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A fundamental goal in population genetics is to understand how variation is arrayed over natural landscapes. From first principles we know that common features such as heterogeneous population densities and barriers to dispersal should shape genetic variation over space, however there are few tools currently available that can deal with these ubiquitous complexities. Geographically referenced single nucleotide polymorphism (SNP) data are increasingly accessible, presenting an opportunity to study genetic variation across geographic space in myriad species. We present a new inference method that uses geo‐referenced SNPs and a deep neural network to estimate spatially heterogeneous maps of population density and dispersal rate. Our neural network trains on simulated input and output pairings, where the input consists of genotypes and sampling locations generated from a continuous space population genetic simulator, and the output is a map of the true demographic parameters. We benchmark our tool against existing methods and discuss qualitative differences between the different approaches; in particular, our program is unique because it infers the magnitude of both dispersal and density as well as their variation over the landscape, and it does so using SNP data. Similar methods are constrained to estimating relative migration rates, or require identity‐by‐descent blocks as input. We applied our tool to empirical data from North American grey wolves, for which it estimated mostly reasonable demographic parameters, but was affected by incomplete spatial sampling. Genetic based methods like ours complement other, direct methods for estimating past and present demography, and we believe will serve as valuable tools for applications in conservation, ecology and evolutionary biology. An open source software package implementing our method is available from https://github.com/kr‐colab/mapNN. [ABSTRACT FROM AUTHOR]

Migratory divides are proposed to be catalysts for speciation across a diversity of taxa. However, it is difficult to test the relative contributions of migratory behaviour vs. other divergent traits to reproductive isolation. Comparing hybrid zones with and without migratory divides offers a rare opportunity to directly examine the contribution of divergent migratory behaviour to reproductive barriers. We show that across replicate sampling transects of two pairs of barn swallow (Hirundo rustica) subspecies, strong reproductive isolation coincided with a migratory divide spanning 20 degrees of latitude. A third subspecies pair exhibited no evidence for a migratory divide and hybridised extensively. Within migratory divides, overwintering habitats were associated with assortative mating, implicating a central contribution of divergent migratory behaviour to reproductive barriers. The remarkable geographic coincidence between migratory divides and genetic breaks supports a long‐standing hypothesis that the Tibetan Plateau is a substantial barrier contributing to the diversity of Siberian avifauna. [ABSTRACT FROM AUTHOR]

Accounting for historical demographic features, such as the strength and timing of gene flow and divergence times between closely related lineages, is vital for many inferences in evolutionary biology. Approximate Bayesian computation (ABC) is one method commonly used to estimate demographic parameters. However, the DNA sequences used as input for this method, often microsatellites or RADseq loci, usually represent a small fraction of the genome. Whole genome sequencing (WGS) data, on the other hand, have been used less often with ABC, and questions remain about the potential benefit of, and how to best implement, this type of data; we used pseudo‐observed data sets to explore such questions. Specifically, we addressed the potential improvements in parameter estimation accuracy that could be associated with WGS data in multiple contexts; namely, we quantified the effects of (a) more data, (b) haplotype‐based summary statistics, and (c) locus length. Compared with a hypothetical RADseq data set with 2.5 Mbp of data, using a 1 Gbp data set consisting of 100 Kbp sequences led to substantial gains in the accuracy of parameter estimates, which was mostly due to haplotype statistics and increased data. We also quantified the effects of including (a) locus‐specific recombination rates, and (b) background selection information in ABC analyses. Importantly, assuming uniform recombination or ignoring background selection had a negative effect on accuracy in many cases. Software and results from this method validation study should be useful for future demographic history analyses. [ABSTRACT FROM AUTHOR]

Drosophila suzukii recently invaded North America and Europe. Populations in Hawaii, California, New York and Nova Scotia are polymorphic for Wolbachia, typically with <20% infection frequency. The Wolbachia in D. suzukii, denoted wSuz, is closely related to wRi, the variant prevalent in continental populations of D. simulans. wSuz is also nearly identical to Wolbachia found in D. subpulchrella, plausibly D. suzukii's sister species. This suggests vertical Wolbachia transmission through cladogenesis ('cladogenic transmission'). The widespread occurrence of 7-20% infection frequencies indicates a stable polymorphism. wSuz is imperfectly maternally transmitted, with wild infected females producing on average 5-10% uninfected progeny. As expected from its low frequency, wSuz produces no cytoplasmic incompatibility (CI), that is, no increased embryo mortality when infected males mate with uninfected females, and no appreciable sex-ratio distortion. The persistence of wSuz despite imperfect maternal transmission suggests positive fitness effects. Assuming a balance between selection and imperfect transmission, we expect a fitness advantage on the order of 20%. Unexpectedly, Wolbachia-infected females produce fewer progeny than do uninfected females. We do not yet understand the maintenance of wSuz in D. suzukii. The absence of detectable CI in D. suzukii and D. subpulchrella makes it unlikely that CI-based mechanisms could be used to control this species without transinfection using novel Wolbachia. Contrary to their reputation as horizontally transmitted reproductive parasites, many Wolbachia infections are acquired through introgression or cladogenesis and many cause no appreciable reproductive manipulation. Such infections, likely to be mutualistic, may be central to understanding the pervasiveness of Wolbachia among arthropods. [ABSTRACT FROM AUTHOR]

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).

A fundamental goal in population genetics is to understand how variation is arrayed over natural landscapes. From first principles we know that common features such as heterogeneous population densities and barriers to dispersal should shape genetic variation over space, however there are few tools currently available that can deal with these ubiquitous complexities. Geographically referenced single nucleotide polymorphism (SNP) data are increasingly accessible, presenting an opportunity to study genetic variation across geographic space in myriad species. We present a new inference method that uses geo-referenced SNPs and a deep neural network to estimate spatially heterogeneous maps of population density and dispersal rate. Our neural network trains on simulated input and output pairings, where the input consists of genotypes and sampling locations generated from a continuous space population genetic simulator, and the output is a map of the true demographic parameters. We benchmark our tool against existing methods and discuss qualitative differences between the different approaches; in particular, our program is unique because it infers the magnitude of both dispersal and density as well as their variation over the landscape, and it does so using SNP data. Similar methods are constrained to estimating relative migration rates, or require identity by descent blocks as input. We applied our tool to empirical data from North American grey wolves, for which it estimated mostly reasonable demographic parameters, but was affected by incomplete spatial sampling. Genetic based methods like ours complement other, direct methods for estimating past and present demography, and we believe will serve as valuable tools for applications in conservation, ecology, and evolutionary biology. An open source software package implementing our method is available from https://github.com/kr-colab/mapNN .

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., Competing Interests: ML, MC, GG, NP, GT, SB, VC, JC, IE, BH, AH, XH, LI, EN, JO, VP, AP, DP, MP, MR, CS, JS, AT, ST, PU, JV, RW, AW, YW, FB, RC, GG, RG, JK, AK, AR, PR, DS, IG No competing interests declared, AG is an employee of Embark Veterinary, Inc. The author declares that no other competing interests exist, JA is an employee of Ancestry DNA. The author declares that no other competing interests exist, AB is an employee of 54Gene, Inc. The author declares that no other competing interests exist, (© 2023, Lauterbur et al.)

Differentiation is often heterogeneous across the genomes of diverging populations. Despite substantial recent progress, much work remains to improve our abilities to connect genomic patterns to underlying evolutionary processes. Crosstalk between theoretical and empirical research has shaped the field of evolutionary genetics since its foundation and needs to be greatly enhanced for modern datasets. We leverage recent insights from theoretical and empirical studies to identify existing gaps and suggest pathways across them. We stress the importance of reporting empirical data in standardized ways to enable meta-analyses and to facilitate parameterization of analyses and models. Additionally, a more comprehensive view of potential mechanisms - especially considering variable recombination rates and ubiquitous background selection - and their interactions should replace common, oversimplified assumptions., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

To explain differences in gut microbial communities we must determine how processes regulating microbial community assembly (colonization, persistence) differ among hosts and affect microbiota composition. We surveyed the gut microbiota of threespine stickleback (Gasterosteus aculeatus) from 10 geographically clustered populations and sequenced environmental samples to track potential colonizing microbes and quantify the effects of host environment and genotype. Gut microbiota composition and diversity varied among populations. These among-population differences were associated with multiple covarying ecological variables: habitat type (lake, stream, estuary), lake geomorphology and food- (but not water-) associated microbiota. Fish genotype also covaried with gut microbiota composition; more genetically divergent populations exhibited more divergent gut microbiota. Our results suggest that population level differences in stickleback gut microbiota may depend more on internal sorting processes (host genotype) than on colonization processes (transient environmental effects).