SLiM 5: Eco-evolutionary simulations across multiple chromosomes and full genomes.

Evolutionary simulations of multiple chromosomes, even up to the scale of full-genome simulations, are becoming increasingly important in population genetics and evolutionary ecology. Unfortunately, the popular simulation framework SLiM has always been intrinsically limited to simulations of a single diploid chromosome. Modeling multiple chromosomes of different types, such as sex chromosomes, has always been cumbersome even with scripting, presenting a substantial barrier to the development of full-genome simulations.

Purifying selection shapes the dynamics of P-element invasion in Drosophila simulans populations.

Background: Transposable elements (TEs) are DNA sequences that can move within a host genome. Many new TE insertions have deleterious effects on their host and are therefore removed by purifying selection. The genomic distribution of TEs thus reflects a balance between new insertions and purifying selection.

Resource-explicit interactions in spatial population models.

1. Continuous-space population models can yield significantly different results from their panmictic counterparts when assessing evolutionary, ecological, or population-genetic processes. However, the computational burden of spatial models is typically much greater than that of panmictic models due to the overhead of determining which individuals interact with one another and how strongly they interact.

Population Genetics Meets Ecology: A Guide to Individual-Based Simulations in Continuous Landscapes.

Individual-based simulation has become an increasingly crucial tool for many fields of population biology. However, continuous geography is important to many applications, and implementing realistic and stable simulations in continuous space presents a variety of difficulties, from modeling choices to computational efficiency. This paper aims to be a practical guide to spatial simulation, helping researchers to implement individual-based simulations and avoid common pitfalls.

Accessible, realistic genome simulation with selection using stdpopsim.

Selection is a fundamental evolutionary force that shapes patterns of genetic variation across species. However, simulations incorporating realistic selection along heterogeneous genomes in complex demographic histories are challenging, limiting our ability to benchmark statistical methods aimed at detecting selection and to explore theoretical predictions. stdpopsim is a community-maintained simulation library that already provides an extensive catalog of species-specific population genetic models.

PROCEED v6.1: Phenotypic rates of change evolutionary and ecological database.

Populations must continuously respond to environmental change or risk extinction. These responses can be measured as phenotypic rates of change, which allow researchers to predict their contemporary evolutionary responses. In 1999, a database of phenotypic rates of change in wild populations was compiled.

Gaussian Process Emulation for Exploring Complex Infectious Disease Models.

Epidemiological models that aim for a high degree of biological realism by simulating every individual in a population are unavoidably complex, with many free parameters, which makes systematic explorations of their dynamics computationally challenging. This study investigates the potential of Gaussian Process emulation to overcome this obstacle. To simulate disease dynamics, we developed an abstract individual-based model that is loosely inspired by dengue, incorporating some key features shaping dengue epidemics such as social structure, human movement, and seasonality.

Population genetics meets ecology: a guide to individual-based simulations in continuous landscapes.

Individual-based simulation has become an increasingly crucial tool for many fields of population biology. However, continuous geography is important to many applications, and implementing realistic and stable simulations in continuous space presents a variety of difficulties, from modeling choices to computational efficiency. This paper aims to be a practical guide to spatial simulation, helping researchers to implement individual-based simulations and avoid common pitfalls.

e3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology.

Infectious disease dynamics are driven by the complex interplay of epidemiological, ecological, and evolutionary processes. Accurately modeling these interactions is crucial for understanding pathogen spread and informing public health strategies. However, existing simulators often fail to capture the dynamic interplay between these processes, resulting in oversimplified models that do not fully reflect real-world complexities in which the pathogen's genetic evolution dynamically influences disease transmission.

Resource-explicit interactions in spatial population models.

Continuous-space population models can yield significantly different results from their panmictic counterparts when assessing evolutionary, ecological, or population-genetic processes. However, the computational burden of spatial models is typically much greater than that of panmictic models due to the overhead of determining which individuals interact with one another and how strongly they interact. Though these calculations are necessary to model local competition that regulates the population density, they can lead to prohibitively long runtimes.

Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations.

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic datasets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge.

Inference of the distribution of fitness effects of mutations is affected by single nucleotide polymorphism filtering methods, sample size and population structure.

The distribution of fitness effects (DFE) of new mutations has been of interest to evolutionary biologists since the concept of mutations arose. Modern population genomic data enable us to quantify the DFE empirically, but few studies have examined how data processing, sample size and cryptic population structure might affect the accuracy of DFE inference. We used simulated and empirical data (from Arabidopsis lyrata) to show the effects of missing data filtering, sample size, number of single nucleotide polymorphisms (SNPs) and population structure on the accuracy and variance of DFE estimates.

SLiM 4: Multispecies Eco-Evolutionary Modeling.

AbstractThe SLiM software framework for genetically explicit forward simulation has been widely used in population genetics. However, it has been largely restricted to modeling only a single species, which has limited its broader utility in evolutionary biology. Indeed, to our knowledge no general-purpose, flexible modeling framework exists that provides support for simulating multiple species while also providing other key features, such as explicit genetics and continuous space.

: a framework for spatio-temporal population genomic simulations on geographic landscapes.

One of the goals of population genetics is to understand how evolutionary forces shape patterns of genetic variation over time. However, because populations evolve across both time and space, most evolutionary processes also have an important spatial component, acting through phenomena such as isolation by distance, local mate choice, or uneven distribution of resources. This spatial dimension is often neglected, partly due to the lack of tools specifically designed for building and evaluating complex spatio-temporal population genetic models.

Inversion invasions: when the genetic basis of local adaptation is concentrated within inversions in the face of gene flow.

Across many species where inversions have been implicated in local adaptation, genomes often evolve to contain multiple, large inversions that arise early in divergence. Why this occurs has yet to be resolved. To address this gap, we built forward-time simulations in which inversions have flexible characteristics and can invade a metapopulation undergoing spatially divergent selection for a highly polygenic trait.

Individual-based Modeling of Genome Evolution in Haplodiploid Organisms.

Ants, bees, wasps, bark beetles, and other species have haploid males and diploid females. Although such haplodiploid species play key ecological roles and are threatened by environmental changes, no general framework exists for simulating their genetic evolution. Here, we use the SLiM simulation environment to build a novel model for individual-based forward simulation of genetic evolution in haplodiploids.

Selection on the gametophyte: Modeling alternation of generations in plants.

Premise: The degree of gametophyte dependence on the sporophyte life stage is a major feature that differentiates the life cycles of land plants, yet the evolutionary consequences of this difference remain poorly understood. Most evolutionary models assume organisms are either haploid or diploid for their entire lifespan, which is not appropriate for simulating plant life cycles. Here, we introduce (imulating ploid-ploid volution), a new, simple Python program for implementing simulations with biphasic life cycles and analyzing their results, using SLiM 3 as a simulation back end.

Seeing the forest for the trees: Assessing genetic offset predictions from gradient forest.

Gradient Forest (GF) is a machine learning algorithm designed to analyze spatial patterns of biodiversity as a function of environmental gradients. An offset measure between the GF-predicted environmental association of adapted alleles and a new environment (GF Offset) is increasingly being used to predict the loss of environmentally adapted alleles under rapid environmental change, but remains mostly untested for this purpose. Here, we explore the robustness of GF Offset to assumption violations, and its relationship to measures of fitness, using SLiM simulations with explicit genome architecture and a spatial metapopulation.

Migration restores hybrid incompatibility driven by mitochondrial-nuclear sexual conflict.

In the mitochondrial genome, sexual asymmetry in transmission allows the accumulation of male-harming mutations since selection acts only on the effect of the mutation in females. Called the 'Mother's Curse', this phenomenon induces a selective pressure for nuclear variants that compensate for this reduction in male fitness. Previous work has demonstrated the existence of these interactions and their potential to act as Dobzhansky-Muller incompatibilities, contributing to reproductive isolation between populations.

Estimating the potential for coral adaptation to global warming across the Indo-West Pacific.

The potential of reef-building corals to adapt to increasing sea-surface temperatures is often debated but has rarely been comprehensively modeled on a region-wide scale. We used individual-based simulations to model adaptation to warming in a coral metapopulation comprising 680 reefs and representing the whole of the Central Indo-West Pacific. Encouragingly, some reefs-most notably Vietnam, Japan, Taiwan, New Caledonia and the southern half of the Great Barrier Reef-exhibited high capacity for adaptation and, in our model, maintained coral cover even under a rapid "business-as-usual" warming scenario throughout the modeled period (200 years).

Evolutionary Modeling in SLiM 3 for Beginners.

The SLiM forward genetic simulation framework has proved to be a powerful and flexible tool for population genetic modeling. However, as a complex piece of software with many features that allow simulating a diverse assortment of evolutionary models, its initial learning curve can be difficult. Here we provide a step-by-step demonstration of how to build a simple evolutionary model in SLiM 3, to help new users get started.

Tree-sequence recording in SLiM opens new horizons for forward-time simulation of whole genomes.

There is an increasing demand for evolutionary models to incorporate relatively realistic dynamics, ranging from selection at many genomic sites to complex demography, population structure, and ecological interactions. Such models can generally be implemented as individual-based forward simulations, but the large computational overhead of these models often makes simulation of whole chromosome sequences in large populations infeasible. This situation presents an important obstacle to the field that requires conceptual advances to overcome.

SLiM 3: Forward Genetic Simulations Beyond the Wright-Fisher Model.

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.