Systematic Biology最新文献

Adaptive radiation involves diversification along multiple trait axes, producing phenotypically diverse, species-rich lineages. Theory generally predicts that multi-trait evolution occurs via a "stages" model, with some traits saturating early in a lineage's history, and others diversifying later. Despite its multidimensional nature, however, we know surprisingly little about how different suites of traits evolve during adaptive radiation. Here, we investigated the rate, pattern, and timing of morphological and physiological evolution in the anole lizard adaptive radiation from the Caribbean island of Hispaniola. Rates and patterns of morphological and physiological diversity are largely unaligned, corresponding to independent selective pressures associated with structural and thermal niches. Cold tolerance evolution reflects parapatric divergence across elevation, rather than niche partitioning within communities. Heat tolerance evolution and the preferred temperature evolve more slowly than cold tolerance, reflecting behavioral buffering, particularly in edge-habitat species (a pattern associated with the Bogert effect). In contrast to the nearby island of Puerto Rico, closely related anoles on Hispaniola do not sympatrically partition thermal niche space. Instead, allopatric and parapatric separation across biogeographic and environmental boundaries serves to keep morphologically similar close relatives apart. The phenotypic diversity of this island's adaptive radiation accumulated largely as a by-product of time, with surprisingly few exceptional pulses of trait evolution. A better understanding of the processes that guide multidimensional trait evolution (and nuance therein) will prove key in determining whether the stages model should be considered a common theme of adaptive radiation.

Reticulation between incipient lineages is a common feature of diversification. We examine these phenomena in the Pisgah clade of Desmognathus salamanders from the southern Appalachian Mountains of the eastern United States. The group contains four to seven species exhibiting two discrete phenotypes, aquatic "shovel-nosed" and semi-aquatic "black-bellied" forms. These ecomorphologies are ancient and have apparently been transmitted repeatedly between lineages through introgression. Geographically proximate populations of both phenotypes exhibit admixture, and at least two black-bellied lineages have been produced via reticulations between shovel-nosed parentals, suggesting potential hybrid speciation dynamics. However, computational constraints currently limit our ability to reconstruct network radiations from gene-tree data. Available methods are limited to level-1 networks wherein reticulations do not share edges, and higher-level networks may be non-identifiable in many cases. We present a heuristic approach to recover information from higher-level networks across a range of potentially identifiable empirical scenarios, supported by theory and simulation. When extrinsic information indicates the location and direction of reticulations, our method can successfully estimate a reduced possible set of non-level-1 networks. Phylogenomic data support a single backbone topology with up to five overlapping hybrid edges in the Pisgah clade. These results suggest an unusual mechanism of ecomorphological hybrid speciation, wherein a binary threshold trait causes some hybrid populations to shift between microhabitat niches, promoting ecological divergence between sympatric hybrids and parentals. This contrasts with other well-known systems in which hybrids exhibit intermediate, novel, or transgressive phenotypes. The genetic basis of these phenotypes is unclear and further data are needed to clarify the evolutionary basis of morphological changes with ecological consequences.

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 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.

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 5 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 synthesize 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 2 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.

Accurately reconstructing the reticulate histories of polyploids remains a central challenge for understanding plant evolution. Although phylogenetic networks can provide insights into relationships among polyploid lineages, inferring networks may be hindered by the complexities of homology determination in polyploid taxa. We use simulations to show that phasing alleles from allopolyploid individuals can improve phylogenetic network inference under the multispecies coalescent by obtaining the true network with fewer loci compared with haplotype consensus sequences or sequences with heterozygous bases represented as ambiguity codes. Phased allelic data can also improve divergence time estimates for networks, which is helpful for evaluating allopolyploid speciation hypotheses and proposing mechanisms of speciation. To achieve these outcomes in empirical data, we present a novel pipeline that leverages a recently developed phasing algorithm to reliably phase alleles from polyploids. This pipeline is especially appropriate for target enrichment data, where the depth of coverage is typically high enough to phase entire loci. We provide an empirical example in the North American Dryopteris fern complex that demonstrates insights from phased data as well as the challenges of network inference. We establish that our pipeline (PATÉ: Phased Alleles from Target Enrichment data) is capable of recovering a high proportion of phased loci from both diploids and polyploids. These data may improve network estimates compared with using haplotype consensus assemblies by accurately inferring the direction of gene flow, but statistical nonidentifiability of phylogenetic networks poses a barrier to inferring the evolutionary history of reticulate complexes.

The ideal approach to Bayesian phylogenetic inference is to estimate all parameters of interest jointly in a single hierarchical model. However, this is often not feasible in practice due to the high computational cost. Instead, phylogenetic pipelines generally consist of sequential analyses, whereby a single point estimate from a given analysis is used as input for the next analysis (e.g., a single multiple sequence alignment is used to estimate a gene tree). In this framework, uncertainty is not propagated from step to step, which can lead to inaccurate or spuriously confident results. Here, we formally develop and test a sequential inference approach for Bayesian phylogenetic inference, which uses importance sampling to generate observations for the next step of an analysis pipeline from the posterior distribution produced in the previous step. Our sequential inference approach presented here not only accounts for uncertainty between analysis steps but also allows for greater flexibility in software choice (and hence model availability) and can be computationally more efficient than the traditional joint inference approach when multiple models are being tested. We show that our sequential inference approach is identical in practice to the joint inference approach only if sufficient information in the data is present (a narrow posterior distribution) and/or sufficiently many important samples are used. Conversely, we show that the common practice of using a single point estimate can be biased, for example, a single phylogeny estimate can transform an unrooted phylogeny into a time-calibrated phylogeny. We demonstrate the theory of sequential Bayesian inference using both a toy example and an empirical case study of divergence-time estimation in insects using a relaxed clock model from transcriptome data. In the empirical example, we estimate 3 posterior distributions of branch lengths from the same data (DNA character matrix with a GTR+Γ+I substitution model, an amino acid data matrix with empirical substitution models, and an amino acid data matrix with the PhyloBayes CAT-GTR model). Finally, we apply 3 different node-calibration strategies and show that divergence time estimates are affected by both the data source and underlying substitution process to estimate branch lengths as well as the node-calibration strategies. Thus, our new sequential Bayesian phylogenetic inference provides the opportunity to efficiently test different approaches for divergence time estimation, including branch-length estimation from other software.

Time-dependent birth-death sampling models have been used in numerous studies for inferring past evolutionary dynamics in different biological contexts, e.g. speciation and extinction rates in macroevolutionary studies, or effective reproductive number in epidemiological studies. These models are branching processes where lineages can bifurcate, die, or be sampled with time-dependent birth, death, and sampling rates, generating phylogenetic trees. It has been shown that in some subclasses of such models, different sets of rates can result in the same distributions of reconstructed phylogenetic trees, and therefore the rates become unidentifiable from the trees regardless of their size. Here we show that widely used time-dependent fossilised birth-death (FBD) models are identifiable. This subclass of models makes more realistic assumptions about the fossilisation process and certain infectious disease transmission processes than the unidentifiable birth-death sampling models. Namely, FBD models assume that sampled lineages stay in the process rather than being immediately removed upon sampling. Identifiability of the time-dependent FBD model justifies using statistical methods that implement this model to infer the underlying temporal diversification or epidemiological dynamics from phylogenetic trees or directly from molecular or other comparative data. We further show that the time-dependent fossilised-birth-death model with an extra parameter, the removal after sampling probability, is unidentifiable. This implies that in scenarios where we do not know how sampling affects lineages we are unable to infer this extra parameter together with birth, death, and sampling rates solely from trees.

Variation in gene tree estimates is widely observed in empirical phylogenomic data and is often assumed to be the result of biological processes. However, a recent study using tetrapod mitochondrial genomes to control for biological sources of variation due to their haploid, uniparentally inherited, and non-recombining nature found that levels of discordance among mitochondrial gene trees were comparable to those found in studies that assume only biological sources of variation. Additionally, they found that several of the models of sequence evolution chosen to infer gene trees were doing an inadequate job fitting the sequence data. These results indicated that significant amounts of gene tree discordance in empirical data may be due to poor fit of sequence evolution models, and that more complex and biologically realistic models may be needed. To test how the fit of sequence evolution models relates to gene tree discordance, we analyzed the same mitochondrial datasets as the previous study using two additional, more complex models of sequence evolution that each includes a different biologically realistic aspect of the evolutionary process: a covarion model to incorporate site-specific rate variation across lineages (heterotachy), and a partitioned model to incorporate variable evolutionary patterns by codon position. Our results show that both additional models fit the data better than the models used in the previous study, with the covarion being consistently and strongly preferred as tree size increases. However, even these more preferred models still inferred highly discordant mitochondrial gene trees, thus deepening the mystery around what we label the "Mito-Phylo Paradox" and leading us to ask whether the observed variation could, in fact, be biological in nature after all.