Population genetic clustering methods are widely used to detect hybridization events between closely related populations within species, as well as between deeply diverged lineages across phylogenetic time scales. Their strengths and limitations in the latter cases, however, remain poorly explored. This study presents a systematic evaluation of the performance of the most popular population clustering method, STRUCTURE, under a variety of cross-species hybridization scenarios, including hybrid speciation, as well as introgression involving ghost (i.e., extinct or unsampled) lineages or otherwise. Our simulations demonstrate that STRUCTURE performs well in identifying hybrids and their parental donors when admixture happens very recently between sampled extant lineages, but generally fails to detect signals of admixture when hybridization occurs in deep time or when gene flow stems from ghost lineages. We find that symmetrical parental contribution in cases of hybrid speciation will often be revealed as extremely asymmetrical in STRUCTURE, especially when the admixture event occurred a long time ago. Our results suggest that population genetic clustering methods may be inefficient for detecting ancient or ghost admixtures, which may partly explain why ghost introgression has escaped the attention of evolutionary biologists until recently.

Multimedia representations of phylogenies can only broaden the audience experiencing results of the field. I developed a bioinformatics pipeline for representation of phylogenies as audio and video, optimized to enable conversion of an extremely wide range of phylogeny structures (from tens to tens of thousands of terminals). I also compiled and standardized a set of contemporary phylogenetics results comprising only ones that were provisioned in analyzable form supplementary to a publication and that had been assigned a Creative Commons license by the authors. Fifty-six such phylogenies were audified and the resultant media files were made easily accessible. This work provisionally addresses a problematic gap in public information on phylogenetics for nonvisual modalities, and exemplifies how evolutionary biologists might better respond to obligations in widening participation.

Large phylogenies derived from publicly available genetic sequences are becoming a popular and indispensable tool in addressing core questions in ecology and evolution, as well as in tackling challenging conservation issues. Optimizing taxonomic coverage and data quality is essential for improving the precision and reliability of phylogenetic reconstructions and evolutionary inferences. Here we present PyNCBIminer, a user-friendly software that automates the assembly of large DNA data sets from GenBank for phylogenetic reconstruction using the supermatrix method. PyNCBIminer uses the iterative BLAST procedure to retrieve genetic sequences accurately and efficiently from GenBank. The state-of-the-art strategies also serve to improve taxa coverage and the quality of target DNA markers. PyNCBIminer is designed to efficiently handle large data sets, but it is also suitable for medium and small data sets. It is open source and freely available at GitHub (https://github.com/Xiaoting-Xu/PyNCBIminer) and Gitee (https://gitee.com/xiaotingxu/PyNCBIminer). Its utility and performance are demonstrated through the assembly of phylogenetic data sets encompassing several genetic markers of varying sizes for the angiosperm order Dipsacales. PyNCBIminer holds an advantage over similar programs in that it performs the majority of computations on the NCBI server, eliminating the need for users to build and maintain large local databases and reducing the demands on their computers. In addition, it integrates other commonly used phylogenetic analysis software, providing users from various backgrounds with convenient options for retrieving and assembling GenBank sequence data, along with flexible features that allow for user-defined parameters and strategies.

Assessing sampling biases caused by nonrandom specimen collecting is crucial in systematics, biogeography, and conservation. Nevertheless, research on the collecting biases of vascular plants in China remains limited. Here, we investigated the collecting status of gymnosperms in China using 48 673 herbarium specimens representing 180 Chinese gymnosperm species. The spatial and temporal patterns, collector and determiner biases, and phylogenetic and taxon biases were studied to comprehend the collecting bias of gymnosperms in China during 1900–2021. Meanwhile, we assessed the inventory completeness of gymnosperms to identify collecting hotspots and coldspots. The results showed that gymnosperms predominantly inhabit mountainous areas in China. The historical collecting of gymnosperms in China can be divided into two distinct stages with four peaks of collecting activities. The distribution of collected or identified specimens per individual displays significant skewness, and the collecting of gymnosperms has the issues of overcollecting or undersampling. Inventory completeness remains deficient, and collecting intensity is weak in both northern China and the range of 22°–25° N in the southern region (i.e., central Yunnan, southern Guangxi, and eastern Guangdong). Additionally, observation of the spatial distribution reveals both hotspots and coldspots scattered across mountain regions, lacking clustering. This study revealed a highly polarized collecting pattern for gymnosperms in China. Consequently, we recommend optimizing collecting measures, targeted specimen collecting, and continuous dynamic monitoring of gymnosperms in future collecting efforts. These recommendations hold relevance not only for gymnosperms in China but also for other land plants worldwide.

Since the concept of the tree of life was introduced about 150 years ago, a considerable fraction of the scientific community has focused its efforts on its reconstruction, with remarkable progress during the last two decades with the advent of DNA sequences. However, the assemblage of a comprehensive and explorable tree of life has been a difficult task to achieve due to two main obstacles: i) information is scattered into several individual sources and ii) practical visualization tools for exploring large trees are needed. To overcome both challenges, we aimed to synthesize a family-level tree of life by compiling over 1400 published phylogenetic studies, choosing the source trees that represent the best phylogenetic hypotheses to date based on a set of objective criteria. Moreover, we dated the tree by employing over 550 secondary calibrations using publicly available sequences for more than 5000 taxa, and by incorporating age ranges from the fossil record for over 2800 taxa. Additionally, we developed a mobile app for smartphones to facilitate the visualization and exploration of the resulting tree. Interactive features include exploration by the zooming and panning gestures of touch screens, collapsing branches, visualizing specific clades as subtrees, a search engine, and a timescale to determine extinction and divergence dates, among others. Small illustrations of organisms are displayed at the terminals to better visualize the morphological diversity of life. Our assembled tree currently includes over 7000 families, and its content will be expanded through regular updates to cover all life on earth at family level.

Obtaining reliable species identification of the legume genus Caragana has been challenging. Until now, species identification was mostly carried out utilizing diagnostic morphological characteristics, in addition to some successful applications of secondary chemical compounds. This study was designed to establish a DNA barcoding protocol enabling unambiguous identification of 238 accessions belonging to 67 species of Caragana. The performance of four DNA barcoding regions nrITS, trnH-psbA, matK, and rbcL was explored using three analytical approaches, Pairwise Genetic Distance, Sequence Similarity and Phylogenetic Tree method. The chloroplast regions rbcL and matK showed lower discriminatory power compared with the nuclear region internal transcribed spacer (ITS) and the chloroplast region trnH-psbA. The nrITS outperformed the other regions in the resolution rate. The present study brings forth an efficient barcode locus for Caragana. A barcode based either on a single-locus nrITS or the combination of nrITS and trnH-psbA was found to be most suitable for species discrimination with distinctive barcoding gaps. An attempt has also been made to resolve taxonomic issues in the Caragana opulens complex. DNA barcoding tools when complemented with alpha taxonomic evidence can aid in solving complex systematic problems, especially when taxa are characterized by overlapping traits, such as species belonging to the Fabaceae family.

Identification of Fabaceae family plants traditionally relies on either morphological traits or DNA barcoding, each with limitations in accuracy and efficiency. Deep learning has emerged as a promising tool for integrating multiple data sources, but its full potential remains underexplored. This study aimed to utilize a deep learning model that integrates morphological and molecular data for species identification within the Fabaceae family, bridging the gap between the two methods of identification. The research involved four main phases: (i) data collection; (ii) data preprocessing; (iii) training and testing the model; and (iv) analysis of results. The data comprised DNA barcode sequences retrieved from the BOLD database, and images were collected from different websites. The model was trained for identification on the genera and species levels, with two different barcodes: ITS2 and matK+rbcL. Only species with four available copies of ITS2, matK, and rbcL sequences were selected to ensure consistent input across samples. The final data set included seven genera and 21 species. While the model achieved high accuracy during training, test accuracy remained low (14%–19%), indicating overfitting, likely due to the limited data set size. However, the model demonstrated the ability to evaluate barcode discrimination across genera. Specifically, it highlighted ITS2 and matK+rbcL as having varying levels of effectiveness depending on the genus. These findings introduce a new application for deep learning in plant systematics not only for species identification but also for evaluating barcodes. This approach could help reduce the reliance on trial-and-error in barcode selection and enhance the efficiency of molecular taxonomy.