Systematic Biology最新文献

Time-dependent birth-death sampling models have been used in numerous studies to infer past evolutionary dynamics in different biological contexts, for example,  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 fossilized birth-death (FBD) models are identifiable. This subclass of models makes more realistic assumptions about the fossilization 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. The 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 FBD 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.

Despite significant advances in phylogenetics over the past decades, the deep relationships within Bivalvia (phylum Mollusca) remain inconclusive. Previous efforts based on morphology or several genes have failed to resolve many key nodes in the phylogeny of Bivalvia. Advances have been made recently using transcriptome data, but the phylogenetic relationships within Bivalvia historically lacked consensus, especially within Pteriomorphia and Imparidentia. Here, we inferred the relationships of key lineages within Bivalvia using matrices generated from specifically designed ultraconserved elements (UCEs) with 16 available genomic resources and 85 newly sequenced specimens from 55 families. Our new probes (Bivalve UCE 2k v.1) for target sequencing captured an average of 849 UCEs with 1085 bp in mean length from in vitro experiments. Our results introduced novel schemes from 6 major clades (Protobranchina, Pteriomorphia, Palaeoheterodonta, Archiheterodonta, Anomalodesmata, and Imparidentia), though some inner nodes were poorly resolved, such as paraphyletic Heterodonta in some topologies potentially due to insufficient taxon sampling. The resolution increased when analyzing specific matrices for Pteriomorphia and Imparidentia. We recovered 3 Pteriomorphia topologies different from previously published trees, with the strongest support for ((Ostreida + (Arcida + Mytilida)) + (Pectinida + (Limida + Pectinida))). Limida were nested within Pectinida, warranting further studies. For Imparidentia, our results strongly supported the new hypothesis of (Galeommatida + (Adapedonta + Cardiida)), while the possible non-monophyly of Lucinida was inferred but poorly supported. Overall, our results provide important insights into the phylogeny of Bivalvia and show that target enrichment sequencing of UCEs can be broadly applied to study both deep and shallow phylogenetic relationships.

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 of 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 data sets as the previous study using 2 additional, more complex models of sequence evolution that each include 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.

Biology has become a highly mathematical discipline in which probabilistic models play a central role. As a result, research in the biological sciences is now dependent on computational tools capable of carrying out complex analyses. These tools must be validated before they can be used, but what is understood as validation varies widely among methodological contributions. This may be a consequence of the still embryonic stage of the literature on statistical software validation for computational biology. Our manuscript aims to advance this literature. Here, we describe, illustrate, and introduce new good practices for assessing the correctness of a model implementation with an emphasis on Bayesian methods. We also introduce a suite of functionalities for automating validation protocols. It is our hope that the guidelines presented here help sharpen the focus of discussions on (as well as elevate) expected standards of statistical software for biology.

Evolutionary changes in geographic distribution and larval host plants may promote the rapid diversification of montane insects, but this scenario has been rarely investigated. We studied the rapid radiation of the butterfly genus Colias, which has diversified in mountain ecosystems in Eurasia, Africa, and the Americas. Based on a data set of 150 nuclear protein-coding genetic loci and mitochondrial genomes, we constructed a time-calibrated phylogenetic tree of Colias species with broad taxon sampling. We then inferred their ancestral geographic ranges, historical diversification rates, and the evolution of host use. We found that the most recent common ancestor of Colias was likely geographically widespread and originated ~3.5 Ma. The group subsequently diversified in different regions across the world, often in tandem with geographic expansion events. No aspect of elevation was found to have a direct effect on diversification. The genus underwent a burst of diversification soon after the divergence of the Neotropical lineage, followed by an exponential decline in diversification rate toward the present. The ancestral host repertoire included the legume genera Astragalus and Trifolium but later expanded to include a wide range of Fabaceae genera and plants in more distantly related families, punctuated with periods of host range expansion and contraction. We suggest that the widespread distribution of the ancestor of all extant Colias lineages set the stage for diversification by isolation of populations that locally adapted to the various different environments they encountered, including different host plants. In this scenario, elevation is not the main driver but might have accelerated diversification by isolating populations.