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Immune escape is a critical hallmark of cancer progression and underlies resistance to multiple immunotherapies. However, it remains unclear when the genetic events associated with immune escape occur during cancer development. Here, we integrate functional genomics studies of immunomodulatory genes with a tumor evolution reconstruction approach to infer the evolution of immune escape across 38 cancer types from the Pan-Cancer Analysis of Whole Genomes dataset. Different cancers favor mutations in different immunomodulatory pathways. For example, the antigen presentation machinery is highly mutated in colorectal adenocarcinoma, lung squamous cell carcinoma, and chromophobe renal cell carcinoma, and the protein methylation pathway is highly mutated in bladder transitional cell carcinoma and lung adenocarcinoma. We also observe different timing patterns in multiple immunomodulatory pathways. For instance, mutations impacting genes involved in cellular amino acid metabolism were more likely to happen late in pancreatic adenocarcinoma. Mutations in the glucocorticoid receptor regulatory network pathway tended to occur early, while mutations in the TNF pathways were more likely to occur late in B-cell non-Hodgkin lymphoma. Mutations in the NOD1/2 signaling pathway and DNA binding transcription factor activity tended to happen late in breast adenocarcinoma and ovarian adenocarcinoma. Together, these results delineate the evolutionary trajectories of immune escape in different cancer types and highlight opportunities for improved immunotherapy of cancer., Competing Interests: Author disclosures The authors report no competing interests.

Despite tremendous efforts in the past decades, relationships among main avian lineages remain heavily debated without a clear resolution. Discrepancies have been attributed to diversity of species sampled, phylogenetic method and the choice of genomic regions 1-3 . Here we address these issues by analysing the genomes of 363 bird species 4 (218 taxonomic families, 92% of total). Using intergenic regions and coalescent methods, we present a well-supported tree but also a marked degree of discordance. The tree confirms that Neoaves experienced rapid radiation at or near the Cretaceous-Palaeogene boundary. Sufficient loci rather than extensive taxon sampling were more effective in resolving difficult nodes. Remaining recalcitrant nodes involve species that are a challenge to model due to either extreme DNA composition, variable substitution rates, incomplete lineage sorting or complex evolutionary events such as ancient hybridization. Assessment of the effects of different genomic partitions showed high heterogeneity across the genome. We discovered sharp increases in effective population size, substitution rates and relative brain size following the Cretaceous-Palaeogene extinction event, supporting the hypothesis that emerging ecological opportunities catalysed the diversification of modern birds. The resulting phylogenetic estimate offers fresh insights into the rapid radiation of modern birds and provides a taxon-rich backbone tree for future comparative studies., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Cancers develop and progress as mutations accumulate, and with the advent of single-cell DNA and RNA sequencing, researchers can observe these mutations and their transcriptomic effects and predict proteomic changes with remarkable temporal and spatial precision. However, to connect genomic mutations with their transcriptomic and proteomic consequences, cells with either only DNA data or only RNA data must be mapped to a common domain. For this purpose, we present MaCroDNA, a method that uses maximum weighted bipartite matching of per-gene read counts from single-cell DNA and RNA-seq data. Using ground truth information from colorectal cancer data, we demonstrate the advantage of MaCroDNA over existing methods in accuracy and speed. Exemplifying the utility of single-cell data integration in cancer research, we suggest, based on results derived using MaCroDNA, that genomic mutations of large effect size increasingly contribute to differential expression between cells as Barrett's esophagus progresses to esophageal cancer, reaffirming the findings of the previous studies., (© 2023. The Author(s).)

The evolutionary histories of individual loci in a genome can be estimated independently, but this approach is error-prone due to the limited amount of sequence data available for each gene, which has led to the development of a diverse array of gene tree error correction methods which reduce the distance to the species tree. We investigate the performance of two representatives of these methods: TRACTION and TreeFix. We found that gene tree error correction frequently increases the level of error in gene tree topologies by "correcting" them to be closer to the species tree, even when the true gene and species trees are discordant. We confirm that full Bayesian inference of the gene trees under the multispecies coalescent model is more accurate than independent inference. Future gene tree correction approaches and methods should incorporate an adequately realistic model of evolution instead of relying on oversimplified heuristics., (© The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

Motivation: Single-nucleotide variants (SNVs) are the most common variations in the human genome. Recently developed methods for SNV detection from single-cell DNA sequencing data, such as SCIΦ and scVILP, leverage the evolutionary history of the cells to overcome the technical errors associated with single-cell sequencing protocols. Despite being accurate, these methods are not scalable to the extensive genomic breadth of single-cell whole-genome (scWGS) and whole-exome sequencing (scWES) data., Results: Here, we report on a new scalable method, Phylovar, which extends the phylogeny-guided variant calling approach to sequencing datasets containing millions of loci. Through benchmarking on simulated datasets under different settings, we show that, Phylovar outperforms SCIΦ in terms of running time while being more accurate than Monovar (which is not phylogeny-aware) in terms of SNV detection. Furthermore, we applied Phylovar to two real biological datasets: an scWES triple-negative breast cancer data consisting of 32 cells and 3375 loci as well as an scWGS data of neuron cells from a normal human brain containing 16 cells and approximately 2.5 million loci. For the cancer data, Phylovar detected somatic SNVs with high or moderate functional impact that were also supported by bulk sequencing dataset and for the neuron dataset, Phylovar identified 5745 SNVs with non-synonymous effects some of which were associated with neurodegenerative diseases., Availability and Implementation: Phylovar is implemented in Python and is publicly available at https://github.com/NakhlehLab/Phylovar., (© The Author(s) 2022. Published by Oxford University Press.)

Phylogenomic studies of prokaryotic taxa often assume conserved marker genes are homologous across their length. However, processes such as horizontal gene transfer or gene duplication and loss may disrupt this homology by recombining only parts of genes, causing gene fission or fusion. We show using simulation that it is necessary to delineate homology groups in a set of bacterial genomes without relying on gene annotations to define the boundaries of homologous regions. To solve this problem, we have developed a graph-based algorithm to partition a set of bacterial genomes into Maximal Homologous Groups of sequences (MHGs) where each MHG is a maximal set of maximum-length sequences which are homologous across the entire sequence alignment. We applied our algorithm to a dataset of 19 Enterobacteriaceae species and found that MHGs cover much greater proportions of genomes than markers and, relatedly, are less biased in terms of the functions of the genes they cover. We zoomed in on the correlation between each individual marker and their overlapping MHGs, and show that few phylogenetic splits supported by the markers are supported by the MHGs while many marker-supported splits are contradicted by the MHGs. A comparison of the species tree inferred from marker genes with the species tree inferred from MHGs suggests that the increased bias and lack of genome coverage by markers causes incorrect inferences as to the overall relationship between bacterial taxa., Competing Interests: The authors have declared that no competing interests exist.

Coalescent methods are proven and powerful tools for population genetics, phylogenetics, epidemiology, and other fields. A promising avenue for the analysis of large genomic alignments, which are increasingly common, is coalescent hidden Markov model (coalHMM) methods, but these methods have lacked general usability and flexibility. We introduce a novel method for automatically learning a coalHMM and inferring the posterior distributions of evolutionary parameters using black-box variational inference, with the transition rates between local genealogies derived empirically by simulation. This derivation enables our method to work directly with three or four taxa and through a divide-and-conquer approach with more taxa. Using a simulated data set resembling a human-chimp-gorilla scenario, we show that our method has comparable or better accuracy to previous coalHMM methods. Both species divergence times and population sizes were accurately inferred. The method also infers local genealogies, and we report on their accuracy. Furthermore, we discuss a potential direction for scaling the method to larger data sets through a divide-and-conquer approach. This accuracy means our method is useful now, and by deriving transition rates by simulation, it is flexible enough to enable future implementations of various population models., (© 2021 Liu et al.; Published by Cold Spring Harbor Laboratory Press.)

Trait evolution among a set of species-a central theme in evolutionary biology-has long been understood and analyzed with respect to a species tree. However, the field of phylogenomics, which has been propelled by advances in sequencing technologies, has ushered in the era of species/gene tree incongruence and, consequently, a more nuanced understanding of trait evolution. For a trait whose states are incongruent with the branching patterns in the species tree, the same state could have arisen independently in different species (homoplasy) or followed the branching patterns of gene trees, incongruent with the species tree (hemiplasy). Another evolutionary process whose extent and significance are better revealed by phylogenomic studies is gene flow between different species. In this work, we present a phylogenomic method for assessing the role of hybridization and introgression in the evolution of polymorphic or monomorphic binary traits. We apply the method to simulated evolutionary scenarios to demonstrate the interplay between the parameters of the evolutionary history and the role of introgression in a binary trait's evolution (which we call xenoplasy). Very importantly, we demonstrate, including on a biological data set, that inferring a species tree and using it for trait evolution analysis in the presence of gene flow could lead to misleading hypotheses about trait evolution., Competing Interests: The authors have declared that no competing interests exist.

The COVID-19 pandemic has sparked an urgent need to uncover the underlying biology of this devastating disease. Though RNA viruses mutate more rapidly than DNA viruses, there are a relatively small number of single nucleotide polymorphisms (SNPs) that differentiate the main SARS-CoV-2 lineages that have spread throughout the world. In this study, we investigated 129 RNA-seq data sets and 6928 consensus genomes to contrast the intra-host and inter-host diversity of SARS-CoV-2. Our analyses yielded three major observations. First, the mutational profile of SARS-CoV-2 highlights intra-host single nucleotide variant (iSNV) and SNP similarity, albeit with differences in C > U changes. Second, iSNV and SNP patterns in SARS-CoV-2 are more similar to MERS-CoV than SARS-CoV-1. Third, a significant fraction of insertions and deletions contribute to the genetic diversity of SARS-CoV-2. Altogether, our findings provide insight into SARS-CoV-2 genomic diversity, inform the design of detection tests, and highlight the potential of iSNVs for tracking the transmission of SARS-CoV-2., (© 2021 Sapoval et al.; Published by Cold Spring Harbor Laboratory Press.)

Species tree inference from multilocus data has emerged as a powerful paradigm in the postgenomic era, both in terms of the accuracy of the species tree it produces as well as in terms of elucidating the processes that shaped the evolutionary history. Bayesian methods for species tree inference are desirable in this area as they have been shown not only to yield accurate estimates, but also to naturally provide measures of confidence in those estimates. However, the heavy computational requirements of Bayesian inference have limited the applicability of such methods to very small data sets. In this article, we show that the computational efficiency of Bayesian inference under the multispecies coalescent can be improved in practice by restricting the space of the gene trees explored during the random walk, without sacrificing accuracy as measured by various metrics. The idea is to first infer constraints on the trees of the individual loci in the form of unresolved gene trees, and then to restrict the sampler to consider only resolutions of the constrained trees. We demonstrate the improvements gained by such an approach on both simulated and biological data., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Lateral root (LR) proliferation is a major determinant of soil nutrient uptake. How resource allocation controls the extent of LR growth remains unresolved. We used genetic, physiological, transcriptomic, and grafting approaches to define a role for C-TERMINALLY ENCODED PEPTIDE RECEPTOR 1 (CEPR1) in controlling sucrose-dependent LR growth. CEPR1 inhibited LR growth in response to applied sucrose, other metabolizable sugars, and elevated light intensity. Pathways through CEPR1 restricted LR growth by reducing LR meristem size and the length of mature LR cells. RNA-sequencing of wild-type (WT) and cepr1-1 roots with or without sucrose treatment revealed an intersection of CEP-CEPR1 signalling with the sucrose transcriptional response. Sucrose up-regulated several CEP genes, supporting a specific role for CEP-CEPR1 in the response to sucrose. Moreover, genes with basally perturbed expression in cepr1-1 overlap with WT sucrose-responsive genes significantly. We found that exogenous CEP inhibited LR growth via CEPR1 by reducing LR meristem size and mature cell length. This result is consistent with CEP-CEPR1 acting to curtail the extent of sucrose-dependent LR growth. Reciprocal grafting indicates that LR growth inhibition requires CEPR1 in both the roots and shoots. Our results reveal a new role for CEP-CEPR1 signalling in controlling LR growth in response to sucrose., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

Motivation: Reticulate evolutionary histories, such as those arising in the presence of hybridization, are best modeled as phylogenetic networks. Recently developed methods allow for statistical inference of phylogenetic networks while also accounting for other processes, such as incomplete lineage sorting. However, these methods can only handle a small number of loci from a handful of genomes., Results: In this article, we introduce a novel two-step method for scalable inference of phylogenetic networks from the sequence alignments of multiple, unlinked loci. The method infers networks on subproblems and then merges them into a network on the full set of taxa. To reduce the number of trinets to infer, we formulate a Hitting Set version of the problem of finding a small number of subsets, and implement a simple heuristic to solve it. We studied their performance, in terms of both running time and accuracy, on simulated as well as on biological datasets. The two-step method accurately infers phylogenetic networks at a scale that is infeasible with existing methods. The results are a significant and promising step towards accurate, large-scale phylogenetic network inference., Availability and Implementation: We implemented the algorithms in the publicly available software package PhyloNet (https://bioinfocs.rice.edu/PhyloNet)., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press.)

Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release., Competing Interests: The authors have declared that no competing interests exist.

Reticulate species evolution, such as hybridization or introgression, is relatively common in nature. In the presence of reticulation, species relationships can be captured by a rooted phylogenetic network, and orthologous gene evolution can be modeled as bifurcating gene trees embedded in the species network. We present a Bayesian approach to jointly infer species networks and gene trees from multilocus sequence data. A novel birth-hybridization process is used as the prior for the species network, and we assume a multispecies network coalescent prior for the embedded gene trees. We verify the ability of our method to correctly sample from the posterior distribution, and thus to infer a species network, through simulations. To quantify the power of our method, we reanalyze two large data sets of genes from spruces and yeasts. For the three closely related spruces, we verify the previously suggested homoploid hybridization event in this clade; for the yeast data, we find extensive hybridization events. Our method is available within the BEAST 2 add-on SpeciesNetwork, and thus provides an extensible framework for Bayesian inference of reticulate evolution., (© The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

While methods for genetic species delimitation have noticeably improved in the last decade, this remains a work in progress. Ideally, model based approaches should be applied and considered jointly with other lines of evidence, primarily morphology and geography, in an integrative taxonomy framework. Deep phylogeographic divergences have been reported for several species of Carlia skinks, but only for some eastern taxa have species boundaries been formally tested. The present study does this and revises the taxonomy for two species from northern Australia, Carlia johnstonei and C. triacantha . We introduce an approach that is based on the recently published method StarBEAST2, which uses multilocus data to explore the support for alternative species delimitation hypotheses using Bayes Factors (BFD). We apply this method, jointly with two other multispecies coalescent methods, using an extensive (from 2,163 exons) data set along with measures of 11 morphological characters. We use this integrated approach to evaluate two new candidate species previously revealed in phylogeographic analyses of rainbow skinks (genus Carlia ) in Western Australia. The results based on BFD StarBEAST2, BFD* SNAPP and BPP genetic delimitation, together with morphology, support each of the four recently identified Carlia lineages as separate species. The BFD StarBEAST2 approach yielded results highly congruent with those from BFD* SNAPP and BPP. This supports use of the robust multilocus multispecies coalescent StarBEAST2 method for species delimitation, which does not require a priori resolved species or gene trees. Compared to the situation in C. triacantha , morphological divergence was greater between the two lineages within Kimberley endemic C. johnstonei , which also had deeper divergent histories. This congruence supports recognition of two species within C. johnstonei . Nevertheless, the combined evidence also supports recognition of two taxa within the more widespread C. triacantha . With this work, we describe two new species, Carlia insularis sp. nov and Carlia isostriacantha sp. nov. in the northwest of Australia. This contributes to increasing recognition that this region of tropical Australia has a rich and unique fauna., Competing Interests: The authors declare there are no competing interests.

In this article, we describe the presence of genes encoding close homologues of an endogenous plant peptide, rapid alkalinization factor (RALF), within the genomes of 26 species of phytopathogenic fungi. Members of the RALF family are key growth factors in plants, and the sequence of the RALF active region is well conserved between plant and fungal proteins. RALF1-like sequences were observed in most cases; however, RALF27-like sequences were present in the Sphaerulina musiva and Septoria populicola genomes. These two species are pathogens of poplar and, interestingly, the closest relative to their respective RALF genes is a poplar RALF27-like sequence. RALF peptides control cellular expansion during plant development, but were originally defined on the basis of their ability to induce rapid alkalinization in tobacco cell cultures. To test whether the fungal RALF peptides were biologically active in plants, we synthesized RALF peptides corresponding to those encoded by two sequenced genomes of the tomato pathogen Fusarium oxysporum f. sp. lycopersici. One of these peptides inhibited the growth of tomato seedlings and elicited responses in tomato and Nicotiana benthamiana typical of endogenous plant RALF peptides (reactive oxygen species burst, induced alkalinization and mitogen-activated protein kinase activation). Gene expression analysis confirmed that a RALF-encoding gene in F. oxysporum f. sp. lycopersici was expressed during infection on tomato. However, a subsequent reverse genetics approach revealed that the RALF peptide was not required by F. oxysporum f. sp. lycopersici for infection on tomato roots. This study has demonstrated the presence of functionally active RALF peptides encoded within phytopathogens that harbour an as yet undetermined role in plant-pathogen interactions., (© 2016 BSPP AND JOHN WILEY & SONS LTD.)

Fully Bayesian multispecies coalescent (MSC) methods like *BEAST estimate species trees from multiple sequence alignments. Today thousands of genes can be sequenced for a given study, but using that many genes with *BEAST is intractably slow. An alternative is to use heuristic methods which compromise accuracy or completeness in return for speed. A common heuristic is concatenation, which assumes that the evolutionary history of each gene tree is identical to the species tree. This is an inconsistent estimator of species tree topology, a worse estimator of divergence times, and induces spurious substitution rate variation when incomplete lineage sorting is present. Another class of heuristics directly motivated by the MSC avoids many of the pitfalls of concatenation but cannot be used to estimate divergence times. To enable fuller use of available data and more accurate inference of species tree topologies, divergence times, and substitution rates, we have developed a new version of *BEAST called StarBEAST2. To improve convergence rates we add analytical integration of population sizes, novel MCMC operators and other optimizations. Computational performance improved by 13.5× and 13.8× respectively when analyzing two empirical data sets, and an average of 33.1× across 30 simulated data sets. To enable accurate estimates of per-species substitution rates, we introduce species tree relaxed clocks, and show that StarBEAST2 is a more powerful and robust estimator of rate variation than concatenation. StarBEAST2 is available through the BEAUTi package manager in BEAST 2.4 and above., (© The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Background: Small, secreted signaling peptides work in parallel with phytohormones to control important aspects of plant growth and development. Genes from the C-TERMINALLY ENCODED PEPTIDE (CEP) family produce such peptides which negatively regulate plant growth, especially under stress, and affect other important developmental processes. To illuminate how the CEP gene family has evolved within the plant kingdom, including its emergence, diversification and variation between lineages, a comprehensive survey was undertaken to identify and characterize CEP genes in 106 plant genomes., Results: Using a motif-based system developed for this study to identify canonical CEP peptide domains, a total of 916 CEP genes and 1,223 CEP domains were found in angiosperms and for the first time in gymnosperms. This defines a narrow band for the emergence of CEP genes in plants, from the divergence of lycophytes to the angiosperm/gymnosperm split. Both CEP genes and domains were found to have diversified in angiosperms, particularly in the Poaceae and Solanaceae plant families. Multispecies orthologous relationships were determined for 22% of identified CEP genes, and further analysis of those groups found selective constraints upon residues within the CEP peptide and within the previously little-characterized variable region. An examination of public Oryza sativa RNA-Seq datasets revealed an expression pattern that links OsCEP5 and OsCEP6 to panicle development and flowering, and CEP gene trees reveal these emerged from a duplication event associated with the Poaceae plant family., Conclusions: The characterization of the plant-family specific CEP genes OsCEP5 and OsCEP6, the association of CEP genes with angiosperm-specific development processes like panicle development, and the diversification of CEP genes in angiosperms provides further support for the hypothesis that CEP genes have been integral to the evolution of novel traits within the angiosperm lineage. Beyond these findings, the comprehensive set of CEP genes and their properties reported here will be a resource for future research on CEP genes and peptides.

The role of MtCEP1, a member of the CEP (C-terminally encoded peptide) signaling peptide family, was examined in Medicago truncatula root development. MtCEP1 was expressed in root tips, vascular tissue, and young lateral organs, and was up-regulated by low nitrogen levels and, independently, by elevated CO2. Overexpressing MtCEP1 or applying MtCEP1 peptide to roots elicited developmental phenotypes: inhibition of lateral root formation, enhancement of nodulation, and the induction of periodic circumferential root swellings, which arose from cortical, epidermal, and pericycle cell divisions and featured an additional cortical cell layer. MtCEP peptide addition to other legume species induced similar phenotypes. The enhancement of nodulation by MtCEP1 is partially tolerant to high nitrate, which normally strongly suppresses nodulation. These nodules develop faster, are larger, and fix more nitrogen in the absence and presence of inhibiting nitrate levels. At 25mM nitrate, nodules formed on pre-existing swelling sites induced by MtCEP1 overexpression. RNA interference-mediated silencing of several MtCEP genes revealed a negative correlation between transcript levels of MtCEP1 and MtCEP2 with the number of lateral roots. MtCEP1 peptide-dependent phenotypes were abolished or attenuated by altering or deleting key residues in its 15 amino acid domain. RNA-Seq analysis revealed that 89 and 116 genes were significantly up- and down-regulated, respectively, by MtCEP1 overexpression, including transcription factors WRKY, bZIP, ERF, and MYB, homologues of LOB29, SUPERROOT2, and BABY BOOM. Taken together, the data suggest that the MtCEP1 peptide modulates lateral root and nodule development in M. truncatula.

Plant root architecture is regulated by the initiation and modulation of cell division in regions containing pluripotent stem cells known as meristems. In roots, meristems are formed early in embryogenesis, in the case of the root apical meristem (RAM), and during organogenesis at the site of lateral root or, in legumes, nodule formation. Root meristems can also be generated in vitro from leaf explants cultures supplemented with auxin. microRNAs (miRNAs) have emerged as regulators of many key biological functions in plants including root development. To identify key miRNAs involved in root meristem formation in Medicago truncatula, we used deep sequencing to compare miRNA populations. Comparisons were made between: (1) the root tip (RT), containing the RAM and the elongation zone (EZ) tissue and (2) root forming callus (RFC) and non-root forming callus (NRFC). We identified 83 previously reported miRNAs, 24 new to M. truncatula, in 44 families. For the first time in M. truncatula, members of conserved miRNA families miR165, miR181 and miR397 were found. Bioinformatic analysis identified 38 potential novel miRNAs. Selected miRNAs and targets were validated using Taqman miRNA assays and 5' RACE. Many miRNAs were differentially expressed between tissues, particularly RFC and NRFC. Target prediction revealed a number of miRNAs to target genes previously shown to be differentially expressed between RT and EZ or RFC and NRFC and important in root development. Additionally, we predict the miRNA/target relationships for miR397 and miR160 to be conserved in M. truncatula. Amongst the predictions, were AUXIN RESPONSE FACTOR 10, targeted by miR160 and a LACCASE-like gene, targeted by miR397, both are miRNA/target pairings conserved in other species.