Systematic Biology最新文献

Advances in genomics have greatly enhanced our understanding of mountain biodiversity, providing new insights into the complex and dynamic mechanisms that drive the formation of mountain biotas. These span from broad biogeographic patterns to population dynamics and adaptations to these environments. However, significant challenges remain in integrating large-scale and fine-scale findings to develop a comprehensive understanding of mountain biodiversity. One significant challenge is the lack of genomic data, particularly in historically understudied arid regions where reptiles are a particularly diverse vertebrate group. In the present study, we assembled a de novo genome-wide SNP dataset for the complete endemic reptile fauna of a mountain range (19 described species with more than 600 specimens sequenced), and integrated state-of-the-art biogeographic analyses at the population, species, and community level. Thus, we provide a holistic integration of how a whole endemic reptile community has originated, diversified and dispersed through a mountain system. Our results show that reptiles independently colonized the Hajar Mountains of southeastern Arabia 11 times. After colonization, species delimitation methods suggest high levels of within-mountain diversification, supporting up to 49 deep lineages. This diversity is strongly structured following local topography, with the highest peaks acting as a broad barrier to gene flow among the entire community. Interestingly, orogenic events do not seem key drivers of the biogeographic history of reptiles in this system. Instead, past climatic events seem to have had a major role in this community assemblage. We observe an increase of vicariant events from Late Pliocene onwards, coinciding with an unstable climatic period of rapid shifts between hyper-arid and semiarid conditions that led to the ongoing desertification of Arabia. We conclude that paleoclimate, and particularly extreme aridification, acted as a main driver of diversification in arid mountain systems which is tangled with the generation of highly adapted endemicity. Overall, our study does not only provide a valuable contribution to understanding the evolution of mountain biodiversity, but also offers a flexible and scalable approach that can be reproduced into any taxonomic group and at any discrete environment.

Cyanobacteria are the only prokaryotes to have evolved oxygenic photosynthesis paving the way for complex life. Studying the evolution and ecological niche of cyanobacteria and their ancestors is crucial for understanding the intricate dynamics of biosphere evolution. These organisms frequently deal with environmental stressors such as salinity and drought, and they employ compatible solutes as a mechanism to cope with these challenges. Compatible solutes are small molecules that help maintain cellular osmotic balance in high salinity environments, such as marine waters. Their production plays a crucial role in salt tolerance, which, in turn, influences habitat preference. Among the five known compatible solutes produced by cyanobacteria (sucrose, trehalose, glucosylglycerol, glucosylglycerate, and glycine betaine), their synthesis varies between individual strains. In this study, we work in a Bayesian stochastic mapping framework, integrating multiple sources of information about compatible solute biosynthesis in order to predict the ancestral habitat preference of Cyanobacteria. Through extensive model selection analyses and statistical tests for correlation, we identify glucosylglycerol and glucosylglycerate as the most significantly correlated with habitat preference, while trehalose exhibits the weakest correlation. Additionally, glucosylglycerol, glucosylglycerate, and glycine betaine show high loss/gain rate ratios, indicating their potential role in adaptability, while sucrose and trehalose are less likely to be lost due to their additional cellular functions. Contrary to previous findings, our analyses predict that the last common ancestor of Cyanobacteria (living at around 3180 Ma) had a 97% probability of a high salinity habitat preference and was likely able to synthesise glucosylglycerol and glucosylglycerate. Nevertheless, cyanobacteria likely colonized low-salinity environments shortly after their origin, with an 89% probability of the first cyanobacterium with low-salinity habitat preference arising prior to the Great Oxygenation Event (2460 Ma). Stochastic mapping analyses provide evidence of cyanobacteria inhabiting early marine habitats, aiding in the interpretation of the geological record. Our age estimate of ~2590 Ma for the divergence of two major cyanobacterial clades (Macro- and Microcyanobacteria) suggests that these were likely significant contributors to primary productivity in marine habitats in the lead-up to the Great Oxygenation Event, and thus played a pivotal role in triggering the sudden increase in atmospheric oxygen.

Models have always been central to inferring molecular evolution and to reconstructing phylogenetic trees. Their use typically involves the development of a mechanistic framework reflecting our understanding of the underlying biological processes, such as nucleotide substitu- tions, and the estimation of model parameters by maximum likelihood or Bayesian inference. However, deriving and optimizing the likelihood of the data is not always possible under complex evolutionary scenarios or even tractable for large datasets, often leading to unrealistic simplifying assumptions in the fitted models. To overcome this issue, we coupled stochastic simulations of genome evolution with a new supervised deep learning model to infer key parameters of molecular evolution. Our model is designed to directly analyze multiple sequence alignments and estimate per-site evolutionary rates and divergence, without requiring a known phylogenetic tree. The accuracy of our predictions matched that of likelihood-based phylogenetic inference, when rate heterogeneity followed a simple gamma distribution, but it strongly exceeded it under more complex patterns of rate variation, such as codon models. Our approach is highly scalable and can be efficiently applied to genomic data, as we showed on a dataset of 26 million nucleotides from the clownfish clade. Our simulations also showed that the integration of per-site rates obtained by deep learning within a Bayesian framework led to significantly more accu- rate phylogenetic inference, particularly with respect to the estimated branch lengths. We thus propose that future advancements in phylogenetic analysis will benefit from a semi-supervised learning approach that combines deep-learning estimation of substitution rates, which allows for more flexible models of rate variation, and probabilistic inference of the phylogenetic tree, which guarantees interpretability and a rigorous assessment of statistical support.

The role of interspecific hybridization has recently seen increasing attention, especially in the context of diversification dynamics. Genomic research has now made it abundantly clear that both hybridization and introgression - the exchange of genetic material through hybridization and backcrossing - are far more common than previously thought. Besides cases of ongoing or recent genetic exchange between taxa, an increasing number of studies report "ancient introgression" - referring to results of hybridization that took place in the distant past. However, it is not clear whether commonly used methods for the detection of introgression are applicable to such old systems, given that most of these methods were originally developed for analyses at the level of populations and recently diverged species, affected by recent or ongoing genetic exchange. In particular, the assumption of constant evolutionary rates, which is implicit in many commonly used approaches, is more likely to be violated as evolutionary divergence increases. To test the limitations of introgression detection methods when being applied to old systems, we simulated thousands of genomic datasets under a wide range of settings, with varying degrees of among-species rate variation and introgression. Using these simulated datasets, we showed that some commonly applied statistical methods, including the D-statistic and certain tests based on sets of local phylogenetic trees, can produce false-positive signals of introgression between divergent taxa that have different rates of evolution. These misleading signals are caused by the presence of homoplasies occurring at different rates in different lineages. To distinguish between the patterns caused by rate variation and genuine introgression, we developed a new test that is based on the expected clustering of introgressed sites along the genome, and implemented this test in the program Dsuite.

The nine-banded armadillo (Dasypus novemcinctus) is the most widespread xenarthran species across the Americas. Recent studies have suggested it is composed of four morphologically and genetically distinct lineages of uncertain taxonomic status. To address this issue, we used a museomic approach to sequence 80 complete mitogenomes and capture 997 nuclear loci for 71 Dasypus individuals sampled across the entire distribution. We carefully cleaned up potential genotyping errors and cross contaminations that could blur species boundaries by mimicking gene flow. Our results unambiguously support four distinct lineages within the D. novemcinctus complex. We found cases of mito-nuclear phylogenetic discordance but only limited contemporary gene flow confined to the margins of the lineage distributions. All available evidence including the restricted gene flow, phylogenetic reconstructions based on both mitogenomes and nuclear loci, and phylogenetic delimitation methods consistently supported the four lineages within D. novemcinctus as four distinct species. Comparable genetic differentiation values to other recognized Dasypus species further reinforced their status as valid species. Considering congruent morphological results from previous studies, we provide an integrative taxonomic view to recognise four species within the D. novemcinctus complex: D. novemcinctus, D. fenestratus, D. mexicanus, and D. guianensis sp. nov., a new species endemic of the Guiana Shield that we describe here. The two available individuals of D. mazzai and D. sabanicola were consistently nested within D. novemcinctus lineage and their status remains to be assessed. The present work offers a case study illustrating the power of museomics to reveal cryptic species diversity within a widely distributed and emblematic species of mammals.

A fundamental objective of evolutionary biology is to understand the origin of independently evolving species. Phylogenetic studies of species radiations rarely are able to document ongoing speciation; instead, modes of speciation, entailing geographic separation and/or ecological differentiation, are posited retrospectively. The Oreinotinus clade of Viburnum has radiated recently from north to south through the cloud forests of Mexico and Central America to the Central Andes. Our analyses support a hypothesis of incipient speciation in Oreinotinus at the southern edge of its geographic range, from central Peru to northern Argentina. Although several species and infraspecific taxa of have been recognized in this area, multiple lines of evidence and analytical approaches (including analyses of phylogenetic relationships, genetic structure, leaf morphology, and climatic envelopes) favor the recognition of just a single species, V. seemenii. We show that what has previously been recognized as V. seemenii f. minor has recently occupied the drier Tucuman-Bolivian forest region from Samaipata in Bolivia to Salta in northern Argentina. Plants in these populations form a well-supported clade with a distinctive genetic signature and they have evolved smaller, narrower leaves. We interpret this as the beginning of a within-species divergence process that has elsewhere in the neotropics resulted repeatedly in Viburnum species with a particular set of leaf ecomorphs. Specifically, the southern populations are in the process of evolving the small, glabrous, and entire leaf ecomorph that has evolved in four other montane areas of endemism. As predicted based on our studies of leaf ecomorphs in Chiapas, Mexico, these southern populations experience generally drier conditions, with large diurnal temperature fluctuations. In a central portion of the range of V. seemenii, characterized by wetter climatic conditions, we also document what may be the initial differentiation of the leaf ecomorph with larger, pubescent, and toothy leaves. The emergence of these ecomorphs thus appears to be driven by adaptation to subtly different climatic conditions in separate geographic regions, as opposed to parapatric differentiation along elevational gradients as suggested by Viburnum species distributions in other parts of the neotropics.

The popularity of relaxed clock Bayesian inference of clade origin timings has generated several recent publications with focal results considerably older than the fossils of the clades in question. Here, we critically examine two such clades: the animals (with a focus on the bilaterians) and the mammals (with a focus on the placentals). Each example displays a set of characteristic pathologies which, although much commented on, are rarely corrected for. We conclude that in neither case does the molecular clock analysis provide any evidence for an origin of the clade deeper than what is suggested by the fossil record. In addition, both these clades have other features (including, in the case of the placental mammals, proximity to a large mass extinction) that allow us to generate precise expectations of the timings of their origins. Thus, in these instances, the fossil record can provide a powerful test of molecular clock methodology, and why it goes astray, and we have every reason to think these problems are general. [Cambrian explosion; mammalian evolution; molecular clocks.].

In recent years, the study of hybridization and introgression has made significant progress, with ghost introgression-the transfer of genetic material from extinct or unsampled lineages to extant species-emerging as a key area for research. Accurately identifying ghost introgression, however, presents a challenge. To address this issue, we focused on simple cases involving 3 species with a known phylogenetic tree. Using mathematical analyses and simulations, we evaluated the performance of popular phylogenetic methods, including HyDe and PhyloNet/MPL, and the full-likelihood method, Bayesian Phylogenetics and Phylogeography (BPP), in detecting ghost introgression. Our findings suggest that heuristic approaches relying on site-pattern counts or gene-tree topologies struggle to differentiate ghost introgression from introgression between sampled non-sister species, frequently leading to incorrect identification of donor and recipient species. The full-likelihood method BPP uses multilocus sequence alignments directly-hence taking into account both gene-tree topologies and branch lengths, by contrast, is capable of detecting ghost introgression in phylogenomic datasets. We analyzed a real-world phylogenomic dataset of 14 species of Jaltomata (Solanaceae) to showcase the potential of full-likelihood methods for accurate inference of introgression.