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    MovCLUfish : A data mining tool for discovering fish movement patterns from individual-based models

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    Type
    Article
    Authors
    Politikos, Dimitrios V. cc
    Kleftogiannis, Dimitrios A. cc
    Tsiaras, Kostas
    Rose, Kenneth A.
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2021-02-15
    Online Publication Date
    2021-02-15
    Print Publication Date
    2021-04
    Embargo End Date
    2022-02-15
    Submitted Date
    2020-03-31
    Permanent link to this record
    http://hdl.handle.net/10754/667524
    
    Metadata
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    Abstract
    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.
    Citation
    Politikos, D. V., Kleftogiannis, D., Tsiaras, K., & Rose, K. A. (2021). MovCLUfish  : A data mining tool for discovering fish movement patterns from individual-based models. Limnology and Oceanography: Methods. doi:10.1002/lom3.10421
    Sponsors
    The research of Dimitris Kleftogiannis was funded by the KAUST Base Research Funds to professor Panos Kalnis. We thank Nikolaos Zarokanelos for his useful comments. We express our appreciation to the two reviewers for the careful reading of the manuscript and their thoughtful comments.
    Publisher
    Wiley
    Journal
    Limnology and Oceanography: Methods
    DOI
    10.1002/lom3.10421
    Additional Links
    https://onlinelibrary.wiley.com/doi/10.1002/lom3.10421
    ae974a485f413a2113503eed53cd6c53
    10.1002/lom3.10421
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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