New SLiM3 paper - more flexible genetic simulations

The SLiM forward population genetic simulation software has been discussed a few times recently, so I thought it would be useful to note that there is a brand new paper about it is out at Molecular Biology and Evolution (I just received an email alert a few minutes ago).

This paper details recent updates to SLiM’s functionality, including the option to make models more spatially explicit and to model scenarios that go beyond the standard Wright-Fischer population genetics model (named after Sewall Wright and Ronald Fisher, two of the founders of the modern synthesis), which makes several assumptions that are not always realistic.

The paper is open source and can be found here:


With the desire to model population genetic processes under increasingly realistic scenarios, forward genetic simulations have become a critical part of the toolbox of modern evolutionary biology. The SLiM forward genetic simulation framework is one of the most powerful and widely used tools in this area. However, its foundation in the Wright–Fisher model has been found to pose an obstacle to implementing many types of models; it is difficult to adapt the Wright–Fisher model, with its many assumptions, to modeling ecologically realistic scenarios such as explicit space, overlapping generations, individual variation in reproduction, density-dependent population regulation, individual variation in dispersal or migration, local extinction and recolonization, mating between subpopulations, age structure, fitness-based survival and hard selection, emergent sex ratios, and so forth. In response to this need, we here introduce SLiM 3, which contains two key advancements aimed at abolishing these limitations. First, the new non-Wright–Fisher or “nonWF” model type provides a much more flexible foundation that allows the easy implementation of all of the above scenarios and many more. Second, SLiM 3 adds support for continuous space, including spatial interactions and spatial maps of environmental variables. We provide a conceptual overview of these new features, and present several example models to illustrate their use.


This is great. @jordan will love this.

Yes, that was a good read and helped me understand a bit more of how SLiM works (especially the generation cycles comparison).

I still don’t know what I can do with it practically (I’m using it to learn at this point) but I think it’s fun to play with.

You could use it in that genetics and ancestry course you are mulling over designing with @Troendle.

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I’m considering using it to create some modeling assignments for my undergraduate evolution course.

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