DNA barcoding confirms the validity of Anthidium melanopygum Friese, 1917 stat. nov. (Hymenoptera: Megachilidae) as a distinct species of Western Asia

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Abstract

Heinrich Friese described Anthidium spiniventris [sic] from Palestine in 1899, and A. melanopygum as a “variety” of it from Turkey in 1917. While A. melanopygum was subsequently recognized as a subspecies of A. spiniventre, a morphological examination of new material of both taxa suggests that these taxa represent distinct species. This was also confirmed by genetic barcoding of the mitochondrial cytochrome c oxidase subunit 1 (COI) gene, which revealed the two taxa form distinct clades with an average genetic distance of 5.69%, while the genetic within-group distance of these two taxa was only 0.14% for A. melanopygum and 0% for A. spiniventre. Anthidium melanopygum has a wide distribution that extends from Greece and Bulgaria in the west across Turkey and Iran to Turkmenistan in the east. By contrast, A. spiniventre has a restricted, disjunct distribution with isolated populations in the southern Levant and Iran.

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MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

Sudhir Kumar, Glen Stecher, Michael KaWah Li … (2018)

The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.

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MEGA11: Molecular Evolutionary Genetics Analysis Version 11

Koichiro Tamura, Glen Stecher, Sudhir Kumar (2021)

The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor , and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net .

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bold: The Barcode of Life Data System (http://www.barcodinglife.org)

Sujeevan Ratnasingham, Paul Hebert (2007)

The Barcode of Life Data System (bold) is an informatics workbench aiding the acquisition, storage, analysis and publication of DNA barcode records. By assembling molecular, morphological and distributional data, it bridges a traditional bioinformatics chasm. bold is freely available to any researcher with interests in DNA barcoding. By providing specialized services, it aids the assembly of records that meet the standards needed to gain BARCODE designation in the global sequence databases. Because of its web-based delivery and flexible data security model, it is also well positioned to support projects that involve broad research alliances. This paper provides a brief introduction to the key elements of bold, discusses their functional capabilities, and concludes by examining computational resources and future prospects.