A method for identifying aggressive Chinese mitten crabs through interactive behavior

Abstract

This invention discloses a method for identifying aggressive Chinese mitten crabs through interactive behavior, specifically including the following steps: (1) establishing an interactive behavior spectrum of Chinese mitten crabs; (2) obtaining interactive behavior characteristics of aggressive and non-aggressive crabs through data analysis; (3) distinguishing aggressive and non-aggressive crabs using interactive behavior characteristics; and (4) verifying the distinction between aggressive and non-aggressive crabs. Step (1) summarizes and describes all interactive behaviors that occur after two crabs meet through fighting experiments, forming an interactive behavior spectrum. The fighting experiment apparatus includes a cylindrical experimental tank with a partition and a video recording system. The video recording system includes a camera placed directly above the experimental tank. This invention elucidates the formation mechanism of aggressive crabs, enabling the development of artificial interference techniques to screen out aggressive crab individuals. Aggressive and non-aggressive crabs can be raised separately, effectively reducing fighting behavior and contributing to the improvement of the yield and quality of Chinese mitten crabs.

Description

Technical Field

[0001] This application relates to the field of production and aquaculture technology of Chinese mitten crab, specifically to a method for distinguishing between aggressive and non-aggressive Chinese mitten crabs. Background Technology

[0002] In recent years, the farming of Chinese mitten crabs has shifted from lake enclosure farming to pond ecological farming, initiating a new "green transformation" model. Under high-density pond farming conditions, fighting behavior intensifies. The loss of appendages or body injuries caused by fighting can lead to decreased immunity, reduced foraging efficiency and mating success rates, and cannibalism, severely impacting the survival, growth, quality, and economic value of the Chinese mitten crab.

[0003] In addition, because the missing limbs prevent them from feeding normally, a large amount of feed will accumulate at the bottom of the pond, polluting the aquaculture environment and reducing the level of green and ecological aquaculture.

[0004] Fighting crabs are relatively common among Chinese mitten crabs. Their aggressive behavior often leads to greater energy loss, more severe limb amputation, or even death. The higher the proportion of fighting crabs in aquaculture, the greater the economic loss. Summary of the Invention

[0005] This invention provides a method for identifying aggressive Chinese mitten crabs through interactive behavior. The purpose is to identify aggressive crab individuals and separate aggressive crabs from non-aggressive crabs for breeding, thereby improving the yield and quality of Chinese mitten crabs.

[0006] This invention is achieved through the following technical solution:

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

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

[0009] (2) Through data analysis, the interactive behavior characteristics of aggressive and non-aggressive crabs were obtained;

[0010] (3) Use interactive behavior characteristics to distinguish between good fighting crabs and bad fighting crabs;

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

[0012] Furthermore, step (1) summarizes and describes all the interactive behaviors that occur after the two crabs meet through fighting experiments, forming an interactive behavior spectrum.

[0013] Furthermore, the apparatus for the fighting experiment includes a cylindrical experimental water tank with a partition and a video recording system, which includes a camera positioned directly above the experimental water tank.

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

[0015] Furthermore, step (2) uses 12 common behaviors in the interaction behavior spectrum as variables to perform principal component analysis. The specific analysis steps are as follows:

[0016] 1) Standardize the raw data to eliminate the influence of dimensions (calculated using SPSS).

[0017] Suppose there are m index variables for principal component analysis: x1, x2, ..., x m There are n evaluation objects, and the value of the j-th indicator of the i-th evaluation object is x. The values ​​of each indicator are then converted into standardized indicators.

[0018]

[0019] in, Let s be the sample mean of the j-th indicator. j Let be the sample standard deviation of the j-th indicator, corresponding to Standardized indicator variables: This allows for the standardization of 12 interactive behavior indicators for all individuals;

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

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

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

[0023] 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 The feature vectors form m new index variables;

[0024]

[0025] Where y1 is the first principal component PC1, y2 is the second principal component PC2, ..., y m It is the m-th principal component PCM;

[0026] Principal components with eigenvalues ​​greater than 1 (PC) were selected as the analysis objects. The first three principal components were retained, explaining a total of 74.7% of the total variance. PC1 explained 45.74% of the total variance.

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

[0028] Calculate the eigenvalue λ j The information contribution rate and cumulative contribution rate of (j = 1, 2, ..., m) are called... Main component y j Information contribution rate;

[0029] Principal components y1, y2, ... y p The cumulative contribution rate when a p Close to 1(a) r When the values ​​are 0.85, 0.90, 0.95, then select the first p index variables y1, y2, ... y p As p principal components, they replace the original m index variables, thus enabling comprehensive analysis of the p principal components;

[0030] Calculate the overall score Where b j The information contribution rate of the j-th principal component.

[0031] Furthermore, PC1 represents the aggressive type, and two opposing variables can be clearly observed in the loading plot. Grabbing, shoving, forward approach, intimidation, stretching, and patrolling are aggressive behaviors that are positively loaded onto PC1, while stillness and curling up are non-aggressive behaviors that are negatively loaded onto PC1. PC2 explains 19.26% of the total variance and scores highest on probing, wall crawling, and ignoring. Among the 12 fighting behaviors, forward approach, intimidation, grabbing, shoving, stretching, and patrolling are significantly positively correlated; stillness and curling up are significantly positively correlated and negatively correlated with the first six behaviors, while the other four are not statistically significant.

[0032] Furthermore, according to the PCA score chart, aggressive crabs tend to exhibit aggressive behaviors such as grabbing, pushing, approaching forward, intimidating, stretching, and patrolling during fights, while non-aggressive crabs tend to exhibit non-aggressive behaviors such as curling up and remaining still, and ordinary crabs tend to exhibit behaviors such as escaping, crawling along walls, and probing.

[0033] Ultimately, the following behavioral characteristics were identified: approaching positively, raising claws to intimidate, grabbing, pushing, stretching, and patrolling were the behavioral characteristics of aggressive crabs, while curling up and remaining still were the behavioral characteristics of non-aggressive crabs.

[0034] Furthermore, in step (3), K-means cluster analysis is performed on all individuals using the behavioral characteristics of aggressor crabs (grabbing, shoving, forward approach, intimidation, stretching, and patrolling) and the behavioral characteristics of non-aggressor crabs (stillness and curling up) as variables. 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, using the following formula: Select the data objects that are closest to the cluster center, which means selecting the most similar data objects and grouping them into one group;

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

[0038] d) Repeat steps b) and c) until the data object partitioning no longer changes, at which point the clustering is complete;

[0039] K-means clustering analysis showed that all individuals were divided into three categories: aggressive crabs, non-aggressive crabs, and other crabs (individuals other than aggressive and non-aggressive crabs).

[0040] Furthermore, after obtaining the fighting crab and non-fighting crab groups in step (4), fighting experiments were conducted to verify them respectively.

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

[0042] The technical principle of this invention is based on the significant differences in interactive behavior between aggressive and non-aggressive crabs. Under laboratory conditions, aggressive behaviors (grabbing, pushing, forward approach, intimidation, stretching, and patrolling) and non-aggressive behaviors (coiling and remaining still) can be used as variables for K-means cluster analysis to accurately distinguish between aggressive and non-aggressive crab groups. Farmers in the production field can also observe the interactive behavior of crabs during the breeding process to differentiate between aggressive and non-aggressive crabs. For example, individuals that remain still and curl up for extended periods are non-aggressive crabs, while those that move frequently and push or grab other individuals are aggressive crabs.

[0043] Beneficial effects: This invention provides a method for identifying aggressive Chinese mitten crabs through interactive behavior. Under laboratory conditions, it elucidates the formation mechanism of aggressive crabs, enabling the development of artificial interference techniques to screen out aggressive crab individuals. Aggressive crabs can be separated from non-aggressive crabs for breeding, effectively reducing crab fighting behavior and contributing to the improvement of Chinese mitten crab yield and quality. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of a fighting behavior observation system.

[0045] Figure 2 Principal component analysis series diagrams for aggressive crabs, non-aggressive crabs, and ordinary crabs (2a: behavioral loading diagrams in the interaction behavior spectrum, 2b: heatmap of correlations of 12 behaviors, 2c: schematic diagram of PCA scores for aggressive crabs, non-aggressive crabs, and ordinary crabs).

[0046] Figure 3 A schematic diagram illustrating the differences in fighting behavior between aggressive and non-aggressive crabs (3a: number of fights, 3b: number of contacts, 3c: number of close encounters, 3d: duration of a single fight; asterisks on the bars indicate significant differences between the two groups). Detailed Implementation

[0047] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings: These embodiments are implemented based on the technical solution of the present invention, and provide detailed implementation methods and specific operation processes, 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 includes the following steps:

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

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

[0051] Two male crabs with a weight difference of 1%-4% were paired and placed on opposite sides of an experimental tank. A partition was placed in the middle of the tank to prevent them from touching each other. After the crabs had adapted for 10 minutes, the partition was removed, and their fighting was recorded over one hour using a high-definition camera. The recorded video was analyzed, and data was collected. The interaction behavior spectrum includes 15 interaction behaviors, as shown in Table 1.

[0052] Table 1. Interaction Behavior Spectrum of Crabs

[0053]

[0054] Note: Bold text indicates that the interaction behavior was used in PCA analysis. The winner posture, chelicerae clearing, and defensive pincers occurred infrequently and were not significant, so they were not included as PCA variables.

[0055] (2) Through data analysis, the interactive behavior characteristics of aggressive and non-aggressive crabs were obtained;

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

[0057] 1) Standardize the raw data to eliminate the influence of dimensions (calculated using SPSS).

[0058] Suppose there are m indicator variables for principal component analysis: x1, x2, ..., x m There are n evaluation objects, and the value of the j-th indicator of the i-th evaluation object is x. The values ​​of each indicator are then converted into standardized indicators.

[0059]

[0060] in, Let s be the sample mean of the j-th indicator. j Let be the sample standard deviation of the j-th indicator, corresponding to Standardized indicator variables: This allows for the standardization of 12 interactive behavior indicators for all individuals;

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

[0062] Correlation coefficient matrix R = (r ij ) m×m ,in In the formula r ii =1,r ij =r ji ,r ij It is the correlation coefficient between the i-th indicator and the j-th indicator;

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

[0064] 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 index variables are composed of feature vectors;

[0065]

[0066] Where y1 is the first principal component PC1, y2 is the second principal component PC2, ..., y m It is the m-th principal component PCM;

[0067] Principal components with eigenvalues ​​greater than 1 (PC) were selected as the analysis objects. The first three principal components were retained, explaining a total of 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 composite score.

[0069] Calculate the eigenvalue λ j The information contribution rate and cumulative contribution rate of (j = 1, 2, ..., m) are called... Main component y j Information contribution rate;

[0070] Principal components y1, y2, ... y p The cumulative contribution rate when a p Close to 1(a) r When the values ​​are 0.85, 0.90, 0.95, then select the first p index variables y1, y2, ... y p As p principal components, they replace the original m index variables, thus enabling comprehensive analysis of the p principal components.

[0071] Calculate the overall score Where b j The information contribution rate of the j-th principal component.

[0072] PC1 is an aggressive type, and two opposite variables can be clearly observed in the load diagram. Figure 2 a), where grabbing, pushing, forward approach, intimidation, stretching, and patrolling are aggressive behaviors and are positively loaded onto PC1, while stillness and curling up are non-aggressive behaviors and are negatively loaded onto PC1. Figure 2 a) PC2 explained 19.26% of the total variance and scored highest on probing, wall crawling, and ignoring. It also showed a significant positive correlation among the 12 fighting behaviors: positive approach, intimidation, grabbing, pushing, stretching, and patrolling. Figure 2 b, p<0.05); there was a significant positive correlation between stillness and curling up and a negative correlation with the first six behaviors (b, p<0.05); Figure 2 (b, p<0.05), while the other four did not show statistical significance.

[0073] According to the PCA score chart, aggressive crabs tend to exhibit more aggressive behaviors during fights, such as grabbing, pushing, approaching directly, intimidating, stretching, and surveying. Non-aggressive crabs tend to exhibit less aggressive behaviors, such as curling up and remaining still. Ordinary crabs tend to exhibit behaviors such as escaping, crawling along walls, and probing. Figure 2 c);

[0074] Ultimately, the following behavioral characteristics were identified: approaching positively, raising claws to intimidate, grabbing, pushing, stretching, and patrolling were the behavioral characteristics of aggressive crabs, while curling up and remaining still were the behavioral characteristics of non-aggressive crabs.

[0075] Table 2 records the first three principal components (PCs) of the interaction behavior.

[0076]

[0077]

[0078] Note: Bold text indicates the load contributed by the corresponding PC, with an absolute value > 0.4.

[0079] (3) Use interactive behavior characteristics to distinguish between good fighting crabs and bad fighting crabs;

[0080] Step (3) uses the behavioral characteristics of fighting crabs (grabbing, pushing, approaching forward, intimidating, stretching out, and patrolling) and the behavioral characteristics of non-fighting crabs (stillness and curling up) 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, using the following formula: Select the data objects that are closest to the cluster center, which means selecting the most similar data objects and grouping them into one group;

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

[0084] d) Repeat steps b) and c) until the data object partitioning no longer changes, at which point the clustering is complete;

[0085] K-means clustering analysis showed that all individuals were divided into three categories: aggressive crabs, non-aggressive crabs, and other crabs (individuals other than aggressive and non-aggressive crabs).

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

[0087] After obtaining the fighting crab and non-fighting crab groups in step (4), fighting experiments were conducted to verify the results. The experimental setup was the same as described above. Figure 1 .

[0088] In the fighting experiment, data such as the number of fights, the number of close encounters, the number of touches, and the duration of each fight were obtained as evidence to verify the aggressive nature of the crabs. The results showed that the number of fights (p<0.01), the number of touches (p<0.01), the number of close encounters (p<0.05), and the duration of each fight (p<0.01) were significantly higher in the aggressive crab group than in the non-aggressive crab group (e.g., the non-aggressive crab group). Figure 3 ).

[0089] The technical principle of this invention is based on the significant differences in interactive behavior between aggressive and non-aggressive crabs. Under laboratory conditions, aggressive behaviors (grabbing, pushing, forward approach, intimidation, stretching, and patrolling) and non-aggressive behaviors (coiling and remaining still) can be used as variables for K-means cluster analysis to accurately distinguish between aggressive and non-aggressive crab groups. Farmers in the production field can also observe the interactive behavior of crabs during the breeding process to differentiate between aggressive and non-aggressive crabs. For example, individuals that remain still and curl up for extended periods are non-aggressive crabs, while those that move frequently and push or grab other individuals are aggressive crabs.

[0090] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for identifying aggressive Chinese mitten crabs through interactive behavior, characterized in that, Specifically, the steps include the following: (1) Establish the interactive behavior spectrum of Chinese mitten crab. The interactive behavior spectrum includes fifteen interactive behaviors such as positive approach, raising claws to intimidate, grabbing, pushing, victorious posture, stretching, claw cleaning, patrolling, probing, crawling along the wall, stillness, curling up, escaping, ignoring, and defensive clamping. (2) Through data analysis, the interactive behavior characteristics of fighting crabs and non-fighting crabs were obtained. Principal component analysis was performed using 12 common behaviors in the interactive behavior spectrum as variables. The 12 common behaviors include positive approach, claw intimidation, grabbing, pushing, stretching, patrolling, probing, crawling along the wall, remaining still, curling up, escaping, and ignoring. Specifically, the following steps are included: 1) Standardize the original data to eliminate the influence of dimensions. Assume there are m indicator variables for principal component analysis: x1, x2, …, x m There are n evaluation objects, and the value of the j-th indicator of the i-th evaluation object is x. The values ​​of each indicator are converted into standardized indicators. : ,in, Let s be the sample mean of the j-th indicator. j Let be the sample standard deviation of the j-th indicator, corresponding to Standardized indicator variables: This allows for the standardization of 12 interactive behavior indicators for all individuals; 2) Establish the correlation coefficient matrix R between the variables. Correlation coefficient matrix ,in , in the formula , It is the correlation coefficient between the i-th indicator and the j-th indicator; 3) Calculate the eigenvalues ​​and eigenvectors of the correlation coefficient matrix R. Calculate the eigenvalues ​​of the correlation coefficient matrix R. And the corresponding eigenvectors u1, u2, …u m, in Each feature vector forms m new index variables; Where y1 is the first principal component PC1, y2 is the second principal component PC2, ..., y m It is the m-th principal component PCM; Principal components with eigenvalues ​​greater than 1 (PC) were selected as the analysis objects. The first three principal components were retained, explaining 74.7% of the total variance, with PC1 explaining 45.74% of the total variance. 4) Write out the principal components and calculate the composite score. Calculate the eigenvalue λ j The information contribution rate and cumulative contribution rate of the subset (j=1, 2, …, m) are called the principal components y. j Information contribution rate: , Principal components y1, y2, …y p The cumulative contribution rate, when a p Close to 1(a) r When the values ​​are 0.85, 0.90, 0.95, then select the first p index variables y1, y2, …y p As p principal components, they replace the original m index variables, thus enabling comprehensive analysis of the p principal components; Calculate the overall score , where b j The information contribution rate of the j-th principal component; PCA analysis shows that aggressive crabs tend to exhibit aggressive behaviors such as grabbing, pushing, approaching, intimidating, stretching, and patrolling during fights, while non-aggressive crabs tend to exhibit non-aggressive behaviors such as curling up and remaining still, and ordinary crabs tend to exhibit behaviors such as escaping, crawling along walls, and probing. Ultimately, the following behavioral characteristics were identified: approaching positively, raising claws to intimidate, grabbing, pushing, stretching, and patrolling were the behavioral characteristics of aggressive crabs, while curling up and remaining still were the behavioral characteristics of non-aggressive crabs. (3) Use interactive behavior characteristics to distinguish between good fighting crabs and bad fighting crabs. Use the behavioral characteristics of good fighting crabs and bad fighting crabs as variables to perform K-means cluster analysis on all individuals. Through K-means cluster analysis, all individuals are divided into three categories: good fighting crabs, bad fighting crabs and other crabs. (4) Verification of aggressive and non-aggressive crabs.

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

3. The method for identifying aggressive Chinese mitten crabs through interactive behavior according to claim 1, characterized in that, The apparatus for the fighting experiment includes a cylindrical experimental water tank with a partition and a video recording system, which includes a camera positioned 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 opposite sides of an experimental tank. A partition was placed in the middle of the tank to prevent them from touching each other. After the crabs had adapted for 10 minutes, the partition was removed, and a high-definition camera was used to record the fighting between the crabs over a period of 1 hour. The recorded video was then analyzed and data was collected.

5. The method for identifying aggressive Chinese mitten crabs through interactive behavior according to claim 1, characterized in that, PC1 represents the aggressive type. Grabbing, shoving, forward approach, intimidation, stretching, and patrolling are aggressive behaviors, all positively loaded onto PC1. Stillness and curling up are non-aggressive behaviors, both negatively loaded onto PC1. PC2 explains 19.26% of the total variance and scores highest on probing, wall crawling, and ignoring. Among the 12 fighting behaviors, forward approach, intimidation, grabbing, shoving, stretching, and patrolling are significantly positively correlated; stillness and curling up are significantly positively correlated and negatively correlated with the first six behaviors; the other four are not statistically significant.

6. The method for identifying aggressive Chinese mitten crabs through interactive behavior according to claim 1, characterized in that, The specific steps of the K-means clustering analysis in step (3) 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, using the following formula: Select the data objects that are closest to the cluster center, which means selecting the most similar data objects and grouping them into one group; c) After all data objects have been divided, recalculate the average value of each of the k groups and use it as the cluster center of each group; d) Repeat steps b) and c) until the partitioning of the data objects no longer changes, at which point the clustering is complete.

7. The method for identifying aggressive Chinese mitten crabs through interactive behavior according to claim 1, characterized in that, After obtaining the fighting crab and non-fighting crab groups in step (4), fighting experiments were conducted to verify them.

8. The method for identifying aggressive Chinese mitten crabs through interactive behavior according to claim 7, characterized in that, The number of fights, the number of close encounters, the number of contacts, and the duration of each fight were obtained in the fighting experiment as a basis for verifying the aggressive nature of the crabs. The results showed that the number of fights, the number of contacts, the number of close encounters, and the duration of each fight were significantly higher in the aggressive crab group than in the non-aggressive crab group.

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