SLiM

SLiM simulates forward-time population genetic and evolutionary scenarios, enabling explicit modeling of demography, selection, spatial structure, multi-chromosome genomes, and multi-species ecological interactions.

Key Features:

• Non-Wright-Fisher model (SLiM 3): Implements a non-Wright-Fisher (nonWF) model supporting explicit space, overlapping generations, individual variation in reproduction and dispersal, density-dependent population regulation, local extinction and recolonization, mating between subpopulations, age structure, fitness-based survival, hard selection, and emergent sex ratios.
• Continuous space support: Supports continuous space for spatial interactions and mapping of environmental variables.
• Tree-sequence recording: Stores the full genealogical history of genomes, allows omission of neutral mutations during forward-time simulation and their later addition, enables recapitation (neutral burn-in post-simulation), permits direct examination of genealogical trees along the genome, and produces a compact representation analyzable with the msprime Python package.
• Multi-chromosome simulation (SLiM 5): Simulates up to 256 chromosomes including autosomes, sex chromosomes (X, Y, Z, W), and haploid mitochondrial and chloroplast DNA, and integrates chromosome-aware reproduction with VCF input/output and tree-sequence recording.
• Multi-species support (SLiM 4): Supports simulation of multiple species and ecological interactions such as predation, parasitism, and mutualism for community-, ecosystem- and coevolutionary-level modeling.

Scientific Applications:

• Population genetics simulations: Modeling selection at multiple genomic sites, complex demography, and population structure in forward time.
• Eco-evolutionary dynamics: Exploring ecological interactions, coevolution, community and ecosystem processes, and biodiversity dynamics.
• Genomic-scale simulations: Conducting full-genome simulations with multi-chromosome architectures and tree-sequence recording for large populations.
• Polygenic trait evolution: Studying evolution of polygenic quantitative traits under environmental selection pressures.
• Genealogical and coalescent analyses: Reconstructing and analyzing genealogies using tree-sequence recording, recapitation, and downstream analysis with msprime.
• Conservation and management scenarios: Simulating local extinction, recolonization, and other processes relevant to biodiversity and conservation.

Methodology:

Performs forward-time simulation using the non-Wright-Fisher (nonWF) model; employs tree-sequence recording to store genealogical histories while omitting neutral mutations for later addition and supports recapitation; models explicit continuous space, overlapping generations, individual-level reproduction and dispersal, density dependence, local extinction/recolonization, age structure, fitness-based survival and hard selection; supports multi-chromosome genomes (up to 256 chromosomes), multiple species, and VCF input/output, with analysis interoperability via the msprime Python package.