Global arthropod beta-diversity is spatially and temporally structured by latitude.

Global biodiversity gradients are generally expected to reflect greater species replacement closer to the equator. However, empirical validation of global biodiversity gradients largely relies on vertebrates, plants, and other less diverse taxa. Here we assess the temporal and spatial dynamics of global arthropod biodiversity dynamics using a beta-diversity framework. Sampling includes 129 sampling sites whereby malaise traps are deployed to monitor temporal changes in arthropod communities. Overall, we encountered more than 150,000 unique barcode index numbers (BINs) (i.e. species proxies). We assess between site differences in community diversity using beta-diversity and the partitioned components of species replacement and richness difference. Global total beta-diversity (dissimilarity) increases with decreasing latitude, greater spatial distance and greater temporal distance. Species replacement and richness difference patterns vary across biogeographic regions. Our findings support long-standing, general expectations of global biodiversity patterns. However, we also show that the underlying processes driving patterns may be regionally linked.

PROTAX-GPU: a scalable probabilistic taxonomic classification system for DNA barcodes.

DNA-based identification is vital for classifying biological specimens, yet methods to quantify the uncertainty of sequence-based taxonomic assignments are scarce. Challenges arise from noisy reference databases, including mislabelled entries and missing taxa. PROTAX addresses these issues with a probabilistic approach to taxonomic classification, advancing on methods that rely solely on sequence similarity. It provides calibrated probabilistic assignments to a partially populated taxonomic hierarchy, accounting for taxa that lack references and incorrect taxonomic annotation. While effective on smaller scales, global application of PROTAX necessitates substantially larger reference libraries, a goal previously hindered by computational barriers. We introduce PROTAX-GPU, a scalable algorithm capable of leveraging the global Barcode of Life Data System (>14 million specimens) as a reference database. Using graphics processing units (GPU) to accelerate similarity and nearest-neighbour operations and the JAX library for Python integration, we achieve over a 1000 × speedup compared with the central processing unit (CPU)-based implementation without compromising PROTAX's key benefits. PROTAX-GPU marks a significant stride towards real-time DNA barcoding, enabling quicker and more efficient species identification in environmental assessments. This capability opens up new avenues for real-time monitoring and analysis of biodiversity, advancing our ability to understand and respond to ecological dynamics. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

BOLD v4: A Centralized Bioinformatics Platform for DNA-Based Biodiversity Data.

BOLD, the Barcode of Life Data System, supports the acquisition, storage, validation, analysis, and publication of DNA barcodes, activities requiring the integration of molecular, morphological, and distributional data. Its pivotal role in curating the reference library of DNA barcodes, coupled with its data management and analysis capabilities, makes it a central resource for biodiversity science. It enables rapid, accurate identification of specimens and also reveals patterns of genetic diversity and evolutionary relationships among taxa.Launched in 2005, BOLD has become an increasingly powerful tool for advancing the understanding of planetary biodiversity. It currently hosts 17 million specimen records and 14 million barcodes that provide coverage for more than a million species from every continent and ocean. The platform has the long-term goal of providing a consistent, accurate system for identifying all species of eukaryotes.BOLD's integrated analytical tools, full data lifecycle support, and secure collaboration framework distinguish it from other biodiversity platforms. BOLD v4 brought enhanced data management and analysis capabilities as well as novel functionality for data dissemination and publication. Its next version will include features to strengthen its utility to the research community, governments, industry, and society-at-large.

Minimalist revision of Mesochorus Gravenhorst, 1829 (Hymenoptera: Ichneumonidae: Mesochorinae) from Área de Conservación Guanacaste, Costa Rica, with 158 new species and host records for 129 species / Revisión minimalista de Mesochorus Gravenhorst, 1829 (Hymenoptera: Ichneumonidae: Mesochorinae) del Área de Conservación Guanacaste, Costa Rica, con 158 especies nuevas y registros de hospedantes para 129 especies

Introduction: Species of Mesochorus are found worldwide and members of this genus are primarily hyperparasitoids of Ichneumonoidea and Tachinidae. Objectives: To describe species of Costa Rican Mesochorus reared from caterpillars and to a lesser extent Malaise-trapped. Methods: The species are diagnosed by COI mtDNA barcodes, morphological inspection, and host data. A suite of images and host data (plant, caterpillar, and primary parasitoid) are provided for each species. Results: A total of 158 new species of Mesochorus. Sharkey is the taxonomic authority for all. Conclusions: This demonstrates a practical application of DNA barcoding that can be applied to the masses of undescribed neotropical insect species in hyperdiverse groups.

Introducción: Las especies de Mesochorus se encuentran en todo el mundo y los miembros de este género son principalmente hiperparasitoides de las familias Ichneumonoidea y Tachinidae. Objetivos: Describir las especies de Mesochorus costarricenses obtenidas de orugas y en menor medida por trampas Malaise. Métodos: Las especies se diagnosticaron mediante el uso de código de barra molecular por COI del ADNmt, inspección morfológica y datos del huésped. Se proporciona un conjunto de imágenes y datos de los huéspedes (planta, oruga y parasitoide primario) para cada especie. Resultados: Se encontró un total de 158 nuevas especies de Mesochorus. Sharkey es la autoridad taxonómica para todas las especies. Conclusiones: Se demuestra una aplicación práctica del código de barras de ADN que se puede aplicar a grandes cantidades de especies de insectos neotropicales no descritas para grupos hiperdiversos.

DNA barcodes reveal striking arthropod diversity and unveil seasonal patterns of variation in the southern Atlantic Forest.

The Atlantic Forest harbors 7% of global biodiversity and possesses high levels of endemism, but many of its component taxa remain unstudied. Due to the importance of tropical forests and the urgency to protect them, there is a compelling need to address this knowledge gap. To provide more information on its arthropod fauna, a Malaise trap was deployed for 12 months in a semi-degraded area of the southern Upper Paraná ecoregion of the Atlantic Forest. All specimens were DNA barcoded and the Barcode Index Number (BIN) system was employed to assign each specimen to a species proxy. DNA barcodes were obtained from 75,500 arthropods that included representatives of 8,651 BINs. Nearly 81% of these BINs were first records, highlighting the high rates of endemism and lack of study of arthropods from the Atlantic Forest. Diptera was the most abundant order, followed by Hemiptera, Lepidoptera and Hymenoptera. Diptera was also the most species-rich order, followed by Hymenoptera, Lepidoptera, and Coleoptera, a result consistent with studies in other biogeographic regions. Insects were most abundant in winter and most diverse in autumn and winter. This pattern, however, was caused mainly by the dynamics of dipteran diversity as other orders differed in their seasonal variation. The BIN composition of the insect community varied sharply through the year and also differed between the two consecutive summers included in the sampling period. The study of the 38 commonest BINs showed that seasonal patterns of abundance were not order-specific. Temperature had the strongest impact on seasonal abundance variation. Our results highlight the striking and understudied arthropod diversity of the highly fragmented Atlantic Forest, the predominance of dipterans, and the fact that abundance and richness in this insect community peak in the coolest months. Standardized studies like this generate fast and reliable biodiversity inventories and unveil ecological patterns, thus providing valuable information for conservation programs.

More discussion of minimalist species descriptions and clarifying some misconceptions contained in Meier et al. 2021.

This is a response to a preprint version of "A re-analysis of the data in Sharkey et al.'s (2021) minimalist revision reveals that BINs do not deserve names, but BOLD Systems needs a stronger commitment to open science", https://www.biorxiv.org/content/10.1101/2021.04.28.441626v2. Meier et al. strongly criticized Sharkey et al.'s publication in which 403 new species were deliberately minimally described, based primarily on COI barcode sequence data. Here we respond to these criticisms. The following points are made: 1) Sharkey et al. did not equate BINs with species, as demonstrated in several examples in which multiple species were found to be in single BINs. 2) We reiterate that BINs were used as a preliminary sorting tool, just as preliminary morphological identification commonly sorts specimens based on color and size into unit trays; despite BINs and species concepts matching well over 90% of species, this matching does not equate to equality. 3) Consensus barcodes were used only to provide a diagnosis to conform to the rules of the International Code of Zoological Nomenclature just as consensus morphological diagnoses are. The barcode of a holotype is definitive and simply part of its cellular morphology. 4) Minimalist revisions will facilitate and accelerate future taxonomic research, not hinder it. 5) We refute the claim that the BOLD sequences of Plesiocoelusvanachterbergi are pseudogenes and demonstrate that they simply represent a frameshift mutation. 6) We reassert our observation that morphological evidence alone is insufficient to recognize species within species-rich higher taxa and that its usefulness lies in character states that are congruent with molecular data. 7) We show that in the cases in which COI barcodes code for the same amino acids in different putative species, data from morphology, host specificity, and other ecological traits reaffirm their utility as indicators of genetically distinct lineages.

Minimalist revision and description of 403 new species in 11 subfamilies of Costa Rican braconid parasitoid wasps, including host records for 219 species.

Three new genera are described: Michener (Proteropinae), Bioalfa (Rogadinae), and Hermosomastax (Rogadinae). Keys are given for the New World genera of the following braconid subfamilies: Agathidinae, Braconinae, Cheloninae, Homolobinae, Hormiinae, Ichneutinae, Macrocentrinae, Orgilinae, Proteropinae, Rhysipolinae, and Rogadinae. In these subfamilies 416 species are described or redescribed. Most of the species have been reared and all but 13 are new to science. A consensus sequence of the COI barcodes possessed by each species is employed to diagnose the species, and this approach is justified in the introduction. Most descriptions consist of a lateral or dorsal image of the holotype, a diagnostic COI consensus barcode, the Barcode Index Number (BIN) code with a link to the Barcode of Life Database (BOLD), and the holotype specimen information required by the International Code of Zoological Nomenclature. The following species are treated and those lacking authorship are newly described here with authorship attributable to Sharkey except for the new species of Macrocentrinae which are by Sharkey & van Achterberg: AGATHIDINAE: Aerophiluspaulmarshi, Mesocoelusdavidsmithi, Neothlipsisbobkulai, Plesiocoelusvanachterbergi, Pneumagathiserythrogastra (Cameron, 1905), Therophilusbobwhartoni, T.donaldquickei, T.gracewoodae, T.maetoi, T.montywoodi, T.penteadodiasae, Zacremnopsbrianbrowni, Z.coatlicue Sharkey, 1990, Zacremnopscressoni (Cameron, 1887), Z.ekchuah Sharkey, 1990, Z.josefernandezi, Zelomorphasarahmeierottoae. BRACONINAE: Braconalejandromarini, B.alejandromasisi, B.alexamasisae, B.andresmarini, B.andrewwalshi, B.anniapicadoae, B.anniemoriceae, B.barryhammeli, B.bernardoespinozai, B.carlossanabriai, B.chanchini, B.christophervallei, B.erasmocoronadoi, B.eugeniephillipsae, B.federicomatarritai, B.frankjoycei, B.gerardovegai, B.germanvegai, B.isidrochaconi, B.jimlewisi, B.josejaramilloi, B.juanjoseoviedoi, B.juliodiazi, B.luzmariaromeroae, B.manuelzumbadoi, B.marialuisariasae, B.mariamartachavarriae, B.mariorivasi, B.melissaespinozae, B.nelsonzamorai, B.nicklaphami, B.ninamasisae, B.oliverwalshi, B.paulamarinae, B.rafamoralesi, B.robertofernandezi, B.rogerblancoi, B.ronaldzunigai, B.sigifredomarini, B.tihisiaboshartae, B.wilberthbrizuelai, Digonogastramontylloydi, D.montywoodi, D.motohasegawai, D.natwheelwrighti, D.nickgrishini. CHELONINAE: Adeliusadrianguadamuzi, A.gauldi Shimbori & Shaw, 2019, A.janzeni Shimbori & Shaw, 2019, Ascogastergloriasihezarae, A.grettelvegae, A.guillermopereirai, A.gustavoecheverrii, A.katyvandusenae, A.luisdiegogomezi, Chelonusalejandrozaldivari, C.gustavogutierrezi, C.gustavoinduni, C.harryramirezi, C.hartmanguidoi, C.hazelcambroneroae, C.iangauldi, C.isidrochaconi, C.janecheverriae, C.jeffmilleri, C.jennyphillipsae, C.jeremydewaardi, C.jessiehillae, C.jesusugaldei, C.jimlewisi, C.jimmilleri, C.jimwhitfieldi, C.johanvalerioi, C.johnburnsi, C.johnnoyesi, C.jorgebaltodanoi, C.jorgehernandezi, C.josealfredohernandezi, C.josefernandeztrianai, C.josehernandezcortesi, C.josemanuelperezi, C.josephinerodriguezae, C.juanmatai, C.junkoshimurae, C.kateperezae, C.luciariosae, C.luzmariaromeroae, C.manuelpereirai, C.manuelzumbadoi, C.marianopereirai, C.maribellealvarezae, C.markmetzi, C.markshawi, C.martajimenezae, C.mayrabonillae, C.meganmiltonae, C.melaniamunozae, C.michaelstroudi, C.michellevanderbankae, C.mingfangi, C.minorcarmonai, C.monikaspringerae, C.moniquegilbertae, C.motohasegawai, C.nataliaivanovae, C.nelsonzamorai, C.normwoodleyi, C.osvaldoespinozai, C.pamelacastilloae, C.paulgoldsteini, C.paulhansoni, C.paulheberti, C.petronariosae, C.ramyamanjunathae, C.randallgarciai, C.rebeccakittelae, C.robertoespinozai, C.robertofernandezi, C.rocioecheverriae, C.rodrigogamezi, C.ronaldzunigai, C.rosibelelizondoae, C.rostermoragai, C.ruthfrancoae, C.scottmilleri, C.scottshawi, C.sergioriosi, C.sigifredomarini, C.stevearonsoni, C.stevestroudi, C.sujeevanratnasinghami, C.sureshnaiki, C.torbjornekremi, C.yeimycedenoae, Leptodrepanaalexisae, L.erasmocoronadoi, L.felipechavarriai, L.freddyquesadai, L.gilbertfuentesi, L.manuelriosi, Phanerotomaalmasolisae, P.alvaroherrerai, P.anacordobae, P.anamariamongeae, P.andydeansi, P.angelagonzalezae, P.angelsolisi, P.barryhammeli, P.bernardoespinozai, P.calixtomoragai, P.carolinacanoae, P.christerhanssoni, P.christhompsoni, P.davesmithi, P.davidduthiei, P.dirksteinkei, P.donquickei, P.duniagarciae, P.duvalierbricenoi, P.eddysanchezi, P.eldarayae, P.eliethcantillanoae, P.jenopappi, Pseudophanerotomaalanflemingi, Ps.albanjimenezi, Ps.alejandromarini, Ps.alexsmithi, Ps.allisonbrownae, Ps.bobrobbinsi. HOMOLOBINAE: Exasticolusjennyphillipsae, E.randallgarciai, E.robertofernandezi, E.sigifredomarini, E.tomlewinsoni. HORMIINAE: Hormiusanamariamongeae, H.angelsolisi, H.anniapicadoae, H.arthurchapmani, H.barryhammeli, H.carmenretanae, H.carloswalkeri, H.cesarsuarezi, H.danbrooksi, H.eddysanchezi, H.erikframstadi, H.georgedavisi, H.grettelvegae, H.gustavoinduni, H.hartmanguidoi, H.hectoraritai, H.hesiquiobenitezi, H.irenecanasae, H.isidrochaconi, H.jaygallegosi, H.jimbeachi, H.jimlewisi, H.joelcracrafti, H.johanvalerioi, H.johnburleyi, H.joncoddingtoni, H.jorgecarvajali, H.juanmatai, H.manuelzumbadoi, H.mercedesfosterae, H.modonnellyae, H.nelsonzamorai, H.pamelacastilloae, H.raycypessi, H.ritacolwellae, H.robcolwelli, H.rogerblancosegurai, H.ronaldzunigai, H.russchapmani, H.virginiaferrisae, H.warrenbrighami, H.willsflowersi. ICHNEUTINAE: Oligoneuruskriskrishtalkai, O.jorgejimenezi, Paroligoneuruselainehoaglandae, P.julianhumphriesi, P.mikeiviei. MACROCENTRINAE: Austrozelejorgecampabadali, A.jorgesoberoni, Dolichozelegravitarsis (Muesebeck, 1938), D.josefernandeztrianai, D.josephinerodriguezae, Hymenochaoniakalevikulli, H.kateperezae, H.katherinebaillieae, H.katherineellisonae, H.katyvandusenae, H.kazumifukunagae, H.keithlangdoni, H.keithwillmotti, H.kenjinishidai, H.kimberleysheldonae, H.krisnorvigae, H.lilianamadrigalae, H.lizlangleyae, Macrocentrusfredsingeri, M.geoffbarnardi, M.gregburtoni, M.gretchendailyae, M.grettelvegae, M.gustavogutierrezi, M.hannahjamesae, M.harisridhari, M.hillaryrosnerae, M.hiroshikidonoi, M.iangauldi, M.jennyphillipsae, M.jesseausubeli, M.jessemaysharkae, M.jimwhitfieldi, M.johnbrowni, M.johnburnsi, M.jonathanfranzeni, M.jonathanrosenbergi, M.jorgebaltodanoi, M.lucianocapelli. ORGILINAE: Orgilusamyrossmanae, O.carrolyoonae, O.christhompsoni, O.christinemcmahonae, O.dianalipscombae, O.ebbenielsoni, O.elizabethpennisiae, O.evertlindquisti, O.genestoermeri, O.jamesriegeri, O.jeanmillerae, O.jeffmilleri, O.jerrypowelli, O.jimtiedjei, O.johnlundbergi, O.johnpipolyi, O.jorgellorentei, O.larryspearsi, O.marlinricei, O.mellissaespinozae, O.mikesmithi, O.normplatnicki, O.peterrauchi, O.richardprimacki, O.sandraberriosae, O.sarahmirandae, O.scottmilleri, O.scottmorii, Stantoniabillalleni, S.brookejarvisae, S.donwilsoni, S.erikabjorstromae, S.garywolfi, S.henrikekmani, S.luismirandai, S.miriamzunzae, S.quentinwheeleri, S.robinkazmierae, S.ruthtifferae. PROTEROPINAE: Hebichneutestricolor Sharkey & Wharton, 1994, Proteropsiangauldi, P.vickifunkae, Michenercharlesi. RHYSIPOLINAE: Pseudorhysipolisluisfonsecai, P. mailyngonzalezaeRhysipolisjulioquirosi. ROGADINAE: Aleiodesadrianaradulovae, A.adrianforsythi, A.agnespeelleae, A.alaneaglei, A.alanflemingi, A.alanhalevii, A.alejandromasisi, A.alessandracallejae, A.alexsmithi, A.alfonsopescadori, A.alisundermieri, A.almasolisae, A.alvarougaldei, A.alvaroumanai, A.angelsolisi, A.annhowdenae, A.bobandersoni, A.carolinagodoyae, A.charlieobrieni, A.davefurthi, A.donwhiteheadi, A.doylemckeyi, A.frankhovorei, A.henryhowdeni, A.inga Shimbori & Shaw, 2020, A.johnchemsaki, A.johnkingsolveri, A.gonodontovorus Shimbori & Shaw, 2020, A.manuelzumbadoi, A.mayrabonillae, A.michelledsouzae, A.mikeiviei, A.normwoodleyi, A.pammitchellae, A.pauljohnsoni, A.rosewarnerae, A.steveashei, A.terryerwini, A.willsflowersi, Bioalfapedroleoni, B.alvarougaldei, B.rodrigogamezi, Choreborogasandydeansi, C.eladiocastroi, C.felipechavarriai, C.frankjoycei, Clinocentrusandywarreni, Cl.angelsolisi, Cystomastaxalexhausmanni, Cy.angelagonzalezae, Cy.ayaigarashiae, Hermosomastaxclavifemorus Quicke sp. nov., Heterogamusdonstonei, Pseudoyeliconesbernsweeneyi, Stiropiusbencrairi, S.berndkerni, S.edgargutierrezi, S.edwilsoni, S.ehakernae, Triraphisbillfreelandi, T.billmclarneyi, T.billripplei, T.bobandersoni, T.bobrobbinsi, T.bradzlotnicki, T.brianbrowni, T.brianlaueri, T.briannestjacquesae, T.camilocamargoi, T.carlosherrerai, T.carolinepalmerae, T.charlesmorrisi, T.chigiybinellae, T.christerhanssoni, T.christhompsoni, T.conniebarlowae, T.craigsimonsi, T.defectus Valerio, 2015, T.danielhubi, T.davidduthiei, T.davidwahli, T.federicomatarritai, T.ferrisjabri, T.mariobozai, T.martindohrni, T.matssegnestami, T.mehrdadhajibabaei, T.ollieflinti, T.tildalauerae, Yeliconesdirksteinkei, Y.markmetzi, Y.monserrathvargasae, Y.tricolor Quicke, 1996. Y.woldai Quicke, 1996. The following new combinations are proposed: Neothlipsissmithi (Ashmead), new combination for Microdussmithi Ashmead, 1894; Neothlipsispygmaeus (Enderlein), new combination for Microduspygmaeus Enderlein, 1920; Neothlipsisunicinctus (Ashmead), new combination for Microdusunicinctus Ashmead, 1894; Therophilusanomalus (Bortoni and Penteado-Dias) new combination for Plesiocoelusanomalus Bortoni and Penteado-Dias, 2015; Aerophilusareolatus (Bortoni and Penteado-Dias) new combination for Plesiocoelusareolatus Bortoni and Penteado-Dias, 2015; Pneumagathiserythrogastra (Cameron) new combination for Agathiserythrogastra Cameron, 1905. Dolichozelecitreitarsis (Enderlein), new combination for Paniscozelecitreitarsis Enderlein, 1920. Dolichozelefuscivertex (Enderlein) new combination for Paniscozelefuscivertex Enderlein, 1920. Finally, Bassusbrooksi Sharkey, 1998 is synonymized with Agathiserythrogastra Cameron, 1905; Paniscozelegriseipes Enderlein, 1920 issynonymized with Dolichozelekoebelei Viereck, 1911; Paniscozelecarinifrons Enderlein, 1920 is synonymized with Dolichozelefuscivertex (Enderlein, 1920); and Paniscozelenigricauda Enderlein,1920 is synonymized with Dolichozelequaestor (Fabricius, 1804). (originally described as Ophionquaestor Fabricius, 1804).

Ultra-Conserved Elements and morphology reciprocally illuminate conflicting phylogenetic hypotheses in Chalcididae (Hymenoptera, Chalcidoidea).

Recent technical advances combined with novel computational approaches have promised the acceleration of our understanding of the tree of life. However, when it comes to hyperdiverse and poorly known groups of invertebrates, studies are still scarce. As published phylogenies will be rarely challenged by future taxonomists, careful attention must be paid to potential analytical bias. We present the first molecular phylogenetic hypothesis for the family Chalcididae, a group of parasitoid wasps, with a representative sampling (144 ingroups and seven outgroups) that covers all described subfamilies and tribes, and 82% of the known genera. Analyses of 538 Ultra-Conserved Elements (UCEs) with supermatrix (RAxML and IQTREE) and gene tree reconciliation approaches (ASTRAL, ASTRID) resulted in highly supported topologies in overall agreement with morphology but reveal conflicting topologies for some of the deepest nodes. To resolve these conflicts, we explored the phylogenetic tree space with clustering and gene genealogy interrogation methods, analyzed marker and taxon properties that could bias inferences and performed a thorough morphological analysis (130 characters encoded for 40 taxa representative of the diversity). This joint analysis reveals that UCEs enable attainment of resolution between ancestry and convergent/divergent evolution when morphology is not informative enough, but also shows that a systematic exploration of bias with different analytical methods and a careful analysis of morphological features is required to prevent publication of artifactual results. We highlight a GC content bias for maximum-likelihood approaches, an artifactual mid-point rooting of the ASTRAL tree and a deleterious effect of high percentage of missing data (>85% missing UCEs) on gene tree reconciliation methods. Based on the results we propose a new classification of the family into eight subfamilies and ten tribes that lay the foundation for future studies on the evolutionary history of Chalcididae.

A DNA barcode library for the butterflies of North America.

Although the butterflies of North America have received considerable taxonomic attention, overlooked species and instances of hybridization continue to be revealed. The present study assembles a DNA barcode reference library for this fauna to identify groups whose patterns of sequence variation suggest the need for further taxonomic study. Based on 14,626 records from 814 species, DNA barcodes were obtained for 96% of the fauna. The maximum intraspecific distance averaged 1/4 the minimum distance to the nearest neighbor, producing a barcode gap in 76% of the species. Most species (80%) were monophyletic, the others were para- or polyphyletic. Although 15% of currently recognized species shared barcodes, the incidence of such taxa was far higher in regions exposed to Pleistocene glaciations than in those that were ice-free. Nearly 10% of species displayed high intraspecific variation (>2.5%), suggesting the need for further investigation to assess potential cryptic diversity. Aside from aiding the identification of all life stages of North American butterflies, the reference library has provided new perspectives on the incidence of both cryptic and potentially over-split species, setting the stage for future studies that can further explore the evolutionary dynamics of this group.

Torix Rickettsia are widespread in arthropods and reflect a neglected symbiosis.

BACKGROUND: Rickettsia are intracellular bacteria best known as the causative agents of human and animal diseases. Although these medically important Rickettsia are often transmitted via haematophagous arthropods, other Rickettsia, such as those in the Torix group, appear to reside exclusively in invertebrates and protists with no secondary vertebrate host. Importantly, little is known about the diversity or host range of Torix group Rickettsia. RESULTS: This study describes the serendipitous discovery of Rickettsia amplicons in the Barcode of Life Data System (BOLD), a sequence database specifically designed for the curation of mitochondrial DNA barcodes. Of 184,585 barcode sequences analysed, Rickettsia is observed in ∼0.41% of barcode submissions and is more likely to be found than Wolbachia (0.17%). The Torix group of Rickettsia are shown to account for 95% of all unintended amplifications from the genus. A further targeted PCR screen of 1,612 individuals from 169 terrestrial and aquatic invertebrate species identified mostly Torix strains and supports the "aquatic hot spot" hypothesis for Torix infection. Furthermore, the analysis of 1,341 SRA deposits indicates that Torix infections represent a significant proportion of all Rickettsia symbioses found in arthropod genome projects. CONCLUSIONS: This study supports a previous hypothesis that suggests that Torix Rickettsia are overrepresented in aquatic insects. In addition, multiple methods reveal further putative hot spots of Torix Rickettsia infection, including in phloem-feeding bugs, parasitoid wasps, spiders, and vectors of disease. The unknown host effects and transmission strategies of these endosymbionts make these newly discovered associations important to inform future directions of investigation involving the understudied Torix Rickettsia.

Debar: A sequence-by-sequence denoiser for COI-5P DNA barcode data.

DNA barcoding and metabarcoding are now widely used to advance species discovery and biodiversity assessments. High-throughput sequencing (HTS) has expanded the volume and scope of these analyses, but elevated error rates introduce noise into sequence records that can inflate estimates of biodiversity. Denoising -the separation of biological signal from instrument (technical) noise-of barcode and metabarcode data currently employs abundance-based methods which do not capitalize on the highly conserved structure of the cytochrome c oxidase subunit I (COI) region employed as the animal barcode. This manuscript introduces debar, an R package that utilizes a profile hidden Markov model to denoise indel errors in COI sequences introduced by instrument error. In silico studies demonstrated that debar recognized 95% of artificially introduced indels in COI sequences. When applied to real-world data, debar reduced indel errors in circular consensus sequences obtained with the Sequel platform by 75%, and those generated on the Ion Torrent S5 by 94%. The false correction rate was less than 0.1%, indicating that debar is receptive to the majority of true COI variation in the animal kingdom. In conclusion, the debar package improves DNA barcode and metabarcode workflows by aiding the generation of more accurate sequences aiding the characterization of species diversity.

?Addendum to a minimalist revision of Costa Rican Braconidae: 28 new species and 23 host records.

Twenty-nine species are treated, most of which have host caterpillar and food plant records, and all but one are new to science. The first host record for the agathidine genus Amputoearinus is given. Gnathopleurajosequesadai Sharkey, sp. nov. is reported as a hyperparasitoid of fly larvae, the first such record for the genus. The following new species are diagnosed primarily using COI barcode data; Sharkey is the authority for all: Agathidinae: Aerophilusdavidwagneri, Aerophilusfundacionbandorum, Aerophilusnicklaphami, Lytopylusdavidstopaki, Lytopylusdavidschindeli; Alysiinae: Gnathopleurajosequesadai; Braconinae: Braconandreamezae, Braconfranklinpaniaguai, Braconrafagutierrezi, Braconguillermoblancoi, Braconoscarmasisi, Braconpauldimaurai, Braconshebadimaurae, Saciremakarendimaurae; Cheloninae: Chelonusminorzunigai; Homolobinae: Homolobusstevestroudi; Macrocentrinae: Macrocentrusmichaelstroudi; Orgilinae: Stantoniagilbertfuentesi; Rhysipolinae: Rhysipolisstevearonsoni; Rogadinae: Aleiodeskaydodgeae, Aleiodeskerrydresslerae, Aleiodesjosesolanoi, Aleiodesjuniorporrasi, Aleiodesrocioecheverri, Aleiodesronaldzunigai, Choreborogasjesseausubeli, Triraphisdoncombi, and Yeliconesmayrabonillae.

Using DNA-barcoded Malaise trap samples to measure impact of a geothermal energy project on the biodiversity of a Costa Rican old-growth rain forest.

We report one year (2013-2014) of biomonitoring an insect community in a tropical old-growth rain forest, during construction of an industrial-level geothermal electricity project. This is the first-year reaction by the species-rich insect biodiversity; six subsequent years are being analyzed now. The site is on the margin of a UNESCO Natural World Heritage Site, Área de Conservación Guanacaste (ACG), in northwestern Costa Rica. This biomonitoring is part of Costa Rica's ongoing efforts to sustainably retain its wild biodiversity through biodevelopmental integration with its societies. Essential tools are geothermal engineering needs, entomological knowledge, insect species-rich forest, government-NGO integration, common sense, DNA barcoding for species-level identification, and Malaise traps. This research is tailored for integration with its society at the product level. We combine an academic view with on-site engineering decisions. This biomonitoring requires alpha-level DNA barcoding combined with centuries of morphology-based entomological taxonomy and ecology. Not all desired insect community analyses are performed; they are for data from subsequent years combined with this year. We provide enough analysis to be used by both guilds now. This biomonitoring has shown, for the first year, that the geothermal project impacts only the biodiversity within a zone less than 50 m from the project margin.

coil: an R package for cytochrome c oxidase I (COI) DNA barcode data cleaning, translation, and error evaluation.

Biological conclusions based on DNA barcoding and metabarcoding analyses can be strongly influenced by the methods utilized for data generation and curation, leading to varying levels of success in the separation of biological variation from experimental error. The 5' region of cytochrome c oxidase subunit I (COI-5P) is the most common barcode gene for animals, with conserved structure and function that allows for biologically informed error identification. Here, we present coil ( https://CRAN.R-project.org/package=coil ), an R package for the pre-processing and frameshift error assessment of COI-5P animal barcode and metabarcode sequence data. The package contains functions for placement of barcodes into a common reading frame, accurate translation of sequences to amino acids, and highlighting insertion and deletion errors. The analysis of 10 000 barcode sequences of varying quality demonstrated how coil can place barcode sequences in reading frame and distinguish sequences containing indel errors from error-free sequences with greater than 97.5% accuracy. Package limitations were tested through the analysis of COI-5P sequences from the plant and fungal kingdoms as well as the analysis of potential contaminants: nuclear mitochondrial pseudogenes and Wolbachia COI-5P sequences. Results demonstrated that coil is a strong technical error identification method but is not reliable for detecting all biological contaminants.

BOLD and GenBank revisited - Do identification errors arise in the lab or in the sequence libraries?

Applications of biological knowledge, such as forensics, often require the determination of biological materials to a species level. As such, DNA-based approaches to identification, particularly DNA barcoding, are attracting increased interest. The capacity of DNA barcodes to assign newly encountered specimens to a species relies upon access to informatics platforms, such as BOLD and GenBank, which host libraries of reference sequences and support the comparison of new sequences to them. As parameterization of these libraries expands, DNA barcoding has the potential to make valuable contributions in diverse applied contexts. However, a recent publication called for caution after finding that both platforms performed poorly in identifying specimens of 17 common insect species. This study follows up on this concern by asking if the misidentifications reflected problems in the reference libraries or in the query sequences used to test them. Because this reanalysis revealed that missteps in acquiring and analyzing the query sequences were responsible for most misidentifications, a workflow is described to minimize such errors in future investigations. The present study also revealed the limitations imposed by the lack of a polished species-level taxonomy for many groups. In such cases, applications can be strengthened by mapping the geographic distributions of sequence-based species proxies rather than waiting for the maturation of formal taxonomic systems based on morphology.

A reference library for Canadian invertebrates with 1.5 million barcodes, voucher specimens, and DNA samples.

The reliable taxonomic identification of organisms through DNA sequence data requires a well parameterized library of curated reference sequences. However, it is estimated that just 15% of described animal species are represented in public sequence repositories. To begin to address this deficiency, we provide DNA barcodes for 1,500,003 animal specimens collected from 23 terrestrial and aquatic ecozones at sites across Canada, a nation that comprises 7% of the planet's land surface. In total, 14 phyla, 43 classes, 163 orders, 1123 families, 6186 genera, and 64,264 Barcode Index Numbers (BINs; a proxy for species) are represented. Species-level taxonomy was available for 38% of the specimens, but higher proportions were assigned to a genus (69.5%) and a family (99.9%). Voucher specimens and DNA extracts are archived at the Centre for Biodiversity Genomics where they are available for further research. The corresponding sequence and taxonomic data can be accessed through the Barcode of Life Data System, GenBank, the Global Biodiversity Information Facility, and the Global Genome Biodiversity Network Data Portal.

Metabarcoding a diverse arthropod mock community.

Although DNA metabarcoding is an attractive approach for monitoring biodiversity, it is often difficult to detect all the species present in a bulk sample. In particular, sequence recovery for a given species depends on its biomass and mitome copy number as well as the primer set employed for PCR. To examine these variables, we constructed a mock community of terrestrial arthropods comprised of 374 species. We used this community to examine how species recovery was impacted when amplicon pools were constructed in four ways. The first two protocols involved the construction of bulk DNA extracts from different body segments (Bulk Abdomen, Bulk Leg). The other protocols involved the production of DNA extracts from single legs which were then merged prior to PCR (Composite Leg) or PCR-amplified separately (Single Leg) and then pooled. The amplicons generated by these four treatments were then sequenced on three platforms (Illumina MiSeq, Ion Torrent PGM and Ion Torrent S5). The choice of sequencing platform did not substantially influence species recovery, although the Miseq delivered the highest sequence quality. As expected, species recovery was most efficient from the Single Leg treatment because amplicon abundance varied little among taxa. Among the three treatments where PCR occurred after pooling, the Bulk Abdomen treatment produced a more uniform read abundance than the Bulk Leg or Composite Leg treatment. Primer choice also influenced species recovery and evenness. Our results reveal how variation in protocols can have substantial impacts on perceived diversity unless sequencing coverage is sufficient to reach an asymptote.

Expedited assessment of terrestrial arthropod diversity by coupling Malaise traps with DNA barcoding 1.

Monitoring changes in terrestrial arthropod communities over space and time requires a dramatic increase in the speed and accuracy of processing samples that cannot be achieved with morphological approaches. The combination of DNA barcoding and Malaise traps allows expedited, comprehensive inventories of species abundance whose cost will rapidly decline as high-throughput sequencing technologies advance. Aside from detailing protocols from specimen sorting to data release, this paper describes their use in a survey of arthropod diversity in a national park that examined 21 194 specimens representing 2255 species. These protocols can support arthropod monitoring programs at regional, national, and continental scales.

A Sequel to Sanger: amplicon sequencing that scales.

BACKGROUND: Although high-throughput sequencers (HTS) have largely displaced their Sanger counterparts, the short read lengths and high error rates of most platforms constrain their utility for amplicon sequencing. The present study tests the capacity of single molecule, real-time (SMRT) sequencing implemented on the SEQUEL platform to overcome these limitations, employing 658 bp amplicons of the mitochondrial cytochrome c oxidase I gene as a model system. RESULTS: By examining templates from more than 5000 species and 20,000 specimens, the performance of SMRT sequencing was tested with amplicons showing wide variation in GC composition and varied sequence attributes. SMRT and Sanger sequences were very similar, but SMRT sequencing provided more complete coverage, especially for amplicons with homopolymer tracts. Because it can characterize amplicon pools from 10,000 DNA extracts in a single run, the SEQUEL can reduce greatly reduce sequencing costs in comparison to first (Sanger) and second generation platforms (Illumina, Ion). CONCLUSIONS: SMRT analysis generates high-fidelity sequences from amplicons with varying GC content and is resilient to homopolymer tracts. Analytical costs are low, substantially less than those for first or second generation sequencers. When implemented on the SEQUEL platform, SMRT analysis enables massive amplicon characterization because each instrument can recover sequences from more than 5 million DNA extracts a year.

Machine Learned Replacement of N-Labels for Basecalled Sequences in DNA Barcoding.

This study presents a machine learning method that increases the number of identified bases in Sanger Sequencing. The system post-processes a KB basecalled chromatogram. It selects a recoverable subset of N-labels in the KB-called chromatogram to replace with basecalls (A,C,G,T). An N-label correction is defined given an additional read of the same sequence, and a human finished sequence. Corrections are added to the dataset when an alignment determines the additional read and human agree on the identity of the N-label. KB must also rate the replacement with quality value of in the additional read. Corrections are only available during system training. Developing the system, nearly 850,000 N-labels are obtained from Barcode of Life Datasystems, the premier database of genetic markers called DNA Barcodes. Increasing the number of correct bases improves reference sequence reliability, increases sequence identification accuracy, and assures analysis correctness. Keeping with barcoding standards, our system maintains an error rate of percent. Our system only applies corrections when it estimates low rate of error. Tested on this data, our automation selects and recovers: 79 percent of N-labels from COI (animal barcode); 80 percent from matK and rbcL (plant barcodes); and 58 percent from non-protein-coding sequences (across eukaryotes).