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Updated: Jul 31, 2025

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SLiM 4: Multispecies Eco-Evolutionary Modeling.

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    The SLiM 4 software now simulates multiple species and their ecological interactions, advancing evolutionary biology and conservation research. This framework enables complex community and ecosystem modeling previously not possible.

    Keywords:
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    Area of Science:

    • Evolutionary Biology
    • Computational Biology
    • Ecology

    Background:

    • The SLiM software framework is a key tool for genetically explicit forward simulations in population genetics.
    • Previous versions were limited to single-species modeling, hindering research on multi-species interactions and community dynamics.
    • A flexible, general-purpose framework for multi-species simulation with explicit genetics and continuous space was lacking.

    Purpose of the Study:

    • To introduce SLiM 4, a new version of the SLiM software framework.
    • To enable the simulation of multiple species and their ecological interactions within a single modeling framework.
    • To facilitate research on higher biological levels, including communities, ecosystems, and coevolutionary processes.

    Main Methods:

    • Development of SLiM 4, extending the SLiM framework to support multiple species.
    • Implementation of ecological interactions such as predation, parasitism, and mutualism between simulated species.
    • Illustrative examples demonstrating the new multi-species simulation capabilities.

    Main Results:

    • SLiM 4 now supports the simulation of genetically explicit, multi-species populations.
    • The framework incorporates ecological interactions (predation, parasitism, mutualism) between species.
    • Demonstrated utility of SLiM 4 for modeling complex ecological and evolutionary dynamics.

    Conclusions:

    • SLiM 4 addresses a critical gap in evolutionary and ecological modeling software.
    • The enhanced framework supports research into community ecology, eco-evolutionary dynamics, and biodiversity.
    • This advancement is crucial for understanding evolutionary ecology and informing conservation strategies.