Heterogeneity and concordance in locus-specific differentiation and introgression between species of towhees.

The maintenance or breakdown of reproductive isolation is an observable outcome of secondary contact between species. In cases where hybrids beyond the F1 are formed, the representation of each species' ancestry can vary dramatically among genomic regions. This genomic heterogeneity in ancestry and introgression can offer insight into evolutionary processes, particularly if introgression is compared in multiple hybrid zones. Similarly, considerable heterogeneity exists across the genome in the extent to which populations and species have diverged, reflecting the combined effects of different evolutionary processes on genetic variation. We studied hybridization across two hybrid zones of two phenotypically well-differentiated bird species in Mexico (Pipilo maculatus and P. ocai), to investigate genomic heterogeneity in differentiation and introgression. Using genotyping-by-sequencing (GBS) and hierarchical Bayesian models, we genotyped 460 birds at over 41 000 single nucleotide polymorphism (SNP) loci. We identified loci exhibiting extreme introgression relative to the genome-wide expectation using a Bayesian genomic cline model. We also estimated locus-specific FST and identified loci with exceptionally high genetic divergence between the parental species. We found some concordance of locus-specific introgression in the two independent hybrid zones (6-20% of extreme loci shared across zones), reflecting areas of the genome that experience similar gene flow when the species interact. Additionally, heterogeneity in introgression and divergence across the genome revealed another subset of loci under the influence of locally specific factors. These results are consistent with a history in which reproductive isolation has been influenced by a common set of loci in both hybrid zones, but where local environmental and stochastic factors also lead to genomic differentiation.

The genomic consequences of adaptive divergence and reproductive isolation between species of manakins.

The processes of adaptation and speciation are expected to shape genomic variation within and between diverging species. Here we analyze genomic heterogeneity of genetic differentiation and introgression in a hybrid zone between two bird species (Manacus candei and M. vitellinus) using 59 100 SNPs, a whole genome assembly, and Bayesian models. Measures of genetic differentiation (FST) and introgression (genomic cline center [α] and rate [ß]) were highly heterogeneous among loci. We identified thousands of loci with elevated parameter estimates, some of which are likely to be associated with variation in fitness in Manacus populations. To analyze the genomic organization of differentiation and introgression, we mapped SNPs onto a draft assembly of the M. vitellinus genome. Estimates of FST, α, and ß were autocorrelated at very short physical distances (< 100 bp), but much less so beyond this. In addition, average statistical associations (linkage disequilibrium) between SNPs were generally low and were not higher in admixed populations than in populations of the parental species. Although they did not occur with a constant probability across the genome, loci with elevated FST, α, and ß were not strongly co-localized in the genome. Contrary to verbal models that predict clustering of loci involved in adaptation and isolation in discrete genomic regions, these results are consistent with the hypothesis that genetic regions involved in adaptive divergence and reproductive isolation are scattered throughout the genome. We also found that many loci were characterized by both exceptional genetic differentiation and introgression, consistent with the hypothesis that loci involved in isolation are also often characterized by a history of divergent selection. However, the concordance between isolation and differentiation was only partial, indicating a complex architecture and history of loci involved in isolation.

Specificity, rank preference, and the colonization of a non-native host plant by the Melissa blue butterfly.

Animals often express behavioral preferences for different types of food or other resources, and these preferences can evolve or shift following association with novel food types. Shifts in preference can involve at least two phenomena: a change in rank preference or a change in specificity. The former corresponds to a change in the order in which hosts are preferred, while a shift in specificity can be an increase in the tendency to utilize multiple hosts. These possibilities have been examined in relatively few systems that include extensive population-level replication. The Melissa blue butterfly, Lycaeides melissa, has colonized exotic alfalfa, Medicago sativa, throughout western North America. We assayed the host preferences of 229 females from ten populations associated with novel and native hosts. In four out of five native-associated populations, a native host was preferred over the exotic host, while preference for a native host characterized only two out of five of the alfalfa-associated populations. Across all individuals from alfalfa-associated populations, there appears to have been a decrease in specificity: females from these populations lay fewer eggs on the native host and more eggs on the exotic relative to females from native-host populations. However, females from alfalfa-associated populations did not lay more eggs on a third plant species, which suggests that preferences for specific hosts in this system can potentially be gained and lost independently. Geographic variation in oviposition preference in L. melissa highlights the value of surveying a large number of populations when studying the evolution of a complex behavioral trait.

The n = 1 constraint in population genomics.

A key objective of population genomics is to identify portions of the genome that have been shaped by natural selection rather than by neutral divergence. A previously recognized but underappreciated challenge to this objective is that observations of allele frequencies across genomes in natural populations often correspond to a single, unreplicated instance of the outcome of evolution. This is because the composition of each individual genomic region and population is expected to be the outcome of a unique array of evolutionary processes. Given a single observation, inference of the evolutionary processes that led to the observed state of a locus is associated with considerable uncertainty. This constraint on inference can be ameliorated by utilizing multi-allelic (e.g. DNA haplotypes) rather than bi-allelic markers, by analysing two or more populations with certain models and by utilizing studies of replicated experimental evolution. Future progress in population genomics will follow from research that recognizes the 'n = 1 constraint' and that utilizes appropriate and explicit evolutionary models for analysis.

Predicting patch occupancy reveals the complexity of host range expansion.

Specialized plant-insect interactions are a defining feature of life on earth, yet we are only beginning to understand the factors that set limits on host ranges in herbivorous insects. To better understand the recent adoption of alfalfa as a host plant by the Melissa blue butterfly, we quantified arthropod assemblages and plant metabolites across a wide geographic region while controlling for climate and dispersal inferred from population genomic variation. The presence of the butterfly is successfully predicted by direct and indirect effects of plant traits and interactions with other species. Results are consistent with the predictions of a theoretical model of parasite host range in which specialization is an epiphenomenon of the many barriers to be overcome rather than a consequence of trade-offs in developmental physiology.

Variable patterns of introgression in two sculpin hybrid zones suggest that genomic isolation differs among populations.

Theory predicts that reproductive isolation may be due to intrinsic genetic incompatibilities or extrinsic ecological factors. Therefore, an understanding of the genetic basis of isolation may require analyses of evolutionary processes in situ to include environmental factors. Here we study genetic isolation between populations of sculpins (Cottus) at 168 microsatellites. Genomic clines were fit using 480 individuals sampled across independent natural hybrid zones that have formed between one invading species and two separate populations of a resident species. Our analysis tests for deviations from neutral patterns of introgression at individual loci based on expectations given genome-wide admixture. Roughly 51% of the loci analysed displayed significant deviations. An overall deficit of interspecific heterozygotes in 26% and 21% of the loci suggests that widespread underdominance drives genomic isolation. At the same time, selection promotes introgression of almost 30% of the markers, which implies that hybridization may increase the fitness of admixed individuals. Cases of overdominance or epistatic interactions were relatively rare. Despite the similarity of the two hybrid zones in their overall genomic composition, patterns observed at individual loci show little correlation between zones and many fit different genotypic models of fitness. At this point, it remains difficult to determine whether these results are due to differences in external selection pressures or cryptic genetic differentiation of distinct parental populations. In the future, data from mapped genetic markers and on variation of ecological factors will provide additional insights into the contribution of these factors to variation in the evolutionary consequences of hybridization.

bgc: Software for Bayesian estimation of genomic clines.

Introgression in admixed populations can be used to identify candidate loci that might underlie adaptation or reproductive isolation. The Bayesian genomic cline model provides a framework for quantifying variable introgression in admixed populations and identifying regions of the genome with extreme introgression that are potentially associated with variation in fitness. Here we describe the bgc software, which uses Markov chain Monte Carlo to estimate the joint posterior probability distribution of the parameters in the Bayesian genomic cline model and designate outlier loci. This software can be used with next-generation sequence data, accounts for uncertainty in genotypic state, and can incorporate information from linked loci on a genetic map. Output from the analysis is written to an HDF5 file for efficient storage and manipulation. This software is written in C++. The source code, software manual, compilation instructions and example data sets are available under the GNU Public License at http://sites.google.com/site/bgcsoftware/.