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).

Evolution is more repeatable in the introduction than range expansion phase of colonization.

How repeatable is evolution at genomic and phenotypic scales? We studied the repeatability of evolution during 8 generations of colonization using replicated microcosm experiments with the red flour beetle, Tribolium castaneum. Based on the patterns of shared allele frequency changes that occurred in populations from the same generation or experimental location, we found adaptive evolution to be more repeatable in the introduction and establishment phases of colonization than in the spread phase, when populations expand their range. Lastly, by studying changes in allele frequencies at conserved loci, we found evidence for the theoretical prediction that range expansion reduces the efficiency of selection to purge deleterious alleles. Overall, our results increase our understanding of adaptive evolution during colonization, demonstrating that evolution can be highly repeatable while also showing that stochasticity still plays an important role.

Cephalopod Sex Determination and its Ancient Evolutionary Origin.

Sex chromosomes are critical elements of sexual reproduction in many animal and plant taxa, however they show incredible diversity and rapid turnover even within clades. Until now, the mechanism of sex determination in cephlaopods has been a mystery. Using a chromosome-level genome assembly generated with long read sequencing, we report the first evidence for genetic sex determination in cephalopods. We have uncovered a sex chromosome in California two-spot octopus (Octopus bimaculoides ) in which males/females have ZZ/ZO karyotypes respectively. We show that the octopus Z chromosome is an evolutionary outlier with respect to divergence and repetitive element content as compared to autosomes and that it is present in all cephalopods that we have examined including Nautilus, the outgroup to squids and octopuses. Our results suggest that the cephalopod Z chromosome system originated before the split of all extant cephalopod lineages, over 480 million years ago and has been conserved to the present, making it the among the oldest conserved animal sex chromosome systems known.

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.

Dispersal inference from population genetic variation using a convolutional neural network.

The geographic nature of biological dispersal shapes patterns of genetic variation over landscapes, making it possible to infer properties of dispersal from genetic variation data. Here, we present an inference tool that uses geographically distributed genotype data in combination with a convolutional neural network to estimate a critical population parameter: the mean per-generation dispersal distance. Using extensive simulation, we show that our deep learning approach is competitive with or outperforms state-of-the-art methods, particularly at small sample sizes. In addition, we evaluate varying nuisance parameters during training-including population density, demographic history, habitat size, and sampling area-and show that this strategy is effective for estimating dispersal distance when other model parameters are unknown. Whereas competing methods depend on information about local population density or accurate inference of identity-by-descent tracts, our method uses only single-nucleotide-polymorphism data and the spatial scale of sampling as input. Strikingly, and unlike other methods, our method does not use the geographic coordinates of the genotyped individuals. These features make our method, which we call "disperseNN," a potentially valuable new tool for estimating dispersal distance in nonmodel systems with whole genome data or reduced representation data. We apply disperseNN to 12 different species with publicly available data, yielding reasonable estimates for most species. Importantly, our method estimated consistently larger dispersal distances than mark-recapture calculations in the same species, which may be due to the limited geographic sampling area covered by some mark-recapture studies. Thus genetic tools like ours complement direct methods for improving our understanding of dispersal.

Rapid, nonparallel genomic evolution of Brassica rapa (field mustard) under experimental drought.

While we know that climate change can potentially cause rapid phenotypic evolution, our understanding of the genetic basis and degree of genetic parallelism of rapid evolutionary responses to climate change is limited. In this study, we combined the resurrection approach with an evolve-and-resequence design to examine genome-wide evolutionary changes following drought. We exposed genetically similar replicate populations of the annual plant Brassica rapa derived from a field population in southern California to four generations of experimental drought or watered conditions in a greenhouse. Genome-wide sequencing of ancestral and descendant population pools identified hundreds of SNPs that showed evidence of rapidly evolving in response to drought. Several of these were in stress response genes, and two were identified in a prior study of drought response in this species. However, almost all genetic changes were unique among experimental populations, indicating that the evolutionary changes were largely nonparallel, despite the fact that genetically similar replicates of the same founder population had experienced controlled and consistent selection regimes. This nonparallelism of evolution at the genetic level is potentially because of polygenetic adaptation allowing for multiple different genetic routes to similar phenotypic outcomes. Our findings help to elucidate the relationship between rapid phenotypic and genomic evolution and shed light on the degree of parallelism and predictability of genomic evolution to environmental change.

De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes.

We report de novo genome assemblies, transcriptomes, annotations, and methylomes for the 26 inbreds that serve as the founders for the maize nested association mapping population. The number of pan-genes in these diverse genomes exceeds 103,000, with approximately a third found across all genotypes. The results demonstrate that the ancient tetraploid character of maize continues to degrade by fractionation to the present day. Excellent contiguity over repeat arrays and complete annotation of centromeres revealed additional variation in major cytological landmarks. We show that combining structural variation with single-nucleotide polymorphisms can improve the power of quantitative mapping studies. We also document variation at the level of DNA methylation and demonstrate that unmethylated regions are enriched for cis-regulatory elements that contribute to phenotypic variation.

Evolution of pathogen response genes associated with increased disease susceptibility during adaptation to an extreme drought in a Brassica rapa plant population.

BACKGROUND: Pathogens are key components in natural and agricultural plant systems. There is evidence of evolutionary changes in disease susceptibility as a consequence of climate change, but we know little about the underlying genetic basis of this evolution. To address this, we took advantage of a historical seed collection of a Brassica rapa population, which we previously demonstrated evolved an increase in disease susceptibility to a necrotrophic fungal pathogen following a drought. RESULTS: Previously, we combined a resurrection experiment with genome-wide sequencing of 124 pooled ancestral and descendant plants. Here, using these previously generated sequence data (Franks et al. in Mol Ecol 25(15):3622-3631, 2016), we show that well-characterized necrotrophic fungal pathogen response (NFPR) genes have evolved, as indicated by changes in allele frequency, between ancestors and descendants, with several of them identified as extreme FST outliers. The jasmonic acid (JA) signaling pathway in particular seems to underlie the evolution of disease susceptibility, in addition to its well characterized role in plastic disease response. We identify a list of 260 genes that are both NFPR genes and are differentially expressed in response to drought, based on publicly available data. We present evidence that five of these genes evolved between ancestors and descendants, suggesting that the drought acted as the evolutionary driver, and that the accompanying increase in disease susceptibility may have been a consequence of genetic pleiotropy. CONCLUSIONS: Our study provides evidence that for this population, standing variation in NFPR genes is affected by natural selection related to climate change. Our results reveal potentially important candidates that may underlie trait evolution in both crops and natural systems. Additionally, this trade-off between adaptation to biotic and abiotic stresses is an example of how climate change can have diverse and unexpected consequences.

rdmc: An Open Source R Package Implementing Convergent Adaptation Models of Lee and Coop (2017).

The availability of whole genome sequencing data from multiple related populations creates opportunities to test sophisticated population genetic models of convergent adaptation. Recent work by Lee and Coop (2017) developed models to infer modes of convergent adaption at local genomic scales, providing a rich framework for assessing how selection has acted across multiple populations at the tested locus. Here I present, rdmc, an R package that builds on the existing software implementation of Lee and Coop (2017) that prioritizes ease of use, portability, and scalability. I demonstrate installation and comprehensive overview of the package's current utilities.

A resurrection study reveals limited evolution of thermal performance in response to recent climate change across the geographic range of the scarlet monkeyflower.

Evolutionary rescue can prevent populations from declining under climate change, and should be more likely at high-latitude, "leading" edges of species' ranges due to greater temperature anomalies and gene flow from warm-adapted populations. Using a resurrection study with seeds collected before and after a 7-year period of record warming, we tested for thermal adaptation in the scarlet monkeyflower Mimulus cardinalis. We grew ancestors and descendants from northern-edge, central, and southern-edge populations across eight temperatures. Despite recent climate anomalies, populations showed limited evolution of thermal performance curves. However, one southern population evolved a narrower thermal performance breadth by 1.31°C, which matches the direction and magnitude of the average decrease in seasonality experienced. Consistent with the climate variability hypothesis, thermal performance breadth increased with temperature seasonality across the species' geographic range. Inconsistent with performance trade-offs between low and high temperatures across populations, we did not detect a positive relationship between thermal optimum and mean temperature. These findings fail to support the hypothesis that evolutionary response to climate change is greatest at the leading edge, and suggest that the evolution of thermal performance is unlikely to rescue most populations from the detrimental effects of rapidly changing climate.

Genetic Diversity and Thermal Performance in Invasive and Native Populations of African Fig Flies.

During biological invasions, invasive populations can suffer losses of genetic diversity that are predicted to negatively impact their fitness/performance. Despite examples of invasive populations harboring lower diversity than conspecific populations in their native range, few studies have linked this lower diversity to a decrease in fitness. Using genome sequences, we show that invasive populations of the African fig fly, Zaprionus indianus, have less genetic diversity than conspecific populations in their native range and that diversity is proportionally lower in regions of the genome experiencing low recombination rates. This result suggests that selection may have played a role in lowering diversity in the invasive populations. We next use interspecific comparisons to show that genetic diversity remains relatively high in invasive populations of Z. indianus when compared with other closely related species. By comparing genetic diversity in orthologous gene regions, we also show that the genome-wide landscape of genetic diversity differs between invasive and native populations of Z. indianus indicating that invasion not only affects amounts of genetic diversity but also how that diversity is distributed across the genome. Finally, we use parameter estimates from thermal performance curves for 13 species of Zaprionus to show that Z. indianus has the broadest thermal niche of measured species, and that performance does not differ between invasive and native populations. These results illustrate how aspects of genetic diversity in invasive species can be decoupled from measures of fitness, and that a broad thermal niche may have helped facilitate Z. indianus's range expansion.

Stochastic processes drive rapid genomic divergence during experimental range expansions.

Range expansions are crucibles for rapid evolution, acting via both selective and neutral mechanisms. While selection on traits such as dispersal and fecundity may increase expansion speed, neutral mechanisms arising from repeated bottlenecks and genetic drift in edge populations (i.e. gene surfing) could slow spread or make it less predictable. Thus, it is necessary to disentangle the effects of selection from neutral mechanisms to robustly predict expansion dynamics. This is difficult to do with expansions in nature, as replicated expansions are required to distinguish selective and neutral processes in the genome. Using replicated microcosms of the red flour beetle ( Tribolium castaneum), we identify a robust signature of stochastic, neutral mechanisms in genomic changes arising over only eight generations of expansion and assess the role of standing variation and de novo mutations in driving these changes. Average genetic diversity was reduced within edge populations, but with substantial among-replicate variability in the changes at specific genomic windows. Such variability in genomic changes is consistent with a large role for stochastic, neutral processes. This increased genomic divergence among populations was mirrored by heightened variation in population size and expansion speed, suggesting that stochastic variation in the genome could increase unpredictability of range expansions.

Grow Where You Thrive, or Where Only You Can Survive? An Analysis of Performance Curve Evolution in a Clade with Diverse Habitat Affinities.

Performance curves are valuable tools for quantifying the fundamental niches of organisms and testing hypotheses about evolution, life-history trade-offs, and the drivers of variation in species' distribution patterns. Here, we present a novel Bayesian method for characterizing performance curves that facilitates comparisons among species. We then use this model to quantify and compare the hydrological performance curves of 14 different taxa in the genus Lasthenia, an ecologically diverse clade of plants that collectively occupy a variety of habitats with unique hydrological features, including seasonally flooded wetlands called vernal pools. We conducted a growth chamber experiment to measure each taxon's fitness across five hydrological treatments that ranged from severe drought to extended flooding, and we identified differences in hydrological performance curves that explain their associations with vernal pool and terrestrial habitats. Our analysis revealed that the distribution of vernal pool taxa in the field does not reflect their optimal hydrological environments: all taxa, regardless of habitat affinity, have highest fitness under similar hydrological conditions of saturated soil without submergence. We also found that a taxon's relative position across flood gradients within vernal pools is best predicted by the height of its performance curve. These results demonstrate the utility of our approach for generating insights into when and how performance curves evolve among taxa as they diversify into distinct environments. To facilitate its use, the modeling framework has been developed into an R package.

Genetics of alternative splicing evolution during sunflower domestication.

Alternative splicing enables organisms to produce the diversity of proteins necessary for multicellular life by using relatively few protein-coding genes. Although differences in splicing have been identified among divergent taxa, the shorter-term evolution of splicing is understudied. The origins of novel splice forms, and the contributions of alternative splicing to major evolutionary transitions, are largely unknown. This study used transcriptomes of wild and domesticated sunflowers to examine splice differentiation and regulation during domestication. We identified substantial splicing divergence between wild and domesticated sunflowers, mainly in the form of intron retention. Transcripts with divergent splicing were enriched for seed-development functions, suggesting that artificial selection impacted splicing patterns. Mapping of quantitative trait loci (QTLs) associated with 144 differential splicing cases revealed primarily trans-acting variation affecting splicing patterns. A large proportion of identified QTLs contain known spliceosome proteins and are associated with splicing variation in multiple genes. Examining a broader set of wild and domesticated sunflower genotypes revealed that most differential splicing patterns in domesticated sunflowers likely arose from standing variation in wild Helianthus annuus and gained frequency during the domestication process. However, several domesticate-associated splicing patterns appear to be introgressed from other Helianthus species. These results suggest that sunflower domestication involved selection on pleiotropic regulatory alleles. More generally, our findings indicate that substantial differences in isoform abundances arose rapidly during a recent evolutionary transition and appear to contribute to adaptation and population divergence.

Genetic Architecture of Capitate Glandular Trichome Density in Florets of Domesticated Sunflower (Helianthus annuus L.).

Capitate glandular trichomes (CGT), one type of glandular trichomes, are most common in Asteraceae species. CGT can produce various secondary metabolites such as sesquiterpene lactones (STLs) and provide durable resistance to insect pests. In sunflower, CGT-based host resistance is effective to combat the specialist pest, sunflower moth. However, the genetic basis of CGT density is not well understood in sunflower. In this study, we identified two major QTL controlling CGT density in sunflower florets by using a F4 mapping population derived from the cross HA 300 × RHA 464 with a genetic linkage map constructed from genotyping-by-sequencing data and composed of 2121 SNP markers. One major QTL is located on chromosome 5, which explained 11.61% of the observed phenotypic variation, and the second QTL is located on chromosome 6, which explained 14.06% of the observed phenotypic variation. The QTL effects and the association between CGT density and QTL support interval were confirmed in a validation population which included 39 sunflower inbred lines with diverse genetic backgrounds. We also identified two strong candidate genes in the QTL support intervals, and the functions of their orthologs in other plant species suggested their potential roles in regulating capitate glandular trichome density in sunflower. Our results provide valuable information to sunflower breeding community for developing host resistance to sunflower insect pests.

Rapid genome-wide evolution in Brassica rapa populations following drought revealed by sequencing of ancestral and descendant gene pools.

There is increasing evidence that evolution can occur rapidly in response to selection. Recent advances in sequencing suggest the possibility of documenting genetic changes as they occur in populations, thus uncovering the genetic basis of evolution, particularly if samples are available from both before and after selection. Here, we had a unique opportunity to directly assess genetic changes in natural populations following an evolutionary response to a fluctuation in climate. We analysed genome-wide differences between ancestors and descendants of natural populations of Brassica rapa plants from two locations that rapidly evolved changes in multiple phenotypic traits, including flowering time, following a multiyear late-season drought in California. These ancestor-descendant comparisons revealed evolutionary shifts in allele frequencies in many genes. Some genes showing evolutionary shifts have functions related to drought stress and flowering time, consistent with an adaptive response to selection. Loci differentiated between ancestors and descendants (FST outliers) were generally different from those showing signatures of selection based on site frequency spectrum analysis (Tajima's D), indicating that the loci that evolved in response to the recent drought and those under historical selection were generally distinct. Very few genes showed similar evolutionary responses between two geographically distinct populations, suggesting independent genetic trajectories of evolution yielding parallel phenotypic changes. The results show that selection can result in rapid genome-wide evolutionary shifts in allele frequencies in natural populations, and highlight the usefulness of combining resurrection experiments in natural populations with genomics for studying the genetic basis of adaptive evolution.

The genomics of adaptation, divergence and speciation: a congealing theory.

In this issue, Flaxman et al. () report the results of sophisticated whole-genome simulations of speciation with gene flow, enhancing our understanding of the process by building on previous single-locus, multilocus and analytical works. Their findings provide us with new insights about how genomes can diverge and the importance of statistical and chromosomal linkage in facilitating reproductive isolation. The authors characterize the conditions under which, even with high gene flow and weak divergent selection, reproductive isolation between populations can occur due to the emergent stochastic process of genomewide congealing, where numerous statistically or physically linked loci of small effect allow selection to limit effective migration rates. The initial congealing event can occur within a broad range conditions, and once initiated, the self-reinforcing process leads to rapid divergence and ultimately two reproductively isolated populations. Flaxman et al.'s () work is a valuable contribution to our understanding of speciation with gene flow and in making a more predictive field of evolutionary genomics and speciation.