A Phylogenetic Framework to Simulate Synthetic Interspecies RNA-Seq Data.

Interspecies RNA-Seq datasets are increasingly common, and have the potential to answer new questions about the evolution of gene expression. Single-species differential expression analysis is now a well-studied problem that benefits from sound statistical methods. Extensive reviews on biological or synthetic datasets have provided the community with a clear picture on the relative performances of the available methods in various settings. However, synthetic dataset simulation tools are still missing in the interspecies gene expression context. In this work, we develop and implement a new simulation framework. This tool builds on both the RNA-Seq and the phylogenetic comparative methods literatures to generate realistic count datasets, while taking into account the phylogenetic relationships between the samples. We illustrate the usefulness of this new framework through a targeted simulation study, that reproduces the features of a recently published dataset, containing gene expression data in adult eye tissue across blind and sighted freshwater crayfish species. Using our simulated datasets, we perform a fair comparison of several approaches used for differential expression analysis. This benchmark reveals some of the strengths and weaknesses of both the classical and phylogenetic approaches for interspecies differential expression analysis, and allows for a reanalysis of the crayfish dataset. The tool has been integrated in the R package compcodeR, freely available on Bioconductor.

The Cauchy Process on Phylogenies: A Tractable Model for Pulsed Evolution.

Phylogenetic comparative methods use random processes, such as the Brownian Motion, to model the evolution of continuous traits on phylogenetic trees. Growing evidence for non-gradual evolution motivated the development of complex models, often based on Lévy processes. However, their statistical inference is computationally intensive and currently relies on approximations, high-dimensional sampling, or numerical integration. We consider here the Cauchy Process (CP), a particular pure-jump Lévy process in which the trait increment along each branch follows a centered Cauchy distribution with a dispersion proportional to its length. In this work, we derive an exact algorithm to compute both the joint probability density of the tip trait values of a phylogeny under a CP and the ancestral trait values and branch increments posterior densities in quadratic time. A simulation study shows that the CP generates patterns in comparative data that are distinct from any Gaussian process, and that restricted maximum likelihood parameter estimates and root trait reconstruction are unbiased and accurate for trees with 200 tips or less. The CP has only two parameters but is rich enough to capture complex-pulsed evolution. It can reconstruct posterior ancestral trait distributions that are multimodal, reflecting the uncertainty associated with the inference of the evolutionary history of a trait from extant taxa only. Applied on empirical datasets taken from the Evolutionary Ecology and Virology literature, the CP suggests nuanced scenarios for the body size evolution of Greater Antilles Lizards and for the geographical spread of the West Nile Virus epidemics in North America, both consistent with previous studies using more complex models. The method is efficiently implemented in C with an R interface in package cauphy, which is open source and freely available online.

The evolution of Ericaceae flowers and their pollination syndromes at a global scale.

PREMISE: Floral evolution in large clades is difficult to study not only because of the number of species involved, but also because they often are geographically widespread and include a diversity of outcrossing pollination systems. The cosmopolitan blueberry family (Ericaceae) is one such example, most notably pollinated by bees and multiple clades of nectarivorous birds. METHODS: We combined data on floral traits, pollination ecology, and geography with a comprehensive phylogeny to examine the structuring of floral diversity across pollination systems and continents. We focused on ornithophilous systems to test the hypothesis that some Old World Ericaceae were pollinated by now-extinct hummingbirds. RESULTS: Despite some support for floral differentiation at a continental scale, we found a large amount of variability within and among landmasses, due to both phylogenetic conservatism and parallel evolution. We found support for floral differentiation in anther and corolla traits across pollination systems, including among different ornithophilous systems. Corolla traits show inconclusive evidence that some Old World Ericaceae were pollinated by hummingbirds, while anther traits show stronger evidence. Some major shifts in floral traits are associated with changes in pollination system, but shifts within bee systems are likely also important. CONCLUSIONS: Studying the floral evolution of large, morphologically diverse, and widespread clades is feasible. We demonstrate that continent-specific radiations have led to widespread parallel evolution of floral morphology. We show that traits outside of the perianth may hold important clues to the ecological history of lineages.

Markov-Modulated Continuous-Time Markov Chains to Identify Site- and Branch-Specific Evolutionary Variation in BEAST.

Markov models of character substitution on phylogenies form the foundation of phylogenetic inference frameworks. Early models made the simplifying assumption that the substitution process is homogeneous over time and across sites in the molecular sequence alignment. While standard practice adopts extensions that accommodate heterogeneity of substitution rates across sites, heterogeneity in the process over time in a site-specific manner remains frequently overlooked. This is problematic, as evolutionary processes that act at the molecular level are highly variable, subjecting different sites to different selective constraints over time, impacting their substitution behavior. We propose incorporating time variability through Markov-modulated models (MMMs), which extend covarion-like models and allow the substitution process (including relative character exchange rates as well as the overall substitution rate) at individual sites to vary across lineages. We implement a general MMM framework in BEAST, a popular Bayesian phylogenetic inference software package, allowing researchers to compose a wide range of MMMs through flexible XML specification. Using examples from bacterial, viral, and plastid genome evolution, we show that MMMs impact phylogenetic tree estimation and can substantially improve model fit compared to standard substitution models. Through simulations, we show that marginal likelihood estimation accurately identifies the generative model and does not systematically prefer the more parameter-rich MMMs. To mitigate the increased computational demands associated with MMMs, our implementation exploits recent developments in BEAGLE, a high-performance computational library for phylogenetic inference. [Bayesian inference; BEAGLE; BEAST; covarion, heterotachy; Markov-modulated models; phylogenetics.].

Inference of Adaptive Shifts for Multivariate Correlated Traits.

To study the evolution of several quantitative traits, the classical phylogenetic comparative framework consists of a multivariate random process running along the branches of a phylogenetic tree. The Ornstein-Uhlenbeck (OU) process is sometimes preferred to the simple Brownian motion (BM) as it models stabilizing selection toward an optimum. The optimum for each trait is likely to be changing over the long periods of time spanned by large modern phylogenies. Our goal is to automatically detect the position of these shifts on a phylogenetic tree, while accounting for correlations between traits, which might exist because of structural or evolutionary constraints. We show that, in the presence of shifts, phylogenetic Principal Component Analysis fails to decorrelate traits efficiently, so that any method aiming at finding shifts needs to deal with correlation simultaneously. We introduce here a simplification of the full multivariate OU model, named scalar OU, which allows for noncausal correlations and is still computationally tractable. We extend the equivalence between the OU and a BM on a rescaled tree to our multivariate framework. We describe an Expectation-Maximization (EM) algorithm that allows for a maximum likelihood estimation of the shift positions, associated with a new model selection criterion, accounting for the identifiability issues for the shift localization on the tree. The method, freely available as an R-package (PhylogeneticEM) is fast, and can deal with missing values. We demonstrate its efficiency and accuracy compared to another state-of-the-art method ($\ell$1ou) on a wide range of simulated scenarios and use this new framework to reanalyze recently gathered data sets on New World Monkeys and Anolis lizards.

Phylogenetic Comparative Methods on Phylogenetic Networks with Reticulations.

The goal of phylogenetic comparative methods (PCMs) is to study the distribution of quantitative traits among related species. The observed traits are often seen as the result of a Brownian Motion (BM) along the branches of a phylogenetic tree. Reticulation events such as hybridization, gene flow or horizontal gene transfer, can substantially affect a species' traits, but are not modeled by a tree. Phylogenetic networks have been designed to represent reticulate evolution. As they become available for downstream analyses, new models of trait evolution are needed, applicable to networks. We develop here an efficient recursive algorithm to compute the phylogenetic variance matrix of a trait on a network, in only one preorder traversal of the network. We then extend the standard PCM tools to this new framework, including phylogenetic regression with covariates (or phylogenetic ANOVA), ancestral trait reconstruction, and Pagel's $\lambda$ test of phylogenetic signal. The trait of a hybrid is sometimes outside of the range of its two parents, for instance because of hybrid vigor or hybrid depression. These two phenomena are rather commonly observed in present-day hybrids. Transgressive evolution can be modeled as a shift in the trait value following a reticulation point. We develop a general framework to handle such shifts and take advantage of the phylogenetic regression view of the problem to design statistical tests for ancestral transgressive evolution in the evolutionary history of a group of species. We study the power of these tests in several scenarios and show that recent events have indeed the strongest impact on the trait distribution of present-day taxa. We apply those methods to a data set of Xiphophorus fishes, to confirm and complete previous analysis in this group. All the methods developed here are available in the Julia package PhyloNetworks.

PhyloNetworks: A Package for Phylogenetic Networks.

PhyloNetworks is a Julia package for the inference, manipulation, visualization, and use of phylogenetic networks in an interactive environment. Inference of phylogenetic networks is done with maximum pseudolikelihood from gene trees or multi-locus sequences (SNaQ), with possible bootstrap analysis. PhyloNetworks is the first software providing tools to summarize a set of networks (from a bootstrap or posterior sample) with measures of tree edge support, hybrid edge support, and hybrid node support. Networks can be used for phylogenetic comparative analysis of continuous traits, to estimate ancestral states or do a phylogenetic regression. The software is available in open source and with documentation at https://github.com/crsl4/PhyloNetworks.jl.

Assessment of intra-specific variability in Saprolegnia parasitica populations of aquaculture facilities in British Columbia, Canada.

Among the Saprolegnia species found in aquaculture facilities, S. parasitica is recognized as the primary fish pathogen and remains an ongoing concern in fish health management. Until recently, these pathogens were kept in check by use of malachite green; due to its toxicity, this chemical has now been banned from use in many countries. It is difficult to predict and control S. parasitica outbreaks in freshwater systems and there is a need to understand the population genetic structure of this pathogen. Genetic characterization of this species in aquaculture systems would provide information to track introductions and determine possible sources of inoculum. Degenerate PCR primers containing short sequence repeats were used to create microsatellite-associated genetic markers (random amplified microsatellites) for the comparison of S. parasitica isolates collected primarily from commercial Atlantic salmon aquaculture systems in British Columbia, Canada, over a 15 mo period to describe their spatial and temporal variability. The frequencies of amplified products were compared and the population genetic diversity was measured using Nei's genetic distance and Shannon's information index, while the species population structure was evaluated by phylogenetic analysis. S. parasitica was detected in all facilities sampled. Genetic diversity was low but not clonal, most likely due to repeated introduction events and a low level of sexual recombination over time. A better understanding of pathogen population structure will assist the development of effective preventative measures and targeted treatments for disease outbreaks.

How fast are viruses spreading in the wild?

Genomic data collected from viral outbreaks can be exploited to reconstruct the dispersal history of viral lineages in a two-dimensional space using continuous phylogeographic inference. These spatially explicit reconstructions can subsequently be used to estimate dispersal metrics allowing to unveil the dispersal dynamics and evaluate the capacity to spread among hosts. Heterogeneous sampling intensity of genomic sequences can however impact the accuracy of dispersal insights gained through phylogeographic inference. In our study, we implement a simulation framework to evaluate the robustness of three dispersal metrics - a lineage dispersal velocity, a diffusion coefficient, and an isolation-by-distance signal metric - to the sampling effort. Our results reveal that both the diffusion coefficient and isolation-by-distance signal metrics appear to be robust to the number of samples considered for the phylogeographic reconstruction. We then use these two dispersal metrics to compare the dispersal pattern and capacity of various viruses spreading in animal populations. Our comparative analysis reveals a broad range of isolation-by-distance patterns and diffusion coefficients mostly reflecting the dispersal capacity of the main infected host species but also, in some cases, the likely signature of rapid and/or long-distance dispersal events driven by human-mediated movements through animal trade. Overall, our study provides key recommendations for the lineage dispersal metrics to consider in future studies and illustrates their application to compare the spread of viruses in various settings.

Modeling the velocity of evolving lineages and predicting dispersal patterns.

Accurate estimation of the dispersal velocity or speed of evolving organisms is no mean feat. In fact, existing probabilistic models in phylogeography or spatial population genetics generally do not provide an adequate framework to define velocity in a relevant manner. For instance, the very concept of instantaneous speed simply does not exist under one of the most popular approaches that models the evolution of spatial coordinates as Brownian trajectories running along a phylogeny [30]. Here, we introduce a new family of models - the so-called "Phylogenetic Integrated Velocity" (PIV) models - that use Gaussian processes to explicitly model the velocity of evolving lineages instead of focusing on the fluctuation of spatial coordinates over time. We describe the properties of these models and show an increased accuracy of velocity estimates compared to previous approaches. Analyses of West Nile virus data in the U.S.A. indicate that PIV models provide sensible predictions of the dispersal of evolving pathogens at a one-year time horizon. These results demonstrate the feasibility and relevance of predictive phylogeography in monitoring epidemics in time and space.

Comparative Analysis of Transcriptomes of Ophiostoma novo-ulmi ssp. americana Colonizing Resistant or Sensitive Genotypes of American Elm.

The Ascomycete Ophiostoma novo-ulmi threatens elm populations worldwide. The molecular mechanisms underlying its pathogenicity and virulence are still largely uncharacterized. As part of a collaborative study of the O. novo-ulmi-elm interactome, we analyzed the O. novo-ulmi ssp. americana transcriptomes obtained by deep sequencing of messenger RNAs recovered from Ulmus americana saplings from one resistant (Valley Forge, VF) and one susceptible (S) elm genotypes at 0 and 96 h post-inoculation (hpi). Transcripts were identified for 6424 of the 8640 protein-coding genes annotated in the O. novo-ulmi nuclear genome. A total of 1439 genes expressed in planta had orthologs in the PHI-base curated database of genes involved in host-pathogen interactions, whereas 472 genes were considered differentially expressed (DEG) in S elms (370 genes) and VF elms (102 genes) at 96 hpi. Gene ontology (GO) terms for processes and activities associated with transport and transmembrane transport accounted for half (27/55) of GO terms that were significantly enriched in fungal genes upregulated in S elms, whereas the 22 GO terms enriched in genes overexpressed in VF elms included nine GO terms associated with metabolism, catabolism and transport of carbohydrates. Weighted gene co-expression network analysis identified three modules that were significantly associated with higher gene expression in S elms. The three modules accounted for 727 genes expressed in planta and included 103 DEGs upregulated in S elms. Knockdown- and knockout mutants were obtained for eight O. novo-ulmi genes. Although mutants remained virulent towards U. americana saplings, we identified a large repertoire of additional candidate O. novo-ulmi pathogenicity genes for functional validation by loss-of-function approaches.

Functional categorization of unique expressed sequence tags obtained from the yeast-like growth phase of the elm pathogen Ophiostoma novo-ulmi.

BACKGROUND: The highly aggressive pathogenic fungus Ophiostoma novo-ulmi continues to be a serious threat to the American elm (Ulmus americana) in North America. Extensive studies have been conducted in North America to understand the mechanisms of virulence of this introduced pathogen and its evolving population structure, with a view to identifying potential strategies for the control of Dutch elm disease. As part of a larger study to examine the genomes of economically important Ophiostoma spp. and the genetic basis of virulence, we have constructed an expressed sequence tag (EST) library using total RNA extracted from the yeast-like growth phase of O. novo-ulmi (isolate H327). RESULTS: A total of 4,386 readable EST sequences were annotated by determining their closest matches to known or theoretical sequences in public databases by BLASTX analysis. Searches matched 2,093 sequences to entries found in Genbank, including 1,761 matches with known proteins and 332 matches with unknown (hypothetical/predicted) proteins. Known proteins included a collection of 880 unique transcripts which were categorized to obtain a functional profile of the transcriptome and to evaluate physiological function. These assignments yielded 20 primary functional categories (FunCat), the largest including Metabolism (FunCat 01, 20.28% of total), Sub-cellular localization (70, 10.23%), Protein synthesis (12, 10.14%), Transcription (11, 8.27%), Biogenesis of cellular components (42, 8.15%), Cellular transport, facilitation and routes (20, 6.08%), Classification unresolved (98, 5.80%), Cell rescue, defence and virulence (32, 5.31%) and the unclassified category, or known sequences of unknown metabolic function (99, 7.5%). A list of specific transcripts of interest was compiled to initiate an evaluation of their impact upon strain virulence in subsequent studies. CONCLUSIONS: This is the first large-scale study of the O. novo-ulmi transcriptome. The expression profile obtained from the yeast-like growth phase of this species will facilitate a multigenic approach to gene expression studies to assess their role in the determination of pathogenicity for this species. The identification and evaluation of gene targets in such studies will be a prerequisite to the development of biological control strategies for this pathogen.

Rivers and landscape ecology of a plant virus, Rice yellow mottle virus along the Niger Valley.

To investigate the spread of Rice yellow mottle virus (RYMV) along the Niger River, regular sampling of virus isolates was conducted along 500 km of the Niger Valley in the Republic of Niger and was complemented by additional sampling in neighbouring countries in West Africa and Central Africa. The spread of RYMV into and within the Republic of Niger was inferred as a continuous process using a Bayesian statistical framework applied previously to reconstruct its dispersal history in West Africa, East Africa, and Madagascar. The spatial resolution along this section of the Niger River was the highest implemented for RYMV and possibly for any plant virus. We benefited from the results of early field surveys of the disease for the validation of the phylogeographic reconstruction and from the well-documented history of rice cultivation changes along the Niger River for their interpretation. As a prerequisite, the temporal signal of the RYMV data sets was revisited in the light of recent methodological advances. The role of the hydrographic network of the Niger Basin in RYMV spread was examined, and the link between virus population dynamics and the extent of irrigated rice was assessed. RYMV was introduced along the Niger River in the Republic of Niger in the early 1980s from areas to the southwest of the country where rice was increasingly grown. Viral spread was triggered by a major irrigation scheme made of a set of rice perimeters along the river valley. The subsequent spatial and temporal host continuity and the inoculum build-up allowed for a rapid spread of RYMV along the Niger River, upstream and downstream, over hundreds of kilometres, and led to the development of severe epidemics. There was no evidence of long-distance dissemination of the virus through natural water. Floating rice in the main meanders of the Middle Niger did not contribute to virus dispersal from West Africa to Central Africa. RYMV along the Niger River is an insightful example of how agricultural intensification favours pathogen emergence and spread.

Suppression of polygalacturonase gene expression in the phytopathogenic fungus Ophiostoma novo-ulmi by RNA interference.

The fungal pathogen, Ophiostomo novo-ulmi, has been responsible for the rapid decline of American elm (Ulmus americana) across North America and remains a serious threat to surviving elm populations. The production of pectinolytic polygalacturonase enzymes has been implicated as a virulence factor for many fungal pathogens, including O. novo-ulmi. Previous work has shown that the targeted disruption of the endopolygalacturonase gene locus epg1 of O. novo-ulmi reduced, but did not eliminate pectinase activity. In the present study, we evaluated the use of RNA interference (RNAi) as a method of suppressing expression of the epg1 locus in O. novo-ulmi and compared its efficiency to the gene disruption method. While there was a reduction in epg1-specific mRNA transcripts and in the amount of polygalacturonase enzyme secreted for both methods of gene regulation, neither method completely suppressed the expression of pectinase activity. There was, however, a significantly greater reduction in both transcript levels and secreted enzyme observed for some of the RNAi transformants. As the first demonstration of RNAi in O. novo-ulmi, this method of gene regulation shows promise in future studies of gene expression and pathogenicity.

nosoi: A stochastic agent-based transmission chain simulation framework in r.

The transmission process of an infectious agent creates a connected chain of hosts linked by transmission events, known as a transmission chain. Reconstructing transmission chains remains a challenging endeavour, except in rare cases characterized by intense surveillance and epidemiological inquiry. Inference frameworks attempt to estimate or approximate these transmission chains but the accuracy and validity of such methods generally lack formal assessment on datasets for which the actual transmission chain was observed.We here introduce nosoi, an open-source r package that offers a complete, tunable and expandable agent-based framework to simulate transmission chains under a wide range of epidemiological scenarios for single-host and dual-host epidemics. nosoi is accessible through GitHub and CRAN, and is accompanied by extensive documentation, providing help and practical examples to assist users in setting up their own simulations.Once infected, each host or agent can undergo a series of events during each time step, such as moving (between locations) or transmitting the infection, all of these being driven by user-specified rules or data, such as travel patterns between locations. nosoi is able to generate a multitude of epidemic scenarios, that can-for example-be used to validate a wide range of reconstruction methods, including epidemic modelling and phylodynamic analyses. nosoi also offers a comprehensive framework to leverage empirically acquired data, allowing the user to explore how variations in parameters can affect epidemic potential. Aside from research questions, nosoi can provide lecturers with a complete teaching tool to offer students a hands-on exploration of the dynamics of epidemiological processes and the factors that impact it. Because the package does not rely on mathematical formalism but uses a more intuitive algorithmic approach, even extensive changes of the entire model can be easily and quickly implemented.

Le processus de transmission d'un agent infectieux crée une chaîne connectée d'hôtes, connue sous le nom de chaîne de transmission. Reconstruire cette dernière reste une entreprise difficile, sauf dans de rares cas caractérisés par une surveillance épidémiologique intense. Il existe des méthodes d'inférence pour estimer ou approximer ces chaînes de transmission. Cependant la précision et la validité de ces méthodes ne sont généralement pas formellement évaluées sur des jeux de données pour lesquelles la chaîne de transmission est connue.Nous présentons ici nosoi, un progiciel r libre qui propose une infrastructure individu­centrée complète, réglable et extensible, pour simuler des chaînes de transmission dans une grande variété de scénarios épidémiologiques. nosoi est disponible sur GitHub et le CRAN, et est accompagné d'une documentation étendue munie d'exemples pratiques détaillés permettant aux utilisateurs de paramétrer aisément leurs propres scénarios de simulation.Une fois infecté, chaque hôte ou agent peut subir une série d'événements à chaque pas de temps, tel que bouger (entre deux lieux) ou transmettre l'infection, chacun de ces événements étant spécifié par des règles ou des données fournies par l'utilisateur. nosoi est capable de générer une multitude de scénarios épidémiques, pouvant, par exemple, être utilisés pour valider certaines méthodes de reconstruction, dont les modélisations épidémiologiques et les analyses phylodynamiques. nosoi offre également une infrastructure complète pour tirer parti de données acquises empiriquement, permettant l'exploration de l'influence des divers paramètres d'intérêt sur le potentiel épidémique. En plus de son utilisation en recherche, nosoi procure aux enseignants universitaires un outil complet pour explorer avec leurs étudiants la dynamique des processus épidémiologiques et ses paramètres. Ce progiciel ne reposant pas sur un formalisme mathématique mais utilisant une approche algorithmique plus intuitive, des changements même drastiques de l'entièreté du modèle peuvent être facilement et rapidement implémentés.

Disrupting Healthcare Silos: Addressing Data Volume, Velocity and Variety With a Cloud-Native Healthcare Data Ingestion Service.

Healthcare enterprises are starting to adopt cloud computing due to its numerous advantages over traditional infrastructures. This has become a necessity because of the increased volume, velocity and variety of healthcare data, and the need to facilitate data correlation and large-scale analysis. Cloud computing infrastructures have the power to offer continuous acquisition of data from multiple heterogeneous sources, efficient data integration, and big data analysis. At the same time, security, availability, and disaster recovery are critical factors aiding towards the adoption of cloud computing. However, the migration of healthcare workloads to cloud is not straightforward due to the vagueness in healthcare data standards, heterogeneity and sensitive nature of healthcare data, and many regulations that govern its usage. This paper highlights the need for providing healthcare data acquisition using cloud infrastructures and presents the challenges, requirements, use-cases, and best practices for building a state-of-the-art healthcare data ingestion service on cloud.

Epidemiological hypothesis testing using a phylogeographic and phylodynamic framework.

Computational analyses of pathogen genomes are increasingly used to unravel the dispersal history and transmission dynamics of epidemics. Here, we show how to go beyond historical reconstructions and use spatially-explicit phylogeographic and phylodynamic approaches to formally test epidemiological hypotheses. We illustrate our approach by focusing on the West Nile virus (WNV) spread in North America that has substantially impacted public, veterinary, and wildlife health. We apply an analytical workflow to a comprehensive WNV genome collection to test the impact of environmental factors on the dispersal of viral lineages and on viral population genetic diversity through time. We find that WNV lineages tend to disperse faster in areas with higher temperatures and we identify temporal variation in temperature as a main predictor of viral genetic diversity through time. By contrasting inference with simulation, we find no evidence for viral lineages to preferentially circulate within the same migratory bird flyway, suggesting a substantial role for non-migratory birds or mosquito dispersal along the longitudinal gradient.

Multiple factors behind early diversification of skull morphology in the continental radiation of New World monkeys.

Understanding the origin of diversity is a fundamental problem in evolutionary biology. The null expectation for the evolutionary diversification is that all changes in biological diversity are the result of random processes. Adaptive radiations depart from this expectation as ecological factors and natural selection are supposed to play a central role in driving exceptional diversification. However, it is not well understood how large-scale continental radiations, given their characteristics, fit to these opposing theoretical models. Here, we used phylogenetic comparative methods and geometric morphometrics to study the evolutionary process of cranial diversification in the continental radiation of New World monkeys. Particularly, we tested several alternative evolutionary scenarios for morphological evolution in the clade. Results indicated that despite the platyrrhine radiation being old and geographically widespread, the formative patterns arising from the initial stages of diversification probably associated with an adaptive radiation can still be recognized today. We also show that no single explored factor (e.g., ecological or allometric) can be invoked as a complete explanation for the observed phenotypic diversity patterns in the clade and, moreover, that different cranial regions exhibit particular macroevolutionary patterns. Together, our results highlight the evident complexity behind large-scale evolutionary radiations.

Interfertility and genetic variability among European and North American isolates of the basidiomycete fungus Chondrostereum purpureum.

The conspecificity of Finnish and western Canadian isolates of the decay fungus Chondrostereum purpureum was investigated by several approaches, including the assessment of genetic variability, mating and progeny analysis, and the analysis of selected phenotypic traits. Eight second-generation single spore strains per fungal isolate pairing were investigated with specific genetic markers developed for both Finnish and Canadian parental isolates. Tests of linkage disequilibrium were used to analyze whether these markers assorted independently among single spore strains. This procedure was similarly applied to the third-generation spore progeny. Finally, global non-metric multidimensional scaling was used to analyze independent random amplified microsatellite marker data to assess the genetic variability of the parental Finnish and Canadian isolates, and their second- and third-generation progeny. Our results revealed that the parental isolates from Finland and western Canada were genetically divergent, but no interfertility barriers were identified between these geographically distant fungi. Furthermore, parental genetic markers used in mating studies demonstrated that second- and third-generation spore progenies underwent normal meiosis and genetic recombination without linkage disequilibrium. Based on this work, the studied C. purpureum isolates from Finland and Canada can be considered as belonging to a single biological species, although genetic and limited phenotypic differentiation was observed.

Species composition of the genus Saprolegnia in fin fish aquaculture environments, as determined by nucleotide sequence analysis of the nuclear rDNA ITS regions.

The ITS region of the rDNA gene was compared for Saprolegnia spp. in order to improve our understanding of nucleotide sequence variability within and between species of this genus, determine species composition in Canadian fin fish aquaculture facilities, and to assess the utility of ITS sequence variability in genetic marker development. From a collection of more than 400 field isolates, ITS region nucleotide sequences were studied and it was determined that there was sufficient consistent inter-specific variation to support the designation of species identity based on ITS sequence data. This non-subjective approach to species identification does not rely upon transient morphological features. Phylogenetic analyses comparing our ITS sequences and species designations with data from previous studies generally supported the clade scheme of Diéguez-Uribeondo et al. (2007) and found agreement with the molecular taxonomic cluster system of Sandoval-Sierra et al. (2014). Our Canadian ITS sequence collection will thus contribute to the public database and assist the clarification of Saprolegnia spp. taxonomy. The analysis of ITS region sequence variability facilitated genus- and species-level identification of unknown samples from aquaculture facilities and provided useful information on species composition. A unique ITS-RFLP for the identification of S. parasitica was also described.