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While genetic variation in any species is potentially shaped by a range of processes, phylogeography and landscape genetics are largely concerned with inferring how environmental conditions and landscape features impact neutral intraspecific diversity. However, even as both disciplines have come to utilize SNP data over the last decades, analytical approaches have remained for the most part focused on either broad-scale inferences of historical processes (phylogeography) or on more localized inferences about environmental and/or landscape features (landscape genetics). Here we demonstrate that an artificial intelligence model-based analytical framework can consider both deeper historical factors and landscape-level processes in an integrated analysis. We implement this framework using data collected from two Brazilian anurans, the Brazilian sibilator frog (Leptodactylus troglodytes) and granular toad (Rhinella granulosa). Our results indicate that historical demographic processes shape most the genetic variation in the sibulator frog, while landscape processes primarily influence variation in the granular toad. The machine learning framework used here allows both historical and landscape processes to be considered equally, rather than requiring researchers to make an a priori decision about which factors are important., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

One key research goal of evolutionary biology is to understand the origin and maintenance of genetic variation. In the Cerrado, the South American savanna located primarily in the Central Brazilian Plateau, many hypotheses have been proposed to explain how landscape features (e.g., geographic distance, river barriers, topographic compartmentalization, and historical climatic fluctuations) have promoted genetic structure by mediating gene flow. Here, we asked whether these landscape features have influenced the genetic structure and differentiation in the lizard species Norops brasiliensis (Squamata: Dactyloidae). To achieve our goal, we used a genetic clustering analysis and estimate an effective migration surface to assess genetic structure in the focal species. Optimized isolation-by-resistance models and a simulation-based approach combined with machine learning (convolutional neural network; CNN) were then used to infer current and historical effects on population genetic structure through 12 unique landscape models. We recovered five geographically distributed populations that are separated by regions of lower-than-expected gene flow. The results of the CNN showed that geographic distance is the sole predictor of genetic variation in N. brasiliensis, and that slope, rivers, and historical climate had no discernible influence on gene flow. Our novel CNN approach was accurate (89.5%) in differentiating each landscape model. CNN and other machine learning approaches are still largely unexplored in landscape genetics studies, representing promising avenues for future research with increasingly accessible genomic datasets., (© 2024. The Author(s).)

Bats subject to high rates of fatalities at wind-energy facilities are of high conservation concern due to the long-term, cumulative effects they have, but the impact on broader bat populations can be difficult to assess. One reason is the poor understanding of the geographic source of individual fatalities and whether they constitute migrants or more local individuals. Here, we used stable hydrogen isotopes, trace elements and species distribution models to determine the most likely summer geographic origins of three different bat species ( Lasiurus borealis , L. cinereus , and Lasionycteris noctivagans ) killed at wind-energy facilities in Ohio and Maryland in the eastern United States. In Ohio, 41.6%, 21.3%, 2.2% of all individuals of L. borealis , L. cinereus , and L. noctivagans , respectively, had evidence of movement. In contrast, in Maryland 77.3%, 37.1%, and 27.3% of these same species were classified as migrants. Our results suggest bats killed at a given wind facility are likely derived from migratory as well as resident populations. Finally, there is variation in the proportion of migrants killed between seasons for some species and evidence of philopatry to summer roosts. Overall, these results indicate that the impact of wind-energy facilities on bat populations occurs across a large geographic extent, with the proportion of migrants impacted likely to vary across species and sites. Similar studies should be conducted across a broader geographic scale to understand the impacts on bat populations from wind-energy facilities., Competing Interests: David M. Nelson is an Academic Editor for PeerJ. C.J. Campbell is employed by Bat Conservation International., (©2024 Wieringa et al.)

When designing phylogeographic investigations researchers can choose to collect many different types of molecular markers, including mitochondrial genes or genomes, SNPs from reduced representation protocols, large sequence capture data sets, and even whole genomes. Given that the statistical power and accuracy of various analyses are expected to differ depending on both the type of marker and the amount of data collected, an exploration of the variance across methodological results as a function of marker type should provide valuable information to researchers. Here we collect mitochondrial Cytochrome b sequences, whole mitochondrial genomes, single nucleotide polymorphisms (SNP)s isolated using a genotype by sequencing (GBS) protocol, sequences from ultraconserved elements, and low-coverage nuclear genomes from the North American water vole (Microtus richardsoni). We estimate genetic distances, population genetic structure, and historical demography using data from each of these datasets and compare the results across markers. As anticipated, the results exhibit differences across marker types, particularly in terms of the resolution offered by different analyses. A cost-benefit analysis indicates that SNPs collected using a GBS protocol are the most cost-effective molecular marker, with inferences that mirror those collected from the whole genome data at a fraction of the cost per sample., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Duckett et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Intraspecific genetic diversity is a key aspect of biodiversity. Quaternary climatic change and glaciation influenced intraspecific genetic diversity by promoting range shifts and population size change. However, the extent to which glaciation affected genetic diversity on a global scale is not well established. Here we quantify nucleotide diversity, a common metric of intraspecific genetic diversity, in more than 38,000 plant and animal species using georeferenced DNA sequences from millions of samples. Results demonstrate that tropical species contain significantly more intraspecific genetic diversity than nontropical species. To explore potential evolutionary processes that may have contributed to this pattern, we calculated summary statistics that measure population demographic change and detected significant correlations between these statistics and latitude. We find that nontropical species are more likely to deviate from neutral expectations, indicating that they have historically experienced dramatic fluctuations in population size likely associated with Pleistocene glacial cycles. By analyzing the most comprehensive data set to date, our results imply that Quaternary climate perturbations may be more important as a process driving the latitudinal gradient in species richness than previously appreciated., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Society for the Study of Evolution (SSE) and European Society for Evolutionary Biology (ESEN).)

The formation of the Baja California Peninsula (BCP) has impacted the microevolutionary dynamics of different species in ways that depend on biological traits such as dispersal capacity. Plants with relatively low levels of vagility have exhibited high genetic divergence between the BCP and Continental mainland. Brahea armata (Arecaceae) is a palm species inhabiting the northern part of the BCP and Sonora; its distribution occurs in isolated oases of vegetation. We aimed to evaluate the influence of the formation of the BCP on the genetic structure of B. armata using nuclear microsatellites and chloroplast markers (cpDNA) to compare patterns of genetic diversity and structure with previous published studies. Because gene flow through seeds is usually more limited compared to pollen flow, we expect to find stronger genetic structure at (cpDNA) than at nuclear markers. Moreover, larger genetic structure might also be explained by the smaller effective population size of cpDNA. We analyzed six microsatellite markers and two cpDNA regions. The main results indicated high levels of genetic differentiation among isolated populations located in the BCP, while low genetic differentiation was found between southern populations of the BCP and Sonora, suggesting long distance gene flow. In contrast, chloroplast markers indicated high levels of genetic structure between BCP and Sonora populations, suggesting asymmetrical gene flow between pollen (measured by nuclear microsatellites) and seed (cpDNA markers). This study provides valuable information on genetic diversity of B. armata that can be relevant for conservation and management; and develops microsatellites markers that can be transferred to other Brahea species., (© 2023. The Author(s) under exclusive licence to The Botanical Society of Japan.)

Comparisons of intraspecific genetic diversity across species can reveal the roles of geography, ecology, and life history in shaping biodiversity. The wide availability of mitochondrial DNA (mtDNA) sequences in open-access databases makes this marker practical for conducting analyses across several species in a common framework, but patterns may not be representative of overall species diversity. Here, we gather new and existing mtDNA sequences and genome-wide nuclear data (genotyping-by-sequencing; GBS) for 30 North American squamate species sampled in the Southeastern and Southwestern United States. We estimated mtDNA nucleotide diversity for 2 mtDNA genes, COI (22 species alignments; average 16 sequences) and cytb (22 species; average 58 sequences), as well as nuclear heterozygosity and nucleotide diversity from GBS data for 118 individuals (30 species; 4 individuals and 6,820 to 44,309 loci per species). We showed that nuclear genomic diversity estimates were highly consistent across individuals for some species, while other species showed large differences depending on the locality sampled. Range size was positively correlated with both cytb diversity (phylogenetically independent contrasts: R2 = 0.31, P = 0.007) and GBS diversity (R2 = 0.21; P = 0.006), while other predictors differed across the top models for each dataset. Mitochondrial and nuclear diversity estimates were not correlated within species, although sampling differences in the data available made these datasets difficult to compare. Further study of mtDNA and nuclear diversity sampled across species' ranges is needed to evaluate the roles of geography and life history in structuring diversity across a variety of taxonomic groups., (© The American Genetic Association. 2023.)

Vocalizations in animals, particularly birds, are critically important behaviors that influence their reproductive fitness. While recordings of bioacoustic data have been captured and stored in collections for decades, the automated extraction of data from these recordings has only recently been facilitated by artificial intelligence methods. These have yet to be evaluated with respect to accuracy of different automation strategies and features. Here, we use a recently published machine learning framework to extract syllables from ten bird species ranging in their phylogenetic relatedness from 1 to 85 million years, to compare how phylogenetic relatedness influences accuracy. We also evaluate the utility of applying trained models to novel species. Our results indicate that model performance is best on conspecifics, with accuracy progressively decreasing as phylogenetic distance increases between taxa. However, we also find that the application of models trained on multiple distantly related species can improve the overall accuracy to levels near that of training and analyzing a model on the same species. When planning big-data bioacoustics studies, care must be taken in sample design to maximize sample size and minimize human labor without sacrificing accuracy., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Provost et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Patterns of genetic diversity within species contain information the history of that species, including how they have responded to historical climate change and how easily the organism is able to disperse across its habitat. More than 40,000 phylogeographic and population genetic investigations have been published to date, each collecting genetic data from hundreds of samples. Despite these millions of data points, meta-analyses are challenging because the synthesis of results across hundreds of studies, each using different methods and forms of analysis, is a daunting and time-consuming task. It is more efficient to proceed by repurposing existing data and using automated data analysis. To facilitate data repurposing, we created a database (phylogatR) that aggregates data from different sources and conducts automated multiple sequence alignments and data curation to provide users with nearly ready-to-analyse sets of data for thousands of species. Two types of scientific research will be made easier by phylogatR: large meta-analyses of thousands of species that can address classic questions in evolutionary biology and ecology, and student- or citizen- science based investigations that will introduce a broad range of people to the analysis of genetic data. phylogatR enhances the value of existing data via the creation of software and web-based tools that enable these data to be recycled and reanalysed and increase accessibility to big data for research laboratories and classroom instructors with limited computational expertise and resources., (© 2022 The Authors. Molecular Ecology Resources published by John Wiley & Sons Ltd.)

Pleistocene glacial cycles drastically changed the distributions of taxa endemic to temperate rainforests in the Pacific Northwest, with many experiencing reduced habitat suitability during glacial periods. In this study, we investigate whether glacial cycles promoted intraspecific divergence and whether subsequent range changes led to secondary contact and gene flow. For seven invertebrate species endemic to the PNW, we estimated species distribution models (SDMs) and projected them onto current and historical climate conditions to assess how habitat suitability changed during glacial cycles. Using single nucleotide polymorphism (SNP) data from these species, we assessed population genetic structure and used a machine-learning approach to compare models with and without gene flow between populations upon secondary contact after the last glacial maximum (LGM). Finally, we estimated divergence times and rates of gene flow between populations. SDMs suggest that there was less suitable habitat in the North Cascades and Northern Rocky Mountains during glacial compared to interglacial periods, resulting in reduced habitat suitability and increased habitat fragmentation during the LGM. Our genomic data identify population structure in all taxa, and support gene flow upon secondary contact in five of the seven taxa. Parameter estimates suggest that population divergences date to the later Pleistocene for most populations. Our results support a role of refugial dynamics in driving intraspecific divergence in the Cascades Range. In these invertebrates, population structure often does not correspond to current biogeographic or environmental barriers. Rather, population structure may reflect refugial lineages that have since expanded their ranges, often leading to secondary contact between once isolated lineages., (© 2022 John Wiley & Sons Ltd.)

Bayesian skyline plots (BSPs) are a useful tool for making inferences about demographic history. For example, researchers typically apply BSPs to test hypotheses regarding how climate changes have influenced intraspecific genetic diversity over time. Like any method, BSP has assumptions that may be violated in some empirical systems (e.g., the absence of population genetic structure), and the naïve analysis of data collected from these systems may lead to spurious results. To address these issues, we introduce P2C2M.Skyline, an R package designed to assess model adequacy for BSPs using posterior predictive simulation. P2C2M.Skyline uses a phylogenetic tree and the log file output from Bayesian Skyline analyses to simulate posterior predictive datasets and then compares this null distribution to statistics calculated from the empirical data to check for model violations. P2C2M.Skyline was able to correctly identify model violations when simulated datasets were generated assuming genetic structure, which is a clear violation of BSP model assumptions. Conversely, P2C2M.Skyline showed low rates of false positives when models were simulated under the BSP model. We also evaluate the P2C2M.Skyline performance in empirical systems, where we detected model violations when DNA sequences from multiple populations were lumped together. P2C2M.Skyline represents a user-friendly and computationally efficient resource for researchers aiming to make inferences from BSP., Competing Interests: The authors have declared that no competing interests exist.

Phylogeographic studies base inferences on large data sets and complex demographic models, but these models are applied in ways that could mislead researchers and compromise their inference. Researchers face three challenges associated with the use of models: (i) 'model selection', or the identification of an appropriate model for analysis; (ii) 'evaluation of analytical results', or the interpretation of the biological significance of the resulting parameter estimates, delimitations, and topologies; and (iii) 'model evaluation', or the use of statistical approaches to assess the fit of the model to the data. The field collectively invests most of its energy in point (ii) without considering the other points; we argue that attention to points (i) and (iii) is essential to phylogeographic inference., Competing Interests: Declaration of interests None declared by authors., (Copyright © 2021. Published by Elsevier Ltd.)

SignificanceOnly an estimated 1 to 10% of Earth's species have been formally described. This discrepancy between the number of species with a formal taxonomic description and actual number of species (i.e., the Linnean shortfall) hampers research across the biological sciences. To explore whether the Linnean shortfall results from poor taxonomic practice or not enough taxonomic effort, we applied machine-learning techniques to build a predictive model to identify named species that are likely to contain hidden diversity. Results indicate that small-bodied species with large, climatically variable ranges are most likely to contain hidden species. These attributes generally match those identified in the taxonomic literature, indicating that the Linnean shortfall is caused by societal underinvestment in taxonomy rather than poor taxonomic practice.

Phylogeography investigates historical drivers of the geographical distribution of intraspecific lineages. Particular attention has been given to ecological, climatic and geological processes in the diversification of the Neotropical biota. Several species sampled across the South American diagonal of open formations (DOF), comprising the Caatinga, Cerrado and Chaco biomes, experienced range shifts coincident with Quaternary climatic changes. However, comparative studies across different spatial, temporal and biological scales on DOF species are still meagre. Here, we combine phylogeographical model selection and machine learning predictive frameworks to investigate the influence of Pleistocene climatic changes on several plant and animal species from the DOF. We assembled mitochondrial/chloroplastic DNA sequences in public repositories and inferred the demographic responses of 44 species, comprising 70 intraspecific lineages of plants, lizards, frogs, spiders and insects. We then built a random forest model using biotic and abiotic information to identify the best predictors of demographic responses in the Pleistocene. Finally, we assessed the temporal synchrony of species demographic responses with hierarchical approximate Bayesian computation. Biotic variables related to population connectivity, gene flow and habitat preferences largely predicted how species responded to Pleistocene climatic changes, and demographic changes were synchronous primarily during the Middle Pleistocene. Although 22 (~31%) lineages underwent demographic expansion, presumably associated with the spread of aridity during the glacial Pleistocene periods, our findings suggest that nine lineages (~13%) exhibited the opposite response due to taxon-specific attributes., (© 2021 John Wiley & Sons Ltd.)

The growing availability of genetic data sets, in combination with machine learning frameworks, offers great potential to answer long-standing questions in ecology and evolution. One such question has intrigued population geneticists, biogeographers, and conservation biologists: What factors determine intraspecific genetic diversity? This question is challenging to answer because many factors may influence genetic variation, including life history traits, historical influences, and geography, and the relative importance of these factors varies across taxonomic and geographic scales. Furthermore, interpreting the influence of numerous, potentially correlated variables is difficult with traditional statistical approaches. To address these challenges, we analysed repurposed data using machine learning and investigated predictors of genetic diversity, focusing on Nearctic amphibians as a case study. We aggregated species traits, range characteristics, and >42,000 genetic sequences for 299 species using open-access scripts and various databases. After identifying important predictors of nucleotide diversity with random forest regression, we conducted follow-up analyses to examine the roles of phylogenetic history, geography, and demographic processes on intraspecific diversity. Although life history traits were not important predictors for this data set, we found significant phylogenetic signal in genetic diversity within amphibians. We also found that salamander species at northern latitudes contained low genetic diversity. Data repurposing and machine learning provide valuable tools for detecting patterns with relevance for conservation, but concerted efforts are needed to compile meaningful data sets with greater utility for understanding global biodiversity., (© 2020 John Wiley & Sons Ltd.)

The discipline of phylogeography has evolved rapidly in terms of the analytical toolkit used to analyse large genomic data sets. Despite substantial advances, analytical tools that could potentially address the challenges posed by increased model complexity have not been fully explored. For example, deep learning techniques are underutilized for phylogeographic model selection. In non-model organisms, the lack of information about their ecology and evolution can lead to uncertainty about which demographic models are appropriate. Here, we assess the utility of convolutional neural networks (CNNs) for assessing demographic models in South American lizards in the genus Norops. Three demographic scenarios (constant, expansion, and bottleneck) were considered for each of four inferred population-level lineages, and we found that the overall model accuracy was higher than 98% for all lineages. We then evaluated a set of 26 models that accounted for evolutionary relationships, gene flow, and changes in effective population size among the four lineages, identifying a single model with an estimated overall accuracy of 87% when using CNNs. The inferred demography of the lizard system suggests that gene flow between non-sister populations and changes in effective population sizes through time, probably in response to Pleistocene climatic oscillations, have shaped genetic diversity in this system. Approximate Bayesian computation (ABC) was applied to provide a comparison to the performance of CNNs. ABC was unable to identify a single model among the larger set of 26 models in the subsequent analysis. Our results demonstrate that CNNs can be easily and usefully incorporated into the phylogeographer's toolkit., (© 2021 John Wiley & Sons Ltd.)

The process of diversification can be studied at the phylogeographic level by attempting to identify the environmental features that promote and maintain population divergence. Here we investigate diversification in Rhinella granulosa, a Neotropical toad from northeastern Brazil, by testing a range of hypotheses that encompass different putative mechanisms reducing gene flow among populations. We sequenced single nucleotide polymorphisms and examined individual predictions related to the role of geographic barriers (rivers), ecological gradients, historical habitat stability, and spatial variation in climate seasonality, also known as the asynchrony of seasons hypothesis. This hypothesis postulates that temporal asynchrony of wet and dry seasons over short distances causes parapatric populations to become isolated by time. After determining genetic structure, inferring past distributions, ranking demographic models, and estimating the power of monthly climatic variables, our results identified two populations that are not associated with geographic barriers, biome gradients, or historical refugia. Instead, they are predicted by spatial variation in monthly rainfall and minimum temperature, consistent with the asynchrony of seasons hypothesis, supported also by our comparative framework using multiple matrix regression and linear mixed effects modeling. Due to the toad's life history, climate likely mediates gene flow directly, with genetic differentiation being provoked by neutral mechanisms related to climate driven population isolation, and/or by natural selection against migrants from populations with different breeding times. The asynchrony of seasons hypothesis is seldom considered in phylogeographic studies, but our results indicate that it should be tested in systems where breeding is tightly coupled with climate., (© 2021. The Author(s), under exclusive licence to The Genetics Society.)

Voles of the genus Microtus are important research organisms, yet genomic resources are lacking. Such resources would benefit future studies of immunology, phylogeography, cryptic diversity, and more. We sequenced and assembled nuclear genomes from two subspecies of water vole ( Microtus richardsoni ) and from the montane vole ( Microtus montanus ). The water vole genomes were sequenced with Illumina and 10× Chromium plus Illumina sequencing, resulting in assemblies with ∼1600,000 and ∼30,000 scaffolds, respectively. The montane vole was also assembled into ∼13,000 scaffolds using Illumina sequencing. Mitochondrial genome assemblies were also performed for both species. Structural and functional annotation for the best water vole nuclear genome resulted in ∼24,500 annotated genes, with 83% of these having functional annotations. Assembly quality statistics for our nuclear assemblies fall within the range of genomes previously published in the genus Microtus , making the water vole and montane vole genomes useful additions to currently available genomic resources., Competing Interests: SP is the director of Iridian Genomes, Inc. The other authors declare that they have no competing interests., (© The Author(s) 2021.)

The Northern Rocky Mountain ecosystem supports rich biological diversity with many endemic and rare species. Extant endemics display two biogeographic patterns: widespread species with fragmented populations, and narrow-range endemics. These distributions are shown by the congeneric snails Anguispira kochi occidentalis and Anguispira nimapuna . These two taxa are disjunct from the remaining species of the genus, which achieves its greatest diversity in eastern North America. Given the disjunct nature of A. k. occidentalis and A. nimapuna , we here present a mtDNA phylogeny of the genus that includes both eastern and western species to assess the phylogenetic position of A. k. occidentalis and A. nimapuna . We then reconstruct the demographic history of A. k. occidentalis and A. nimapuna by analysing current patterns of genetic variation and interpreting the results considering the historical biogeography of the region. Both A. k. occidentalis and A. nimapuna represent unique taxa that are genetically and geographically distinct from their congeners. The current distribution and genetic structure of A. k. occidentalis has been shaped by both historical isolation in refugia and more recent northward shifts, whereas A. nimapuna is represented by two populations with shallow divergence in an area of long-term habitat stability., (© 2021 The Linnean Society of London, Biological Journal of the Linnean Society.)

Understanding seasonal variation in the distribution and movement patterns of migratory species is essential to monitoring and conservation efforts. While there are many species of migratory bats in North America, little is known about their seasonal movements. In terms of conservation, this is important because the bat fatalities from wind energy turbines are significant and may fluctuate seasonally. Here we describe seasonally resolved distributions for the three species that are most impacted by wind farms ( Lasiurus borealis (eastern red bat), L. cinereus (hoary bat) and Lasionycteris noctivagans (silver-haired bat)) and use these distributions to infer their most likely migratory pathways. To accomplish this, we collected 2,880 occurrence points from the Global Biodiversity Information Facility over five decades in North America to model species distributions on a seasonal basis and used an ensemble approach for modeling distributions. This dataset included 1,129 data points for L. borealis , 917 for L. cinereus and 834 for L. noctivagans . The results suggest that all three species exhibit variation in distributions from north to south depending on season, with each species showing potential migratory pathways during the fall migration that follow linear features. Finally, we describe proposed migratory pathways for these three species that can be used to identify stop-over sites, assess small-scale migration and highlight areas that should be prioritized for actions to reduce the effects of wind farm mortality., Competing Interests: The authors declare that they have no competing interests., (© 2021 Wieringa et al.)

The expansion of the wind energy industry has had benefits in terms of increased renewable energy production but has also led to increased mortality of migratory bats due to interactions with wind turbines. A key question that could guide bat-related management activities is identifying the geographic origin of bats killed at wind-energy facilities. Generating this information requires developing new methods for identifying the geographic sources of individual bats. Here we explore the viability of assigning geographic origin using trace element analyses of fur to infer the summer molting location of eastern red bats ( Lasiurus borealis ). Our approach is based on the idea that the concentration of trace elements in bat fur is related through the food chain to the amount of trace elements present in the soil, which varies across large geographic scales. Specifically, we used inductively coupled plasma-mass spectrometry to determine the concentration of fourteen trace elements in fur of 126 known-origin eastern red bats to generate a basemap for assignment throughout the range of this species in eastern North America. We then compared this map to publicly available soil trace element concentrations for the U.S. and Canada, used a probabilistic framework to generate likelihood-of-origin maps for each bat, and assessed how well trace element profiles predicted the origins of these individuals. Overall, our results suggest that trace elements allow successful assignment of individual bats 80% of the time while reducing probable locations in half. Our study supports the use of trace elements to identify the geographic origin of eastern red and perhaps other migratory bats, particularly when combined with data from other biomarkers such as genetic and stable isotope data., Competing Interests: David M. Nelson is an Academic Editor for PeerJ., (©2020 Wieringa et al.)

Range expansions are a potential outcome of changes in habitat suitability, which commonly result as a consequence of climate change. Hypotheses on such changes in the geographic distribution of a certain species can be evaluated using population genetic structure and demography. In this study we explore the population genetic structure, genetic variability, demographic history of, and habitat suitability for Amblyomma americanum, a North American tick species that is a known vector of several pathogenic microorganisms. We used a double digestion restriction site-associated DNA sequencing technique (dd-RAD seq) and discovered 8,181 independent single nucleotide polymorphisms (SNPs) in 189 ticks from across the geographic range of the species. Genetic diversity was low, particularly when considering the broad geographic range of this species. The edge populations were less diverse than populations belonging to the historic range, possibly indicative of a range expansion, but this hypothesis was not statistically supported by a test based on genetic data. Nonetheless, moderate levels of population structure and substructure were detected between geographic regions. For New England, demographic and species distribution models support a scenario where A. americanum was present in more northern locations in the past, underwent a bottleneck, and subsequently recovered. These results are consistent with a hypothesis that this species is re-establishing in this area, rather than one focused on range expansion from the south. This hypothesis is consistent with old records describing the presence of A. americanum in the northeastern US in the early colonial period., (© 2020 John Wiley & Sons Ltd.)

Most approaches to species delimitation to date have considered divergence-only models. Although these models are appropriate for allopatric speciation, their failure to incorporate many of the population-level processes that drive speciation, such as gene flow (e.g., in sympatric speciation), places an unnecessary limit on our collective understanding of the processes that produce biodiversity. To consider these processes while inferring species boundaries, we introduce the R-package delimitR and apply it to identify species boundaries in the reticulate taildropper slug (Prophysaon andersoni). Results suggest that secondary contact is an important mechanism driving speciation in this system. By considering process, we both avoid erroneous inferences that can be made when population-level processes such as secondary contact drive speciation but only divergence is considered, and gain insight into the process of speciation in terrestrial slugs. Further, we apply delimitR to three published empirical datasets and find results corroborating previous findings. Finally, we evaluate the performance of delimitR using simulation studies, and find that error rates are near zero when comparing models that include lineage divergence and gene flow for three populations with a modest number of Single Nucleotide Polymorphisms (SNPs; 1500) and moderate divergence times (<100,000 generations). When we apply delimitR to a complex model set (i.e., including divergence, gene flow, and population size changes), error rates are moderate (∼0.15; 10,000 SNPs), and, when present, misclassifications occur among highly similar models., (© 2019 The Authors. Evolution © 2019 The Society for the Study of Evolution.)

Phylogenetic estimation under the multispecies coalescent model (MSCM) assumes all incongruence among loci is caused by incomplete lineage sorting. Therefore, applying the MSCM to datasets that contain incongruence that is caused by other processes, such as gene flow, can lead to biased phylogeny estimates. To identify possible bias when using the MSCM, we present P2C2M.SNAPP. P2C2M.SNAPP is an R package that identifies model violations using posterior predictive simulation. P2C2M.SNAPP uses the posterior distribution of species trees output by the software package SNAPP to simulate posterior predictive datasets under the MSCM, and then uses summary statistics to compare either the empirical data or the posterior distribution to the posterior predictive distribution to identify model violations. In simulation testing, P2C2M.SNAPP correctly classified up to 83% of datasets (depending on the summary statistic used) as to whether or not they violated the MSCM model. P2C2M.SNAPP represents a user-friendly way for researchers to perform posterior predictive model checks when using the popular SNAPP phylogenetic estimation program. It is freely available as an R package, along with additional program details and tutorials., Competing Interests: The authors declare that they have no competing interests., (© 2020 Duckett et al.)

Adaptive radiations are defined as rapid diversification with phenotypic innovation led by colonization to new environments. Notably, adaptive radiations can occur in parallel when habitats with similar selective pressures are accessible promoting convergent adaptions. Although convergent evolution appears to be a common process, it is unclear what are the main drivers leading the reappearance of morphologies or ecological roles. We explore this question in Myotis bats, the only Chiropteran genus with a worldwide distribution. Three foraging strategies-gleaning, trawling, and aerial netting-repeatedly evolved in several regions of the world, each linked to characteristic morphologies recognized as ecomorphs. Phylogenomic, morphometric, and comparative approaches were adopted to investigate convergence of such foraging strategies and skull morphology as well as factors that explain diversification rates. Genomic and morphometric data were analyzed from ∼80% extant taxa. Results confirm that the ecomorphs evolved multiple times, with trawling evolving more often and foliage gleaning most recently. Skull morphology does not reflect common ancestry and evolves convergently with foraging strategy. Although diversification rates have been roughly constant across the genus, speciation rates are area-dependent and higher in taxa with temperate distributions. Results suggest that in this species-rich group of bats, first, stochastic processes have led divergence into multiple lineages. Then, natural selection in similar niches has promoted repeated adaptation of phenotypes and foraging strategies. Myotis bats are thus a remarkable case of ecomorphological convergence and an emerging model system for investigating the genomic basis of parallel adaptive radiation., (© 2019 The Author(s). Evolution © 2019 The Society for the Study of Evolution.)

The conservation status of most plant species is currently unknown, despite the fundamental role of plants in ecosystem health. To facilitate the costly process of conservation assessment, we developed a predictive protocol using a machine-learning approach to predict conservation status of over 150,000 land plant species. Our study uses open-source geographic, environmental, and morphological trait data, making this the largest assessment of conservation risk to date and the only global assessment for plants. Our results indicate that a large number of unassessed species are likely at risk and identify several geographic regions with the highest need of conservation efforts, many of which are not currently recognized as regions of global concern. By providing conservation-relevant predictions at multiple spatial and taxonomic scales, predictive frameworks such as the one developed here fill a pressing need for biodiversity science., Competing Interests: The authors declare no conflict of interest.

While genetic exchange between nonsister species was traditionally considered to be rare in mammals, analyses of molecular data in multiple systems suggest that it may be common. Interspecific gene flow, if present, is problematic for phylogenetic inference, particularly for analyses near the species level. Here, we explore how to detect and account for gene flow during phylogeny estimation using data from a clade of North American Myotis bats where previous results have led researchers to suspect that gene flow among lineages is present. Initial estimates of phylogenetic networks and species trees indicate that subspecies described within Myotis lucifugus are paraphyletic. In order to explore the extent to which gene flow is likely to interfere with phylogeny estimation, we use posterior predictive simulation and a novel Approximate Bayesian Computation approach based on gene tree distances. The former indicates that the species tree model is a poor fit to the data, and the latter provides evidence that a species tree with gene flow is a better fit. Taken together, we present evidence that the currently recognized M. lucifugus subspecies are paraphyletic, exchange alleles with other Myotis species in regions of secondary contact, and should be considered independent evolutionary lineages despite their morphological similarity.

Model selection approaches in phylogeography have allowed researchers to evaluate the support for competing demographic histories, which provides a mode of inference and a measure of uncertainty in understanding climatic and spatial influences on intraspecific diversity. Here, to rank all models in the comparison set and determine what proportion of the total support the top-ranked model garners, we conduct model selection using two analytical approaches-allele frequency-based, implemented in fastsimcoal2, and gene tree-based, implemented in phrapl. We then expand this model selection framework by including an assessment of absolute fit of the models to the data. For this, we utilize DNA isolated from existing natural history collections that span the distribution of red alder (Alnus rubra) in the Pacific Northwest of North America to generate genomic data for the evaluation of 13 demographic scenarios. The quality of DNA recovered from herbarium specimen leaf tissue was assessed for its utility and effectiveness in demographic model selection, specifically in the two approaches mentioned. We present strong support for the use of herbarium tissue in the generation of genomic DNA, albeit with the inclusion of additional quality control checks prior to library preparation and analyses with multiple approaches that incorporate various data. Analyses with allele frequency spectra and gene trees predominantly support A. rubra having experienced an ancient vicariance event with intermittent and frequent gene flow between the disjunct populations. Additionally, the data consistently fit the most frequently selected model, corroborating the model selection techniques. Finally, these results suggest that the A. rubra disjunct populations do not represent separate species., (© 2018 John Wiley & Sons Ltd.)

Carnivorous plants are striking examples of evolutionary convergence, displaying complex and often highly similar adaptations despite lack of shared ancestry. Using available carnivorous plant genomes along with non-carnivorous reference taxa, this study examines the convergence of functional overrepresentation of genes previously implicated in plant carnivory. Gene Ontology (GO) coding was used to quantitatively score functional representation in these taxa, in terms of proportion of carnivory-associated functions relative to all functional sequence. Statistical analysis revealed that, in carnivorous plants as a group, only two of the 24 functions tested showed a signal of substantial overrepresentation. However, when the four carnivorous taxa were analyzed individually, 11 functions were found to be significant in at least one taxon. Though carnivorous plants collectively may show overrepresentation in functions from the predicted set, the specific functions that are overrepresented vary substantially from taxon to taxon. While it is possible that some functions serve a similar practical purpose such that one taxon does not need to utilize both to achieve the same result, it appears that there are multiple approaches for the evolution of carnivorous function in plant genomes. Our approach could be applied to tests of functional convergence in other systems provided on the availability of genomes and annotation data for a group., Competing Interests: The authors declare there are no competing interests.

While genetic diversity within species is influenced by both geographical distance and environmental gradients, it is unclear what other factors are likely to promote population genetic structure. Using a machine learning framework and georeferenced DNA sequences from more than 8000 species, we demonstrate that geographical attributes of the species range, including total size, latitude and elevation, are the most important predictors of which species are likely to contain structured genetic variation. While latitude is well known as an important predictor of biodiversity, our work suggests that it also plays a key role in shaping diversity within species., (© 2018 The Author(s).)

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., (Copyright © 2017 Elsevier Inc. All rights reserved.)

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.]., (© The Author(s) 2017. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

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.]., (© The Author(s) 2017. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Phylogeographic data sets have grown from tens to thousands of loci in recent years, but extant statistical methods do not take full advantage of these large data sets. For example, approximate Bayesian computation (ABC) is a commonly used method for the explicit comparison of alternate demographic histories, but it is limited by the "curse of dimensionality" and issues related to the simulation and summarization of data when applied to next-generation sequencing (NGS) data sets. We implement here several improvements to overcome these difficulties. We use a Random Forest (RF) classifier for model selection to circumvent the curse of dimensionality and apply a binned representation of the multidimensional site frequency spectrum (mSFS) to address issues related to the simulation and summarization of large SNP data sets. We evaluate the performance of these improvements using simulation and find low overall error rates (~7%). We then apply the approach to data from Haplotrema vancouverense, a land snail endemic to the Pacific Northwest of North America. Fifteen demographic models were compared, and our results support a model of recent dispersal from coastal to inland rainforests. Our results demonstrate that binning is an effective strategy for the construction of a mSFS and imply that the statistical power of RF when applied to demographic model selection is at least comparable to traditional ABC algorithms. Importantly, by combining these strategies, large sets of models with differing numbers of populations can be evaluated., (© 2017 John Wiley & Sons Ltd.)

Biogeographic barriers have long been implicated as drivers of biological diversification, but how these barriers influence co-occurring taxa can vary depending on factors intrinsic to the organism and in their relationships with other species. Due to the interdependence among taxa, ecological communities present a compelling opportunity to explore how interactions among species may lead to a shared response to historical events. Here we collect single nucleotide polymorphism data from five commensal arthropods associated with the Sarracenia alata carnivorous pitcher plant, and test for codiversification across the Mississippi River, a major biogeographic barrier in the southeastern United States. Population genetic structure in three of the ecologically dependent arthropods mirrors that of the host pitcher plant, with divergence time estimates suggesting two of the species (the pitcher plant moth Exyra semicrocea and a flesh fly Sarcophaga sarraceniae) dispersed synchronously across this barrier along with the pitcher plant. Patterns in population size and genetic diversity suggest the plant and ecologically dependent arthropods dispersed from east to west across the Mississippi River. In contrast, species less dependent on the plant ecologically show discordant phylogeographic patterns. This study demonstrates that ecological relationships may be an important predictor of codiversification, and supports recent suggestions that organismal trait data should be prominently featured in comparative phylogeographic investigations., (© 2017 John Wiley & Sons Ltd.)

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.]., (© The Author(s) 2016. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Identifying units of biological diversity is a major goal of organismal biology. An increasing literature has focused on the importance of cryptic diversity, defined as the presence of deeply diverged lineages within a single species. While most discoveries of cryptic lineages proceed on a taxon-by-taxon basis, rapid assessments of biodiversity are needed to inform conservation policy and decision-making. Here, we introduce a predictive framework for phylogeography that allows rapidly identifying cryptic diversity. Our approach proceeds by collecting environmental, taxonomic and genetic data from codistributed taxa with known phylogeographic histories. We define these taxa as a reference set, and categorize them as either harbouring or lacking cryptic diversity. We then build a random forest classifier that allows us to predict which other taxa endemic to the same biome are likely to contain cryptic diversity. We apply this framework to data from two sets of disjunct ecosystems known to harbour taxa with cryptic diversity: the mesic temperate forests of the Pacific Northwest of North America and the arid lands of Southwestern North America. The predictive approach presented here is accurate, with prediction accuracies placed between 65% and 98.79% depending of the ecosystem. This seems to indicate that our method can be successfully used to address ecosystem-level questions about cryptic diversity. Further, our application for the prediction of the cryptic/non-cryptic nature of unknown species is easily applicable and provides results that agree with recent discoveries from those systems. Our results demonstrate that the transition of phylogeography from a descriptive to a predictive discipline is possible and effective., (© 2016 The Author(s).)

Phylogeographic research investigates biodiversity at the interface between populations and species, in a temporal and geographic context. Phylogeography has benefited from analytical approaches that allow empiricists to estimate parameters of interest from the genetic data (e.g., θ = 4Neμ, population divergence, gene flow), and the widespread availability of genomic data allow such parameters to be estimated with greater precision. However, the actual inferences made by phylogeographers remain dependent on qualitative interpretations derived from these parameters' values and as such may be subject to overinterpretation and confirmation bias. Here we argue in favor of using an objective approach to phylogeographic inference that proceeds by calculating the probability of multiple demographic models given the data and the subsequent ranking of these models using information theory. We illustrate this approach by investigating the diversification of two sister species of four-eyed frogs of northeastern Brazil using single nucleotide polymorphisms obtained via restriction-associated digest sequencing. We estimate the composite likelihood of the observed data given nine demographic models and then rank these models using Akaike information criterion. We demonstrate that estimating parameters under a model that is a poor fit to the data is likely to produce values that lead to spurious phylogeographic inferences. Our results strongly imply that identifying which parameters to estimate from a given system is a key step in the process of phylogeographic inference and is at least as important as being able to generate precise estimates of these parameters. They also illustrate that the incorporation of model uncertainty should be a component of phylogeographic hypothesis tests.

Pilosocereus machrisii and P. aurisetus are cactus species within the P. aurisetus complex, a group of eight cacti that are restricted to rocky habitats within the Neotropical savannas of eastern South America. Previous studies have suggested that diversification within this complex was driven by distributional fragmentation, isolation leading to allopatric differentiation, and secondary contact among divergent lineages. These events have been associated with Quaternary climatic cycles, leading to the hypothesis that the xerophytic vegetation patches which presently harbor these populations operate as refugia during the current interglacial. However, owing to limitations of the standard phylogeographic approaches used in these studies, this hypothesis was not explicitly tested. Here we use Approximate Bayesian Computation to refine the previous inferences and test the role of different events in the diversification of two species within P. aurisetus group. We used molecular data from chloroplast DNA and simple sequence repeats loci of P. machrisii and P. aurisetus, the two species with broadest distribution in the complex, in order to test if the diversification in each species was driven mostly by vicariance or by long-dispersal events. We found that both species were affected primarily by vicariance, with a refuge model as the most likely scenario for P. aurisetus and a soft vicariance scenario most probable for P. machrisii. These results emphasize the importance of distributional fragmentation in these species, and add support to the hypothesis of long-term isolation in interglacial refugia previously proposed for the P. aurisetus species complex diversification.

Groups of codistributed species that responded in a concerted manner to environmental events are expected to share patterns of evolutionary diversification. However, the identification of such groups has largely been based on qualitative, post hoc analyses. We develop here two methods (posterior predictive simulation [PPS], Kuhner-Felsenstein [K-F] analysis of variance [ANOVA]) for the analysis of codistributed species that, given a group of species with a shared pattern of diversification, allow empiricists to identify those taxa that do not codiversify (i.e., "outlier" species). The identification of outlier species makes it possible to jointly estimate the evolutionary history of co-diversifying taxa. To evaluate the approaches presented here, we collected data from Páramo dipterans, identified outlier species, and estimated a "community tree" from species that are identified as having codiversified. Our results demonstrate that dipteran communities from different Páramo habitats in the same mountain range are more closely related than communities in other ranges. We also conduct simulation testing to evaluate this approach. Results suggest that our approach provides a useful addition to comparative phylogeographic methods, while identifying aspects of the analysis that require careful interpretation. In particular, both the PPS and K-F ANOVA perform acceptably when there are one or two outlier species, but less so as the number of outliers increases. This is likely a function of the corresponding degradation of the signal of community divergence; without a strong signal from a codiversifying community, there is no dominant pattern from which to detect an outlier species. For this reason, both the magnitude of K-F distance distribution and outside knowledge about the phylogeographic history of each putative member of the community should be considered when interpreting the results., (© 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.)

Comparative phylogeographic investigations have identified congruent phylogeographic breaks in co-distributed species in nearly every region of the world. The qualitative assessments of phylogeographic patterns traditionally used to identify such breaks, however, are limited because they rely on identifying monophyletic groups across species and do not account for coalescent stochasticity. Only long-standing phylogeographic breaks are likely to be obvious; many species could have had a concerted response to more recent landscape events, yet possess subtle signs of phylogeographic congruence because ancestral polymorphism has not completely sorted. Here, we introduce Phylogeographic Concordance Factors (PCFs), a novel method for quantifying phylogeographic congruence across species. We apply this method to the Sarracenia alata pitcher plant system, a carnivorous plant with a diverse array of commensal organisms. We explore whether a group of ecologically associated arthropods have co-diversified with the host pitcher plant, and identify if there is a positive correlation between ecological interaction and PCFs. Results demonstrate that multiple arthropods share congruent phylogeographic breaks with S. alata, and provide evidence that the level of ecological association can be used to predict the degree of similarity in the phylogeographic pattern. This study outlines an approach for quantifying phylogeographic congruence, a central concept in biogeographic research., (© 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.)

The Pilosocereus aurisetus complex consists of eight cactus species with a fragmented distribution associated to xeric enclaves within the Cerrado biome in eastern South America. The phylogeny of these species is incompletely resolved, and this instability complicates evolutionary analyses. Previous analyses based on both plastid and microsatellite markers suggested that this complex contained species with inherent phylogeographic structure, which was attributed to recent diversification and recurring range shifts. However, limitations of the molecular markers used in these analyses prevented some questions from being properly addressed. In order to better understand the relationship among these species and make a preliminary assessment of the genetic structure within them, we developed anonymous nuclear loci from pyrosequencing data of 40 individuals from four species in the P. aurisetus complex. The data obtained from these loci were used to identify genetic clusters within species, and to investigate the phylogenetic relationship among these inferred clusters using a species tree methodology. Coupled with a palaeodistributional modelling, our results reveal a deep phylogenetic and climatic disjunction between two geographic lineages. Our results highlight the importance of sampling more regions from the genome to gain better insights on the evolution of species with an intricate evolutionary history. The methodology used here provides a feasible approach to develop numerous genealogical molecular markers throughout the genome for non-model species. These data provide a more robust hypothesis for the relationship among the lineages of the P. aurisetus complex., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Genetic analyses can identify the scale at which wildlife species are impacted by human activities, and provide demographic information useful for management. Here, we use thousands of nuclear DNA genetic loci to assess whether genetic structure occurs within Lasiurus cinereus (Hoary Bat), L. borealis (Red Bat), and Lasionycteris noctivagans (Silver-Haired Bat) bats found at a wind turbine site in Ohio, and to also estimate demographic parameters in each of these three groups. Our specific goals are to: 1) demonstrate the feasibility of isolating RADseq loci from these tree bat species, 2) test for genetic structure within each species, including any structure that may be associated with time (migration period), and 3) use coalescent-based modeling approaches to estimate genetically-effective population sizes and patterns of population size changes over evolutionary timescales. Thousands of loci were successfully genotyped for each species, demonstrating the value of RADseq for generating polymorphic loci for population genetic analyses in these bats. There was no evidence for genetic differentiation between groups of samples collected at different times throughout spring and fall migration, suggesting that individuals from each species found at the wind facility are from single panmictic populations. Estimates of present-day effective population sizes varied across species, but were consistently large, on the order of 10(5)-10(6). All populations show evidence of expansions that date to the Pleistocene. These results, along with recent work also suggesting limited genetic structure in bats across North America, argue that additional biomarker systems such as stable-isotopes or trace elements should be investigated as alternative and/or complementary approaches to genetics for sourcing individuals collected at single wind farm sites.

Understanding if the members of an ecological community have co-diversified is a central concern of evolutionary biology, as co-diversification suggests prolonged association and possible coevolution. By sampling associated species from an ecosystem, researchers can better understand how abiotic and biotic factors influence diversification in a region. In particular, studies of co-distributed species that interact ecologically can allow us to disentangle the effect of how historical processes have helped shape community level structure and interactions. Here we investigate the Sarracenia alata pitcher plant system, an ecological community where many species from disparate taxonomic groups live inside the fluid-filled pitcher leaves. Direct sequencing of the eukaryotes present in the pitcher plant fluid enables us to better understand how a host plant can shape and contribute to the genetic structure of its associated inquilines, and to ask whether genetic variation in the taxa are structured in a similar manner to the host plant. We used 454 amplicon-based metagenomics to demonstrate that the pattern of genetic diversity in many, but not all, of the eukaryotic community is similar to that of S. alata, providing evidence that associated eukaryotes share an evolutionary history with the host pitcher plant. Our work provides further evidence that a host plant can influence the evolution of its associated commensals.

Bayesian inference operates under the assumption that the empirical data are a good statistical fit to the analytical model, but this assumption can be challenging to evaluate. Here, we introduce a novel r package that utilizes posterior predictive simulation to evaluate the fit of the multispecies coalescent model used to estimate species trees. We conduct a simulation study to evaluate the consistency of different summary statistics in comparing posterior and posterior predictive distributions, the use of simulation replication in reducing error rates and the utility of parallel process invocation towards improving computation times. We also test P2C2M on two empirical data sets in which hybridization and gene flow are suspected of contributing to shared polymorphism, which is in violation with the coalescent model: Tamias chipmunks and Myotis bats. Our results indicate that (i) probability-based summary statistics display the lowest error rates, (ii) the implementation of simulation replication decreases the rate of type II errors, and (iii) our r package displays improved statistical power compared to previous implementations of this approach. When probabilistic summary statistics are used, P2C2M corroborates the assumption that genealogies collected from Tamias and Myotis are not a good fit to the multispecies coalescent model. Taken as a whole, our findings argue that an assessment of the fit of the multispecies coalescent model should accompany any phylogenetic analysis that estimates a species tree., (© 2015 John Wiley & Sons Ltd.)

The gut microbiota of vertebrates are essential to host health. Most non-model vertebrates, however, lack even a basic description of natural gut microbiota biodiversity. Here, we sampled 116 intestines from 59 Neotropical bird species and used the V6 region of the 16S rRNA molecule as a microbial fingerprint (average coverage per bird ~80,000 reads). A core microbiota of Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria was identified, as well as several gut-associated genera. We tested 18 categorical variables associated with each bird for significant correlation to the gut microbiota; host taxonomic categories were most frequently significant and explained the most variation. Ecological variables (e.g., diet, foraging stratum) were also frequently significant but explained less variation. Little evidence was found for a significant influence of geographic space. Finally, we suggest that microbial sampling during field collection of organisms would propel biological understanding of evolutionary history and ecological significance of host-associated microbiota.

Supportive data related to the article "Anonymous nuclear markers reveal taxonomic incongruence and long-term disjunction in a cactus species complex with continental-island distribution in South America" (Perez et al., 2016) [1]. Here, we present pyrosequencing results, primer sequences, a cpDNA phylogeny, and a species tree phylogeny.

Microsatellite markers (also known as SSRs, Simple Sequence Repeats) are widely used in plant science and are among the most informative molecular markers for population genetic investigations, but the development of such markers presents substantial challenges. In this report, we discuss how next generation sequencing can replace the cloning, Sanger sequencing, identification of polymorphic loci, and testing cross-amplification that were previously required to develop microsatellites. We report the development of a large set of microsatellite markers for five species of the Neotropical cactus genus Pilosocereus using a restriction-site-associated DNA sequencing (RAD-seq) on a Roche 454 platform. We identified an average of 165 microsatellites per individual, with the absolute numbers across individuals proportional to the sequence reads obtained per individual. Frequency distribution of the repeat units was similar in the five species, with shorter motifs such as di- and trinucleotide being the most abundant repeats. In addition, we provide 72 microsatellites that could be potentially amplified in the sampled species and 22 polymorphic microsatellites validated in two populations of the species Pilosocereus machrisii. Although low coverage sequencing among individuals was observed for most of the loci, which we suggest to be more related to the nature of the microsatellite markers and the possible bias inserted by the restriction enzymes than to the genome size, our work demonstrates that an NGS approach is an efficient method to isolate multispecies microsatellites even in non-model organisms.

Allopatry is commonly used to predict boundaries in species delimitation investigations under the assumption that currently allopatric distributions are indicative of reproductive isolation; however, species ranges are known to change over time. Incorporating a temporal perspective of geographic distributions should improve species delimitation; to explore this, we investigate three species of western Plethodon salamanders that have shifted their ranges since the end of the Pleistocene. We generate species distribution models (SDM) of the current range, hindcast these models onto a climatic model 21 Ka, and use three molecular approaches to delimit species in an integrated fashion. In contrast to expectations based on the current distribution, we detect no independent lineages in species with allopatric and patchy distributions (Plethodon vandykei and Plethodon larselli). The SDMs indicate that probable habitat is more expansive than their current range, especially during the last glacial maximum (LGM) (21 Ka). However, with a contiguous distribution, two independent lineages were detected in Plethodon idahoensis, possibly due to isolation in multiple glacial refugia. Results indicate that historical SDMs are a better predictor of species boundaries than current distributions, and strongly imply that researchers should incorporate SDM and hindcasting into their investigations and the development of species hypotheses., (© The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)