## 2 Materials and Methods
### Data Preprocessing and Analysing
#### 2.2.3 Fishing Behaviors Characterization  
(1) Testing differences in fishing characteristics.  
A one-way ANOVA with repetitions was conducted on the different fishing characteristics of different vessel states identified by the neural network model to test whether there were significant differences in different vessel states. The vessel state was considered as a factor, whereas fishing characteristics were a dependent variable. This part was tested using IBM SPSS Statistics 22.  
(2) Marking netting order  
The VMS data with casting and hauling states were obtained. A single fishing vessel can only be one state at a time, and casting and hauling of different nets were alternately carried out. Therefore, the adjacent VMS data in order of time with the same state were classified as the same netting order. Subsequently, the VMS data with casting and hauling states were marked with netting order.  
(3) Obtaining the net positions, azimuths, and effective net lengths  
Because the anchors fix the _A. chinensis_ stow nets in the ocean current, the point set of the casting state represents the fishing positions of the vessels. The mean center toolbox was used to process the VMS data with the casting state to obtain the net positions. In addition, all the beginning and ending points of the point sets with the hauling state at each fishing time were extracted and connected into lines. Then, the azimuths and actual distribution distances of all nets were calculated by adding the geometric attributes. Finally, the effective lengths of fishing nets were obtained from the ratio of the maximum effective net length to the corresponding actual distribution distance.  
(4) Calculation of fishing duration  
The intermediate points of the VMS data in the casting and hauling states were considered the beginning and ending points of fishing. Therefore, the positioning time of these two points was regarded as the beginning and ending time of fishing for each net. The fishing duration of the nets was calculated using Equation (2).  
\[T_{k}=E_{k}-B_{k} \tag{2}\]  
where \(E_{k}\) and \(B_{k}\) are the ending and beginning times of the kth net, respectively, and \(T_{k}\) is the fishing duration (h) of the kth net.  
(5) Estimating fishing effort  
Fishing effort is an important parameter in fishery resource management and evaluation. The calculation process is shown in Equation (3).  
\[E=L\times h\times T \tag{3}\]  
where \(E\) is the amount of fishing effort (m\({}^{2}\cdot\)h) put into the catch, \(L\) is the effective net length (m), \(h\) is the water depth (m), and \(T\) is the fishing duration (h). Because the net height used for _A. chinensis_ fishing is higher than the fishing sea depth, it is more reasonable to set h as the water depth when estimating fishing effort.  
(6) Analysis of fishing intensity  
Using the kernel function (Equation (4)) to analyze the fishing effort of each net can visually reflect the distribution of the _A. chinensis_ fishing intensity in the sea area.  
\[\mathbf{D}=\frac{3\Big{(}1-Scale^{2}\Big{)}^{2}}{\pi r^{2}} \tag{4}\]  
In Equation (4), \(r\) is the search radius of the nuclear analysis of the fishing effort, and _Scale_ is the ratio of the distance from the raster center point to the net position pointto the finding radius. In this study, the \(r\) and grid resolution were set to 3000 m and 10 m, respectively.  
Parts (2) to (6) were processed using ArcGIS 10.8.