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      Species review of the genus Boreophilia Benick from North America (Coleoptera, Staphylinidae, Aleocharinae, Athetini): Systematics, habitat, and distribution


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          Fourteen species of the genus Boreophilia Benick are now recognized in North America. Boreophilia insecuta (Eppelsheim), reported by Lohse (1990) from North America, is a misidentification of a new species, which is described here as B. neoinsecuta Klimaszewski, sp. n., and the true B. insecuta (Epp.) does not occur in North America. An additional new species is found in Alaska, and described as B. beringi Klimaszewski & Brunke, sp. n. The following three species are synonymized (second name being valid): Boreophilia herschelensis Klimaszewski & Godin, 2012, with Boreophilia vega (Fenyes, 1920); Boreophilia manitobensis Lohse, 1990, with B. caseyi Lohse, 1990; and B. angusticornis (Bernahuer, 1907) with B. subplana (J Sahlberg, 1880), based on study of genital structures and external morphology. Atheta gelida J Sahlberg, 1887, and Atheta munsteri Bernhauer, 1902, considered as Boreophilia in recent publications, are transferred to the genus Atheta Thomson, subgenus Dimetrota . Boreostiba piligera (J Sahlberg) is transferred to Boreophilia based on morphology and the results of our phylogenetic analysis. Boreophilia nearctica is recorded from Alberta and B. nomensis is recorded from British Columbia for the first time. Each valid species is illustrated by color image of habitus, and black and white images of genitalia and tergite and sternite VIII. A new key to all Nearctic species of the genus is provided. DNA barcode data were available for nine of the 14 species, which we downloaded, analyzed, and used as additional evidence for the taxonomic conclusions reached herein.

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          Most cited references29

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          IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

          Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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            Inference from Iterative Simulation Using Multiple Sequences

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              MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

              Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.

                Author and article information

                Pensoft Publishers
                20 May 2019
                : 848
                : 57-102
                [1 ] Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, 1055 du PEPS, PO Box 10380, Stn. Sainte-Foy, Québec, QC, G1V 4C7, Canada Natural Resources Canada Québec Canada
                [2 ] University of Alaska Museum, 1962 Yukon Drive, Fairbanks, Alaska 99775-6960, USA University of Alaska Fairbanks United States of America
                [3 ] Canadian National Collection of Insects, Agriculture and Agri-Food Canada, Ottawa, ON, K1A 0C6, Canada Agriculture and Agri-Food Canada Ottawa Canada
                Author notes
                Corresponding author: Jan Klimaszewski ( jan.klimaszewski@ 123456canada.ca )

                Academic editor: Volker Assing

                34846 urn:lsid:zoobank.org:pub:E43FDDC8-EAEE-47E2-9ED4-C86C929D1AA3

                This is an open access article distributed under the terms of the CC0 Public Domain Dedication.

                Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, 1055 du P.E.P.S., PO Box 10380, Stn. Sainte-Foy, Québec, QC, Canada G1V 4C7
                Research Article


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