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Some species introduced into new geographical areas from their native ranges wreak ecological and economic havoc in their new environment. Although many studies have searched for either species or habitat characteristics that predict invasiveness of exotic species, the match between characteristics of the invader and those of members of the existing native community may be essential to understanding invasiveness. Here, we find that one metric, the phylogenetic relatedness of an invader to the native community, provides a predictive tool for invasiveness. Using a phylogenetic supertree of all grass species in California, we show that highly invasive grass species are, on average, significantly less related to native grasses than are introduced but noninvasive grasses. The match between the invader and the existing native community may explain why exotic pest species are not uniformly noxious in all novel habitats. Relatedness of invaders to the native biota may be one useful criterion for prioritizing management efforts of exotic species.

Despite the increasingly available phylogenetic hypotheses for multiple taxonomic groups, most of them do not include all species. In phylogenetic ecology, there is still a strong demand to have phylogenies with all species in a study included. The existing software tools to graft species to backbone megatrees, however, are mostly limited to a specific taxonomic group such as plants or fishes. Here, I introduce a new user‐friendly R package, ‘rtrees', that can assemble phylogenies from existing or user‐provided megatrees. For most common taxonomic groups, users can only provide a vector of species scientific names to get a phylogeny or a set of posterior phylogenies from megatrees. It is my hope that ‘rtrees' can provide an easy, flexible, and reliable way to assemble phylogenies from megatrees, facilitating the progress of phylogenetic ecology.

Background Genome-scale phylogenetic analysis based on core gene sets is routinely used in microbiological research. However, the techniques are still not approachable for individuals with little bioinformatics experience. Here, we present EasyCGTree, a user-friendly and cross-platform pipeline to reconstruct genome-scale maximum-likehood (ML) phylogenetic tree using supermatrix (SM) and supertree (ST) approaches. Results EasyCGTree was implemented in Perl programming languages and was built using a collection of published reputable programs. All the programs were precompiled as standalone executable files and contained in the EasyCGTree package. It can run after installing Perl language environment. Several profile hidden Markov models (HMMs) of core gene sets were prepared in advance to construct a profile HMM database (PHD) that was enclosed in the package and available for homolog searching. Customized gene sets can also be used to build profile HMM and added to the PHD via EasyCGTree. Taking 43 genomes of the genus Paracoccus as the testing data set, consensus (a variant of the typical SM), SM, and ST trees were inferred via EasyCGTree successfully, and the SM trees were compared with those inferred via the pipelines UBCG and bcgTree, using the metrics of cophenetic correlation coefficients (CCC) and Robinson–Foulds distance (topological distance). The results suggested that EasyCGTree can infer SM trees with nearly identical topology (distance < 0.1) and accuracy (CCC > 0.99) to those of trees inferred with the two pipelines. Conclusions EasyCGTree is an all-in-one automatic pipeline from input data to phylogenomic tree with guaranteed accuracy, and is much easier to install and use than the reference pipelines. In addition, ST is implemented in EasyCGTree conveniently and can be used to explore prokaryotic evolutionary signals from a different perspective. The EasyCGTree version 4 is freely available for Linux and Windows users at Github ( https://github.com/zdf1987/EasyCGTree4 ).

In phylogenomics, incongruences between gene trees, resulting from both artifactual and biological reasons, are known to decrease the signal-to-noise ratio and complicate species tree inference. The amount of data handled today in classical phylogenomic analyses precludes manual error detection and removal. However, a simple and efficient way to automate the identification of outlier sequences is still missing.Here, we present PhylteR, a method that allows a rapid and accurate detection of outlier sequences in phylogenomic datasets, i.e. species from individual gene trees that do not follow the general trend. PhylteR relies on DISTATIS, an extension of multidimensional scaling to 3 dimensions to compare multiple distance matrices at once. In PhylteR, distance matrices obtained either directly from multiple sequence alignments or extracted from individual gene phylogenies represent evolutionary distances between species according to each gene.On simulated datasets, we show that PhylteR identifies outliers with more sensitivity and precision than a comparable existing method. On a biological dataset of 14,463 genes for 53 species previously assembled for Carnivora phylogenomics, we show (i) that PhylteR identifies as outliers sequences that can be considered as such by other means, and (ii) that the removal of these sequences improves the concordance between the gene trees and the species tree. Thanks to the generation of numerous graphical outputs, PhylteR also allows for the rapid and easy visual characterisation of the dataset at hand, thus aiding in the precise identification of errors.PhylteR is distributed as an R package on CRAN and as containerized versions (docker and singularity).

Evolutionary relationships have remained unresolved in many well-studied groups, even though advances in next-generation sequencing and analysis, using approaches such as transcriptomics, anchored hybrid enrichment, or ultraconserved elements, have brought systematics to the brink of whole genome phylogenomics. Recently, it has become possible to sequence the entire genomes of numerous non-biological models in parallel at reasonable cost, particularly with shotgun sequencing. Here we identify orthologous coding sequences from whole-genome shotgun sequences, which we then use to investigate the relevance and power of phylogenomic relationship inference and time-calibrated tree estimation. We study an iconic group of butterflies - swallowtails of the family Papilionidae - that has remained phylogenetically unresolved, with continued debate about the timing of their diversification. Low-coverage whole genomes were obtained using Illumina shotgun sequencing for all genera. Genome assembly coupled to BLAST-based orthology searches allowed extraction of 6,621 orthologous protein-coding genes for 45 Papilionidae species and 16 outgroup species (with 32% missing data after cleaning phases). Supermatrix phylogenomic analyses were performed with both maximum-likelihood (IQ-TREE) and Bayesian mixture models (PhyloBayes) for amino acid sequences, which produced a fully resolved phylogeny providing new insights into controversial relationships. Species tree reconstruction from gene trees was performed with ASTRAL and SuperTriplets and recovered the same phylogeny. We estimated gene site concordant factors to complement traditional node-support measures, which strengthens the robustness of inferred phylogenies. Bayesian estimates of divergence times based on a reduced dataset (760 orthologs and 12% missing data) indicate a mid-Cretaceous origin of Papilionoidea around 99.2 million years ago (Ma) (95% credibility interval: 68.6-142.7 Ma) and Papilionidae around 71.4 Ma (49.8-103.6 Ma), with subsequent diversification of modern lineages well after the Cretaceous-Paleogene event. These results show that shotgun sequencing of whole genomes, even when highly fragmented, represents a powerful approach to phylogenomics and molecular dating in a group that has previously been refractory to resolution.

The impact of incomplete lineage sorting (ILS) on phylogenetic conflicts among genes, and the related issue of whether to account for ILS in species tree reconstruction, are matters of intense controversy. Here, focusing on full-genome data in placental mammals, we empirically test two assumptions underlying current usage of tree-building methods that account for ILS. We show that in this data set (i) distinct exons from a common gene do not share a common genealogy, and (ii) ILS is only a minor determinant of the existing phylogenetic conflict. These results shed new light on the relevance and conditions of applicability of ILS-aware methods in phylogenomic analyses of protein coding sequences.

Transcriptome-based exon capture methods provide an approach to recover several hundred markers from genomic DNA, allowing for robust phylogenetic estimation at deep timescales. We applied this method to a highly diverse group of venomous marine snails, Conoidea, for which published phylogenetic trees remain mostly unresolved for the deeper nodes. We targeted 850 protein coding genes (678,322 bp) in ca. 120 samples, spanning all (except one) known families of Conoidea and a broad selection of non-Conoidea neogastropods. The capture was successful for most samples, although capture efficiency decreased when DNA libraries were of insufficient quality and/or quantity (dried samples or low starting DNA concentration) and when targeting the most divergent lineages. An average of 75.4% of proteins was recovered, and the resulting tree, reconstructed using both supermatrix (IQ-tree) and supertree (Astral-II, combined with the Weighted Statistical Binning method) approaches, are almost fully supported. A reconstructed fossil-calibrated tree dates the origin of Conoidea to the Lower Cretaceous. We provide descriptions for two new families. The phylogeny revealed in this study provides a robust framework to reinterpret changes in Conoidea anatomy through time. Finally, we used the phylogeny to test the impact of the venom gland and radular type on diversification rates. Our analyses revealed that repeated losses of the venom gland had no effect on diversification rates, while families with a breadth of radula types showed increases in diversification rates, thus suggesting that trophic ecology may have an impact on the evolution of Conoidea.

An r-uniform supertree is a connected and acyclic hypergraph of which each edge has r vertices, where r ? 3. We propose the concept of matching energy for an r-uniform hypergraph, which is defined as the sum of the absolute value of all the eigenvalues of its matching polynomial. With the aid of the matching polynomial of an r-uniform supertree, three pairs of r-uniform supertrees with the same spectral radius and the same matching energy are constructed, and two infinite families of r-uniform supertrees with the same spectral radius and the same matching energy are characterized. Some known results about the graphs with the same spectra regarding to their adjacency matrices can be naturally deduced from our new results.

Progress in sequencing technology allows researchers to assemble ever-larger supermatrices for phylogenomic inference. However, current phylogenomic studies often rest on patchy data sets, with some having 80% missing (or ambiguous) data or more. Though early simulations had suggested that missing data per se do not harm phylogenetic inference when using sufficiently large data sets, Lemmon et al. (Lemmon AR, Brown JM, Stanger-Hall K, Lemmon EM. 2009. The effect of ambiguous data on phylogenetic estimates obtained by maximum likelihood and Bayesian inference. Syst Biol. 58:130–145.) have recently cast doubt on this consensus in a study based on the introduction of parsimony-uninformative incomplete characters. In this work, we empirically reassess the issue of missing data in phylogenomics while exploring possible interactions with the model of sequence evolution. First, we note that parsimony-uninformative incomplete characters are actually informative in a probabilistic framework. A reanalysis of Lemmon’s data set with this in mind gives a very different interpretation of their results and shows that some of their conclusions may be unfounded. Second, we investigate the effect of the progressive introduction of missing data in a complete supermatrix (126 genes × 39 species) capable of resolving animal relationships. These analyses demonstrate that missing data perturb phylogenetic inference slightly beyond the expected decrease in resolving power. In particular, they exacerbate systematic errors by reducing the number of species effectively available for the detection of multiple substitutions. Consequently, large sparse supermatrices are more sensitive to phylogenetic artifacts than smaller but less incomplete data sets, which argue for experimental designs aimed at collecting a modest number (∼50) of highly covered genes. Our results further confirm that including incomplete yet short-branch taxa (i.e., slowly evolving species or close outgroups) can help to eschew artifacts, as predicted by simulations. Finally, it appears that selecting an adequate model of sequence evolution (e.g., the site-heterogeneous CAT model instead of the site-homogeneous WAG model) is more beneficial to phylogenetic accuracy than reducing the level of missing data.

An understanding of the balance of interspecific competition and the physical environment in structuring organismal communities is crucial because those communities structured primarily by their physical environment typically exhibit greater sensitivity to environmental change than those structured predominantly by competitive interactions. Here, using detailed phylogenetic and functional information, we investigate this question in macrofaunal assemblages from Northwest Atlantic Ocean continental slopes, a high seas region projected to experience substantial environmental change through the current century. We demonstrate assemblages to be both phylogenetically and functionally under-dispersed, and thus conclude that the physical environment, not competition, may dominate in structuring deep-ocean communities. Further, we find temperature and bottom trawling intensity to be among the environmental factors significantly related to assemblage diversity. These results hint that deep-ocean communities are highly sensitive to their physical environment and vulnerable to environmental perturbation, including by direct disturbance through fishing, and indirectly through the changes brought about by climate change.