A method for correcting offshore fish resource investigation data

Abstract

The present application relates to the technical field of fishery resources investigation and resource assessment, in particular to a kind of offshore floating fish resource investigation data correction method.The method uses multi-mesh streamer to investigate and sample target floating fish, obtains fishing sample corresponding to different mesh sizes;Determine fish body length parameters and girth parameters, establish the corresponding relationship between fish body length and girth, wherein the relationship model of seasonally fork length and girth is established for scomber japonicus, and the fitting model of body length and girth is established for sardinella zunasi;According to the corrected body length composition data, output resource quantity evaluation results.By introducing fish girth parameters, seasonal fatness correction and large individual compensation mechanism, the unified correction of investigation data of fish of different seasons and different body types is realized, and the accuracy, comparability and resource assessment reliability of resource investigation results are improved.

Description

Technical Field

[0001] This invention relates to fisheries resource survey technology, specifically to a method for correcting nearshore pelagic fish resource survey data, belonging to the fields of fisheries resource assessment and fishing gear selectivity analysis. This method is particularly suitable for processing survey data of mid-to-upper-level migratory fish, such as sardines and mackerel, and can standardize and correct survey data and assess resource abundance for fish of different seasons and lengths, thereby providing a scientific basis for fisheries resource management, Total Allowable Catching (TAC) setting, and sustainable fisheries management. Background Technology

[0002] Nearshore pelagic fish resources are an important component of my country's marine fishery resources, among which sardines, mackerel, and other mid-to-upper-level migratory fish play a vital role in marine ecosystems and fishery production. Accurately understanding their resource quantity, population structure, and variation patterns is an important foundation for conducting fishery resource assessments, determining the Total Allowable Catch (TAC), and implementing scientific conservation management.

[0003] Currently, surveys of nearshore floatfish resources typically employ multi-mesh drift nets. These nets are generally composed of multiple meshes of different sizes arranged sequentially. By utilizing the different mesh sizes to capture fish of varying lengths, the survey sample covers the fish's body length composition. During the survey, the survey vessel deploys the drift nets at predetermined survey stations. After a certain soaking time, the nets are retrieved, and data on the species, quantity, and body length composition of fish caught at each mesh size are collected. This data is then used to estimate the population structure, resource density, and resource abundance of the target fish species.

[0004] However, drift gillnets are essentially a typical selective fishing gear. Their capture process is not random but rather influenced by both fish morphology and mesh structure parameters. Once fish enter the net, they are typically caught through gill trapping, wedging, piercing, or entanglement. All of these capture methods are closely related to the matching relationship between the fish's body girth and the mesh circumference. When the fish's body girth and mesh circumference are within a suitable matching range, the probability of the fish being caught is high; when the fish is too small or too large, the probability of capture decreases significantly. Therefore, fish of different lengths have different probabilities of entering and remaining in the net under the same mesh conditions, thus forming the inherent selective characteristic of drift gillnets.

[0005] For resource surveys, the catch data obtained from the surveys are already influenced by the selectivity of the fishing gear, often resulting in discrepancies between the measured body length composition of the catch and the true body length composition of fish populations in the sea area. Directly using survey catch data for resource estimation can easily lead to overestimation or underestimation of the populations of certain body length groups, thus affecting the accuracy of the resource assessment results. Therefore, it is necessary to establish a reasonable selection correction method to standardize and correct the survey data.

[0006] In existing technologies, resource survey departments typically conduct year-round surveys using fixed mesh combinations and directly estimate resource levels based on catch length frequency, or use empirical models based on body length parameters to correct the survey data. While these methods can reflect fish school structure characteristics to some extent, they still have the following shortcomings:

[0007] First, current techniques do not fully consider the impact of seasonal changes in fish morphology on the selectivity of drift gillnets. Taking mackerel as an example, during the post-spawning stage, mackerel are in a low fatness state, with relatively slender bodies; while during the fattening stage, fatness increases significantly, and the carcass circumference increases substantially. With the same tail fork length, the corresponding carcass circumference parameters differ significantly between seasons. Since the drift gillnet capture process mainly depends on the matching relationship between the fish's carcass circumference and the mesh circumference, the optimal capture length for the same mesh size differs in different seasons. Existing survey methods typically use uniform selectivity parameters to process year-round survey data, making it difficult to accurately reflect the selectivity differences caused by seasonal changes in body shape, thus affecting the comparability of survey results between different seasons.

[0008] Secondly, most existing technologies use fish body length as the basis for selectivity analysis, neglecting the crucial parameter of fish carcass circumference, which directly determines the netting process. In reality, whether fish can pass through the mesh, form gill entrapments, and remain stably in the net primarily depends on the geometrical relationship between the fish's carcass circumference and the mesh's inner perimeter. Simply using body length as a parameter to build a selectivity model makes it difficult to accurately characterize the actual fish capture process, thus affecting the accuracy of the selectivity analysis results.

[0009] Furthermore, existing survey methods generally underestimate the size of large fish individuals. Due to the limited coverage of survey net mesh combinations, the probability of fish exceeding a certain length being caught by existing meshes decreases significantly, resulting in an insufficient sample size of large individuals. In resource assessment, directly using survey catch data for statistical analysis can easily lead to an underestimation of the number and resource quantity of older individuals, thereby affecting the scientific validity of population age structure analysis and resource management decisions.

[0010] Furthermore, for major economically important floaty fish resources such as sardines and mackerel, there is currently a lack of a unified survey data correction technology system that can simultaneously integrate fish body girth parameters, mesh selectivity parameters, and seasonal changes in condition factor. Existing technologies struggle to achieve standardized conversion and unified evaluation of survey data across different fish species and seasons, limiting the application value of survey data in long-term resource dynamic monitoring.

[0011] Therefore, how to establish a data correction method for nearshore pelagic fish resource surveys based on the relationship between fish body girth parameters and mesh selectivity, fully consider the seasonal changes in fish body size, selectively correct the survey data for fish of different body length groups, and compensate for the survey bias of large individuals, thereby improving the accuracy, comparability, and standardization of resource survey results, has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0012] In view of this, the purpose of this invention is to provide a method for correcting nearshore pelagic fish resource survey data, so as to achieve unified correction of survey data for fish of different seasons and body lengths, thereby improving the accuracy and comparability of resource assessment. To achieve the above objective, this invention provides the following technical solution:

[0013] In one embodiment of the present invention, a method for correcting nearshore pelagic fish resource survey data is provided, comprising the following steps:

[0014] S1. Use multi-mesh drift gillnets to investigate and sample the target floating fish, and obtain catch samples corresponding to different mesh sizes;

[0015] S2. Measure the body length and mantle circumference parameters of the fish caught, wherein the mantle circumference parameters include at least one of the following: periocular mantle circumference, posterior edge mantle circumference of the operculum, base mantle circumference of the dorsal fin, and maximum mantle circumference;

[0016] S3. Based on the body length and mantle circumference parameters, establish the correspondence between the body length and mantle circumference of the target flounder. For mackerel, establish the relationship between tail fork length and mantle circumference according to the season. For sardines, establish a linear fitting relationship.

[0017] Furthermore, in one embodiment of the present invention, the target fish includes sardines and mackerel, and the seasonal division of mackerel includes at least two stages: the low fattening period after spawning and the high fattening period.

[0018] Furthermore, in step S3, when establishing the body length-carcass circumference relationship, a fatness parameter is introduced to reflect seasonal body size differences and correct for deviations in tail fork length and carcass circumference.

[0019] Furthermore, in step S4, the inner perimeter of each mesh size is matched with the fish body circumference parameter to establish a selectivity curve for each mesh size, which is used to represent the relative capture probability of fish of different body lengths.

[0020] Further, in step S5, the measured fish body length composition data is weighted and corrected. The correction method includes dividing the measured number of fish by the selectivity coefficient of the corresponding body length group to obtain the corrected number of fish.

[0021] Preferably, a large-size individual compensation item is set for mackerel with a tail fork length of 300mm or more. The compensation item is calculated based on the selectivity curves corresponding to 82mm and 106mm mesh sizes to make up for the underestimation error caused by insufficient sampling of large individuals in traditional mesh combinations.

[0022] Furthermore, in step S6, the corrected body length composition data is used to output fish population evaluation data, including the corrected body length frequency distribution, quantity index, weight index, or population estimate.

[0023] Optionally, the multi-mesh drift net in step S1 includes net sheets with different mesh sizes ranging from at least 18 mm to 121 mm. The net sheet layout ratio can be dynamically adjusted according to the fatness of the mackerel and the season to optimize the sampling coverage of fish of different body lengths.

[0024] Furthermore, the measurement of the carcass circumference in step S2 can be performed using a periorbital carcass circumference measurement band, a gill cover posterior edge measuring scale, or digital image analysis to ensure high-precision measurement data.

[0025] Furthermore, the selective curve plotting, weighted correction, and large-scale individual compensation in steps S4 to S6 can be implemented by computer programs to achieve rapid processing of survey data and standardized output of resource quantities.

[0026] Through the above technical solution, the present invention can achieve the following effects:

[0027] 1. Standardize and correct survey data for different seasons: Introduce a fatness parameter and consider seasonal differences in body size to scientifically correct the relationship between the tail fork length and body circumference of mackerel in different seasons, thereby improving data comparability.

[0028] 2. Improve the accuracy of survey data: Establish a selectivity curve by matching mesh size with body girth, and perform weighted correction on the measured catch data to correct the capture bias of fish of different body lengths by traditional methods.

[0029] 3. Correcting the underestimation of large individuals: The compensation item for large individuals effectively makes up for the insufficient sampling of large fish, improving the accuracy of resource estimation.

[0030] 4. Enhance data processing efficiency: Computer programs enable automated processing, achieving rapid and standardized resource quantity output, providing a reliable basis for fisheries management and TAC formulation.

[0031] 5. Wide applicability: The method can be applied to surveys of different types of nearshore floating fish resources, and has high adaptability and scalability.

[0032] In summary, this invention provides a scientific, systematic, and highly accurate method for correcting nearshore floating fish resource survey data, achieving standardization of survey data and accuracy of resource quantity assessment, which can effectively support fisheries resource management and sustainable utilization decision-making. Attached Figure Description

[0033] Figure 1 This is a flowchart illustrating the nearshore floating fish resource survey data correction method of the present invention, showing the complete processing flow from multi-mesh drift gillnet sampling, measurement of carcass girth and body length parameters, establishment of body length-carcass girth relationship, selective curve plotting, weighted correction, to output of resource quantity evaluation data.

[0034] Figure 2 This is a schematic diagram of the selective correction principle of the gillnet in this invention. It illustrates the matching relationship between different mesh sizes and fish body circumference, as well as the formation process of the selective curve, reflecting the variation law of the probability of fish entering the net with body length and body circumference. Detailed Implementation

[0035] In view of the problems existing in nearshore pelagic fish resource survey methods, such as uncorrected seasonal body size differences, survey bias caused by net selectivity, and low catch rates of large individuals, this invention provides a survey data correction method based on fish body girth parameters, seasonal condition factor correction, and mesh selectivity curves. By measuring the body length and body girth parameters of the catch samples, establishing a body length-body girth relationship model, constructing selectivity curves, and combining large individual compensation, this invention can achieve unified correction of fish survey data for different seasons and different body length groups, improving the accuracy and comparability of resource assessment results. The implementation process, operational details, and technical effects of the method of this invention are described in detail below with reference to the accompanying drawings and specific embodiments, so as to enable those skilled in the art to understand and implement it.

[0036] Example 1: A method for correcting nearshore flounder resource survey data based on mantle girth parameters

[0037] The following is in conjunction with the appendix Figure 1 and attached Figure 2 The present invention provides a detailed description of the method for correcting nearshore floating fish resource survey data.

[0038] like Figure 1 As shown in this embodiment, a method for correcting nearshore pelagic fish resource survey data is provided. This method is applicable to the standardization processing of survey data on pelagic fish such as sardines and mackerel. The method establishes a correspondence between fish body girth parameters and mesh selectivity to correct the surveyed catch data, thereby improving the accuracy and comparability of resource assessment results.

[0039] At the start of the survey, multi-mesh drift nets were deployed at pre-set survey stations within the target survey area. These multi-mesh drift nets consisted of multiple net panels with different mesh sizes, ranging from 18mm to 121mm, ensuring that fish of varying lengths could be caught. After the survey, the fish samples recovered from the nets were sorted and basic survey information, including fish species composition, mesh size, and the corresponding number of fish caught, was recorded.

[0040] After obtaining the fish samples, biological measurements were performed on each fish. For mackerel samples, the tail fork length was measured; for sardine samples, the body length or tail fork length was measured. Simultaneously, the carcass circumference was measured. The carcass circumference can be measured using one or more of the following methods, depending on the survey requirements: periorbital circumference, posterior edge circumference of the gill cover, circumference at the base of the dorsal fin, and maximum circumference. Preferably, a flexible measuring tape is used to directly measure the circumference of corresponding parts of the fish body; in automated survey scenarios, digital image acquisition equipment can also be used to acquire images of the fish body contour, and the carcass circumference parameters can be automatically extracted using image recognition software.

[0041] After the measurements are completed, the fish species information, body length parameters, mantle girth parameters, mesh size, survey time, and survey area information are entered into the survey database, which serves as the data basis for subsequent selective analysis.

[0042] This invention posits that the capture of fish by drift gillnets is essentially controlled by the matching relationship between the fish's body circumference and the inner circumference of the mesh. For example... Figure 2 As shown, when a fish swims and comes into contact with the net, it may be captured by means of gill trapping, wedging, piercing, or entanglement, and all of these capture methods are closely related to the fish's body girth. Therefore, compared to the traditional method of establishing a selectivity model using only body length parameters, using body girth parameters can more realistically reflect the actual process of the fish entering the net.

[0043] Based on the above understanding, this embodiment first establishes the correspondence between fish body length and carcass circumference using survey samples. For sardines, a linear fitting relationship between body length and carcass circumference can be established; for mackerel, a correspondence model between tail fork length and carcass circumference is established according to different survey periods. In some implementations, a condition factor can be further introduced as a correction factor to reflect the seasonal changes in fish body size, thereby improving the accuracy of carcass circumference prediction results.

[0044] After obtaining the correspondence between body length and carcass circumference, the inner perimeter of the mesh corresponding to each mesh size is further calculated, and the fish carcass circumference parameter is matched and analyzed with the inner perimeter of the mesh. For each mesh size, the capture situation of fish in different body length groups is statistically analyzed, and the corresponding relative capture probability is calculated, thereby establishing the selectivity curve corresponding to each mesh specification. The selectivity curve reflects the probability of fish of different body lengths being captured by the corresponding mesh.

[0045] Subsequently, the measured catch data obtained from the survey were corrected based on the established selectivity curve. Specifically, for any fish body length group, the measured number of fish was inversely weighted according to the selectivity coefficient corresponding to that body length group. During the correction process, the measured number of fish was divided by the corresponding selectivity coefficient to obtain the corrected number of fish. Through the above processing, the deviation in body length composition caused by the selectivity of the fishing nets can be effectively eliminated, making the corrected body length frequency distribution closer to the true structure of the fish population in the sea area.

[0046] After correcting the data for each body length group, the corrected body length frequency distribution is further constructed, and resource evaluation indicators are calculated accordingly. These resource evaluation indicators may include quantity index, biomass index, catch per unit catch effort, and estimated resource quantity. Preferably, the above-mentioned selective curve fitting, data correction, and resource quantity evaluation processes can all be completed automatically by computer programs, thereby achieving standardized processing and rapid output of survey data.

[0047] Through the above technical solution, this embodiment establishes a mesh selectivity model using fish body girth parameters and corrects the survey data by combining selectivity coefficients. Compared with traditional resource survey methods based on body length parameters, it can more accurately reflect the true body length composition of fish populations, reduce systematic errors caused by net selectivity, improve the accuracy and stability of resource survey results, and provide reliable data support for fishery resource assessment and the formulation of total allowable catch.

[0048] Example 2: Seasonal Selectivity Correction Method for Mackerel

[0049] Based on Example 1, this example further optimizes and explains the problem of seasonal body size changes that exist during the mackerel resource survey.

[0050] During their long-term investigation, the inventors discovered that mackerel, as a typical mid-to-upper-level migratory fish, exhibits significant differences in body shape characteristics at different growth and physiological stages. In particular, during the period from the end of spawning to the fattening stage, the fish's body fatness varies considerably, and even individuals with the same tail fork length may show significant differences in their body circumference parameters.

[0051] Traditional resource survey methods typically employ a uniform body length selectivity model to process year-round survey data, assuming that fish of the same body length have the same probability of being caught in different seasons. However, the inventors' research revealed that the capture process of mackerel using drift gillnets is primarily influenced by the matching relationship between the fish's body circumference and the mesh circumference, rather than simply being determined by the tail fork length. Therefore, when the mackerel's condition changes, even if the tail fork length remains consistent, its actual probability of entering the net will change, leading to systematic errors when using a uniform selectivity model for resource assessment.

[0052] Based on the above understanding, this embodiment classifies the mackerel survey samples according to the survey period.

[0053] In one implementation, June to July is designated as the low-feeding-to-full-age period after spawning, and September to November is designated as the high-feeding-to-full-age period.

[0054] The survey results show that during the post-spawning stage, mackerel consume a large amount of energy reserves, resulting in a relatively slender body and a smaller carcass circumference for the same tail fork length. However, during the fattening stage, the fish accumulate more fat, resulting in a higher fatness and a significantly larger carcass circumference for the same tail fork length.

[0055] For example, the following results were obtained from a portion of the survey sample:

[0056] Table 1. Statistical results of seasonal differences in body size of mackerel

[0057] season Sample size n Average tail fork length (mm) Average maximum body circumference (mm) Average fatness June-July 458 298.4 162.3 11.8 September to November 512 287.5 171.6 14.1

[0058] As can be seen from Table 1, although the average tail fork length of mackerel was relatively similar between the two periods, there was a significant difference in the average maximum body circumference, indicating that using a single body length parameter cannot accurately reflect the actual state of the fish when it enters the net.

[0059] Therefore, this embodiment establishes a model of the correspondence between tail fork length and body circumference under different seasons.

[0060] For samples from the post-spawning, low-fat-to-full-age stage, a first caudal fork length-carcass girth relationship model can be established:

[0061] G1=f1(FL)

[0062] Wherein, G1 represents the carcass circumference parameter in the post-spawning stage, and FL represents the tail fork length.

[0063] For fattening samples at the end of their fattening period, a second tail fork length-carcass girth relationship model can be established:

[0064] G2=f2(FL)

[0065] G2 represents the carcass circumference parameter during the fattening stage.

[0066] Furthermore, a correction model can be established by introducing the fullness parameter K as a correction factor:

[0067] G=f(FL,K)

[0068] in:

[0069] G represents the circumference of the fish's body;

[0070] FL indicates the length of the tail fork;

[0071] K represents the fullness parameter.

[0072] By introducing the fatness parameter, we can more accurately describe the changes in fish body shape in different seasons.

[0073] After obtaining the tail fork length-body circumference relationship model for different seasons, the body circumference parameters of the corresponding fish were calculated, and further matched with the inner perimeter of the mesh corresponding to each mesh size.

[0074] Subsequently, selectivity curves were established for the post-spawning and fattening stages, respectively.

[0075] As the fish's body circumference increases during the fattening stage, the optimal capture length for the same mesh size will shift towards a smaller body length; while in the post-spawning stage, as the fish's body circumference decreases, the optimal capture length for the same mesh size will shift towards a larger body length.

[0076] Therefore, the same mesh size has different selectivity characteristics in different seasons.

[0077] When correcting resource survey data, the seasonal type is first determined based on the survey time. Then, the tail fork length-body girth model and selective curve parameters for the corresponding season are called to selectively correct the survey data.

[0078] The reverse weighting method described in Example 1 is still used in the correction process. The corrected tail number is calculated based on the selectivity coefficient of the corresponding body length group, and the body length frequency distribution is reconstructed.

[0079] By using the method of this embodiment, survey data obtained in different seasons can be compared under a unified standard, effectively reducing the selective drift error caused by seasonal changes in fish body fatness, and improving the stability and comparability of resource survey results.

[0080] Meanwhile, the seasonal mantle correction model established in this embodiment can more realistically reflect the actual fishing process of mackerel. Compared with the traditional year-round uniform selective model, it can significantly improve the accuracy of resource estimation and provide a more reliable data foundation for dynamic monitoring of mackerel resources and the formulation of total allowable catch.

[0081] Example 3: Compensation and Correction Method for Low Catch Rate of Large Mackerel

[0082] Based on Examples 1 and 2, this example provides a solution to the problem of insufficient capture rate of large individuals in mackerel surveys.

[0083] The inventors discovered that when the tail fork length of mackerel exceeds approximately 300mm, the capture probability of traditional drift gillnet combinations decreases significantly, leading to a severe shortage of large-sized mackerel in the survey sample. This, in turn, results in an underestimation of older fish populations in resource estimation. To address this issue, this embodiment introduces compensation measures for larger individuals and adjusts the mesh configuration to increase the capture coverage of larger fish.

[0084] During implementation, for mackerel samples with a tail fork length of 300mm or more, the corresponding carcass circumference parameters were first calculated based on the seasonal tail fork length-carcass circumference model established in Example 2. Subsequently, by comparing the matching relationship between the fish carcass circumference and the inner perimeter of each mesh, the body length group with a significantly reduced capture probability was identified.

[0085] Preferably, based on the existing mesh size combination, nets with mesh sizes of 82mm and 106mm are added to ensure that mackerel with a tail fork length of 300-350mm can still be caught with a high probability. As shown in Table 3, the optimal catch length and selectivity curve parameters corresponding to different mesh sizes are used to calculate the selectivity coefficient for this length group.

[0086] Mesh size (mm) Optimal length selection (mm) 43 165 55 208 72 265 82 302 106 344

[0087] During data correction, for body length groups with a selectivity coefficient below a preset threshold (e.g., 0.5), a large-size individual compensation term is introduced. The formula for calculating the corrected mantissa is:

[0088] Corrected last digit = Measured last digit ÷ (Selectivity coefficient + Compensation coefficient)

[0089] The compensation coefficient is calculated based on the selectivity curves corresponding to 82mm and 106mm mesh sizes to ensure that large individuals are fully reflected in the corrected tail count.

[0090] This method not only compensates for the underestimation error caused by insufficient sampling of large individuals in traditional mesh combinations, but also ensures the comparability and continuity of mackerel survey data in different seasons throughout the year under a unified standard.

[0091] Furthermore, the corrected tail data were used to construct a corrected body length frequency distribution and to calculate resource quantity evaluation indicators, including quantity index, weight index, and estimated resource quantity. A comparison between the corrected and uncorrected data is shown in Table 4.

[0092] Length group (mm) Actual measured last digits Correcting the last digits 250-275 526 548 275-300 437 482 300-325 112 241 325-350 31 86

[0093] As shown in Table 4, by introducing compensation for large-sized individuals, the number of mackerel with a tail fork length of 300mm or more increased significantly after correction, which significantly reduced the underestimation of large individuals and improved the accuracy of resource assessment.

[0094] This embodiment, combining the seasonal mantle size model and selectivity curve from Embodiment 2, further addresses the problem of low catch rates for large mackerel individuals, achieves unified correction of survey data for mackerel of different lengths and in different seasons, provides reliable support for accurate resource estimation, and provides a scientific basis for fisheries management decisions.

[0095] Example 4: Sardine Carcass Girth-Body Length Model Correction Method

[0096] Based on the aforementioned Examples 1 to 3, this example provides a specific technical implementation plan for the correction of sardine resource survey data.

[0097] Long-term investigations have revealed that sardines are primarily caught in drift gillnets by wedging or gill trapping, and their entry into the net is closely related to the matching relationship between the fish's carcass circumference and the mesh circumference. Compared to a single parameter such as tail fork length or body length, carcass circumference more accurately reflects the actual probability of a fish being caught in the net.

[0098] During the investigation, sardine samples were first collected using a multi-mesh drift gillnet, following the method described in Example 1, and the body length and mantle circumference parameters of each sample were recorded. Measurements could be taken at the periorbital mantle circumference, the posterior edge of the operculum, or the maximum mantle circumference; alternatively, digital image analysis could be used to automatically obtain the fish's outline and mantle circumference parameters.

[0099] Subsequently, a linear fitting model between sardine body length and carcass girth was established using sample data. For example, the following linear relationship can be used for modeling:

[0100]

[0101] in:

[0102] G represents the fish's body circumference (unit: mm);

[0103] L is the body length or tail fork length of the fish (unit: mm).

[0104] a and b are the model parameters obtained through least squares fitting.

[0105] This model can predict the corresponding carcass circumference based on the sardine's body length, thus providing key parameters for subsequent selection analysis.

[0106] After establishing the body length-carcass girth model, the carcass girth parameter was matched with the mesh perimeter corresponding to each mesh size. The capture proportion of fish in different body length groups in each mesh was statistically analyzed, the selectivity coefficient was calculated, and the selectivity curve was plotted. The selectivity curve reflects the relative capture probability under different body length or carcass girth conditions, providing a basis for correcting the survey data.

[0107] During the calibration process, the measured number of fish caught was inversely weighted according to the selectivity coefficient of the corresponding body length group:

[0108]

[0109] This method can effectively eliminate the bias of net selection on sardine survey data, making the corrected body length frequency distribution closer to the true population structure.

[0110] Finally, the corrected tail data were used for resource assessment, including the corrected body length frequency distribution, quantity index, weight index, and resource estimate. Using a computer program to automatically perform selective curve fitting, data correction, and resource calculation enables rapid processing and standardized output of sardine survey data.

[0111] This embodiment introduces a model relating sardine mantle girth parameters to body length, enabling selective correction for different body length groups of sardines. Compared to traditional survey methods that rely solely on body length parameters, this embodiment more accurately reflects the true body length composition of sardines in the sea area, improving the reliability and comparability of resource estimation and providing a scientific basis for fisheries resource management and TAC (Targeted Assessment) formulation.

[0112] Comparative Example 1: Comparison between the conventional uncorrected method and the method of this invention

[0113] In nearshore pelagic fish resource surveys, traditional methods typically rely solely on body length to establish selectivity models, process survey data uniformly throughout the year, and fail to compensate for larger individuals. Based on this, the inventors compared the method of this invention with traditional methods, demonstrating that the method of this invention has significant advantages in accurately reflecting body length composition, correcting seasonal selectivity drift, and mitigating the low catch rate of larger individuals.

[0114] The specific comparison is as follows:

[0115] project Traditional methods Method of the present invention Seasonal processing Unified Selectivity Curve Throughout the Year Seasonal Selectivity Curve Modeling parameters Body length as the main factor Body length + carcass girth + body fat Large individuals Use the measured last digit directly Introducing a compensation term for tail fork length ≥ 300mm Corrected mantissa accuracy Large fish resources are significantly underestimated. The number of large fish tails has increased significantly, closely resembling their actual structure. Output Body length composition deviates from the true group structure The corrected body length composition more closely resembles the actual population structure. Resource assessment stability generally Significantly improved Creative embodiment lack Seasonal carcass model + large individual compensation + selective curve

[0116] Comparative analysis shows that:

[0117] 1. Seasonal correction: Traditional methods ignore the body size differences between the low fatness period and the high fatness period of mackerel after spawning, while the method of this invention establishes seasonally selective curves by relating fatness and carcass size, which significantly enhances the comparability of survey data from different seasons.

[0118] 2. Large Individual Compensation: Traditional methods underestimate the catch rate of mackerel with a tail fork length ≥300mm. The method of this invention increases the mesh size to 82mm and 106mm and introduces a large-size individual compensation term to make the corrected number of tails closer to the actual population structure.

[0119] 3. Body length-carcass girth combination: Compared with single body length modeling, the method of this invention introduces the carcass girth parameter to establish a selectivity curve, which reflects the fish's entry mechanism into the net and can accurately reflect the capture probability of fish in each body length group, thereby improving the accuracy of resource estimation.

[0120] 4. Data Output Standardization: This invention uses computer programs to achieve selective curve fitting, data correction, and resource quantity index output, enabling rapid processing and standardized output, thereby improving survey efficiency and the stability of evaluation results.

[0121] Through this comparative example, the present invention demonstrates significant technological progress in addressing the problems of seasonal selective drift, low capture rate of large individuals, and inaccurate resource assessment that exist in traditional methods, providing a reliable and scientific methodological system for the investigation of nearshore floating fish resources such as mackerel and sardines.

[0122] Further Implementation Method 1: Computer Program Processing Implementation Method

[0123] Building upon Examples 1-4, this further embodiment provides a method for correcting nearshore pelagic fish resource survey data using a computer program. This computer program can run on a general-purpose computer or an embedded data processing terminal, and is used to automatically complete the entire process of mantle girth parameter calculation, selective curve generation, weighted correction, compensation for large individuals, and resource quantity assessment, thereby achieving standardization and rapid processing of survey data.

[0124] This computer program mainly includes the following functional modules:

[0125] 1. Data Import Module

[0126] This module is responsible for importing fish species information, body length parameters, carcass girth parameters, mesh size, number of fish caught, survey time, and survey area information from survey databases or data acquisition equipment. It supports multiple data formats, including Excel, CSV, JSON, or database tables, ensuring compatibility with existing survey systems.

[0127] 2. Body Perimeter Model Construction Module

[0128] Based on the imported data, a body length-carcass girth correlation model is automatically established. For mackerel, fitting models for tail fork length and carcass girth can be constructed according to different seasons, and a fatness parameter can be introduced as a correction factor; for sardines, a linear fitting model can be established. This module can automatically select the best fitting method (linear regression, multiple regression, etc.) and output model parameters and fitting accuracy indicators (such as R-squared). 2 ).

[0129] 3. Selectivity Curve Generation Module

[0130] By combining the body girth model and the inner circumference data of each mesh size, the relative capture probability of fish in different body length groups is calculated, generating selectivity curves corresponding to each mesh size. The module supports visualization of the selectivity curves and can output selectivity coefficients for subsequent correction.

[0131] 4. Correction data calculation module

[0132] The measured tail count is reverse-weighted and corrected to calculate the corrected tail count. For mackerel with a tail fork length exceeding a preset threshold (e.g., 300mm), a large individual compensation term can be automatically applied. The module can perform weighted calculations based on the body length group selectivity coefficient, condition factor correction coefficient, and compensation coefficient, and generate a corrected body length frequency distribution.

[0133] 5. Resource Quantity Assessment Module

[0134] Based on the corrected body length data, resource quantity assessment indicators are calculated, including quantity index, weight index, biomass index, and estimated resource quantity. The module can output standardized reports, including tables and graphs, such as body length frequency distribution maps, selectivity curves, and resource quantity change trend maps.

[0135] 6. Report Generation Module

[0136] It automatically generates standardized survey reports from calibration results, selectivity curves, resource quantity indicators, and technical analysis results, supporting PDF, Excel, or graphic file output, which facilitates management decision-making and scientific research analysis.

[0137] In addition, this computer program allows you to set operating parameter options, including:

[0138] Selection of measurement sites for carcass circumference (circumorbital carcass circumference, posterior edge of gill cover carcass circumference, base of dorsal fin carcass circumference, maximum carcass circumference)

[0139] Automatic switching on seasonally selective curves

[0140] Adjustment of compensation threshold and compensation coefficient for large individuals

[0141] Output report content and format options

[0142] This further implementation method automates the entire process from importing raw survey data to outputting standardized resource quantities. Compared to traditional manual correction methods, this method has the following advantages:

[0143] High efficiency: It can quickly process large amounts of survey data, reducing manual operation time;

[0144] Standardization: Ensure the comparability of data from different survey times, regions, and teams;

[0145] Accuracy Improvement: By using seasonal mantle size models and compensation for large individuals, the consistency between the corrected tail count and the actual fish population structure is improved;

[0146] Scalability: Applicable to different fish species and different survey areas, and the model parameters and mesh combinations can be adjusted according to the survey needs.

[0147] This implementation fully embodies the technical solution of "selective curve plotting, weighted correction and large-scale individual compensation through computer program" as described in claim 10 of the present invention, which not only ensures the feasibility of the method, but also provides technical support for achieving rapid and standardized survey data processing.

[0148] Further Implementation Method 2: Technical Effect Verification Implementation Method

[0149] To verify the effectiveness of the nearshore floating fish resource survey data correction method proposed in this invention, the inventors selected the same batch of survey data and processed it using both traditional resource survey processing methods and the method of this invention, and then compared and analyzed the processing results.

[0150] Traditional methods use uniform selective parameters throughout the year to process resource survey data, establish selective models based solely on body length parameters, and directly use the body length composition of the catch obtained from the survey to estimate resource quantity, without considering fish body girth parameters, seasonal changes in fatness, and compensation factors for large individuals.

[0151] The method of the present invention adopts the technical solutions described in Examples 1 to 4, establishes a selective curve model based on the fish body girth parameters, establishes a mackerel tail fork length-body girth relationship model according to different seasons, and completes the resource quantity estimation after setting a compensation mechanism for large individuals.

[0152] (a) Verification of seasonal body shape changes

[0153] First, mackerel survey samples obtained in different seasons were selected for analysis.

[0154] The statistical results are shown in Table 5.

[0155] Table 5. Comparison of body size characteristics of mackerel in different seasons

[0156] Survey period Sample size Average tail fork length (mm) Average maximum body circumference (mm) Average fatness June to July 458 298.4 162.3 11.8 September to November 512 287.5 171.6 14.1

[0157] As can be seen from Table 5, although the average tail fork length of mackerel was relatively similar between the two periods, there were significant differences in body circumference and fatness.

[0158] This indicates that the body length parameter of a fish cannot fully reflect the actual changes in its body shape.

[0159] Traditional uniform selectivity models cannot accurately reflect the above differences. However, this invention can effectively eliminate selectivity drift errors caused by seasonal body size changes by introducing body fat correction and seasonal body size models.

[0160] (II) Validation of the carcass model

[0161] Further compare the fitting effects of the body length model and the carcass circumference model.

[0162] The statistical results are shown in Table 6.

[0163] Table 6 Comparison of Fitting Results for Different Modeling Methods

[0164] Modeling methods <![CDATA[Coefficient of determination R 2 > Body length model 0.78 Mantle model 0.94

[0165] As can be seen from Table 6, the fitting accuracy of the carcass circumference model is significantly higher than that of the traditional body length model.

[0166] This is because the capture process of drift gillnets is essentially controlled by the matching relationship between the fish's body circumference and the inner circumference of the mesh. Therefore, using the body circumference parameter can more realistically reflect the actual capture process of the fish.

[0167] (III) Verification of the Compensation Effect on Large Individuals

[0168] Further analysis was conducted on mackerel samples with a tail fork length of 300mm or more.

[0169] The resource composition results were calculated using both traditional methods and the method of this invention.

[0170] The statistical results are shown in Table 7.

[0171] Table 7 Comparison results of large individual samples before and after correction

[0172] Length group (mm) Actual measured last digits Corrected endianness 250～275 526 548 275～300 437 482 300～325 112 241 325～350 31 86

[0173] As can be seen from Table 7, in the group with a body length of 300 mm or more, the corrected tail number is significantly higher than the measured tail number.

[0174] This indicates that traditional survey methods significantly underestimate the proportion of large individuals, while the present invention, by introducing a large individual compensation mechanism, can effectively restore the actual proportion of large individuals in the population.

[0175] (iv) Comparison of resource assessment results

[0176] Further compare the resource assessment results of traditional methods with those of the present invention.

[0177] The statistical results are shown in Table 8.

[0178] Table 8 Comparison of Resource Survey and Processing Results

[0179] Comparison items Traditional methods Method of the present invention Should body circumference parameters be considered? no yes Do you consider seasonal changes? no yes Should large-scale individual compensation be provided? no yes Large individual estimation error 38% 7% Stability of survey results generally Significantly improved Authenticity of body length composition lower higher

[0180] As can be seen from Table 8, the method of the present invention is superior to the traditional method in terms of the estimation accuracy of large individuals, the stability of survey results, and the authenticity of body length composition.

[0181] (V) Comprehensive technical effect analysis

[0182] The above verification results demonstrate that the nearshore floating fish resource survey data correction method proposed in this invention can fully utilize fish body girth parameters to establish a selective curve model, and combine the seasonal changes in fish body fatness and the compensation mechanism for large individuals to uniformly correct the survey data.

[0183] Compared with traditional resource survey methods, this invention can not only reduce the systematic error caused by the selectivity of drift nets, but also improve the comparability of survey results in different seasons and effectively improve the problem of underestimation of the resource quantity of large individuals.

[0184] Therefore, this invention can more realistically reflect the population structure characteristics of pelagic fish in the surveyed sea area, improve the accuracy and reliability of resource assessment results, and provide a more scientific data basis for fishery resource management and the formulation of total allowable catch.

[0185] In summary, this invention achieves standardized correction of nearshore planktonic fish resource survey data by introducing fish body girth parameters, seasonal condition factor correction, mesh selectivity curves, and a compensation mechanism for large individuals. This method effectively reduces survey bias caused by drift gillnet selectivity, improves the accuracy and comparability of fish resource assessment results for different seasons and body length groups, and can achieve rapid processing and standardized output through computer programs. It is applicable to nearshore planktonic fish resource surveys, resource quantity assessments, and fisheries management decisions.

[0186] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications, substitutions, combinations, or equivalent modifications can be made to the technical features in the above embodiments without departing from the spirit and substance of the present invention; any modifications, equivalent substitutions, improvements, or combinations made within the concept and principles of the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A method for correcting nearshore pelagic fish resource survey data, characterized in that, The steps include the following: S1. Use multi-mesh drift gillnets to investigate and sample the target floating fish, and obtain catch samples corresponding to different mesh sizes; S2. Measure the body length and mantle circumference parameters of the fish caught, wherein the mantle circumference parameters include at least one of the following: periocular mantle circumference, posterior edge mantle circumference of the operculum, base mantle circumference of the dorsal fin, and maximum mantle circumference; S3. Based on the body length and mantle circumference parameters, establish the correspondence between the body length and mantle circumference of the target flounder. For mackerel, establish the relationship between tail fork length and mantle circumference according to the season. For sardines, establish a linear fitting relationship.

2. The method according to claim 1, characterized in that: The target fish include sardines and mackerel. The seasonal division of mackerel includes at least two stages: the low-fat period after spawning and the high-fat period.

3. The method according to claim 1 or 2, characterized in that: In step S3, when establishing the body length-carcass circumference relationship, a fatness parameter is introduced to reflect seasonal body size differences and correct for deviations in tail fork length and carcass circumference.

4. The method according to any one of claims 1 to 3, characterized in that: In step S4, the inner perimeter of each mesh size is matched with the fish body circumference parameter to establish a selectivity curve for each mesh size, which is used to represent the relative capture probability of fish of different body lengths.

5. The method according to claim 4, characterized in that: In step S5, the measured fish body length composition data are weighted and corrected. The correction method includes dividing the measured number of fish by the selectivity coefficient of the corresponding body length group to obtain the corrected number of fish.

6. The method according to claim 5, characterized in that: For mackerel with a tail fork length of 300mm or more, a large-size individual compensation item is set. The compensation item is calculated based on the selectivity curves corresponding to 82mm and 106mm mesh sizes to make up for the underestimation error caused by insufficient sampling of large individuals in traditional mesh combinations.

7. The method according to any one of claims 1 to 6, characterized in that: In step S6, the corrected body length composition data is used to output fish population evaluation data, including the corrected body length frequency distribution, quantity index, weight index, or population estimate.

8. The method according to any one of claims 1 to 7, characterized in that: The multi-mesh drift net in step S1 includes net sheets with different mesh sizes ranging from at least 18mm to 121mm. The net sheet layout ratio can be dynamically adjusted according to the fatness of the mackerel and the season to optimize the sampling coverage of fish of different body lengths.

9. The method according to any one of claims 1 to 8, characterized in that: The measurement of the carcass circumference in step S2 can be performed using a periorbital carcass circumference measurement band, a gill cover posterior edge measuring scale, or digital image analysis to ensure high-precision measurement data.

10. The method according to any one of claims 1 to 9, characterized in that: The selective curve plotting, weighted correction, and large-scale individual compensation in steps S4 to S6 can be implemented by computer programs to achieve rapid processing of survey data and standardized output of resource quantities.

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