A Nonstationary Markov Model Detects Directional Evolution in Hymenopteran Morphology

Directional evolution has played an important role in shaping the morphological, ecological, and molecular diversity of life. However, standard substitution models assume stationarity of the evolutionary process over the time scale examined, thus impeding the study of directionality. Here we explore a simple, nonstationary model of evolution for discrete data, which assumes that the state frequencies at the root differ from the equilibrium frequencies of the homogeneous evolutionary process along the rest of the tree (i.e., the process is nonstationary, nonreversible, but homogeneous). Within this framework, we develop a Bayesian approach for testing directional versus stationary evolution using a reversible-jump algorithm. Simulations show that when only data from extant taxa are available, the success in inferring directionality is strongly dependent on the evolutionary rate, the shape of the tree, the relative branch lengths, and the number of taxa. Given suitable evolutionary rates (0.1-0.5 expected substitutions between root and tips), accounting for directionality improves tree inference and often allows correct rooting of the tree without the use of an outgroup. As an empirical test, we apply our method to study directional evolution in hymenopteran morphology. We focus on three character systems: wing veins, muscles, and sclerites. We find strong support for a trend toward loss of wing veins and muscles, while stationarity cannot be ruled out for sclerites. Adding fossil and time information in a total-evidence dating approach, we show that accounting for directionality results in more precise estimates not only of the ancestral state at the root of the tree, but also of the divergence times. Our model relaxes the assumption of stationarity and reversibility by adding a minimum of additional parameters, and is thus well suited to studying the nature of the evolutionary process in data sets of limited size, such as morphology and ecolog

Probabilistic Graphical Model Representation in Phylogenetics

Recent years have seen a rapid expansion of the model space explored in statistical phylogenetics, emphasizing the need for new approaches to statistical model representation and software development. Clear communication and representation of the chosen model is crucial for: (1) reproducibility of an analysis, (2) model development and (3) software design. Moreover, a unified, clear and understandable framework for model representation lowers the barrier for beginners and non-specialists to grasp complex phylogenetic models, including their assumptions and parameter/variable dependencies. Graphical modeling is a unifying framework that has gained in popularity in the statistical literature in recent years. The core idea is to break complex models into conditionally independent distributions. The strength lies in the comprehensibility, flexibility, and adaptability of this formalism, and the large body of computational work based on it. Graphical models are well-suited to teach statistical models, to facilitate communication among phylogeneticists and in the development of generic software for simulation and statistical inference. Here, we provide an introduction to graphical models for phylogeneticists and extend the standard graphical model representation to the realm of phylogenetics. We introduce a new graphical model component, tree plates, to capture the changing structure of the subgraph corresponding to a phylogenetic tree. We describe a range of phylogenetic models using the graphical model framework and introduce modules to simplify the representation of standard components in large and complex models. Phylogenetic model graphs can be readily used in simulation, maximum likelihood inference, and Bayesian inference using, for example, Metropolis-Hastings or Gibbs sampling of the posterior distribution

Automatic Alignment in Higher-Order Probabilistic Programming Languages

Probabilistic Programming Languages (PPLs) allow users to encode statistical inference problems and automatically apply an inference algorithm to solve them. Popular inference algorithms for PPLs, such as sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC), are built around checkpoints -- relevant events for the inference algorithm during the execution of a probabilistic program. Deciding the location of checkpoints is, in current PPLs, not done optimally. To solve this problem, we present a static analysis technique that automatically determines checkpoints in programs, relieving PPL users of this task. The analysis identifies a set of checkpoints that execute in the same order in every program run -- they are aligned. We formalize alignment, prove the correctness of the analysis, and implement the analysis as part of the higher-order functional PPL Miking CorePPL. By utilizing the alignment analysis, we design two novel inference algorithm variants: aligned SMC and aligned lightweight MCMC. We show, through real-world experiments, that they significantly improve inference execution time and accuracy compared to standard PPL versions of SMC and MCMC

Evaluation of non-destructive DNA extraction protocols for insect metabarcoding: gentler and shorter is better

DNA metabarcoding can accelerate research on insect diversity, as it is cheap and fast compared to manual sorting and identification. Most metabarcoding protocols require homogenisation of the sample, preventing further work on the specimens. Mild digestion of the tissue by incubation in a lysis buffer has been proposed as an alternative, and, although some mild lysis protocols have already been presented, they have so far not been evaluated against each other. Here, we analyse how two mild lysis buffers (one more aggressive, one gentler in terms of tissue degradation), two different incubation times, and two DNA purification methods (a manual precipitation and an automated protocol) affect the accuracy of retrieving the true composition of mock communities using two mitochondrial markers (COI and 16S). We found that protocol-specific variation in concentration and purity of the DNA extracts produced had little effect on the recovery of species. However, the two lysis treatments differed in quantification of species abundances. Digestion in the gentler buffer and for a shorter time yielded better representation of original sample composition. Digestion in a more aggressive buffer or longer incubation time yielded lower alpha diversity values and increased differences between metabarcoding results and the true species-abundance distribution. We conclude that the details of non-destructive protocols can have a significant effect on metabarcoding performance. A short and mild lysis treatment appears the best choice for recovering the true composition of the sample. This not only improves accuracy, but also comes with a faster processing time than the other treatments

RevBayes: Bayesian Phylogenetic Inference Using Graphical Models and an Interactive Model-Specification Language.

Programs for Bayesian inference of phylogeny currently implement a unique and fixed suite of models. Consequently, users of these software packages are simultaneously forced to use a number of programs for a given study, while also lacking the freedom to explore models that have not been implemented by the developers of those programs. We developed a new open-source software package, RevBayes, to address these problems. RevBayes is entirely based on probabilistic graphical models, a powerful generic framework for specifying and analyzing statistical models. Phylogenetic-graphical models can be specified interactively in RevBayes, piece by piece, using a new succinct and intuitive language called Rev. Rev is similar to the R language and the BUGS model-specification language, and should be easy to learn for most users. The strength of RevBayes is the simplicity with which one can design, specify, and implement new and complex models. Fortunately, this tremendous flexibility does not come at the cost of slower computation; as we demonstrate, RevBayes outperforms competing software for several standard analyses. Compared with other programs, RevBayes has fewer black-box elements. Users need to explicitly specify each part of the model and analysis. Although this explicitness may initially be unfamiliar, we are convinced that this transparency will improve understanding of phylogenetic models in our field. Moreover, it will motivate the search for improvements to existing methods by brazenly exposing the model choices that we make to critical scrutiny. RevBayes is freely available at http://www.RevBayes.com [Bayesian inference; Graphical models; MCMC; statistical phylogenetics.]

The effect of ethanol concentration on the morphological and molecular preservation of insects for biodiversity studies

Traditionally, insects collected for scientific purposes have been dried and pinned, or preserved in 70% ethanol. Both methods preserve taxonomically informative exoskeletal structures well but are suboptimal for preserving DNA for molecular biology. Highly concentrated ethanol (95-100%), preferred as a DNA preservative, has generally been assumed to make specimens brittle and prone to breaking. However, systematic studies on the correlation between ethanol concentration and specimen preservation are lacking. Here, we tested how preservative ethanol concentration in combination with different sample handling regimes affect the integrity of seven insect species representing four orders, and differing substantially in the level of sclerotization. After preservation and treatments (various levels of disturbance), we counted the number of appendages (legs, wings, antennae, or heads) that each specimen had lost. Additionally, we assessed the preservation ofDNAafter long-term storage by comparing the ratio of PCR amplicon copy numbers to an added artificial standard. We found that high ethanol concentrations indeed induce brittleness in insects. However, the magnitude and nature of the effect varied strikingly among species. In general, ethanol concentrations at or above 90% made the insects more brittle, but for species with robust, thicker exoskeletons, this did not translate to an increased loss of appendages. Neither freezing the samples nor drying the insects after immersion in ethanol had a negative effect on the retention of appendages. However, the morphology of the insects was severely damaged if they were allowed to dry. We also found thatDNApreserves less well at lower ethanol concentrations when stored at room temperature for an extended period. However, the magnitude of the effect varies among species; the concentrations at which the number of COI amplicon copies relative to the standard was significantly decreased compared to 95% ethanol ranged from 90% to as low as 50%. While higher ethanol concentrations positively affect long-term DNA preservation, there is a clear trade-off between preserving insects for morphological examination and genetic analysis. The optimal ethanol concentration for the latter is detrimental for the former, and vice versa. These trade-offs need to be considered in large insect biodiversity surveys and other projects aiming to combine molecular work with traditional morphology-based characterization of collected specimens

Establishing arthropod community composition using metabarcoding : Surprising inconsistencies between soil samples and preservative ethanol and homogenate from Malaise trap catches

DNA metabarcoding allows the analysis of insect communities faster and more efficiently than ever before. However, metabarcoding can be conducted through several approaches, and the consistency of results across methods has rarely been studied. We compare the results obtained by DNA metabarcoding of the same communities using two different markers - COI and 16S - and three different sampling methods: (a) homogenized Malaise trap samples (homogenate), (b) preservative ethanol from the same samples, and (c) soil samples. Our results indicate that COI and 16S offer partly complementary information on Malaise trap samples, with each marker detecting a significant number of species not detected by the other. Different sampling methods offer highly divergent estimates of community composition. The community recovered from preservative ethanol of Malaise trap samples is significantly different from that recovered from homogenate. Small and weakly sclerotized insects tend to be overrepresented in ethanol while strong and large taxa are overrepresented in homogenate. For soil samples, highly degenerate COI primers pick up large amounts of nontarget DNA and only 16S provides adequate analyses of insect diversity. However, even with 16S, very little overlap in molecular operational taxonomic unit (MOTU) content was found between the trap and the soil samples. Our results demonstrate that none of the tested sampling approaches is satisfactory on its own. For instance, DNA extraction from preservative ethanol is not a valid replacement for destructive bulk extraction but a complement. In future metabarcoding studies, both should ideally be used together to achieve comprehensive representation of the target community.Peer reviewe