The explosion in population genomic data demands ever more complex modes of analysis, and increasingly these analyses depend on sophisticated simulations. Re-cent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

Adaptation to spatially varying environments has been studied for decades, but advances in sequencing technology are now enabling researchers to investigate the landscape of genetic variation underlying this adaptation genome wide. In this review, we highlight some of the decades-long research on local adaptation in Drosophila melanogaster from well-studied clines in North America and Australia. We explore the evidence for parallel adaptation and identify commonalities in the genes responding to clinal selection across continents as well as discuss instances where patterns differ among clines. We also investigate recent studies utilizing whole-genome data to identify clines in D. melanogaster and a number of other systems. Although connecting segregating genomic variation to variation in phenotypes and fitness remains challenging, clinal genomics is poised to increase our understanding of local adaptation and the selective pressures that drive the extensive phenotypic diversity observed in nature.

Severe insect declines make headlines, but they are rarely based on systematic monitoring outside of Europe. We estimate the rate of change in total butterfly abundance and the population trends for 81 species using 21 years of systematic monitoring in Ohio, USA. Total abundance is declining at 2% per year, resulting in a cumulative 33% reduction in butterfly abundance. Three times as many species have negative population trends compared to positive trends. The rate of total decline and the proportion of species in decline mirror those documented in three comparable long-term European monitoring programs. Multiple environmental changes such as climate change, habitat degradation, and agricultural practices may contribute to these declines in Ohio and shift the makeup of the butterfly community by benefiting some species over others. Our analysis of life-history traits associated with population trends shows an impact of climate change, as species with northern distributions and fewer annual generations declined more rapidly. However, even common and invasive species associated with human-dominated landscapes are declining, suggesting widespread environmental causes for these trends. Declines in common species, although they may not be close to extinction, will have an outsized impact on the ecosystem services provided by insects. These results from the most extensive, systematic insect monitoring program in North America demonstrate an ongoing defaunation in butterflies that on an annual scale might be imperceptible, but cumulatively has reduced butterfly numbers by a third over 20 years.

Accurately inferring the genome-wide landscape of recombination rates in natural populations is a central aim in genomics, as patterns of linkage influence everything from genetic mapping to understanding evolutionary history. Here, we describe recombination landscape estimation using recurrent neural networks (ReLERNN), a deep learning method for estimating a genome-wide recombination map that is accurate even with small numbers of pooled or individually sequenced genomes. Rather than use summaries of linkage disequilibrium as its input, ReLERNN takes columns from a genotype alignment, which are then modeled as a sequence across the genome using a recurrent neural network. We demonstrate that ReLERNN improves accuracy and reduces bias relative to existing methods and maintains high accuracy in the face of demographic model misspecification, missing genotype calls, and genome inaccessibility. We apply ReLERNN to natural populations of African Drosophila melanogaster and show that genome-wide recombination landscapes, although largely correlated among populations, exhibit important population-specific differences. Lastly, we connect the inferred patterns of recombination with the frequencies of major inversions segregating in natural Drosophila populations.

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Knowing the rate at which transposable elements (TEs) insert and delete is critical for understanding their role in genome evolution. We estimated spontaneous rates of insertion and deletion for all known, active TE superfamilies present in a set of Drosophila melanogaster mutation-accumulation (MA) lines using whole genome sequence data. Our results demonstrate that TE insertions far outpace TE deletions in D. melanogaster. We found a significant effect of background genotype on TE activity, with higher rates of insertions in one MA line. We also found significant rate heterogeneity between the chromosomes, with both insertion and deletion rates elevated on the X relative to the autosomes. Further, we identified significant associations between TE activity and chromatin state, and tested for associations between TE activity and other features of the local genomic environment such as TE content, exon content, GC content, and recombination rate. Our results provide the most detailed assessment of TE mobility in any organism to date, and provide a useful benchmark for both addressing theoretical predictions of TE dynamics and for exploring large-scale patterns of TE movement in D. melanogaster and other species.

Native to Asia, the soft-skinned fruit pest Drosophila suzukii has recently invaded the United States and Europe. The eastern United States represents the most recent expansion of their range, and presents an opportunity to test alternative models of colonization history. Here, we investigate the genetic population structure of this invasive fruit fly, with a focus on the eastern United States. We sequenced six X-linked gene fragments from 246 individuals collected from a total of 12 populations. We examine patterns of genetic diversity within and between populations and explore alternative colonization scenarios using approximate Bayesian computation. Our results indicate high levels of nucleotide diversity in this species and suggest that the recent invasions of Europe and the continental United States are independent demographic events. More broadly speaking, our results highlight the importance of integrating population structure into demographic models, particularly when attempting to reconstruct invasion histories. Finally, our simulation results illustrate the general challenge in reconstructing invasion histories using genetic data and suggest that genome-level data are often required to distinguish among alternative demographic scenarios.

Mitochondrial protein translation requires interactions between transfer RNAs encoded by the mitochondrial genome (mt-tRNAs) and mitochondrial aminoacyl tRNA synthetase proteins (mt-aaRS) encoded by the nuclear genome. It has been argued that animal mt-tRNAs have higher deleterious substitution rates relative to their nuclear-encoded counterparts, the cytoplasmic tRNAs (cyt-tRNAs). This dynamic predicts elevated rates of compensatory evolution of mt-aaRS that interact with mt-tRNAs, relative to aaRS that interact with cyt-tRNAs (cyt-aaRS). We find that mt-aaRS do evolve at significantly higher rates (exemplified by higher dN and dN/dS) relative to cyt-aaRS, across mammals, birds, and Drosophila. While this pattern supports a model of compensatory evolution, the level at which a gene is expressed is a more general predictor of protein evolutionary rate. We find that gene expression level explains 10–56% of the variance in aaRS dN/dS, and that cyt-aaRS are more highly expressed in addition to having lower dN/dS values relative to mt-aaRS, consistent with more highly expressed genes being more evolutionarily constrained. Furthermore, we find no evidence of positive selection acting on either class of aaRS protein, as would be expected under a model of compensatory evolution. Nevertheless, the signature of faster mt-aaRS evolution persists in mammalian, but not bird or Drosophila, lineages after controlling for gene expression, suggesting some additional effect of compensatory evolution for mammalian mt-aaRS. We conclude that gene expression is the strongest factor governing differential amino acid substitution rates in proteins interacting with mitochondrial versus cytoplasmic factors, with important differences in mt-aaRS molecular evolution among taxonomic groups.