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, 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 realistic and efficient spatial, individual-based simulations and avoid common pitfalls. To do this, we delve into mechanisms of mating, reproduction, density-dependent feedback, and dispersal, all of which may vary across the landscape, discuss how these affect population dynamics, and describe how to parameterize simulations in convenient ways (for instance, to achieve a desired population density). We also demonstrate how to implement these models using the current version of the individual-based simulator, SLiM. Since SLiM has the capacity to simulate genomes, we also discuss natural selection - in particular, how genetic variation can affect demographic processes. Finally, we provide four short vignettes: simulations of pikas that shift their range up a mountain as temperatures rise; mosquitoes that live in rivers as juveniles and experience seasonally changing habitat; cane toads that expand across Australia, reaching 120 million individuals; and monarch butterflies whose populations are regulated by an explicitly modeled resource (milkweed).

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. We introduce the epidemiological-ecological-evolutionary simulator (e3SIM), an open-source framework that concurrently models the transmission dynamics and molecular evolution of pathogens within a host population while integrating environmental factors. Using an agent-based, discrete-generation, forward-in-time approach, e3SIM incorporates compartmental models, host-population contact networks, and quantitative-trait models for pathogens. This integration allows for realistic simulations of disease spread and pathogen evolution. Key features include a modular and scalable design, flexibility in modeling various epidemiological and population-genetic complexities, incorporation of time-varying environmental factors, and a user-friendly graphical interface. We demonstrate e3SIM's capabilities through simulations of realistic outbreak scenarios with SARS-CoV-2 and Mycobacterium tuberculosis, illustrating its flexibility for studying the genomic epidemiology of diverse pathogen types.

Genetic load and viability of a future restored northern white rhino population.

As biodiversity loss outpaces recovery, conservationists are increasingly turning to novel tools for preventing extinction, including cloning and in vitro gametogenesis of biobanked cells. However, restoration of populations can be hindered by low genetic diversity and deleterious genetic load. The persistence of the northern white rhino (Ceratotherium simum cottoni) now depends on the cryopreserved cells of 12 individuals. These banked genomes have higher genetic diversity than southern white rhinos (C. s. simum), a sister subspecies that successfully recovered from a severe bottleneck, but the potential impact of genetic load is unknown. We estimated how demographic history has shaped genome-wide genetic load in nine northern and 13 southern white rhinos. The bottleneck left southern white rhinos with more fixed and homozygous deleterious alleles and longer runs of homozygosity, whereas northern white rhinos retained more deleterious alleles masked in heterozygosity. To gauge the impact of genetic load on the fitness of a northern white rhino population restored from biobanked cells, we simulated recovery using fitness of southern white rhinos as a benchmark for a viable population. Unlike traditional restoration, cell-derived founders can be reintroduced in subsequent generations to boost lost genetic diversity and relieve inbreeding. In simulations with repeated reintroduction of founders into a restored population, the fitness cost of genetic load remained lower than that borne by southern white rhinos. Without reintroductions, rapid growth of the restored population (>20-30% per generation) would be needed to maintain comparable fitness. Our results suggest that inbreeding depression from genetic load is not necessarily a barrier to recovery of the northern white rhino and demonstrate how restoration from biobanked cells relieves some constraints of conventional restoration from a limited founder pool. Established conservation methods that protect healthy populations will remain paramount, but emerging technologies hold promise to bolster these tools to combat the extinction crisis.

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. Here, we present a novel modeling method in which the resources available to a population are abstractly represented as an additional layer of the simulation. Instead of interacting directly with one another, individuals interact indirectly via this resource layer. We find that this method closely matches other spatial models, yet can dramatically increase the speed of the model, allowing the simulation of much larger populations. Additionally, models structured in this manner exhibit other desirable characteristics, including more realistic spatial dynamics near the edge of the simulated area, and an efficient route for modeling more complex heterogeneous landscapes.

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. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than threefold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed the best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

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. Our analyses focus on three filtering methods-downsampling, imputation and subsampling-with sample sizes of 4-100 individuals. We show that (1) the choice of missing-data treatment directly affects the estimated DFE, with downsampling performing better than imputation and subsampling; (2) the estimated DFE is less reliable in small samples (<8 individuals), and becomes unpredictable with too few SNPs (<5000, the sum of 0- and 4-fold SNPs); and (3) population structure may skew the inferred DFE towards more strongly deleterious mutations. We suggest that future studies should consider downsampling for small data sets, and use samples larger than 4 (ideally larger than 8) individuals, with more than 5000 SNPs in order to improve the robustness of DFE inference and enable comparative analyses.

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. The lack of such software has limited our ability to model higher biological levels such as communities, ecosystems, coevolutionary and eco-evolutionary processes, and biodiversity, which is crucial for many purposes, from extending our basic understanding of evolutionary ecology to informing conservation and management decisions. We here announce the release of SLiM 4, which fills this important gap by adding support for multiple species, including ecological interactions between species such as predation, parasitism, and mutualism, and illustrate its new features with examples.

slendr: 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. To address this methodological gap, we present a new framework for simulating spatially-explicit genomic data, implemented in a new R package called slendr (www.slendr.net), which leverages a SLiM simulation back-end script bundled with the package. With this framework, the users can programmatically and visually encode spatial population ranges and their temporal dynamics (i.e., population displacements, expansions, and contractions) either on real Earth landscapes or on abstract custom maps, and schedule splits and gene-flow events between populations using a straightforward declarative language. Additionally, slendr can simulate data from traditional, non-spatial models, either with SLiM or using an alternative built-in coalescent msprime back end. Together with its R-idiomatic interface to the tskit library for tree-sequence processing and analysis, slendr opens up the possibility of performing efficient, reproducible simulations of spatio-temporal genomic data entirely within the R environment, leveraging its wealth of libraries for geospatial data analysis, statistics, and visualization. Here, we present the design of the slendr R package and demonstrate its features on several practical example workflows.

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. In our simulations, inversions typically arose early in divergence, captured standing genetic variation upon mutation, and then accumulated many small-effect loci over time. Under special conditions, inversions could also arise late in adaptation and capture locally adapted alleles. Polygenic inversions behaved similarly to a single supergene of large effect and were detectable by genome scans. Our results show that characteristics of adaptive inversions found in empirical studies (e.g. multiple large, old inversions that are FST outliers, sometimes overlapping with other inversions) are consistent with a highly polygenic architecture, and inversions do not need to contain any large-effect genes to play an important role in local adaptation. By combining a population and quantitative genetic framework, our results give a deeper understanding of the specific conditions needed for inversions to be involved in adaptation when the genetic architecture is polygenic. This article is part of the theme issue 'Genomic architecture of supergenes: causes and evolutionary consequences'.

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 shadie (Simulating Haploid-Diploid Evolution), a new, simple Python program for implementing simulations with biphasic life cycles and analyzing their results, using SLiM 3 as a simulation back end. Methods: We implemented evolutionary simulations under three realistic plant life cycle models supported in shadie, using either standardized or biologically realistic parameter settings to test how variation in plant life cycles and sexual systems affects patterns of genome diversity. Results: The dynamics of single beneficial mutation fixation did not vary dramatically between different models, but the patterns of spatial variation did differ, demonstrating that different life histories and model parameters affect both genetic diversity and linkage disequilibrium. The rate of linkage disequilibrium decay away from selected sites varied depending on model parameters such as cloning and selfing rates, through their impact on effective population sizes. Discussion: Evolutionary simulations are an exciting, underutilized approach in evolutionary research and education. shadie can aid plant researchers in developing null hypotheses, examining theory, and designing empirical studies, in order to investigate the role of the gametophyte life stage, and the effects of variation in plant life cycles, on plant genome evolution.

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. We compare the fates of adaptive and deleterious mutations and find that selection on recessive mutations is more effective in haplodiploids than in diploids. Our open-source model will foster an understanding of the evolution of sociality and how ecologically important haplodiploid species may respond to changing environments.

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. We evaluate measures of GF Offset in: (1) a neutral model with no environmental adaptation; (2) a monogenic "population genetic" model with a single environmentally adapted locus; and (3) a polygenic "quantitative genetic" model with two adaptive traits, each adapting to a different environment. We found GF Offset to be broadly correlated with fitness offsets under both single locus and polygenic architectures. However, neutral demography, genomic architecture, and the nature of the adaptive environment can all confound relationships between GF Offset and fitness. GF Offset is a promising tool, but it is important to understand its limitations and underlying assumptions, especially when used in the context of predicting maladaptation.

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. However, it is not clear how readily they would give rise to and sustain hybrid incompatibilities. Here, we use computer simulations in SLiM 3 to investigate the consequences of sexually antagonistic mitochondrial-nuclear interactions in a subdivided population. We consider distinct migration schemes and vary the chromosomal location, and consequently the transmission pattern, of nuclear restorers. Disrupting these co-evolved interactions results in less-fit males, skewing the sex ratio toward females. Restoration of male fitness depends on both the chromosomal location of nuclear restorer loci and the migration scheme. Our results show that these interactions may act as Dobzhansky-Muller incompatibilities, but their strength is not enough to drive population isolation. Overall, this model shows the varied ways in which populations can respond to migration's disruption of co-evolved mitochondrial-nuclear interactions.

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). Higher resilience of these reefs was observed under all tested parameter settings except the models prohibiting selection and/or migration during warming. At the same time, the majority of reefs in the region tended to collapse within the first 100 years of warming. The adaptive potential (odds of maintaining high coral cover) of a given reef could be predicted based on two metrics: the reef's present-day temperature, and the proportion of recruits immigrating from warmer locations. The latter metric explains the most variation in adaptive potential, and significantly correlates with actual coral cover changes observed throughout the region between the 1970s and the early 2000s. These findings will help prioritize coral conservation efforts and plan assisted gene flow interventions to boost the adaptive potential of specific coral populations.

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. We will begin with a panmictic neutral model, and build up to a model of the evolution of a polygenic quantitative trait under selection for an environmental phenotypic optimum.

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. 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.

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. The recently developed tree-sequence recording method (Kelleher, Thornton, Ashander, & Ralph, 2018), which stores the genealogical history of all genomes in the simulated population, could provide such an advance. This method has several benefits: (1) it allows neutral mutations to be omitted entirely from forward-time simulations and added later, thereby dramatically improving computational efficiency; (2) it allows neutral burn-in to be constructed extremely efficiently after the fact, using "recapitation"; (3) it allows direct examination and analysis of the genealogical trees along the genome; and (4) it provides a compact representation of a population's genealogy that can be analysed in Python using the msprime package. We have implemented the tree-sequence recording method in SLiM 3 (a free, open-source evolutionary simulation software package) and extended it to allow the recording of non-neutral mutations, greatly broadening the utility of this method. To demonstrate the versatility and performance of this approach, we showcase several practical applications that would have been beyond the reach of previously existing methods, opening up new horizons for the modelling and exploration of evolutionary processes.

asymptoticMK: A Web-Based Tool for the Asymptotic McDonald-Kreitman Test.

The McDonald-Kreitman (MK) test is a widely used method for quantifying the role of positive selection in molecular evolution. One key shortcoming of this test lies in its sensitivity to the presence of slightly deleterious mutations, which can severely bias its estimates. An asymptotic version of the MK test was recently introduced that addresses this problem by evaluating polymorphism levels for different mutation frequencies separately, and then extrapolating a function fitted to that data. Here, we present asymptoticMK, a web-based implementation of this asymptotic MK test. Our web service provides a simple R-based interface into which the user can upload the required data (polymorphism and divergence data for the genomic test region and a neutrally evolving reference region). The web service then analyzes the data and provides plots of the test results. This service is free to use, open-source, and available at http://benhaller.com/messerlab/asymptoticMK.html We provide results from simulations to illustrate the performance and robustness of the asymptoticMK test under a wide range of model parameters.

SLiM 2: Flexible, Interactive Forward Genetic Simulations.

Modern population genomic datasets hold immense promise for revealing the evolutionary processes operating in natural populations, but a crucial prerequisite for this goal is the ability to model realistic evolutionary scenarios and predict their expected patterns in genomic data. To that end, we present SLiM 2: an evolutionary simulation framework that combines a powerful, fast engine for forward population genetic simulations with the capability of modeling a wide variety of complex evolutionary scenarios. SLiM achieves this flexibility through scriptability, which provides control over most aspects of the simulated evolutionary scenarios with a simple R-like scripting language called Eidos. An example SLiM simulation is presented to illustrate the power of this approach. SLiM 2 also includes a graphical user interface for simulation construction, interactive runtime control, and dynamic visualization of simulation output, facilitating easy and fast model development with quick prototyping and visual debugging. We conclude with a performance comparison between SLiM and two other popular forward genetic simulation packages.