seas. Background Art

[0002] The habitat suitability index model is one of the common methods for quantifying the spatial distribution of tuna. Based on the existing studies on tuna habitat models, it is found that: ① In terms of temporal and spatial scales, most studies are based on monthly resolution and focus on high-yield tropical fishing grounds, such as the central and western Pacific Ocean; in contrast, there are few seasonal studies on temperate waters. ② In the selection of model independent variables, environmental variables that affect the distribution of tuna fishing grounds, such as sea surface temperature SST, sea surface height SSH, chlorophyll concentration, etc., are usually selected as common predictors; although fish migration is closely related to its fishing grounds, environmental variable factors based on migration characteristics have not yet been used for quantitative simulation. ③ In the process of model construction, the grouping of environmental factors is manually set by the method of group interval, such as SST is divided into groups with an interval of 0.5℃, which has a strong subjective factor. ④ In the selection and evaluation of the model, the results of the training set and the test set are often used for verification. Summary of the invention

[0003] In order to make up for the deficiencies of the prior art, the present invention provides a seasonal quantitative assessment method for the habitat of skipjack tuna in temperate waters. The patent of the present invention is aimed at skipjack tuna in temperate waters. Based on the characteristics of its seasonal migration, it comprehensively and systematically considers the environmental variables that affect its spatial distribution (including but not limited to the original environmental variable factors), especially the temperature threshold characteristics during its seasonal migration, and adds an isotherm depth index independent variable to the model; and uses the Fisher natural breakpoint method based on statistical test methods to objectively carry out group fitting of environmental factors; and then in the process of model construction, a cross-validation method is used to select the optimal model of the suitability index of skipjack tuna habitat, and the model fitting results are compared and evaluated with the observation data of skipjack tuna, so as to realize the seasonal quantification and assessment of skipjack tuna habitat in temperate waters, and provide a more practical fishing plan for skipjack tuna fishery production.

[0004] The present invention is achieved through the following technical solution: a seasonal quantitative assessment method for the habitat of skipjack tuna in temperate waters, which specifically comprises the following steps:

[0005] Step S1, data selection and collection: the data includes production data and environmental data;

[0006] The production data include the amount of bonito fishing catch, fishing time, operation location (latitude and longitude), and operation days in the Northwest Pacific Ocean with a spatial resolution of 1°×1° and a temporal resolution of seasons;

[0007] The environmental data include eight environmental variables, namely, sea surface height SSH, sea surface temperature SST, sea surface salinity SSS, 17°C isotherm depth D17, mixed layer depth MLD, and gradient indexes SSTG, SSHG and SSSG of SSH, SST and SSS. The temporal resolution of the data is monthly, and the spatial resolution is a non-uniform distribution of variable grids. According to the Sobel theory, SSTG, SSHG and SSSG are used to characterize vortices, and the calculation formulas are as follows:

[0008]

[0009] Where V represents the environmental variable, i and j represent the grid points corresponding to the longitude and latitude of the environmental variable, Sx and Sy represent the processing intensity of the longitude and latitude, and VG represents the corresponding gradient obtained after the environmental variable is processed. This step is completed by Matlab software.

[0010] Step S2, data preprocessing: In production data, the CPUE of catch per unit fishing effort is a proxy indicator of the relative abundance of fishery resources. The CPUE of skipjack tuna is defined as follows:

[0011]

[0012] in It indicates the amount of bonito caught in metric tons; It expresses the fishing effort of skipjack tuna in days;

[0013] In the environmental data, all environmental variable data were preprocessed into seasonal data with a spatial resolution of 1° × 1° through spatial interpolation and temporal averaging to match the spatiotemporal scale of production data;

[0014] Step S3, construction of suitability index SI: SI is used to quantify the probability of a species' distribution under specific environmental variables, usually assuming that it follows a normal distribution;

[0015] First, each environmental variable was divided into 20 groups using the Fisher natural breakpoint grouping method. The calculation formula for the measured SI for each variable and each group was as follows:

[0016]

[0017] Where i and k represent the kth group of the i-th environment variable, respectively. i, k represents the sum of CPUE corresponding to the kth group of the i-th environment variable, CPUE i, min and CPUE i, max They represent the minimum and maximum CPUE values ​​in the i-th environmental variable grouping, respectively;

[0018] Then, a univariate nonlinear fitting equation was used to construct the relationship between each environmental variable and SI; according to the normal relationship between the two, the fitting equation was defined as follows:

[0019]

[0020] Where x is the median value of each environmental variable in each group; SI is the observed value of each environmental variable in each group; a and b parameters are estimated by debugging the nls function of R language, and the fitting equation is simultaneously tested for goodness of fit and significance of parameters;

[0021] Finally, the SI fitting equation parameters and curves of 8 environmental variables were obtained;

[0022] Step S4, Habitat Suitability Index (HSI) model construction: AMM and GMM, two habitat suitability index models based on arithmetic and geometric mean algorithms, were used to conduct further comparative analysis of skipjack tuna habitat quantification. The specific formula is as follows:

[0023]

[0024] Among them C i and SI i Represent the coefficient of the i-th environmental variable and its suitability index, HSI a and HSI g Respectively represent the habitat suitability index based on arithmetic and geometric algorithms; specifically including the following steps:

[0025] Step S4-1, independent variable selection: Spearman rank correlation analysis was used to pre-statistic the correlation between the predictors and the correlation between each predictor and the observed value of the habitat suitability index; the observed value of the habitat suitability index of bonito is defined as follows:

[0026]

[0027] in represents the observed value of skipjack tuna HSI, , , Respectively represent the highest, lowest and relative abundance of each grid point in the skipjack tuna operation data;

[0028] Step S4-2, model selection: using the 10-fold cross validation (k=10) method, the data were sampled 1000 times without replacement, and the data were randomly divided into 10 parts, one of which and the other 9 were designated as the test set and the training set, respectively. The fitted values ​​and observed values ​​of the habitat suitability index of the test set and the training set were subjected to Spearman rank correlation analysis;

[0029] Step S4-3, model evaluation: the performance of the model is evaluated by comparing the fitted value of the habitat suitability index HSI with the relative abundance CPUE of skipjack tuna fishery production;

[0030] Firstly, the habitat suitability index was divided into five groups, representing the different suitability conditions of skipjack tuna habitat; then the ratio of the sum of the relative abundance of skipjack tuna in each group to the sum of the abundance of all groups was calculated; the consistency between the suitable habitat predicted by the model and the observed skipjack tuna abundance indicated the effectiveness of the model.

[0031] As a preferred solution, in the environmental data in step S2, all environmental data are preprocessed into seasonal data with a spatial resolution of 1°×1° through spatial interpolation and temporal averaging, and the spatiotemporal scale of the production data is matched by the mean and griddata functions of Matlab software.

[0032] As a preferred solution, the suitability index SI in step S3 has a value range of 0-1, the highest value 1 indicates that the relative abundance of bonito in this group environment is the highest, and the corresponding environmental variable interval is the most suitable habitat for bonito; the lowest value 0 indicates that this group environment is not suitable for the survival of bonito.

[0033] As a preferred solution, the classification of each environmental variable into 20 groups in step S3 is accomplished by using the R language "classInt" package.

[0034] As a preferred solution, the consistency between the suitable habitat predicted by the model in step S4-3 and the observed relative abundance of skipjack tuna indicates the effectiveness of the model, specifically including that at least 50% of the relative abundance of skipjack tuna is distributed in the habitat suitability index HSI ≥ 3 group, that is, the habitat with HSI ≥ 0.4, indicating that the simulation performance of the model is superior.

[0035] Due to the adoption of the above technical scheme, the present invention has the following beneficial effects compared with the prior art: through the above technical scheme, the patent of the present invention can carry out the distribution prediction of skipjack tuna habitat hotspots in real time or in advance based on the reanalysis of environmental variable data of the ocean model, and can provide skipjack tuna fishery production plans with operation plan references that are more in line with actual fishing needs, such as the location and season of fishing hotspots, thereby practically improving the production efficiency of skipjack tuna fisheries in temperate waters.

[0036] Additional aspects and advantages of the present invention will become apparent from the following description or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0038] Figure 1It is a schematic diagram of the process framework of the present invention;

[0039] Figure 2 This is the fitting curve of the environmental variable suitability index. The black dots represent the SI observation values ​​of each variable; the red lines represent the SI fitting values ​​of each variable;

[0040] Figure 3 This is the correlation matrix of environmental predictors in the habitat suitability index model;

[0041] Figure 4 The cross-validation results of the simulated habitat suitability index and the observed habitat suitability index under different combination schemes of the two algorithms. The horizontal axis represents the combination schemes between 31 different variables, and the vertical axis represents the correlation coefficient between the fitted habitat suitability index and the observed habitat suitability index (p<0.05). The upper and lower figures represent the AMM based on the algorithm average algorithm and the GMM based on the geometric mean algorithm, respectively. Red and green represent the training and test data sets, respectively.

[0042] Figure 5 The optimal parameterization scheme based on arithmetic and geometric mean algorithms for 1975-2018 ( Figure 4 ), the cumulative ratio of relative abundance of skipjack tuna under different habitat suitability indices was evaluated. The grouping basis in the figure refers to Table 4, SPR, SUM, AUT, and WIN represent the four seasons of spring, summer, autumn, and winter, respectively.

[0043] Figure 6 This is the spatial distribution of the skipjack tuna habitat suitability index (2019-2020) simulated by the best arithmetic mean model (with sea surface temperature and 17°C isotherm depth as environmental factors). The black circles represent the observed relative abundance of skipjack tuna, and SPR, SUM, AUT, and WIN represent the four seasons of spring, summer, autumn, and winter, respectively. DETAILED DESCRIPTION

[0044] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0045] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0046] Combine the following Figures 1 to 6 The seasonal quantitative assessment method of the temperate sea skipjack tuna habitat according to the embodiment of the present invention is specifically described.

[0047] like Figure 1 As shown, the present invention proposes a seasonal quantitative assessment method for the habitat of skipjack tuna in temperate waters, which specifically includes the following steps S1, data selection and collection: the data includes production data and environmental data; the production data should use operational data that can represent the temporal and spatial changes of skipjack tuna in temperate waters; the environmental data should select environmental variables that affect the growth and development, migratory distribution, etc. of skipjack tuna, especially focusing on the special requirements of environmental variables during the seasonal migration of skipjack tuna.

[0048] The Western and Central Pacific Fisheries Commission (WCPFC) is an international management organization for tuna (including skipjack tuna). Its statistical production data is widely used in the construction of tuna habitat models and is well representative. Pole fishing is the main method of operation for skipjack tuna in the Northwest Pacific. Therefore, this embodiment selects the skipjack tuna pole fishing operation data of WCPFC as the production data of skipjack tuna in the Northwest Pacific. The data was first made public in November 2021, covering the period from 1975 to 2020. It contains detailed information such as fishing time, operation location, catch, and operation days. The spatial resolution is 1°×1° and the temporal resolution is seasonal.

[0049] Studies have found that the growth, development, and migration distribution of skipjack tuna are related to multiple environmental factors. The population density of skipjack tuna fry is related to sea surface height, sea surface temperature, and sea surface salinity; adult skipjack tuna begins to spawn when the sea surface temperature exceeds 24°C, and reaches a peak at 28°C. Usually, clusters of skipjack tuna appear near anticyclonic vortices, which indicates that there may be a correlation between the distribution of skipjack tuna and environmental gradients or nutrition. The migratory distribution of skipjack tuna is mainly in sea areas where the water temperature is higher than 18°C ​​both horizontally and vertically. The mixed layer depth is crucial for the short-term prediction of skipjack tuna fishing grounds in the northwest Pacific. Taking these influencing factors into account, this embodiment uses five environmental variables, namely sea surface height SSH, sea surface temperature SST, sea surface salinity SSS, 17°C isotherm depth (D17, the indicator closest to the 18°C ​​isotherm depth), and mixed layer depth MLD, which are directly used for the construction of the habitat model of skipjack tuna in the northwest Pacific. The data source of this variable is the fifth version of the Ocean Reanalysis System (ORAS5) of the European Centre for Medium-Range Weather Forecasts. The temporal resolution of the data set is monthly, and the spatial resolution is a non-uniform distribution of variable grids. In addition, according to the Sobel theory, the gradients of the three variables SST, SSS and SSH, denoted as SSTG, SSHG and SSSG, are introduced into the model to characterize the vortex, and the calculation formula is as follows:

[0050]

[0051] Where V represents the environmental variable, i and j represent the grid points corresponding to the longitude and latitude of the environmental variable, Sx and Sy represent the processing intensity of the longitude and latitude, and VG represents the corresponding gradient obtained after the environmental variable is processed. This step is completed by Matlab software.

[0052] Step S2, data preprocessing: In production data, the CPUE of catch per unit fishing effort is a proxy indicator of the relative abundance of fishery resources. The CPUE of skipjack tuna is defined as follows:

[0053]

[0054] in It indicates the amount of bonito caught in metric tons; It expresses the fishing effort of skipjack tuna in days;

[0055] In the environmental data, all environmental data were preprocessed into seasonal data with a spatial resolution of 1°×1° through spatial interpolation and temporal averaging to match the spatiotemporal scale of production data; this was accomplished through the mean and griddata functions of Matlab software;

[0056] Step S3, construction of suitability index SI: The SI index is used to quantify the probability of a species' distribution under specific environmental variables, usually assuming that it follows a normal distribution;

[0057] First, each environmental variable was divided into 20 groups using the Fisher natural breakpoint grouping method (this operation was completed using the R language "classInt" package). The formula for calculating the measured SI for each variable and each group is as follows:

[0058]

[0059] Where i and k represent the kth group of the i-th environment variable, respectively. i, k represents the sum of CPUE corresponding to the kth group of the i-th environment variable, CPUE i, min and CPUE i, max They represent the minimum and maximum CPUE values ​​in the i-th environmental variable grouping, respectively;

[0060] Then, a univariate nonlinear fitting equation was used to construct the relationship between each environmental variable and SI; according to the normal relationship between the two, the fitting equation was defined as follows:

[0061] Where x is the median value of each environmental variable for each group; SI is the observation value of each environmental variable for each group

[0062] The values, a and b parameters are estimated by debugging the nls function of R language, and the goodness of fit and significance test of parameters are performed simultaneously on the fitting equation;

[0063] Finally, the SI fitting equation parameters and their curves of the eight environmental variables are as follows ( Figure 2 , Table 1), suitability index SI, its value range is 0-1, the highest value 1 indicates that the relative abundance of skipjack tuna in this group environment is the highest, and the corresponding environmental variable interval is the most suitable habitat for skipjack tuna; the lowest value 0 indicates that the group environment is not suitable for skipjack tuna survival.

[0064] Table 1 Estimated values, standard deviations, probability values, significance and goodness of fit of the environmental variable suitability index

[0065]

[0066] Table 1 (Continued) Estimated values, standard deviations, probability values, significance and goodness of fit of the environmental variable suitability index

[0067]

[0068] Table 1 (Continued) Estimated values, standard deviations, probability values, significance and goodness of fit of the environmental variable suitability index

[0069]

[0070] In the parameter column of the table, the letters a and b are the two parameters of the univariate nonlinear equation of the fitting suitability index, and the following numbers 1, 2, 3, and 4 represent the four seasons of spring, summer, autumn, and winter respectively; in the significance column, ** represents the significance level of 0.01, and *** represents the significance level of 0.001.

[0071] Step S4, Habitat Suitability Index HSI Model Construction: The Habitat Suitability Index Models AMM and GMM based on the arithmetic mean and geometric mean algorithms are the two most commonly used models in fish habitat models. Through these two models, further comparative analysis of skipjack tuna habitat quantification was carried out, and the specific formulas are as follows:

[0072]

[0073] Among them C i and SI i Represent the coefficient of the i-th environmental variable and its suitability index, HSI a and HSI g Respectively represent the habitat suitability index based on arithmetic and geometric algorithms; specifically including the following steps:

[0074] Step S4-1, independent variable selection: In order to reduce multicollinearity and overfitting in the process of constructing the habitat suitability index model, this embodiment adopts Spearman rank correlation analysis (Spearman method has loose requirements on the distribution type of variable data, and is particularly suitable for non-normally distributed data), and pre-statistics the correlation between the prediction factors and the correlation between each prediction factor and the observed value of the habitat suitability index; wherein the observed value of the habitat suitability index of bonito is defined as follows:

[0075]

[0076] in represents the observed value of skipjack tuna HSI, , , Respectively represent the highest, lowest and relative abundance of each grid point in the skipjack tuna operation data;

[0077] The results showed that there was no collinearity among the eight environmental variable predictors ( Figure 3 ). The results of Spearman correlation analysis between the predictors and the observed values ​​of the habitat suitability index showed that: the environmental suitability index based on sea surface salinity SSS was not statistically correlated with the observed values ​​of the habitat suitability index; the environmental suitability index based on the mixed layer depth MLD and the sea surface salinity gradient SSSG was negatively correlated with the observed values ​​of the habitat suitability index; the environmental suitability index of the remaining five variables was positively correlated with the observed values ​​of the habitat suitability index (p<0.05) (Table 2). This indicates that the contribution of sea surface salinity SSS, mixed layer depth MLD, and sea surface salinity gradient SSSG to the construction of the habitat suitability index model may be very small; therefore, in the subsequent modeling, the habitat suitability index model constructed by the sea surface temperature SST, 17℃ isotherm depth D17, sea surface height SSH, sea surface temperature gradient SSTG, and sea surface height gradient SSHG variables was used, and it was assumed that the contribution of each variable to the model construction was equal. The combined parameterization scheme designed based on these five environmental variables is shown in the following table (Table 3).

[0078] Table 2 Correlation results between the suitability index of each environmental variable and the observed value of the habitat suitability index

[0079] <![CDATA[SI i ]]> SI_SST SI_D17 SI_SSS SI_SSH SI_MLD SI_SSTG SI_SSHG SI_SSSG Correlation coefficient 0.2572 0.2536 -0.0040 0.2197 -0.0709 0.1700 0.0669 -0.1078 Probability value 0.0000 0.0000 0.7289 0.0000 0.0000 0.0000 0.0000 0.0000

[0080] In the table, SI_SST, SI_D17, SI_SSS, SI_SSH, SI_MLD, SI_SSTG, SI_SSHG, and SI_SSSG respectively represent the suitability index of bonito to sea surface temperature, 17°C isotherm depth, sea surface salinity, sea surface height, mixed layer depth, sea surface temperature gradient, sea surface height gradient, and sea surface salinity gradient. The probability value of SI_SSS is 0.7289, which is the objective experimental result of the calculation. 0.7289>0.05 (significance level), indicating that the significance test hypothesis of this result is not established, indicating that there is no correlation between the two variables.

[0081] Table 3 Parameterized combination schemes of suitability index of habitat suitability index model

[0082] Parameterized coefficient variable name <![CDATA[C1 / SI_SST]]> <![CDATA[C2 / SI_D17]]> <![CDATA[C3 / SI_SSH]]> <![CDATA[C4 / SI_SSTG]]> <![CDATA[C5 / SI_SSHG]]> 1 1 0 0 0 0 2 0 1 0 0 0 3 0 0 1 0 0 4 0 0 0 1 0 5 0 0 0 0 1 6 0.5 0.5 0 0 0 7 0.5 0 0.5 0 0 8 0.5 0 0 0.5 0 9 0.5 0 0 0 0.5 10 0 0.5 0.5 0 0 11 0 0.5 0 0.5 0 12 0 0.5 0 0 0.5 13 0 0 0.5 0.5 0 14 0 0 0.5 0 0.5 15 0 0 0 0.5 0.5 16 0.33 0.33 0.33 0 0 17 0.33 0.33 0 0.33 0 18 0.33 0.33 0 0 0.33 19 0.33 0 0.33 0.33 0 20 0.33 0 0.33 0 0.33 21 0.33 0 0 0.33 0.33 22 0 0.33 0.33 0.33 0 23 0 0.33 0.33 0 0.33 24 0 0.33 0 0.33 0.33 25 0 0 0.33 0.33 0.33 26 0.25 0.25 0.25 0.25 0 27 0.25 0.25 0.25 0 0.25 28 0.25 0.25 0 0.25 0.25 29 0.25 0 0.25 0.25 0.25 30 0 0.25 0.25 0.25 0.25 31 0.2 0.2 0.2 0.2 0.2

[0083] In the table, C represents the Ci parameter in the habitat suitability index HSI model; in the table, SI_SST, SI_D17, SI_SSH, SI_SSTG, and SI_SSHG represent the suitability index of bonito to sea surface temperature, 17°C isobath, sea surface height, sea surface temperature gradient, and sea surface height gradient, respectively.

[0084] Step S4-2, model selection: The data of the simulated suitable habitat model from 1975 to 2018 were sampled 1000 times without replacement using a ten-fold cross validation (k=10) method, and the data were randomly divided into 10 parts, one of which was designated as the test set and the other 9 as the training set, respectively. The Spearman rank correlation analysis was performed on the fitted values ​​and observed values ​​of the habitat suitability index of the test set and the training set. The results showed that the habitat model correlation test result based on the sea surface temperature SST and the 17°C isotherm depth D17 environmental variables was the best ( Figure 4 );

[0085] Step S4-3, model evaluation: the performance of the model is evaluated by comparing the HSI fitting value with the CPUE of the relative abundance of skipjack tuna fishery production;

[0086] First, the habitat suitability index was divided into five groups, representing different suitability conditions of skipjack tuna habitat (Table 4); then the ratio of the sum of the relative abundance of skipjack tuna in each group to the sum of the abundance of all groups from 1975 to 2018 was calculated; the consistency between the suitable habitats predicted by the model and the observed abundance of skipjack tuna indicates the effectiveness of the model. Specifically, at least 50% of the relative abundance of skipjack tuna is distributed in the habitat suitability index HSI ≥ 3 groups, that is, habitats with HSI ≥ 0.4, indicating that the simulation performance of the model is superior; this example found that under the optimal fitting scheme (based on the environmental variables of sea surface temperature and 17°C isotherm depth), when the AMM model based on the average algorithm was used for evaluation, about 70% of the relative abundance of skipjack tuna was distributed in suitable habitats in all four seasons; in contrast, the GMM model fitted using the geometric mean algorithm had about 50% (spring and summer) and 60% (autumn and winter) relative abundance distributions in suitable habitats ( Figure 5 ), which shows that the habitat suitability index model based on the average algorithm performs better. To this end, this example further carried out spatial verification of the simulated spatial distribution of the habitat suitability index and the observation data of the relative abundance of skipjack tuna from 2019 to 2020. The results showed that the habitat suitability index model based on the average algorithm can better reflect the distribution of the relative abundance of skipjack tuna ( Figure 6 ).

[0087] Table 4 Habitat suitability index group characteristics

[0088] grade Habitat suitability index Suitable habitat distribution characteristics 1 [0-0.2) Very inappropriate 2 [0.2-0.4) Not suitable 3 [0.4-0.6) Generally suitable 4 [0.6-0.8) More suitable 5 [0.8-1.0] Very suitable

[0089] In summary, this embodiment found that the habitat suitability index model based on the arithmetic mean algorithm, with sea surface temperature and 17°C isotherm depth as prediction factors, optimally simulated the habitat conditions of skipjack tuna in the northwest Pacific Ocean.

[0090] In the description of this specification, the description of the terms "one embodiment", "some embodiments", "specific embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0091] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A seasonal quantitative assessment method for the habitat of skipjack tuna in temperate waters, characterized in that , specifically including the following steps: Step S1, data selection and collection: the data includes production data and environmental data; The production data include fishing catch, fishing time, operation location, and operation days with a spatial resolution of 1°×1° and a temporal resolution of season; The environmental data include eight environmental variables, namely, sea surface height SSH, sea surface temperature SST, sea surface salinity SSS, 17°C isotherm depth D17, mixed layer depth MLD, and gradient indexes SSTG, SSHG and SSSG of SSH, SST and SSS. The temporal resolution of the data is monthly, and the spatial resolution is a non-uniform distribution of variable grids. According to the Sobel theory, SSTG, SSHG and SSSG are introduced into the model to characterize the vortex, and the calculation formula is as follows: ； Where V represents the environmental variable, i and j represent the grid points corresponding to the longitude and latitude of the environmental variable, Sx and Sy represent the processing intensity of the longitude and latitude, and VG represents the corresponding gradient obtained after the environmental variable is processed. This step is completed by Matlab software. Step S2, data preprocessing: In production data, the CPUE of catch per unit fishing effort is a proxy indicator of the relative abundance of fishery resources. The CPUE of skipjack tuna is defined as follows: ； in It indicates the amount of bonito caught in metric tons; It expresses the fishing effort of skipjack tuna in days; For the environmental data, all the environmental data were preprocessed into seasonal data with a spatial resolution of 1° × 1° through spatial interpolation and temporal averaging to match the spatiotemporal scale of the production data; Step S3, construction of suitability index SI: The SI index is used to quantify the probability of a species' distribution under specific environmental variables, usually assuming that it follows a normal distribution; Step S4, Habitat Suitability Index (HSI) model construction: Further comparative analysis of skipjack tuna habitat quantification was carried out using two habitat suitability index models, AMM and GMM, based on arithmetic mean and geometric mean algorithms. The specific formulas are as follows: Among them C i and SI i Represent the coefficient of the i-th environmental variable and its suitability index, HSI a and HSI g Represent the habitat suitability indices based on arithmetic and geometric algorithms, respectively.

2. A seasonal quantitative assessment method for the habitat of skipjack tuna in temperate waters according to claim 1, characterized in that ,In the environmental data in the step S2, all the environmental variable data are preprocessed into seasonal data with a spatial resolution of 1°×1° through spatial interpolation and time averaging processing. The ,temporal scale matching of the production data is completed by the ,mean and griddata functions of Matlab software.

3. A seasonal quantitative assessment method for the habitat of skipjack tuna in temperate waters according to claim 1, characterized in that , the step S3 specifically includes the following steps: First, each environmental variable was divided into 20 groups using the Fisher natural breakpoint grouping method. The calculation formula for the measured SI for each variable and each group was as follows: ； Where i and k represent the kth group of the i-th environment variable, respectively. i, k represents the sum of CPUE corresponding to the kth group of the i-th environment variable, CPUE i, min and CPUE i, max They represent the minimum and maximum CPUE values ​​in the i-th environmental variable grouping, respectively; Then, a univariate nonlinear fitting equation was used to construct the relationship between each environmental variable and SI; according to the normal relationship between the two, the fitting equation was defined as follows: ； Where x is the median value of each environmental variable in each group; SI is the observed value of each environmental variable in each group; a and b parameters are estimated by debugging the nls function of R language, and the fitting equation is simultaneously tested for goodness of fit and significance of parameters; Finally, the SI fitting equation parameters and curves of 8 environmental variables were obtained.

4. A seasonal quantitative assessment method for the habitat of skipjack tuna in temperate waters according to claim 3, characterized in that The suitability index SI in step S3 has a value range of 0-1, the highest value 1 indicates that the relative abundance of bonito in the group environment is the highest, and the corresponding environmental variable interval is the most suitable habitat for bonito; the lowest value 0 indicates that the group environment is not suitable for bonito survival.

5. A seasonal quantitative assessment method for the habitat of skipjack tuna in temperate waters according to claim 3, characterized in that ,The division of each environmental variable into 20 groups in step S3 is ,completed through the “classInt” package in R language.

6. A seasonal quantitative assessment method for the habitat of skipjack tuna in temperate waters according to claim 1, characterized in that , the step S4 specifically includes the following steps: Step S4-1, independent variable selection: Spearman rank correlation analysis was used to pre-statistic the correlation between the predictors and the correlation between each predictor and the observed value of the habitat suitability index; the observed value of the habitat suitability index of bonito is defined as follows: ； in represents the observed value of skipjack tuna HSI, , , Respectively represent the highest, lowest and relative abundance of each grid point in the skipjack tuna operation data; Step S4-2, model selection: using a 10-fold cross validation method with k=10, the data were sampled 1000 times without replacement, and the data were randomly divided into 10 equal parts, one of which and the other 9 were designated as the test set and the training set, respectively. Spearman rank correlation analysis was performed on the fitted values ​​and observed values ​​of the habitat suitability index of the test set and the training set; Step S4-3, model evaluation: the performance of the model is evaluated by comparing the HSI fitted value with the CPUE of the relative abundance of skipjack tuna fishery production; Firstly, the habitat suitability index was divided into five groups, representing different suitability conditions of skipjack tuna habitat; then the ratio of the sum of the relative abundance of skipjack tuna in each group to the sum of the abundance of all groups was calculated; the consistency between the suitable habitat predicted by the model and the observed abundance of skipjack tuna indicated the effectiveness of the model.

7. A seasonal quantitative assessment method for the habitat of skipjack tuna in temperate waters according to claim 6, characterized in that ,The consistency between the suitable habitat predicted by the model in step S4-3 and the observed abundance of skipjack tuna ,indicates the validity of the model, specifically including that at least 50% of the relative abundance of skipjack tuna is distributed in the habitat suitability index HSI ≥ 3 groups, i.e. , habitats with HSI ≥ 0.4, indicating that the simulation performance of the model is superior.

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