Reduced representation approaches produce similar results to whole genome sequencing for some common phylogeographic analyses.

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.

Assessing model adequacy for Bayesian Skyline plots using posterior predictive simulation.

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.

Analysis of biodiversity data suggests that mammal species are hidden in predictable places.

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.

Assessing model adequacy leads to more robust phylogeographic inference.

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.

Genomic Resources for the North American Water Vole (Microtus richardsoni) and the Montane Vole (Microtus montanus).

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.

Identifying model violations under the multispecies coalescent model using P2C2M.SNAPP.

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.

The complete mitochondrial genome of the endangered spotback skate, Atlantoraja castelnaui.

Chondrichthyes are a highly threatened class of organisms, largely due to overfishing and other human activities. The present study describes the complete mitochondrial genome (16,750 bp) of the endangered spotback skate, Atlantoraja castelnaui. The mitogenome is arranged in a typical vertebrate fashion, containing 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes and 1 control region.