Systematic Biology最新文献

The recent rapid radiation of Tillandsia subgenus Tillandsia (Bromeliaceae) provides an attractive system to study the drivers and constraints of species diversification. This species-rich Neotropical monocot clade includes predominantly epiphytic species displaying vast phenotypic diversity. Recent in-depth phylogenomic work revealed that the subgenus originated within the last 7 myr, with one major expansion from South into Central America within the last 5 myr. However, disagreements between phylogenies and lack of resolution at shallow nodes suggest that hybridization may have occurred throughout the radiation, together with frequent incomplete lineage sorting and rapid gene family evolution. We used whole-genome resequencing data to explore the evolutionary history of representative ingroup species employing both tree-based and network approaches. Our results indicate that lineage co-occurrence does not predict relatedness and confirm significant deviations from a tree-like structure, coupled with pervasive gene-tree discordance. Focusing on hybridization, ABBA-BABA and related statistics were used to infer the rates and relative timing of introgression, whereas topology weighting uncovered high heterogeneity of the phylogenetic signal along the genome. High rates of hybridization within and among subclades suggest that, contrary to previous hypotheses, the expansion of subgenus Tillandsia into Central America proceeded through several dispersal events, punctuated by episodes of diversification and gene flow. Network analysis revealed reticulation as a plausible propeller during radiation and establishment across different ecological niches. This work contributes a plant example of prevalent hybridization during rapid species diversification, supporting the hypothesis that interspecific gene flow facilitates explosive diversification.

Bayesian inference has predominantly relied on the Markov chain Monte Carlo (MCMC) algorithm for many years. However, MCMC is computationally laborious, especially for complex phylogenetic models of time trees. This bottleneck has led to the search for alternatives, such as variational Bayes, which can scale better to large data sets. In this paper, we introduce torchtree, a framework written in Python that allows developers to easily implement rich phylogenetic models and algorithms using a fixed tree topology. One can either use automatic differentiation or leverage torchtree's plug-in system to compute gradients analytically for model components for which automatic differentiation is slow. We demonstrate that the torchtree variational inference framework performs similarly to BEAST in terms of speed, and delivers promising approximation results, though accuracy varies across scenarios. Furthermore, we explore the use of the forward Kullback-Leibler (KL) divergence as an optimizing criterion for variational inference, which can handle discontinuous and nondifferentiable models. Our experiments show that inference using the forward KL divergence is frequently faster per iteration compared with the evidence lower bound (ELBO) criterion, although the ELBO-based inference may converge faster in some cases. Overall, torchtree provides a flexible and efficient framework for phylogenetic model development and inference using PyTorch.

A somewhat personal account of the development and acceptance of numerical taxonomic methods during the early years of the journal Systematic Zoology. Includes a few perspectives on the changes in taxonomy and the journal after 75 years.

Unrooted phylogenetic networks are commonly used to represent evolutionary data in the presence of incompatibilities. Although rooted phylogenetic networks offer a more explicit framework for depicting evolutionary histories involving reticulate events, they are reported less frequently, probably due to a lack of tools that are as easily applicable as those for unrooted networks. Here, we introduce PhyloFusion, a fast and user-friendly method for constructing rooted phylogenetic networks from sets of rooted phylogenetic trees. The resulting networks have the tree-child property. The algorithm accommodates trees with unresolved nodes-often resulting from the contraction of low-support edges-as well as some degree of missing taxa. We demonstrate its application to the analysis of functionally related gene groups and show that it can efficiently handle data sets comprising tens of trees or hundreds of taxa. An open source implementation of PhyloFusion is available as part of the SplitsTree app: https://www.github.com/husonlab/splitstree6. All data available here: https://doi.org/10.5061/dryad.k3j9kd5h5.