Comparative Phylogenomic Assessment of Mitochondrial Introgression among Several Species of Chipmunks (Tamias)

Power of Bayesian and Heuristic Tests to Detect Cross-Species Introgression with Reference to Gene Flow in the Tamias quadrivittatus Group of North American Chipmunks

In the past two decades, genomic data have been widely used to detect historical gene flow between species in a variety of plants and animals. The Tamias quadrivittatus group of North America chipmunks, which originated through a series of rapid speciation events, are known to undergo massive amounts of mitochondrial introgression. Yet in a recent analysis of targeted nuclear loci from the group, no evidence for cross-species introgression was detected, indicating widespread cytonuclear discordance. The study used the heuristic method HYDE to detect gene flow, which may suffer from low power. Here we use the Bayesian method implemented in the program BPP to re-analyze these data. We develop a Bayesian test of introgression, calculating the Bayes factor via the Savage-Dickey density ratio using the Markov chain Monte Carlo (MCMC) sample under the model of introgression. We take a stepwise approach to constructing an introgression model by adding introgression events onto a well-supported binary species tree. The analysis detected robust evidence for multiple ancient introgression events affecting the nuclear genome, with introgression probabilities reaching 63%. We estimate population parameters and highlight the fact that species divergence times may be seriously underestimated if ancient cross-species gene flow is ignored in the analysis. We examine the assumptions and performance of HYDE and demonstrate that it lacks power if gene flow occurs between sister lineages or if the mode of gene flow does not match the assumed hybrid-speciation model with symmetrical population sizes. Our analyses highlight the power of likelihood-based inference of cross-species gene flow using genomic sequence data. [Bayesian test; BPP; chipmunks; introgression; MSci; multispecies coalescent; Savage-Dickey density ratio.].

UCE sequencing-derived mitogenomes reveal the timing of mitochondrial replacement in Malagasy shrew tenrecs (Afrosoricida, Tenrecidae, Microgale)

Malagasy shrew tenrecs (Microgale) have increasingly been used to study speciation genetics over the last years. A previous study recently uncovered gene flow between the Shrew-toothed shrew tenrec (M. soricoides) and sympatric southern population of the Pale shrew tenrec (M. fotsifotsy). This gene flow has been suggested to be accompanied by complete mitochondrial replacement in M. fotsifotsy. To explore the temporal framework of this replacement, we assembled mitogenomes from publicly available sequencing data of ultra-conserved elements. We were able to assemble complete and partial mitogenomes for 19 specimens from five species of shrew tenrecs, which represents a multifold increase in mitogenomic resources available for all tenrecs. Phylogenetic inferences and sequence simulations support the close relationship between the mitochondrial lineages of M. soricoides and the southern population of M. fotsifotsy. Based on the nuclear divergence of northern and southern populations of M. fotsifotsy and the mitochondrial divergence between the latter and M. soricoides, there was a mean time window for replacement of ~ 350,000 years. This timeframe implies that the effective size of the ancestral M. fotsifotsy southern population was less 70,000.

Phylogeographic analysis delimits three evolutionary significant units of least chipmunks in North America and identifies unique genetic diversity within the imperiled Peñasco population

Although least chipmunks (Neotamias minimus) are a widely distributed North American species of least concern, the southernmost population, N. m. atristriatus (Peñasco least chipmunk), is imperiled and a candidate for federal listing as a subspecies. We conducted a phylogeographic analysis across the N. minimus range to assess genomic differentiation and distinctiveness of the N. m. atristriatus population. Additionally, we leveraged the historical component of sampling to conduct a temporal analysis of N. minimus genetic diversity and also considered climate change effects on range persistence probability by projecting a species distribution model into the IPCC5 RCP 2.6 and 8.5 scenarios. We identified three geographically structured groups (West, North, and South) that were supported by both mitochondrial and nuclear data. N. m. atristriatus grouped within a unique South subclade but were not reciprocally monophyletic from N. m. operarius, and nuclear genome analyses did not separate N. m. atristriatus, N. m. caryi, and N. m. operarius. Thus, while least chipmunks in the Southwest represent an evolutionary significant unit, subspecies distinctions were not supported and listing of the Peñasco population as a Distinct Population Segment of N. m. operarius may be warranted. Our results also support consideration of populations with North and West mitogenomes as two additional evolutionary significant units. We found that N. minimus genetic diversity declined by ~87% over the last century, and our models predicted substantial future habitat contraction, including the loss of the full contemporary ranges of N. m. atristriatus, N. m. arizonensis, and N. m. chuskaensis.

Supervised machine learning reveals introgressed loci in the genomes of Drosophila simulans and D. sechellia

Hybridization and gene flow between species appears to be common. Even though it is clear that hybridization is widespread across all surveyed taxonomic groups, the magnitude and consequences of introgression are still largely unknown. Thus it is crucial to develop the statistical machinery required to uncover which genomic regions have recently acquired haplotypes via introgression from a sister population. We developed a novel machine learning framework, called FILET (Finding Introgressed Loci via Extra-Trees) capable of revealing genomic introgression with far greater power than competing methods. FILET works by combining information from a number of population genetic summary statistics, including several new statistics that we introduce, that capture patterns of variation across two populations. We show that FILET is able to identify loci that have experienced gene flow between related species with high accuracy, and in most situations can correctly infer which population was the donor and which was the recipient. Here we describe a data set of outbred diploid Drosophila sechellia genomes, and combine them with data from D. simulans to examine recent introgression between these species using FILET. Although we find that these populations may have split more recently than previously appreciated, FILET confirms that there has indeed been appreciable recent introgression (some of which might have been adaptive) between these species, and reveals that this gene flow is primarily in the direction of D. simulans to D. sechellia.

Understanding the extent to which species or diverged populations hybridize in nature is crucially important if we are to understand the speciation process. Accordingly numerous research groups have developed methodology for finding the genetic evidence of such introgression. In this report we develop a supervised machine learning approach for uncovering loci which have introgressed across species boundaries. We show that our method, FILET, has greater accuracy and power than competing methods in discovering introgression, and in addition can detect the directionality associated with the gene flow between species. Using whole genome sequences from Drosophila simulans and Drosophila sechellia we show that FILET discovers quite extensive introgression between these species that has occurred mostly from D. simulans to D. sechellia. Our work highlights the complex process of speciation even within a well-studied system and points to the growing importance of supervised machine learning in population genetics.