Discovery of Ongoing Selective Sweeps within Anopheles Mosquito Populations Using Deep Learning.

Identification of partial sweeps, which include both hard and soft sweeps that have not currently reached fixation, provides crucial information about ongoing evolutionary responses. To this end, we introduce partialS/HIC, a deep learning method to discover selective sweeps from population genomic data. partialS/HIC uses a convolutional neural network for image processing, which is trained with a large suite of summary statistics derived from coalescent simulations incorporating population-specific history, to distinguish between completed versus partial sweeps, hard versus soft sweeps, and regions directly affected by selection versus those merely linked to nearby selective sweeps. We perform several simulation experiments under various demographic scenarios to demonstrate partialS/HIC's performance, which exhibits excellent resolution for detecting partial sweeps. We also apply our classifier to whole genomes from eight mosquito populations sampled across sub-Saharan Africa by the Anopheles gambiae 1000 Genomes Consortium, elucidating both continent-wide patterns as well as sweeps unique to specific geographic regions. These populations have experienced intense insecticide exposure over the past two decades, and we observe a strong overrepresentation of sweeps at insecticide resistance loci. Our analysis thus provides a list of candidate adaptive loci that may be relevant to mosquito control efforts. More broadly, our supervised machine learning approach introduces a method to distinguish between completed and partial sweeps, as well as between hard and soft sweeps, under a variety of demographic scenarios. As whole-genome data rapidly accumulate for a greater diversity of organisms, partialS/HIC addresses an increasing demand for useful selection scan tools that can track in-progress evolutionary dynamics.

Asymmetrical gene flow in five co-distributed syngnathids explained by ocean currents and rafting propensity.

Ocean circulation driving macro-algal rafting is believed to serve as an important mode of dispersal for many marine organisms, leading to predictions on population-level genetic connectivity and the directionality of effective dispersal. Here, we use genome-wide single nucleotide polymorphism data to investigate whether gene flow directionality in two seahorses (Hippocampus) and three pipefishes (Syngnathus) follows the predominant ocean circulation patterns in the Gulf of Mexico and northwestern Atlantic. In addition, we explore whether gene flow magnitudes are predicted by traits related to active dispersal ability and habitat preference. We inferred demographic histories of these co-distributed syngnathid species, and coalescent model-based estimates indicate that gene flow directionality is in agreement with ocean circulation data that predicts eastward and northward macro-algal transport. However, the magnitude to which ocean currents influence this pattern appears strongly dependent on the species-specific traits related to rafting propensity and habitat preferences. Higher levels of gene flow and stronger directionality are observed in Hippocampus erectus, Syngnathus floridae and Syngnathus louisianae, which closely associated with the pelagic macro-algae Sargassum spp., compared to Hippocampus zosterae and the Syngnathus scovelli/Syngnathus fuscus sister-species pair, which prefer near shore habitats and are weakly associated with pelagic Sargassum. This study highlights how the combination of population genomic inference together with ocean circulation data can help explain patterns of population structure and diversity in marine ecosystems.

Quaternary climate changes as speciation drivers in the Amazon floodplains.

The role of climate as a speciation driver in the Amazon has long been discussed. Phylogeographic studies have failed to recover synchronous demographic responses across taxa, although recent evidence supports the interaction between rivers and climate in promoting speciation. Most studies, however, are biased toward upland forest organisms, while other habitats are poorly explored and could hold valuable information about major historical processes. We conducted a comparative phylogenomic analysis of floodplain forest birds to explore the effects of historical environmental changes and current connectivity on population differentiation. Our findings support a similar demographic history among species complexes, indicating that the central portion of the Amazon River basin is a suture zone for taxa isolated across the main Amazonian sub-basins. Our results also suggest that changes in the fluvial landscape induced by climate variation during the Mid- and Late Pleistocene drove population isolation, leading to diversification with subsequent secondary contact.

Comparative phylogeographic inference with genome-wide data from aggregated population pairs.

Comparing divergences across multiple sister population pairs has been a focus in phylogeography since its inception. Initial approaches used organelle genetic data and involved qualitative comparisons of phylogenetic patterns to evaluate hypotheses of shared and variable evolutionary responses. This endeavor has progressed with coalescent model-based statistical techniques and advances in next-generation sequencing, yet there remains a need for methods that can exploit aggregated genomic-scale data within a unified analytical framework. To this end, we introduce the aggregate joint site frequency spectrum (ajSFS) by validating its use within a hierarchical Bayesian framework through several in silico experiments. Subsequently, we applied our method against two published restriction site-associated DNA marker datasets consisting of eight local replicates of a lamprey species pair and six co-distributed passerine taxon pairs, respectively, with the aim of inferring variability in co-divergence and co-migration histories. We found that the lamprey population pairs exhibited temporal synchrony in both co-divergence and collective secondary contact times, yet an idiosyncratic pattern in secondary migration intensities. In contrast, the bird population pairs displayed thoroughly asynchronous co-divergence histories. Our results demonstrate that the ajSFS can be exploited for complex and flexible co-demographic inference, opening up new possibilities for comparative phylogeography and population genomic studies.

Environmental heterogeneity and not vicariant biogeographic barriers generate community-wide population structure in desert-adapted snakes.

Genetic structure can be influenced by local adaptation to environmental heterogeneity and biogeographic barriers, resulting in discrete population clusters. Geographic distance among populations, however, can result in continuous clines of genetic divergence that appear as structured populations. Here, we evaluate the relevant importance of these three factors over a landscape characterized by environmental heterogeneity and the presence of a hypothesized biogeographic barrier in producing population genetic structure within 13 codistributed snake species using a genomic data set. We demonstrate that geographic distance and environmental heterogeneity across western North America contribute to population genomic divergence. Surprisingly, landscape features long thought to contribute to biogeographic barriers play little role in divergence community wide. Our results suggest that isolation by environment is the most important contributor to genomic divergence. Furthermore, we show that models of population clustering that incorporate spatial information consistently outperform nonspatial models, demonstrating the importance of considering geographic distances in population clustering. We argue that environmental and geographic distances as drivers of community-wide divergence should be explored before assuming the role of biogeographic barriers.

Differential Effect of Selection against LINE Retrotransposons among Vertebrates Inferred from Whole-Genome Data and Demographic Modeling.

Variation in LINE composition is one of the major determinants for the substantial size and structural differences among vertebrate genomes. In particular, the larger genomes of mammals are characterized by hundreds of thousands of copies from a single LINE clade, L1, whereas nonmammalian vertebrates possess a much greater diversity of LINEs, yet with orders of magnitude less in copy number. It has been proposed that such variation in copy number among vertebrates is due to differential effect of LINE insertions on host fitness. To investigate LINE selection, we deployed a framework of demographic modeling, coalescent simulations, and probabilistic inference against population-level whole-genome data sets for four model species: one population each of threespine stickleback, green anole, and house mouse, as well as three human populations. Specifically, we inferred a null demographic background utilizing SNP data, which was then exploited to simulate a putative null distribution of summary statistics that was compared with LINE data. Subsequently, we applied the inferred null demographic model with an additional exponential size change parameter, coupled with model selection, to test for neutrality as well as estimate the strength of either negative or positive selection. We found a robust signal for purifying selection in anole and mouse, but a lack of clear evidence for selection in stickleback and human. Overall, we demonstrated LINE insertion dynamics that are not in accordance to a mammalian versus nonmammalian dichotomy, and instead the degree of existing LINE activity together with host-specific demographic history may be the main determinants of LINE abundance.

Pleistocene climatic changes drive diversification across a tropical savanna.

Spatial responses of species to past climate change depend on both intrinsic traits (climatic niche breadth, dispersal rates) and the scale of climatic fluctuations across the landscape. New capabilities in generating and analysing population genomic data, along with spatial modelling, have unleashed our capacity to infer how past climate changes have shaped populations, and by extension, complex communities. Combining these approaches, we uncover lineage diversity across four codistributed lizards from the Australian Monsoonal Tropics and explore how varying climatic tolerances interact with regional climate history to generate common vs. disparate responses to late Pleistocene change. We find more divergent spatial structuring and temporal demographic responses in the drier Kimberley region compared to the more mesic and consistently suitable Top End. We hypothesize that, in general, the effects of species' traits on sensitivity to climate fluctuation will be more evident in climatically marginal regions. If true, this points to the need in climatically marginal areas to craft more species-(or trait)-specific strategies for persistence under future climate change.

Long-term in situ persistence of biodiversity in tropical sky islands revealed by landscape genomics.

Tropical mountains are areas of high species richness and endemism. Two historical phenomena may have contributed to this: (i) fragmentation and isolation of habitats may have promoted the genetic differentiation of populations and increased the possibility of allopatric divergence and speciation and (ii) the mountain areas may have allowed long-term population persistence during global climate fluctuations. These two phenomena have been studied using either species occurrence data or estimating species divergence times. However, only few studies have used intraspecific genetic data to analyse the mechanisms by which endemism may emerge at the microevolutionary scale. Here, we use landscape analysis of genomic SNP data sampled from two high-elevation plant species from an archipelago of tropical sky islands (the Trans-Mexican Volcanic Belt) to test for population genetic differentiation, synchronous demographic changes and habitat persistence. We show that genetic differentiation can be explained by the degree of glacial habitat connectivity among mountains and that mountains have facilitated the persistence of populations throughout glacial/interglacial cycles. Our results support the ongoing role of tropical mountains as cradles for biodiversity by uncovering cryptic differentiation and limits to gene flow.

multi-dice: r package for comparative population genomic inference under hierarchical co-demographic models of independent single-population size changes.

Population genetic data from multiple taxa can address comparative phylogeographic questions about community-scale response to environmental shifts, and a useful strategy to this end is to employ hierarchical co-demographic models that directly test multi-taxa hypotheses within a single, unified analysis. This approach has been applied to classical phylogeographic data sets such as mitochondrial barcodes as well as reduced-genome polymorphism data sets that can yield 10,000s of SNPs, produced by emergent technologies such as RAD-seq and GBS. A strategy for the latter had been accomplished by adapting the site frequency spectrum to a novel summarization of population genomic data across multiple taxa called the aggregate site frequency spectrum (aSFS), which potentially can be deployed under various inferential frameworks including approximate Bayesian computation, random forest and composite likelihood optimization. Here, we introduce the r package multi-dice, a wrapper program that exploits existing simulation software for flexible execution of hierarchical model-based inference using the aSFS, which is derived from reduced genome data, as well as mitochondrial data. We validate several novel software features such as applying alternative inferential frameworks, enforcing a minimal threshold of time surrounding co-demographic pulses and specifying flexible hyperprior distributions. In sum, multi-dice provides comparative analysis within the familiar R environment while allowing a high degree of user customization, and will thus serve as a tool for comparative phylogeography and population genomics.

Inferring responses to climate dynamics from historical demography in neotropical forest lizards.

We apply a comparative framework to test for concerted demographic changes in response to climate shifts in the neotropical lowland forests, learning from the past to inform projections of the future. Using reduced genomic (SNP) data from three lizard species codistributed in Amazonia and the Atlantic Forest (Anolis punctatus, Anolis ortonii, and Polychrus marmoratus), we first reconstruct former population history and test for assemblage-level responses to cycles of moisture transport recently implicated in changes of forest distribution during the Late Quaternary. We find support for population shifts within the time frame of inferred precipitation fluctuations (the last 250,000 y) but detect idiosyncratic responses across species and uniformity of within-species responses across forest regions. These results are incongruent with expectations of concerted population expansion in response to increased rainfall and fail to detect out-of-phase demographic syndromes (expansions vs. contractions) across forest regions. Using reduced genomic data to infer species-specific demographical parameters, we then model the plausible spatial distribution of genetic diversity in the Atlantic Forest into future climates (2080) under a medium carbon emission trajectory. The models forecast very distinct trajectories for the lizard species, reflecting unique estimated population densities and dispersal abilities. Ecological and demographic constraints seemingly lead to distinct and asynchronous responses to climatic regimes in the tropics, even among similarly distributed taxa. Incorporating such constraints is key to improve modeling of the distribution of biodiversity in the past and future.

Urbanization shapes the demographic history of a native rodent (the white-footed mouse, Peromyscus leucopus) in New York City.

How urbanization shapes population genomic diversity and evolution of urban wildlife is largely unexplored. We investigated the impact of urbanization on white-footed mice,Peromyscus leucopus,in the New York City (NYC) metropolitan area using coalescent-based simulations to infer demographic history from the site-frequency spectrum. We assigned individuals to evolutionary clusters and then inferred recent divergence times, population size changes and migration using genome-wide single nucleotide polymorphisms genotyped in 23 populations sampled along an urban-to-rural gradient. Both prehistoric climatic events and recent urbanization impacted these populations. Our modelling indicates that post-glacial sea-level rise led to isolation of mainland and Long Island populations. These models also indicate that several urban parks represent recently isolated P. leucopus populations, and the estimated divergence times for these populations are consistent with the history of urbanization in NYC.

The aggregate site frequency spectrum for comparative population genomic inference.

Understanding how assemblages of species responded to past climate change is a central goal of comparative phylogeography and comparative population genomics, an endeavour that has increasing potential to integrate with community ecology. New sequencing technology now provides the potential to perform complex demographic inference at unprecedented resolution across assemblages of nonmodel species. To this end, we introduce the aggregate site frequency spectrum (aSFS), an expansion of the site frequency spectrum to use single nucleotide polymorphism (SNP) data sets collected from multiple, co-distributed species for assemblage-level demographic inference. We describe how the aSFS is constructed over an arbitrary number of independent population samples and then demonstrate how the aSFS can differentiate various multispecies demographic histories under a wide range of sampling configurations while allowing effective population sizes and expansion magnitudes to vary independently. We subsequently couple the aSFS with a hierarchical approximate Bayesian computation (hABC) framework to estimate degree of temporal synchronicity in expansion times across taxa, including an empirical demonstration with a data set consisting of five populations of the threespine stickleback (Gasterosteus aculeatus). Corroborating what is generally understood about the recent postglacial origins of these populations, the joint aSFS/hABC analysis strongly suggests that the stickleback data are most consistent with synchronous expansion after the Last Glacial Maximum (posterior probability = 0.99). The aSFS will have general application for multilevel statistical frameworks to test models involving assemblages and/or communities, and as large-scale SNP data from nonmodel species become routine, the aSFS expands the potential for powerful next-generation comparative population genomic inference.