Haplotype network branch diversity, a new metric combining genetic and topological diversity to compare the complexity of haplotype networks.

A common way of illustrating phylogeographic results is through the use of haplotype networks. While these networks help to visualize relationships between individuals, populations, and species, evolutionary studies often only quantitatively analyze genetic diversity among haplotypes and ignore other network properties. Here, we present a new metric, haplotype network branch diversity (HBd), as an easy way to quantifiably compare haplotype network complexity. Our metric builds off the logic of combining genetic and topological diversity to estimate complexity previously used by the published metric haplotype network diversity (HNd). However, unlike HNd which uses a combination of network features to produce complexity values that cannot be defined in probabilistic terms, thereby obscuring the values' implication for a sampled population, HBd uses frequencies of haplotype classes to incorporate topological information of networks, keeping the focus on the population and providing easy-to-interpret probabilistic values for randomly sampled individuals. The goal of this study is to introduce this more intuitive metric and provide an R script that allows researchers to calculate diversity and complexity indices from haplotype networks. A group of datasets, generated manually (model dataset) and based on published data (empirical dataset), were used to illustrate the behavior of HBd and both of its terms, haplotype diversity, and a new index called branch diversity. Results followed a predicted trend in both model and empirical datasets, from low metric values in simple networks to high values in complex networks. In short, the new combined metric joins genetic and topological diversity of haplotype networks, into a single complexity value. Based on our analysis, we recommend the use of HBd, as it makes direct comparisons of network complexity straightforward and provides probabilistic values that can readily discriminate situations that are difficult to resolve with available metrics.

Phylogeny of the supertribe Nebriitae (Coleoptera, Carabidae) based on analyses of DNA sequence data.

The phylogeny of the carabid beetle supertribe Nebriitae is inferred from analyses of DNA sequence data from eight gene fragments including one nuclear ribosomal gene (28S), four nuclear-protein coding genes (CAD, topoisomerase 1, PEPCK, and wingless), and three mitochondrial gene fragments (16S + tRNA-Leu + ND1, COI ("barcode" region) and COI ("Pat/Jer" region)). Our taxon sample included 264 exemplars representing 241 species and subspecies (25% of the known nebriite fauna), 39 of 41 currently accepted genera and subgenera (all except Notiokasis and Archileistobrius), and eight outgroup taxa. Separate maximum likelihood (ML) analyses of individual genes, combined ML analyses of nuclear, nuclear protein-coding, and mitochondrial genes, and combined ML and Bayesian analyses of the eight-gene-fragment matrix resulted in a well-resolved phylogeny of the supertribe, with most nodes in the tree strongly supported. Within Nebriitae, 167 internal nodes of the tree (out of the maximum possible 255) are supported by maximum-likelihood bootstrap values of 90% or more. The tribes Notiophilini, Opisthiini, Pelophilini, and Nebriini are well supported as monophyletic but relationships among these are not well resolved. Nippononebria is a distinct genus more closely related to Leistus than Nebria. Archastes, Oreonebria, Spelaeonebria, and Eurynebria, previously treated as distinct genera by some authors, are all nested within a monophyletic genus Nebria. Within Nebria, four major clades are recognized: (1) the Oreonebria Series, including eight subgenera arrayed in two subgeneric complexes (the Eonebria and Oreonebria Complexes); (2) the Nebriola Series, including only subgenus Nebriola; (3) the Nebria Series, including ten subgenera arrayed in two subgeneric complexes, the Boreonebria and Nebria Complexes, with the latter further subdivided into three subgeneric subcomplexes (the Nebria, Epinebriola, and Eunebria Subcomplexes)); and (4) the Catonebria Series, including seven subgenera arrayed in two subgeneric complexes (the Reductonebria and Catonebria Complexes). A strong concordance of biogeography with the inferred phylogeny is noted and some evident vicariance patterns are highlighted. A revised classification, mainly within the Nebriini, is proposed to reflect the inferred phylogeny. Three genus-group taxa (Nippononebria, Vancouveria and Archastes) are given revised status and seven are recognized as new synonymies (Nebriorites Jeannel, 1941 and Marggia Huber, 2014 = Oreonebria Daniel, 1903; Pseudonebriola Ledoux & Roux, 1989 = Boreonebria Jeannel, 1937; Patrobonebria Bänninger, 1923, Paranebria Jeannel, 1937 and Barbonebriola Huber & Schmidt, 2017 = Epinebriola Daniel & Daniel, 1904; and Asionebria Shilenkov, 1982 = Psilonebria Andrewes, 1923). Six new subgenera are proposed and described for newly recognized clades: Parepinebriola Kavanaugh subgen. nov. (type species: Nebria delicata Huber & Schmidt, 2017), Insulanebria Kavanaugh subgen. nov. (type species: Nebria carbonaria Eschscholtz, 1829), Erwinebria Kavanaugh subgen. nov. (type species Nebria sahlbergii Fischer von Waldheim, 1828), Nivalonebria Kavanaugh subgen. nov. (type species: Nebria paradisi Darlington, 1931), Neaptenonebria Kavanaugh subgen. nov. (type species: Nebria ovipennis LeConte, 1878), and Palaptenonebria Kavanaugh subgen. nov. (type species: Nebria mellyi Gebler, 1847). Future efforts to better understand relationships within the supertribe should aim to expand the taxon sampling of DNA sequence data, particularly within subgenera Leistus and Evanoleistus of genus Leistus and the Nebria Complex of genus Nebria.