Method for distinguishing good Eriocheir sinensis through interactive behaviors

Abstract

The invention discloses a method for distinguishing a good Chinese mitten crab through interactive behaviors. The method specifically comprises the following steps: (1) establishing an interactive behavior spectrum of the Chinese mitten crab; (2) obtaining interactive behavior characteristics of the good crabs and the non-good crabs through data analysis; (3) distinguishing the good crabs from the non-good crabs by utilizing the interactive behavior characteristics; and (4) verifying the good crabs and the non-good crabs. In the step (1), all interactive behaviors generated after the two crabs meet are summarized and described through a fighting experiment, and an interactive behavior spectrum is formed. The fighting experiment device comprises a cylindrical experiment water tank with a partition plate and a video recording system, the video recording system comprises a camera, and the camera is placed over the experiment water tank. According to the method, the forming mechanism of the crabs is illustrated, so that a man-made interference technology is formed, the crabs are screened out, the crabs and the non-crabs can be bred separately, the fighting behavior of the crabs is effectively reduced, and the yield and quality of the eriocheir sinensis are improved.

Description

Technical Field

[0001] The present application relates to the technical field of production and breeding of Chinese mitten crabs, and in particular to a method for distinguishing aggressive and non-aggressive Chinese mitten crabs. Background Art

[0002] In recent years, the aquaculture of Chinese mitten crabs has transformed from lake enclosure aquaculture to pond ecological aquaculture, opening up a new model of "green transformation". Under high-density pond aquaculture conditions, fighting behavior intensifies. The loss of appendages or body damage caused by fighting can lead to a decrease in immunity, reduced foraging efficiency and mating success rate, and cannibalism, which seriously affects the survival, growth, quality and economic value of Chinese mitten crabs.

[0003] In addition, due to the inability to feed normally due to limb defects, a large amount of bait will accumulate at the bottom of the pond, polluting the breeding environment and reducing the level of green ecological breeding.

[0004] Aggressive crabs are relatively common individuals among Chinese mitten crabs. Aggressive behavior often causes more energy loss, more serious limb breakage, and even death. The more aggressive crabs there are in the breeding process, the greater the economic losses. Summary of the invention

[0005] The invention provides a method for identifying aggressive Chinese mitten crabs through interactive behaviors, the purpose of which is to identify aggressive crab individuals and culture the aggressive crabs separately from non-aggressive crabs, so as to improve the yield and quality of Chinese mitten crabs.

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

[0007] A method for identifying aggressive Chinese mitten crabs through interactive behavior, specifically comprising the following steps:

[0008] (1) Establish the interactive behavior spectrum of Chinese mitten crab;

[0009] (2) Obtain the interactive behavior characteristics of aggressive and non-aggressive crabs through data analysis;

[0010] (3) using interactive behavior characteristics to distinguish aggressive crabs from nonaggressive crabs;

[0011] (4) Verification of aggressive crabs and non-aggressive crabs.

[0012] Furthermore, in step (1), all interactive behaviors that occur after the two crabs meet are summarized and described through a fighting experiment to form an interactive behavior spectrum.

[0013] Furthermore, the fighting experiment device includes a cylindrical experimental water tank with a partition and a video recording system. The video recording system includes a camera, and the camera is placed directly above the experimental water tank.

[0014] Furthermore, two male crabs with a weight difference of 1%-4% were paired and placed on both sides of the experimental water tank. A partition was placed in the middle of the experimental water tank to prevent the two from touching each other. After the crabs adapted for 10 minutes, the partition was removed and a high-definition camera was used to record the fighting of the crabs within 1 hour. The recorded video was analyzed and data was collected.

[0015] Furthermore, in step (2), principal component analysis is performed using the 12 common behaviors in the interactive behavior spectrum as variables. The specific analysis steps are as follows:

[0016] 1) Standardize the original data to eliminate the impact of dimension (calculated by SPSS)

[0017] Assume that there are m indicator variables for principal component analysis: x 1 ,x 2 ,…,x m , where there are n evaluation objects, the value of the jth indicator of the i-th evaluation object is x, and each indicator value is converted into a standardized indicator

[0018]

[0019] in, is the sample mean of the jth indicator, s j is the sample standard deviation of the jth indicator, corresponding to For standardized indicator variables: Thus, the standardization of 12 interaction behavior indicators of all individuals was obtained;

[0020] 2) Establish the correlation coefficient matrix R between variables

[0021] Correlation coefficient matrix R = (r ij ) m×m ,in In the formula, r ii =1,r ij =r ji ,r ij is the correlation coefficient between the ith indicator and the jth indicator;

[0022] 3) Calculate the eigenvalues ​​and eigenvectors of the correlation coefficient matrix R

[0023] Calculate the eigenvalue λ of the correlation coefficient matrix R 1 ≥λ 2 ≥…≥λ m ≥0, and the corresponding eigenvector u 1 ,u 2 ,…u m , where u j =(u 1j ,u 2j,…u nj ) T , m new indicator variables are composed of feature vectors;

[0024]

[0025] Among them, y 1 is the first principal component PC1,y 2 is the second principal component PC2…,y m is the mth principal component PCM;

[0026] The principal components PC with eigenvalue greater than 1 were selected as the analysis object, and the first three principal components were retained, which explained 74.7% of the total variance. PC1 explained 45.74% of the total variance;

[0027] 4) Write out the principal components and calculate the comprehensive score

[0028] Calculate the eigenvalue λ j =(j=1,2,…,m)’s information contribution rate and cumulative contribution rate are called The principal component y j Information contribution rate;

[0029] The principal component y 1 ,y 2 ,…y p The cumulative contribution rate of a p Close to 1(a r =0.85,0.90,0.95), then select the first p indicator variables y 1 ,y 2 ,…y p , as p principal components, replacing the original m indicator variables, so that the p principal components can be comprehensively analyzed;

[0030] Calculate the composite score where b j is the information contribution rate of the jth principal component.

[0031] Furthermore, PC1 is the aggressive type, and two opposite variables can be clearly observed in the loading diagram, among which grabbing, pushing, forward approach, intimidation, stretching and patrolling are aggressive behaviors and are positively loaded on PC1, and stillness and curling up are non-aggressive behaviors and are reversely loaded on PC1. PC2 explains 19.26% of the total variance and has the highest scores in probing, crawling along the wall and ignoring. At the same time, among the 12 fighting behaviors, forward approach, intimidation, grabbing, pushing, stretching and patrolling are significantly positively correlated; stillness and curling up are significantly positively correlated and negatively correlated with the first six behaviors, and the other four behaviors are not statistically significant.

[0032] Furthermore, according to the PCA score graph, aggressive crabs are more likely to perform aggressive behaviors such as grabbing, pushing, forward approaching, intimidation, stretching, and patrolling during the fight, while non-aggressive crabs are more likely to perform non-aggressive behaviors such as curling up and staying still, and ordinary crabs are more likely to perform behaviors such as escaping, crawling along the wall, and probing;

[0033] Finally, it was found that positive approach, threatening with claws, grabbing, pushing, stretching and patrolling are the interactive behavior characteristics of aggressive crabs, while curling up and staying still are the interactive behavior characteristics of non-aggressive crabs.

[0034] Furthermore, in step (3), the behavioral characteristics of aggressive crabs (grasping, pushing, forward approaching, intimidation, stretching and patrolling) and the behavioral characteristics of non-aggressive crabs (stillness and curling up) are used as variables to perform K-means cluster analysis on all individuals. The specific analysis steps are as follows:

[0035] a) In a set of n data objects, determine the number of clusters k and arbitrarily select k cluster centers as the initial cluster centers;

[0036] b) Calculate the Euclidean distance from each data object to the k cluster centers. The distance formula is: Select the data object with the minimum distance from the cluster center, that is, select the most similar data objects and divide them into a group;

[0037] c) After all data objects are divided, recalculate the average value of each group for each k group and use it as the cluster center of each group;

[0038] d) Repeat b) and c) until the division of data objects no longer changes, and the clustering is completed;

[0039] Through K-means cluster analysis, all individuals were divided into three categories, namely, aggressive crabs, non-aggressive crabs and other crabs (individuals other than aggressive crabs and non-aggressive crabs).

[0040] Furthermore, after obtaining the aggressive crab and non-aggressive crab groups in step (4), fighting experiments are performed on each group for verification.

[0041] Furthermore, data such as the number of fights, number of approaches, number of contacts and fighting time were obtained in the fighting experiment as a basis for verifying the aggressive personality. The results showed that the number of fights (p<0.01), number of contacts (p<0.01), number of approaches (p<0.05) and single fighting time (p<0.01) of the aggressive crab group were significantly higher than those of the non-aggressive crab group.

[0042] The technical principle of the present invention is that there are significant differences in interactive behaviors between aggressive crabs and non-aggressive crabs. Under laboratory conditions, aggressive behaviors (grasping, pushing, forward approaching, intimidation, stretching and patrolling) and non-aggressive behaviors (curling and stillness) can be used as variables to perform K-means cluster analysis to accurately distinguish between aggressive and non-aggressive crab groups. Farmers on the production line can also observe the interactive behaviors of river crabs during the breeding process to distinguish between aggressive and non-aggressive crabs. For example, individuals that remain still and curl up for a long time are non-aggressive crabs, and individuals that move frequently and push and grab other individuals are aggressive crabs.

[0043] Beneficial effects: The method of the present invention is to identify aggressive Chinese mitten crabs through interactive behavior, and to clarify the formation mechanism of aggressive crabs under laboratory conditions so as to form an artificial interference technology to screen out aggressive crab individuals. Aggressive crabs can be cultured separately from non-aggressive crabs, which effectively reduces the fighting behavior of crabs and is beneficial to the improvement of the yield and quality of Chinese mitten crabs. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 Schematic diagram of the fighting behavior observation system.

[0045] Figure 2 A series of principal component analysis diagrams of aggressive crabs, non-aggressive crabs and common crabs (2a: loading diagram of each behavior in the interactive behavior spectrum, 2b: heat map of the correlation of 12 behaviors, 2c: schematic diagram of PCA scores of aggressive crabs, non-aggressive crabs and common crabs).

[0046] Figure 3 Schematic diagram of the difference in fighting behavior between aggressive crabs and non-aggressive crabs (3a: number of fights, 3b: number of contacts, 3c: number of approaches, 3d: duration of a single fight, asterisks on the bar graph indicate significant differences between the two groups). DETAILED DESCRIPTION

[0047] The following is a detailed description of an embodiment of the present invention in conjunction with the accompanying drawings: This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

[0048] A method for identifying aggressive Chinese mitten crabs through interactive behavior, specifically comprising the following steps:

[0049] (1) Establish the interactive behavior spectrum of Chinese mitten crab;

[0050] The step (1) summarizes and describes all interactive behaviors that occur after the two crabs meet through a fighting experiment to form an interactive behavior spectrum. The fighting experiment device includes a cylindrical experimental water tank with a partition and a video recording system. The video recording system includes a camera, which is placed directly above the experimental water tank ( Figure 1 ).

[0051] Two male crabs with a weight difference of 1%-4% were paired and placed on both sides of the experimental water tank. A partition was placed in the middle of the experimental water tank so that the two could not touch each other. After the crabs adapted for 10 minutes, the partition was removed and a high-definition camera was used to record the fighting between the two within 1 hour. The recorded video was analyzed and data was collected. The interactive behavior spectrum includes 15 interactive behaviors, as shown in Table 1.

[0052] Table 1 Interactive behavior spectrum of river crabs

[0053]

[0054] Note: Bold fonts indicate that the interactive behaviors are used for PCA analysis. Winner posture, chelicerae cleaning, and defensive clamping occur less frequently and are not significant, so they are not used as PCA variables.

[0055] (2) Obtain the interactive behavior characteristics of aggressive and non-aggressive crabs through data analysis;

[0056] The step (2) uses the 12 common behaviors in the interactive behavior spectrum (Table 2) as variables to perform principal component analysis. The specific analysis steps are as follows:

[0057] 1) Standardize the original data to eliminate the impact of dimension (calculated by SPSS)

[0058] Assume that there are m indicator variables for principal component analysis: x 1 ,x 2 ,…,x m , where there are n evaluation objects, the value of the jth indicator of the i-th evaluation object is x, and each indicator value is converted into a standardized indicator

[0059]

[0060] in, is the sample mean of the jth indicator, s j is the sample standard deviation of the jth indicator, corresponding to For standardized indicator variables: Thus, the standardization of 12 interaction behavior indicators of all individuals was obtained;

[0061] 2) Establish the correlation coefficient matrix R between variables

[0062] Correlation coefficient matrix R = (r ij ) m×m ,in In the formula, r ii =1,r ij =r ji ,r ijis the correlation coefficient between the ith indicator and the jth indicator;

[0063] 3) Calculate the eigenvalues ​​and eigenvectors of the correlation coefficient matrix R

[0064] Calculate the eigenvalue λ of the correlation coefficient matrix R 1 ≥λ 2 ≥…≥λ m ≥0, and the corresponding eigenvector u 1 ,u 2 ,…u m , where u j =(u 1j ,u 2j ,…u nj ) T , m new indicator variables are composed of feature vectors;

[0065]

[0066] Among them, y 1 is the first principal component PC1,y 2 is the second principal component PC2…,y m is the mth principal component PCM;

[0067] The principal components PC with eigenvalue greater than 1 were selected as the analysis objects, and the first three principal components were retained, which explained 74.7% of the total variance. PC1 explained 45.74% of the total variance (Table 2);

[0068] 5) Write out the principal components and calculate the comprehensive score

[0069] Calculate the eigenvalue λ j =(j=1,2,…,m)’s information contribution rate and cumulative contribution rate are called The principal component y j Information contribution rate;

[0070] The principal component y 1 ,y 2 ,…y p The cumulative contribution rate of a p Close to 1(a r =0.85,0.90,0.95), then select the first p indicator variables y 1 ,y 2 ,…y p , as p principal components, replacing the original m indicator variables, so that a comprehensive analysis of the p principal components can be performed.

[0071] Calculate the composite score where b j is the information contribution rate of the jth principal component.

[0072] PC1 is of the aggressive type, and two opposite variables can be clearly observed in the loading diagram ( Figure 2 a), among which grasping, pushing, approaching, intimidating, stretching and patrolling as aggressive behaviors are all positively loaded on PC1, and static and curling as non-aggressive behaviors are negatively loaded on PC1 ( Figure 2 a), PC2 explained 19.26% of the total variance and had the highest scores in exploration, wall crawling and ignoring. Among the 12 fighting behaviors, positive approach, intimidation, grabbing, pushing, stretching and patrolling were significantly positively correlated ( Figure 2 b, p<0.05); there was a significant positive correlation between static and curled up and a negative correlation with the first six behaviors ( Figure 2 b, p<0.05), and the other four were not statistically significant.

[0073] According to the PCA score chart, aggressive crabs are more likely to perform aggressive behaviors such as grabbing, pushing, forward approach, intimidation, stretching and patrolling during the fight, while non-aggressive crabs are more likely to perform non-aggressive behaviors such as curling up and staying still, and ordinary crabs are more likely to perform behaviors such as escaping, crawling along the wall and probing ( Figure 2 c);

[0074] Finally, it was found that positive approach, threatening with claws, grabbing, pushing, stretching and patrolling are the interactive behavior characteristics of aggressive crabs, while curling up and staying still are the interactive behavior characteristics of non-aggressive crabs.

[0075] Table 2 The first three principal components (PCs) of the interactive behavior

[0076]

[0077]

[0078] Note: Bold indicates the corresponding PC contributes the most to the load, with an absolute value > 0.4.

[0079] (3) using interactive behavior characteristics to distinguish aggressive crabs from nonaggressive crabs;

[0080] In step (3), the behavioral characteristics of aggressive crabs (grasping, pushing, forward approaching, intimidation, stretching and patrolling) and the behavioral characteristics of non-aggressive crabs (stillness and curling up) are used as variables to perform K-means cluster analysis on all individuals. The specific analysis steps are as follows:

[0081] a) In a set of n data objects, determine the number of clusters k and arbitrarily select k cluster centers as the initial cluster centers;

[0082] b) Calculate the Euclidean distance from each data object to the k cluster centers. The distance formula is: Select the data object with the minimum distance from the cluster center, that is, select the most similar data objects and divide them into a group;

[0083] c) After all data objects are divided, recalculate the average value of each group for each k group and use it as the cluster center of each group;

[0084] d) Repeat b) and c) until the division of data objects no longer changes, and the clustering is completed;

[0085] Through K-means cluster analysis, all individuals were divided into three categories, namely, aggressive crabs, non-aggressive crabs and other crabs (individuals other than aggressive crabs and non-aggressive crabs).

[0086] (4) Verification of aggressive crabs and non-aggressive crabs.

[0087] After obtaining the aggressive crab and non-aggressive crab groups in step (4), fighting experiments were performed respectively. The experimental device was the same as Figure 1 .

[0088] In the fighting experiment, data such as the number of fights, the number of approaches, the number of contacts and the fighting time were obtained as the basis for verifying the aggressive personality. The results showed that the number of fights (p<0.01), the number of contacts (p<0.01), the number of approaches (p<0.05) and the single fighting time (p<0.01) of the aggressive crab group were significantly higher than those of the non-aggressive crab group (such as Figure 3 ).

[0089] The technical principle of the present invention is that there are significant differences in interactive behaviors between aggressive crabs and non-aggressive crabs. Under laboratory conditions, aggressive behaviors (grasping, pushing, forward approaching, intimidation, stretching and patrolling) and non-aggressive behaviors (curling and stillness) can be used as variables to perform K-means cluster analysis to accurately distinguish between aggressive and non-aggressive crab groups. Farmers on the production line can also observe the interactive behaviors of river crabs during the breeding process to distinguish between aggressive and non-aggressive crabs. For example, individuals that remain still and curl up for a long time are non-aggressive crabs, and individuals that move frequently and push and grab other individuals are aggressive crabs.

[0090] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for identifying aggressive Chinese mitten crabs through interactive behavior, characterized in that: The specific steps include: (1) Establish the interactive behavior spectrum of Chinese mitten crab; (2) Obtain the interactive behavior characteristics of aggressive and non-aggressive crabs through data analysis; (3) using interactive behavior characteristics to distinguish aggressive crabs from nonaggressive crabs; (4) Verification of aggressive crabs and non-aggressive crabs.

2. The method for identifying aggressive Chinese mitten crabs through interactive behavior according to claim 1, characterized in that: The step (1) summarizes and describes all interactive behaviors that occur after the two crabs meet through a fighting experiment to form an interactive behavior spectrum.

3. The method for identifying aggressive Chinese mitten crabs through interactive behavior according to claim 1, characterized in that: The fighting experiment apparatus includes a cylindrical experimental water tank with a partition and a video recording system. The video recording system includes a camera placed directly above the experimental water tank.

4. The method for identifying aggressive Chinese mitten crabs through interactive behavior according to claim 1, characterized in that: Two male crabs with a weight difference of 1%-4% were paired and placed on both sides of an experimental water tank. A partition was placed in the middle of the experimental water tank to prevent the two from touching each other. After the crabs adapted for 10 minutes, the partition was removed and a high-definition camera was used to record the fighting of the crabs within 1 hour. The recorded video was analyzed and data was collected.

5. The method for identifying aggressive Chinese mitten crabs through interactive behavior according to claim 1, characterized in that: The step (2) uses the 12 common behaviors in the interactive behavior spectrum as variables to perform principal component analysis. The specific analysis steps are as follows: 1) Standardize the original data to eliminate the influence of dimension. Assume that there are m indicator variables for principal component analysis: x1, x2, …, x m , where there are n evaluation objects, the value of the jth indicator of the i-th evaluation object is x, and each indicator value is converted into a standardized indicator in, is the sample mean of the jth indicator, s j is the sample standard deviation of the jth indicator, corresponding to is a standardized indicator variable: Thus, the standardization of 12 interaction behavior indicators of all individuals was obtained; 2) Establish the correlation coefficient matrix R between variables Correlation coefficient matrix R = (r ij ) m×m ,in In the formula, r ii =1,r ij =r ji ,r ij is the correlation coefficient between the ith indicator and the jth indicator; 3) Calculate the eigenvalues ​​and eigenvectors of the correlation coefficient matrix R Calculate the eigenvalues ​​of the correlation coefficient matrix R λ1≥λ2≥…≥λ m ≥0, and the corresponding eigenvectors u1,u2,…u m , where u j =(u 1j ,u 2j ,…u nj ) T , m new indicator variables are composed of feature vectors; Among them, y1 is the first principal component PC1, y2 is the second principal component PC2…, y m is the mth principal component PCM; The principal components PC with eigenvalue greater than 1 were selected as the analysis object, and the first three principal components were retained, which explained 74.7% of the total variance, and PC1 explained 45.74% of the total variance; 6) Write out the principal components and calculate the comprehensive score Calculate the eigenvalue λ j =(j=1,2,…,m)’s information contribution rate and cumulative contribution rate, called The principal component y j Information contribution rate; are the principal components y1,y2,…y p The cumulative contribution rate of a p Close to 1(a r =0.85,0.90,0.95), then select the first p indicator variables y1,y2,…y p , as p principal components, replacing the original m indicator variables, so that the p principal components can be comprehensively analyzed; Calculate the composite score where b j is the information contribution rate of the jth principal component.

6. The method for identifying aggressive Chinese mitten crabs through interactive behavior according to claim 1, characterized in that: PC1 is the aggressive type. Two opposite variables can be observed in the loading diagram. Grasping, pushing, forward approach, intimidation, stretching and patrolling are aggressive behaviors and are positively loaded on PC1. Stillness and curling up are non-aggressive behaviors and are reversely loaded on PC1. PC2 explains 19.26% of the total variance and has the highest scores in probing, crawling along the wall and ignoring. Among the 12 fighting behaviors, forward approach, intimidation, grasping, pushing, stretching and patrolling are significantly positively correlated; stillness and curling up are significantly positively correlated and negatively correlated with the first six behaviors, and the other four behaviors are not statistically significant.

7. The method for identifying aggressive Chinese mitten crabs through interactive behavior according to claim 1, characterized in that: According to the PCA score chart, aggressive crabs are more likely to perform aggressive behaviors such as grabbing, pushing, forward approach, intimidation, stretching and patrolling during the fight, non-aggressive crabs are more likely to perform non-aggressive behaviors such as curling up and staying still, and ordinary crabs are more likely to perform behaviors such as escaping, crawling along the wall and probing; Finally, it was found that positive approach, threatening with claws, grabbing, pushing, stretching and patrolling are the interactive behavior characteristics of aggressive crabs, while curling up and staying still are the interactive behavior characteristics of non-aggressive crabs.

8. The method for identifying aggressive Chinese mitten crabs through interactive behavior according to claim 1, characterized in that: In step (3), the behavioral characteristics of aggressive crabs and non-aggressive crabs are used as variables to perform K-means cluster analysis on all individuals. The specific analysis steps are as follows: a) In a set of n data objects, determine the number of clusters k and arbitrarily select k cluster centers as the initial cluster centers; b) Calculate the Euclidean distance from each data object to the k cluster centers. The distance formula is: Select the data object with the minimum distance from the cluster center, that is, select the most similar data objects and divide them into a group; c) After all data objects are divided, recalculate the average value of each group for each k group and use it as the cluster center of each group; d) Loop through b) and c) until the division of data objects no longer changes, and the clustering is complete; Through K-means cluster analysis, all individuals were divided into three categories, namely aggressive crabs, non-aggressive crabs and other crabs.

9. The method for identifying aggressive Chinese mitten crabs through interactive behavior according to claim 1, characterized in that: After obtaining the aggressive crab and non-aggressive crab groups in step (4), fighting experiments are performed on each group to verify the results.

10. The method for identifying aggressive Chinese mitten crabs through interactive behavior according to claim 1, characterized in that: In the fighting experiment, data such as the number of fights, number of approaches, number of contacts and fighting time were obtained as the basis for verifying the aggressive personality. The results showed that the number of fights, number of contacts, number of approaches and single fighting time of the aggressive crab group were significantly higher than those of the non-aggressive crab group.

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