Spatially explicit individual‐based models (IBMs) are useful tools for simulating the movement of discrete fish individuals within dynamic and heterogeneous environments. However, processing the IBM outputs is complicated because fish individuals are continuously adjusting their behavior in response to changing environmental conditions. Here, we present a new analysis tool, called MovCLUfish, that uses data mining to identify patterns from the trajectories of the individuals generated from IBMs. MovCLUfish is configured to identify features of fish behavior related to occupation (area of fish presence), dynamics of aggregation (how fish individuals are distributed within the area of presence), and mobility (how fish move between subregions). MovCLUfish receives as input the fish locations (longitude, latitude) at fixed times during a specific time period and performs spatial clustering on consecutive timestamps, considering them as moving objects. Fish locations are grouped into clusters whose features (centroid, shape, size, density) are used to provide further information about the spatial distributions. The clusters are analyzed using three built‐in pattern mining methods: tracking moving centroids (TMC), aggregating moving clusters (AMC), and tracking fish mobility (TFM). TMC detects shifts in the distribution of fish over time, AMC visualizes the way fish aggregations change geographically over time, and TFM provides quantitative information on the patterns of exchange and connectivity of individuals among regions within the domain. We describe the workflow of MovCLUfish and illustrate its applicability using output from an IBM model configured for anchovy in the Eastern Mediterranean Sea. Further avenues for improvement and expansion of MovCLUfish are discussed.

中文翻译：

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MovCLUfish：一种数据挖掘工具，用于从基于个体的模型中发现鱼类的运动模式

空间明确的基于个体的模型（IBM）是用于模拟离散鱼类个体在动态和异构环境中的运动的有用工具。但是，处理IBM的输出非常复杂，因为鱼类个体会根据不断变化的环境条件不断调整其行为。在这里，我们介绍了一个称为MovCLUfish的新分析工具，该工具使用数据挖掘从IBM生成的个人的轨迹中识别出模式。MovCLUfish的配置可识别与职业（鱼的存在区域），聚集的动力学（鱼个体在存在区域内的分布方式）以及活动性（鱼在子区域之间的移动）有关的鱼类行为特征。MovCLUfish接收鱼的位置（经度，纬度）在特定时间段内的固定时间进行，并在连续的时间戳上进行空间聚类（将它们视为移动的对象）。鱼的位置被分组为簇，其特征（质心，形状，大小，密度）用于提供有关空间分布的更多信息。使用三种内置的模式挖掘方法对集群进行分析：跟踪移动质心（TMC），聚集移动集群（AMC）和跟踪鱼类迁移率（TFM）。TMC可以检测出鱼类分布随时间的变化，AMC可以直观地看到鱼类聚集随时间发生地理变化的方式，而TFM可以提供有关域内各个区域之间个体交换和连通性模式的定量信息。我们使用来自为地中海东部an鱼配置的IBM模型的输出来描述MovCLUfish的工作流程并说明其适用性。讨论了改进和扩展MovCLUfish的其他途径。