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Coronavirus disease 2019 (COVID-19) is caused by SARS-CoV-2, an emerging virus that utilizes host proteins ACE2 and TMPRSS2 as entry factors. Understanding the factors affecting the pattern and levels of expression of these genes is important for deeper understanding of SARS-CoV-2 tropism and pathogenesis. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci for both ACE2 and TMPRSS2, that vary in frequency across world populations. We find TMPRSS2 is part of a mucus secretory network, highly upregulated by type 2 (T2) inflammation through the action of interleukin-13, and that the interferon response to respiratory viruses highly upregulates ACE2 expression. IL-13 and virus infection mediated effects on ACE2 expression were also observed at the protein level in the airway epithelium. Finally, we define airway responses to common coronavirus infections in children, finding that these infections generate host responses similar to other viral species, including upregulation of IL6 and ACE2. Our results reveal possible mechanisms influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes.

To identify genetic determinants of airway dysfunction, we performed a transcriptome-wide association study for asthma by combining RNA-seq data from the nasal airway epithelium of 681 children, with UK Biobank genetic association data. Our airway analysis identified 95 asthma genes, 58 of which were not identified by transcriptome-wide association analyses using other asthma-relevant tissues. Among these genes were MUC5AC, an airway mucin, and FOXA3, a transcriptional driver of mucus metaplasia. Muco-ciliary epithelial cultures from genotyped donors revealed that the MUC5AC risk variant increases MUC5AC protein secretion and mucus secretory cell frequency. Airway transcriptome-wide association analyses for mucus production and chronic cough also identified MUC5AC. These cis-expression variants were associated with trans effects on expression; the MUC5AC variant was associated with upregulation of non-inflammatory mucus secretory network genes, while the FOXA3 variant was associated with upregulation of type-2 inflammation-induced mucus-metaplasia pathway genes. Our results reveal genetic mechanisms of airway mucus pathobiology.

Air pollution particulate matter 100 genes), which included transcriptional drivers of mucus metaplasia (SPDEF and FOXA3). Exposure to a higher OE dose modified the expression of 1,240 genes and further exacerbated expression responses observed at the moderate dose, including the mucus secretory program. Moreover, the higher OE dose significantly increased the MUC5AC/MUC5B gel-forming mucin expression ratio and strongly downregulated ciliated cell expression programs, including key ciliating cell transcription factors (e.g., FOXJ1 and MCIDAS). Chronic OE stimulation induced mucus metaplasia–like remodeling characterized by increases in MUC5AC(+) secretory cells and MUC5AC mucus secretions. This epithelial remodeling may underlie poor respiratory outcomes associated with high PM(2.5) exposure.

SUMMARY The type 2 cytokine-high asthma endotype (T2H) is characterized by IL-13-driven mucus obstruction of the airways. To further investigate this incompletely understood pathobiology, we characterize IL-13 effects on human airway epithelial cell cultures using single-cell RNA sequencing, finding that IL-13 generates a distinctive transcriptional state for each cell type. Specifically, we discover a mucus secretory program induced by IL-13 in all cell types which converts both mucus and defense secretory cells into a metaplastic state with emergent mucin production and secretion, while leading to ER stress and cell death in ciliated cells. The IL-13-remodeled epithelium secretes a pathologic, mucin-imbalanced, and innate immunity-depleted proteome that arrests mucociliary motion. Signatures of IL-13-induced cellular remodeling are mirrored by transcriptional signatures characteristic of the nasal airway epithelium within T2H versus T2-low asthmatic children. Our results reveal the epithelium-wide scope of T2H asthma and present candidate therapeutic targets for restoring normal epithelial function., In Brief Using airway epithelial cell cultures, Jackson et al. show that IL-13, a driver of type 2-high asthma, induces emergent mucus secretory expression states for each cell type. This program universally diminishes innate airway defense, produces a pathologic mucus secretome that arrests mucociliary movement, and is recapitulated in type 2 inflamed children., Graphical Abstract

Cigarette smoke first interacts with the lung through the cellularly diverse airway epithelium and goes on to drive development of most chronic lung diseases. Here, through single cell RNA-sequencing analysis of the tracheal epithelium from smokers and non-smokers, we generate a comprehensive atlas of epithelial cell types and states, connect these into lineages, and define cell-specific responses to smoking. Our analysis infers multi-state lineages that develop into surface mucus secretory and ciliated cells and then contrasts these to the unique specification of submucosal gland (SMG) cells. Accompanying knockout studies reveal that tuft-like cells are the likely progenitor of both pulmonary neuroendocrine cells and CFTR-rich ionocytes. Our smoking analysis finds that all cell types, including protected stem and SMG populations, are affected by smoking through both pan-epithelial smoking response networks and hundreds of cell-specific response genes, redefining the penetrance and cellular specificity of smoking effects on the human airway epithelium., Chronic lung diseases are characterized by molecular and cellular composition changes. Here the authors use single-cell RNA sequencing to map cell type-specific changes in human tracheal epithelium related to smoking, and to provide evidence for a tuft-like progenitor for pulmonary neuroendocrine cells and ionocytes.

Coronavirus disease 2019 (COVID-19) is caused by SARS-CoV-2, an emerging virus that utilizes host proteins ACE2 and TMPRSS2 as entry factors. Understanding the factors affecting the pattern and levels of expression of these genes is important for deeper understanding of SARS-CoV-2 tropism and pathogenesis. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci for both ACE2 and TMPRSS2, that vary in frequency across world populations. We find TMPRSS2 is part of a mucus secretory network, highly upregulated by type 2 (T2) inflammation through the action of interleukin-13, and that the interferon response to respiratory viruses highly upregulates ACE2 expression. IL-13 and virus infection mediated effects on ACE2 expression were also observed at the protein level in the airway epithelium. Finally, we define airway responses to common coronavirus infections in children, finding that these infections generate host responses similar to other viral species, including upregulation of IL6 and ACE2. Our results reveal possible mechanisms influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes., ACE2 and TMPRSS2 have received recent attention as entry factors for SARS-CoV-2. Here the authors analyze nasal airway transcriptome data from 695 children determining ACE2 and TMPRSS2 expression is induced by viral and type2 inflammation, respectively, and both exhibit eQTLs that vary across world populations.

The demographic history of most species is complex, with multiple evolutionary processes combining to shape the observed patterns of genetic diversity. To infer this history, the discipline of phylogeography has (to date) used models that simplify the historical demography of the focal organism, for example by assuming or ignoring ongoing gene flow between populations or by requiring a priori specification of divergence history. Since no single model incorporates every possible evolutionary process, researchers rely on intuition to choose the models that they use to analyze their data. Here, we describe an approximate likelihood approach that reduces this reliance on intuition. PHRAPL allows users to calculate the probability of a large number of complex demographic histories given a set of gene trees, enabling them to identify the most likely underlying model and estimate parameters for a given system. Available model parameters include coalescence time among populations or species, gene flow, and population size. We describe the method and test its performance in model selection and parameter estimation using simulated data. We also compare model probabilities estimated using our approximate likelihood method to those obtained using standard analytical likelihood. The method performs well under a wide range of scenarios, although this is sometimes contingent on sampling many loci. In most scenarios, as long as there are enough loci and if divergence among populations is sufficiently deep, PHRAPL can return the true model in nearly all simulated replicates. Parameter estimates from the method are also generally accurate in most cases. PHRAPL is a valuable new method for phylogeographic model selection and will be particularly useful as a tool to more extensively explore demographic model space than is typically done or to estimate parameters for complex models that are not readily implemented using current methods. Estimating relevant parameters using the most appropriate demographic model can help to sharpen our understanding of the evolutionary processes giving rise to phylogeographic patterns. [AIC; grid search; isolation-with-migration; migration rate; multispecies coalescent; parameter optimization; population genetics; tree topologies.].

Cigarette smoke first interacts with the lung through the cellularly diverse airway epithelium and goes on to drive development of most chronic lung diseases. Here, through single cell RNA-sequencing analysis of the tracheal epithelium from smokers and nonsmokers, we generated a comprehensive atlas of epithelial cell types and states, connected these into lineages, and defined cell-specific responses to smoking. Our analysis inferred multi-state lineages that develop into surface mucus secretory and ciliated cells and contrasted these to the unique lineage and specialization of submucosal gland (SMG) cells. Our analysis also suggests a lineage relationship between tuft, pulmonary neuroendocrine, and the newly discovered CFTR-rich ionocyte cells. Our smoking analysis found that all cell types, including protected stem and SMG populations, are affected by smoking, through both pan-epithelial smoking response networks and hundreds of cell type-specific response genes, redefining the penetrance and cellular specificity of smoking effects on the human airway epithelium.

Macrophages are well recognized for their dual roles in orchestrating inflammatory responses and regulating tissue repair. In almost all acutely inflamed tissues, 2 main subclasses of macrophages coexist. These include embryonically derived resident tissue macrophages and BM-derived recruited macrophages. While it is clear that macrophage subsets categorized in this fashion display distinct transcriptional and functional profiles, whether all cells within these categories and in the same inflammatory microenvironment share similar functions or whether further specialization exists has not been determined. To investigate inflammatory macrophage heterogeneity on a more granular level, we induced acute lung inflammation in mice and performed single cell RNA sequencing of macrophages isolated from the airspaces during health, peak inflammation, and resolution of inflammation. In doing so, we confirm that cell origin is the major determinant of alveolar macrophage (AM) programing, and, to our knowledge, we describe 2 previously uncharacterized, transcriptionally distinct subdivisions of AMs based on proliferative capacity and inflammatory programing.

Airway epithelial cells (AECs) play a central role in the pathogenesis of many lung diseases. Consequently, advancements in our understanding of the underlying causes of lung diseases, and the development of novel treatments, depend on continued detailed study of these cells. Generation and analysis of high-throughput gene expression data provide an indispensable tool for carrying out the type of broad-scale investigations needed to identify the key genes and molecular pathways that regulate, distinguish, and predict distinct pulmonary pathologies. Of the available technologies for generating genome-wide expression data, RNA sequencing (RNA-seq) has emerged as the most powerful. Hence many researchers are turning to this approach in their studies of lung disease. For the relatively uninitiated, computational analysis of RNA-seq data can be daunting, given the large number of methods and software packages currently available. The aim of this chapter is to provide a broad overview of the major steps involved in processing and analyzing RNA-seq data, with a special focus on methods optimized for data generated from AECs. We take the reader from the point of obtaining sequence reads from the lab to the point of making biological inferences with expression data. Along the way, we discuss the statistical and computational considerations one typically confronts during different phases of analysis and point to key methods, software packages, papers, online guides, and other resources that can facilitate successful RNA-seq analysis.

Context Landscape structure shapes the genetic structure of populations by delimiting spatial patterns of dispersal and reproduction across generations. Thus, descriptions of human-altered landscapes can be used to predict demographic and evolutionary outcomes of populations. Effectively measuring landscape structure to predict genetic structure requires that we understand the relative importance of distinct components of landscape structure (e.g., habitat amount and configuration) in creating spatial patterns of genetic variation. Objectives We thus developed an individual-based simulation model to test predictions about the relative importance of habitat amount and configuration in producing genetic structure. We also investigated the independent relationships between components of landscape structure and the population dynamics that underlie genetic effects. Methods We ran experiments in which we allowed gene flow and population size to vary as emergent outcomes of the interactions between hypothetical populations and heterogeneous landscapes. Results We found that the amount of habitat in a landscape isa muchbetterpredictor ofgenetic structure than is habitat configuration.Thispattern holds across a range of landscapes and dispersal distances and behaviors. When habitat is non-contiguous (i.e., fragmented), habitat amount mediates production of genetic differentiation by regulating both the size and isolation of habitat patches, which in turn regulate population size and gene flow. Conclusions These results suggest that habitat amount, a simple measure that is easy to calculate, may often be the best metric for predicting population genetic structure and that when possible, measures of habitat amount and population size should be incorporated into landscape genetic studies.

Polarization of the airway epithelial cells (AECs) in the airway lumen is critical to the proper function of the mucociliary escalator and maintenance of lung health, but the cellular requirements for polarization of AECs are poorly understood. Using human AECs and cell lines, we demonstrate that cadherin-26 (CDH26) is abundantly expressed in differentiated AECs, localizes to the cell apices near ciliary membranes, and has functional cadherin domains with homotypic binding. We find a unique and non-redundant role for CDH26, previously uncharacterized in AECs, in regulation of cell–cell contact and cell integrity through maintaining cytoskeletal structures. Overexpression of CDH26 in cells with a fibroblastoid phenotype increases contact inhibition and promotes monolayer formation and cortical actin structures. CDH26 expression is also important for localization of planar cell polarity proteins. Knockdown of CDH26 in AECs results in loss of cortical actin and disruption of CRB3 and other proteins associated with apical polarity. Together, our findings uncover previously unrecognized functions for CDH26 in the maintenance of actin cytoskeleton and apicobasal polarity of AECs.

Phylogeography seeks to discover the evolutionary processes that have given rise to organismal and genetic diversity. This requires explicit hypotheses (i.e., models) to be evaluated with genetic data in order to identify those hypotheses that best explain the data. In recent years, advancements in the model-based tools used to estimate phylogeographic parameters of interest such as gene flow, divergence time, and relationships among groups have been made. However, given the complexity of these models, available methods can typically only compare a handful of possible hypotheses, requiring researchers to specify in advance the small set of models to consider. Without formal quantitative approaches to model selection, researchers must rely on their intuition to formulate the model space to be explored. We explore the adequacy of intuitive choices made by researchers during the process of data analysis by reanalyzing 20 empirical phylogeographic datasets using PHRAPL, an objective tool for phylogeographic model selection. We show that the best models for most datasets include both gene flow and population divergence parameters, and that species tree methods (which do not consider gene flow) tend to be overly simplistic for many phylogeographic systems. Objective approaches to phylogeographic model selection offer an important complement to researcher intuition.

Evaluating management options for mitigating the impacts of wildlife–vehicle collisions (WVC) is a major goal for road ecology. Fencing along roads in conjunction with the construction of wildlife road passages has been widely accepted as the most effective way to minimize WVC. However, limited resources often require wildlife managers to focus on a single method of mitigation, yet the relative effectiveness of fences and passages for reducing road mortality and restoring population connectivity is unclear. Using the stone marten (Martes foina, Erxleben, 1777) as a model species, we developed an individual-based, spatially explicit simulation model to develop predictions concerning the relative performance of fencing and passage construction under different rates of road mortality. For five levels each, we varied probability of road mortality, fencing extent, and number of passages in a full factorial design, for a total of 125 management scenarios. We then compared the relative impact of these two mitigation approaches on population abundance (N) and genetic differentiation (Fst) using linear regression. Our results predict that fences are much more effective than passages at mitigating the effects of road mortality on abundance. Moreover, we show that under most circumstances, fences are also more effective than passages at reducing genetic differentiation. This is likely driven by the ability of fencing to eliminate road mortality, which in turn increases genetic diversity, thereby slowing differentiation across the road. However, partial fencing can reduce road mortality nearly as well as full fencing. Moreover, partial fencing also allows adequate population connectivity across roads. Thus, we argue that partial fencing of roads alone may often be the best and most cost-effective management option for road mitigation.

Landscape ecologists are often interested in measuring the effects of an environmental variable on a biological response; however, the strength and direction of effect depend on the size of the area within which the environmental variable is measured. Thus a central objective is to identify the optimal spatial extent within which to measure the environmental variable, i.e. the “scale of effect”. Our objectives are (1) to provide a comprehensive summary of the hypotheses concerning what determines the scale of effect, (2) to provide predictions that can be tested in empirical studies, and (3) to show, with a review of the literature, that most of these predictions have so far been inadequately tested. We propose 14 predictions derived from five hypotheses explaining what determines the scale of effect, and review the literature (if any) supporting each prediction. These predictions involve five types of factors: (A) species traits, (B) landscape variables, (C) biological responses (e.g. abundance vs. occurrence), (D) indirect influences, and (E) regional context of the study. We identify methodological issues that hinder estimation of the scale of effect. Of the 14 predictions, only nine have been tested empirically and only five have received some empirical support. Most support is from simulation studies. Empirical evidence usually does not support predictions. The study of the spatial scale at which landscape variables influence biological outcomes is in its infancy. We provide directions for future research by clarifying predictions concerning the determinants of the scale of effect.

Although recent radiations are fruitful for studying the process of speciation, they are difficult to characterize and require the use of multiple loci and analytical methods that account for processes such as gene flow and genetic drift. Using multilocus sequence data, we combine hierarchical cluster analysis, coalescent species tree inference, and isolation-with-migration analysis to investigate evolutionary relationships among cryptic lineages of North American ground skinks. We also estimate the extent that gene flow has accompanied or followed diversification, and also attempt to account for and minimize the influence of gene flow when reconstructing relationships. The data best support seven largely parapatric populations that are broadly concordant with mitochondrial (mt)DNA phylogeography throughout most of the species range, although they fail to fully represent extensive mtDNA divergence along the Gulf Coast. Relationships within and among three broad geographical groups are well supported, despite evidence of gene flow among them. Rejection of an allopatric divergence model partially depends on the inclusion of samples from near parapatric boundaries in the analyses, suggesting that allopatric divergence followed by recent migration may best explain migration rate estimates. Accounting for geographical variation in patterns of gene flow can improve estimates of migration‐divergence parameters and minimize the influence of contemporary gene flow on phylogenetic inference. © 2012 The Linnean Society of London, Biological Journal of the Linnean Society, 2012, 107, 192‐209.

Roads can have two important effects on populations that impact genetic variation: reduced gene flow and reduced abundance. Reduced gene flow (“barrier effects”) due to road avoidance behavior or road mortality can lead to reduced genetic diversity because genetic drift is enhanced in fragmented populations. Road mortality can also reduce population abundance (“depletion effects”) whenever road-caused mortality outpaces recruitment, also lowering diversity even when barrier effects are inconsequential. Although roads are expected to affect both genetic diversity and fragmentation, most research focuses only on fragmentation. Furthermore, in studies that do investigate road effects on genetic diversity, correlations are usually attributed to barrier effects and little attention is paid to the potentially confounding influence of mortality-caused depletion effects. Here we investigate the relative importance of barrier and depletion effects on genetic diversity of populations separated by a road by performing coalescent simulations wherein these two road effects are varied independently. By simulating wide ranging rates of migration and population decline, we also determine how the importance of these forces changes depending on their relative magnitude. We show that the vast majority of potential variation in genetic diversity is governed by depletion (mortality) rather than barrier effects. We also show that unless migration is sufficiently high and population decline due to mortality is sufficiently low, increasing migration across roads will generally not recoup genetic variation lost due to road mortality. We argue that the genetic effects of road-mediated mortality have been underappreciated and should be more often considered before prioritizing road-mitigation measures.

Invasive species often have dramatic negative effects that lead to the deterioration and loss of biodiversity frequently coupled with the burden of expensive biocontrol programs and subversion of socioeconomic stability. The fauna and flora of oceanic islands are particularly susceptible to invasive species and the increase of global movements of humans and their products since WW II has caused numerous anthropogenic translocations and increased the ills of human-mediated invasions. We use a multi-locus genomic dataset to identify geographic origin, pace, pattern and historical process of an invasive scincid lizard (Carlia) that has been inadvertently introduced to Guam, the Northern Marianas, and Palau. This lizard is of major importance as its introduction is thought to have assisted in the establishment of the invasive brown treesnake (Boiga irregularis) on Guam by providing a food resource. Our findings demonstrate multiple waves of introductions that appear to be concordant with movements of Allied and Imperial Japanese forces in the Pacific during World War II.

Rivers can act as both islands of mesic refugia for terrestrial organisms during times of aridification and barriers to gene flow, though evidence for long-term isolation by rivers is mixed. Understanding the extent to which riverine barrier effects can be heightened for populations trapped in mesic refugia can help explain maintenance and generation of diversity in the face of Pleistocene climate change. Herein, we implement phylogenetic and population genetic approaches to investigate the phylogeographic structure and history of the ground skink, Scincella lateralis, using mtDNA and eight nuclear loci. We then test several predictions of a river- refugia model of diversification. We recover 14 well-resolved mtDNA lineages distributed east―west along the Gulf Coast with a subset of lineages extending northward. In contrast, ncDNA exhibits limited phylogenetic structure or congruence among loci. However, multilocus population structure is broadly congruent with mtDNA patterns and suggests that deep coalescence rather than differential gene flow is responsible for mtDNA―ncDNA discordance. The observed patterns suggest that most lineages originated from population vicariance due to riverine barriers strengthened during the Plio―Pleistocene by a climate-induced coastal distribution. Diversification due to rivers is likely a special case, contingent upon other environmental or biological factors that reinforce riverine barrier effects.

The demographic history of most species is complex, with multiple evolutionary processes combining to shape the observed patterns of genetic diversity. To infer this history, the discipline of phylogeography has (to date) used models that simplify the historical demography of the focal organism, for example by assuming or ignoring ongoing gene flow between populations or by requiring a priori specification of divergence history. Since no single model incorporates every possible evolutionary process, researchers rely on intuition to choose the models that they use to analyze their data. Here, we develop an approach to circumvent this reliance on intuition. PHRAPL allows users to calculate the probability of a large number of demographic histories given their data, enabling them to identify the optimal model and produce accurate parameter estimates for a given system. Using PHRAPL, we reanalyze data from 19 recent phylogeographic investigations. Results indicate that the optimal models for most datasets parameterize both gene flow and population divergence, and suggest that species tree methods (which do not consider gene flow) are overly simplistic for most phylogeographic systems. These results highlight the importance of phylogeographic model selection, and reinforce the role of phylogeography as a bridge between population genetics and phylogenetics.

Regional landscape context influences the fate of local populations, yet the spatial extent of this influence (called the "scale of effect") is difficult to predict. Thus, a major problem for conservation management is to understand the factors governing the scale of effect such that landscape structure surrounding a focal area is measured and managed at the biologically relevant spatial scale. One unresolved question is whether and how scale of effect may depend on the population response measured (e.g., abundance vs. presence/absence). If scales of effect differ across population outcomes of a given species, management based on one outcome may compromise another, further complicating conservation decision making. Here we used an individual-based simulation model to investigate how scales of effect of landscapes that vary in the amount and fragmentation of habitat differ among three population responses (local abundance, presence/absence, and genetic diversity). We also explored how the population response measured affects the relative importance of habitat amount and fragmentation in shaping local populations, and how dispersal distance mediates the magnitude and spatial scale of these effects. We found that the spatial scale most strongly influencing local populations depended on the outcome measured and was predicted to be small for abundance, medium-sized for presence/absence, and large for genetic diversity. Increasing spatial scales likely resulted from increasing temporal scales over which outcomes were regulated (with local genetic diversity being regulated over the largest number of generations). Thus, multiple generations of dispersal and gene flow linked local population patterns to regional population size. The effects of habitat amount dominated the effects of fragmentation for all three outcomes. Increased dispersal distance strongly reduced abundance, but not presence/absence or genetic diversity. Our results suggest that managing protected species at spatial scales based on population abundance data may ignore broader landscape effects on population genetic diversity and persistence, lending support to the importance of managing large buffers surrounding areas of conservation concern.

Species are commonly thought to be evolutionarily independent in a way that populations within a species are not. In recent years, studies that seek to identify evolutionarily independent lineages (i.e., to delimit species) using genetic data have typically adopted multispecies coalescent approaches that assume that evolutionary independence is formed by the differential sorting of ancestral alleles due to genetic drift. However, gene flow appears to be common among populations and nascent species, and while this process may inhibit lineage divergence (and thus independence), it is usually not explicitly considered when delimiting species. In this article, we apply Phylogeographic Inference using Approximate Likelihoods (PHRAPL), a recently described method for phylogeographic model selection, to species delimitation. We describe an approach to delimiting species using PHRAPL that attempts to account for both genetic drift and gene flow, and we compare the method's performance to that of a popular delimitation approach (BPP) using both simulated and empirical datasets. PHRAPL generally infers the correct demographic-delimitation model when the generating model includes gene flow between taxa, given a sufficient amount of data. When the generating model includes only isolation in the recent past, PHRAPL will in some cases fail to differentiate between gene flow and divergence, leading to model misspecification. Nevertheless, the explicit consideration of gene flow by PHRAPL is an important complement to existing delimitation approaches, particularly in systems where gene flow is likely important. [approximate likelihoods; coalescent simulations; genealogical divergence index; Homo sapiens; isolation-with-migration; multispecies coalescent; Sarracenia; Scincella.].

Thirteen polymorphic microsatellite markers were developed in the North American ground skink, Scincella lateralis, for use in landscape and population genetic research. Loci were genotyped for a sample of 37 lizards from southeastern Louisiana. The number of alleles per locus ranged from 5 to 25 and observed heterozygosity ranged from 0.23 to 0.97. The high levels of polymorphism observed in these loci will be useful for a variety of applications.

Despite considerable attention, the long-term impact of rivers on species diversification remains uncertain. Meander loop cutoff (MLC) is one river phenomenon that may compromise a river’s diversifying effects by passively transferring organisms from one side of the river to the other. However, the ability of MLC to promote gene flow across rivers has not been demonstrated empirically. Here, we test several predictions of MLC-mediated gene flow in populations of North American ground skinks (Scincella lateralis) separated by a well-established riverine barrier, the Mississippi River: 1) individuals collected from within meander cutoffs should be more closely related to individuals across the river than on the same side, 2) individuals within meander cutoffs should contain more immigrants than individuals away from meander cutoffs, 3) immigration rates estimated across the river should be highest in the direction of the cutoff event, and 4) the distribution of alleles native to one side of the river should be better predicted by the historical rather than current path of the river. To test these predictions we sampled 13 microsatellite loci and mitochondrial DNA from ground skinks collected near three ancient meander loops. These predictions were generally supported by genetic data, although support was stronger for mtDNA than for microsatellite data. Partial support for genetic divergence of samples within ancient meander loops also provides evidence for the MLC hypothesis. Although a role for MLC-mediated gene flow was supported here for ground skinks, the transient nature of river channels and morphologies may limit the long-term importance of MLC in stemming population divergence across major rivers.

Growing evidence supports the idea that species can diverge in the presence of gene flow. However, most methods of phylogeny estimation do not consider this process, despite the fact that ignoring gene flow is known to bias phylogenetic inference. Furthermore, studies that do consider divergence-with-gene-flow typically do so by estimating rates of gene flow using a isolation-with-migration model (IM), rather than evaluating scenarios of gene flow (such as divergence-with-gene flow or secondary contact) that represent very different types of diversification. In this investigation, we aim to infer the recent phylogenetic history of a clade of western long-eared bats while evaluating a number of different models that parameterize gene flow in a variety of ways. We utilize PHRAPL, a new tool for phylogeographic model selection, to compare the fit of a broad set of demographic models that include divergence, migration, or both among Myotis evotis, $M$. thysanodes and M. keenii. A genomic data set consisting of 808 loci of ultraconserved elements was used to explore such models in three steps using an incremental design where each successive set was informed by, and thus more focused than, the previous set of models. Specifically, the three steps were to (i) assess whether gene flow should be modeled and identify the best topologies, (ii) infer directionality of migration using the best topologies, and (iii) estimate the timing of gene flow. The best model (AIC model weight ${\sim}0.98$) included two divergence events (($M$. evotis, $M$. thysanodes), M. keenii) accompanied by gene flow at the initial stages of divergence. These results provide a striking example of speciation-with-gene-flow in an evolutionary lineage. [Myotis bats; PHRAPL; P2C2M; phylogeographic model selection; speciation with gene flow.].