Widespread gene flow following range expansion in Anna's Hummingbird.

Anthropogenic changes have altered the historical distributions of many North American taxa. As environments shift, ecological and evolutionary processes can combine in complex ways to either stimulate or inhibit range expansion. Here, we examined the role of evolution in a rapid range expansion whose ecological context has been well-documented, Anna's Hummingbird (Calypte anna). Previous studies have suggested that the C. anna range expansion is the result of an ecological release facilitated by human-mediated environmental changes, where access to new food sources have allowed further filling of the abiotic niche. We examined the role of gene flow and adaptation during range expansion from their native California breeding range, north into Canada and east into New Mexico and Texas, USA. Using low coverage whole genome sequencing we found high genetic diversity, low divergence, and little evidence of selection on the northern and eastern expansion fronts. Additionally, there are no clear barriers to gene flow across the native and expanded range. The lack of selective signals between core and expanded ranges could reflect (i) an absence of novel selection pressure in the expanded range (supporting the ecological release hypothesis), (ii) swamping of adaptive variation due to high gene flow, or (iii) limitations of genome scans for detecting small shifts in allele frequencies across many loci. Nevertheless, our results provide an example where strong selection is not apparent during a rapid, contemporary range shift.

Visualizing population structure with variational autoencoders.

Dimensionality reduction is a common tool for visualization and inference of population structure from genotypes, but popular methods either return too many dimensions for easy plotting (PCA) or fail to preserve global geometry (t-SNE and UMAP). Here we explore the utility of variational autoencoders (VAEs)-generative machine learning models in which a pair of neural networks seek to first compress and then recreate the input data-for visualizing population genetic variation. VAEs incorporate nonlinear relationships, allow users to define the dimensionality of the latent space, and in our tests preserve global geometry better than t-SNE and UMAP. Our implementation, which we call popvae, is available as a command-line python program at github.com/kr-colab/popvae. The approach yields latent embeddings that capture subtle aspects of population structure in humans and Anopheles mosquitoes, and can generate artificial genotypes characteristic of a given sample or population.

Predicting geographic location from genetic variation with deep neural networks.

Most organisms are more closely related to nearby than distant members of their species, creating spatial autocorrelations in genetic data. This allows us to predict the location of origin of a genetic sample by comparing it to a set of samples of known geographic origin. Here, we describe a deep learning method, which we call Locator, to accomplish this task faster and more accurately than existing approaches. In simulations, Locator infers sample location to within 4.1 generations of dispersal and runs at least an order of magnitude faster than a recent model-based approach. We leverage Locator's computational efficiency to predict locations separately in windows across the genome, which allows us to both quantify uncertainty and describe the mosaic ancestry and patterns of geographic mixing that characterize many populations. Applied to whole-genome sequence data from Plasmodium parasites, Anopheles mosquitoes, and global human populations, this approach yields median test errors of 16.9km, 5.7km, and 85km, respectively.

Space is the Place: Effects of Continuous Spatial Structure on Analysis of Population Genetic Data.

Real geography is continuous, but standard models in population genetics are based on discrete, well-mixed populations. As a result, many methods of analyzing genetic data assume that samples are a random draw from a well-mixed population, but are applied to clustered samples from populations that are structured clinally over space. Here, we use simulations of populations living in continuous geography to study the impacts of dispersal and sampling strategy on population genetic summary statistics, demographic inference, and genome-wide association studies (GWAS). We find that most common summary statistics have distributions that differ substantially from those seen in well-mixed populations, especially when Wright's neighborhood size is < 100 and sampling is spatially clustered. "Stepping-stone" models reproduce some of these effects, but discretizing the landscape introduces artifacts that in some cases are exacerbated at higher resolutions. The combination of low dispersal and clustered sampling causes demographic inference from the site frequency spectrum to infer more turbulent demographic histories, but averaged results across multiple simulations revealed surprisingly little systematic bias. We also show that the combination of spatially autocorrelated environments and limited dispersal causes GWAS to identify spurious signals of genetic association with purely environmentally determined phenotypes, and that this bias is only partially corrected by regressing out principal components of ancestry. Last, we discuss the relevance of our simulation results for inference from genetic variation in real organisms.

Ecological Release of the Anna's Hummingbird during a Northern Range Expansion.

During range expansions, species can experience rapid population growth if changes in climate or interspecific interactions remove limits on growth rates in novel habitats. Here I document a century of range expansion in the Anna's hummingbird (Calypte anna) and investigate the causes of its recent abundance through a combination of demographic, climatic, and phenological analyses. Christmas Bird Count records indicate that populations have been growing in California since the early twentieth century. Sites across the Pacific Northwest show striking fits to simple models of exponential growth following colonization in the 1960s and 1970s, and nest records indicate that the species now delays the start of the nesting season by at least 16 days in the north. Although the species now occurs in a much wider range of climates than before the range expansion, the fastest growing populations in the northwest are in regions with minimum breeding season temperatures similar to those occupied by the species in its native range. Range expansions in the Anna's hummingbird thus reflect an ecological release likely caused by a mix of introduced plants, human facilitation, and phenological acclimation that allowed a California native to expand across western North America.

Minor allele frequency thresholds strongly affect population structure inference with genomic data sets.

A common method of minimizing errors in large DNA sequence data sets is to drop variable sites with a minor allele frequency (MAF) below some specified threshold. Although widespread, this procedure has the potential to alter downstream population genetic inferences and has received relatively little rigorous analysis. Here we use simulations and an empirical single nucleotide polymorphism data set to demonstrate the impacts of MAF thresholds on inference of population structure-often the first step in analysis of population genomic data. We find that model-based inference of population structure is confounded when singletons are included in the alignment, and that both model-based and multivariate analyses infer less distinct clusters when more stringent MAF cutoffs are applied. We propose that this behaviour is caused by the combination of a drop in the total size of the data matrix and by correlations between allele frequencies and mutational age. We recommend a set of best practices for applying MAF filters in studies seeking to describe population structure with genomic data.

A Migratory Divide in the Painted Bunting (Passerina ciris).

In the painted bunting (Passerina ciris), a North American songbird, populations on the Atlantic coast and interior southern United States are known to be allopatric during the breeding season, but efforts to map connectivity with wintering ranges have been largely inconclusive. Using genomic and morphological data from museum specimens and banded birds, we found evidence of three genetically differentiated painted bunting populations with distinct wintering ranges and molt-migration phenologies. In addition to confirming that the Atlantic coast population remains allopatric throughout the annual cycle, we identified an unexpected migratory divide within the interior breeding range. Populations breeding in Louisiana winter on the Yucatán Peninsula and are parapatric with other interior populations that winter in mainland Mexico and Central America. Across the interior breeding range, genetic ancestry is also associated with variation in wing length, suggesting that selection may be promoting morphological divergence in populations with different migration strategies.

Cryptic speciation and gene flow in a migratory songbird Species Complex: Insights from the Red-Eyed Vireo (Vireo olivaceus).

Migratory species that alternate between sympatry and allopatry over the course of an annual cycle are promising subjects for studies seeking to understand the process of speciation in the absence of strict geographic isolation. Here we sought to identify cryptic species and assess rates of gene flow in a clade of neotropical migrant songbirds in which geography and taxonomy are currently out of sync: the Red-Eyed Vireo (V. olivaceus) Species Complex. Phylogenetic, clustering, and statistical species delimitation analyses found that V. olivaceusincludes two non-sister lineages migrating in opposite directions across the equator. Analyses of gene flow identified low levels of introgression between two species pairs, but none between northern and southern olivaceus. We also identified substantial well-supported conflicts between nuclear and mitochondrial topologies. Although the geographic distribution of mito-nuclear discordance is suggestive of hybridization and mitochondrial capture, we found no evidence of introgression in the nuclear genome of populations with discordant mitochondrial gene trees. Our study finds that species boundaries match breeding range and migratory phenology rather than the existing taxonomy in this group, and demonstrates the utility of genomic data in inferring species boundaries in recently diverged clades.

A multilocus phylogeny of a major New World avian radiation: the Vireonidae.

The family Vireonidae represents one of the most widespread and well-known New World avian radiations, but a robust species-level phylogeny of the group is lacking. Here, we infer a phylogeny of Vireonidae using multilocus data obtained from 221 individuals from 46 of 52 vireonid species (representing all four genera) and five "core Corvoidea" outgroups. Our results show Vireonidae to be monophyletic, consistent with a single colonization of the New World by an Asian ancestor. Cyclarhis and Vireolanius are monophyletic genera that diverged early from the rest of Vireonidae. Hylophilus is polyphyletic, represented by three distinct clades concordant with differences in morphology, habitat, and voice. The poorly known South American species Hylophilus sclateri is embedded within the genus Vireo. Vireo, in turn, consists of several well-supported intrageneric clades. Overall, tropical vireonid species show much higher levels of intraspecific genetic structure than temperate species and several currently recognized species are probably comprised of multiple cryptic species.