Selective sorting of ancestral introgression in maize and teosinte along an elevational cline.

While often deleterious, hybridization can also be a key source of genetic variation and pre-adapted haplotypes, enabling rapid evolution and niche expansion. Here we evaluate these opposing selection forces on introgressed ancestry between maize (Zea mays ssp. mays) and its wild teosinte relative, mexicana (Zea mays ssp. mexicana). Introgression from ecologically diverse teosinte may have facilitated maize's global range expansion, in particular to challenging high elevation regions (> 1500 m). We generated low-coverage genome sequencing data for 348 maize and mexicana individuals to evaluate patterns of introgression in 14 sympatric population pairs, spanning the elevational range of mexicana, a teosinte endemic to the mountains of Mexico. While recent hybrids are commonly observed in sympatric populations and mexicana demonstrates fine-scale local adaptation, we find that the majority of mexicana ancestry tracts introgressed into maize over 1000 generations ago. This mexicana ancestry seems to have maintained much of its diversity and likely came from a common ancestral source, rather than contemporary sympatric populations, resulting in relatively low FST between mexicana ancestry tracts sampled from geographically distant maize populations. Introgressed mexicana ancestry in maize is reduced in lower-recombination rate quintiles of the genome and around domestication genes, consistent with pervasive selection against introgression. However, we also find mexicana ancestry increases across the sampled elevational gradient and that high introgression peaks are most commonly shared among high-elevation maize populations, consistent with introgression from mexicana facilitating adaptation to the highland environment. In the other direction, we find patterns consistent with adaptive and clinal introgression of maize ancestry into sympatric mexicana at many loci across the genome, suggesting that maize also contributes to adaptation in mexicana, especially at the lower end of its elevational range. In sympatric maize, in addition to high introgression regions we find many genomic regions where selection for local adaptation maintains steep gradients in introgressed mexicana ancestry across elevation, including at least two inversions: the well-characterized 14 Mb Inv4m on chromosome 4 and a novel 3 Mb inversion Inv9f surrounding the macrohairless1 locus on chromosome 9. Most outlier loci with high mexicana introgression show no signals of sweeps or local sourcing from sympatric populations and so likely represent ancestral introgression sorted by selection, resulting in correlated but distinct outcomes of introgression in different contemporary maize populations.

Selection and hybridization shaped the rapid spread of African honey bee ancestry in the Americas.

Recent biological invasions offer 'natural' laboratories to understand the genetics and ecology of adaptation, hybridization, and range limits. One of the most impressive and well-documented biological invasions of the 20th century began in 1957 when Apis mellifera scutellata honey bees swarmed out of managed experimental colonies in Brazil. This newly-imported subspecies, native to southern and eastern Africa, both hybridized with and out-competed previously-introduced European honey bee subspecies. Populations of scutellata-European hybrid honey bees rapidly expanded and spread across much of the Americas in less than 50 years. We use broad geographic sampling and whole genome sequencing of over 300 bees to map the distribution of scutellata ancestry where the northern and southern invasions have presently stalled, forming replicated hybrid zones with European bee populations in California and Argentina. California is much farther from Brazil, yet these hybrid zones occur at very similar latitudes, consistent with the invasion having reached a climate barrier. At these range limits, we observe genome-wide clines for scutellata ancestry, and parallel clines for wing length that span hundreds of kilometers, supporting a smooth transition from climates favoring scutellata-European hybrid bees to climates where they cannot survive winter. We find no large effect loci maintaining exceptionally steep ancestry transitions. Instead, we find most individual loci have concordant ancestry clines across South America, with a build-up of somewhat steeper clines in regions of the genome with low recombination rates, consistent with many loci of small effect contributing to climate-associated fitness trade-offs. Additionally, we find no substantial reductions in genetic diversity associated with rapid expansions nor complete dropout of scutellata ancestry at any individual loci on either continent, which suggests that the competitive fitness advantage of scutellata ancestry at lower latitudes has a polygenic basis and that scutellata-European hybrid bees maintained large population sizes during their invasion. To test for parallel selection across continents, we develop a null model that accounts for drift in ancestry frequencies during the rapid expansion. We identify several peaks within a larger genomic region where selection has pushed scutellata ancestry to high frequency hundreds of kilometers past the present cline centers in both North and South America and that may underlie high-fitness traits driving the invasion.

Is your phylogeny informative? Measuring the power of comparative methods.

Phylogenetic comparative methods may fail to produce meaningful results when either the underlying model is inappropriate or the data contain insufficient information to inform the inference. The ability to measure the statistical power of these methods has become crucial to ensure that data quantity keeps pace with growing model complexity. Through simulations, we show that commonly applied model choice methods based on information criteria can have remarkably high error rates; this can be a problem because methods to estimate the uncertainty or power are not widely known or applied. Furthermore, the power of comparative methods can depend significantly on the structure of the data. We describe a Monte Carlo-based method which addresses both of these challenges, and show how this approach both quantifies and substantially reduces errors relative to information criteria. The method also produces meaningful confidence intervals for model parameters. We illustrate how the power to distinguish different models, such as varying levels of selection, varies both with number of taxa and structure of the phylogeny. We provide an open-source implementation in the pmc ("Phylogenetic Monte Carlo") package for the R programming language. We hope such power analysis becomes a routine part of model comparison in comparative methods.