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Global gradients in species biodiversity are expected to reflect tighter packing of species closer to the equator. Yet, empirical validation of these patterns has so far focused on less diverse taxa, with comparable assessments of mega-diverse groups historically constrained by the taxonomic impediment. Here we assess the temporal and spatial turnover dynamics of arthropod communities sampled across 129 globally distributed monitoring sites. Overall, we encountered more than 150,000 unique BINs (i.e., species proxies). We show that global differences in community compositional change are linked to latitudinal, spatial, and temporal gradients, which are largely consistent across biogeographic regions. This general latitudinal imprint on community composition provides a mechanistic underpinning for global biodiversity gradients.

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.

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.

Publisher Copyright: © 2021 The Authors. Molecular Ecology Resources published by John Wiley & Sons Ltd. To associate specimens identified by molecular characters to other biological knowledge, we need reference sequences annotated by Linnaean taxonomy. In this study, we (1) report the creation of a comprehensive reference library of DNA barcodes for the arthropods of an entire country (Finland), (2) publish this library, and (3) deliver a new identification tool for insects and spiders, as based on this resource. The reference library contains mtDNA COI barcodes for 11,275 (43%) of 26,437 arthropod species known from Finland, including 10,811 (45%) of 23,956 insect species. To quantify the improvement in identification accuracy enabled by the current reference library, we ran 1000 Finnish insect and spider species through the Barcode of Life Data system (BOLD) identification engine. Of these, 91% were correctly assigned to a unique species when compared to the new reference library alone, 85% were correctly identified when compared to BOLD with the new material included, and 75% with the new material excluded. To capitalize on this resource, we used the new reference material to train a probabilistic taxonomic assignment tool, FinPROTAX, scoring high success. For the full-length barcode region, the accuracy of taxonomic assignments at the level of classes, orders, families, subfamilies, tribes, genera, and species reached 99.9%, 99.9%, 99.8%, 99.7%, 99.4%, 96.8%, and 88.5%, respectively. The FinBOL arthropod reference library and FinPROTAX are available through the Finnish Biodiversity Information Facility (www.laji.fi) at https://laji.fi/en/theme/protax. Overall, the FinBOL investment represents a massive capacity-transfer from the taxonomic community of Finland to all sectors of society.

International audience; Global insect decline has recently become a cause for major concern, particularly in the tropics where the vast majority of species occurs. Deforestation is suggested as being a major driver of this decline, but how anthropogenic changes in landscape structure affect tropical insect communities has rarely been addressed. We sampled Saturniidae and Sphingidae moths on 27 farms located in Brazilian Amazonia (Para state) and characterised by different deforestation histories. We used functional traits (forewing length, body mass, wing load, trophic niche breadth and resource use strategy), analysed by combining RLQ and null model analyses, to investigate the responses of their taxonomic and functional diversity to landscape change dynamics and current structure. We found that communities had a higher proportion of large and polyphagous species with low wing load in landscapes with low forest quality and relative cover and high land use turnover. This was mainly due to a significant response to deforestation by saturniids, whereas the more mobile sphingids showed no significant landscape-related pattern. We also observed an overall increase of species richness and functional dispersion in landscapes that have been deforested for a long time when compared with more recent agricultural settlements. Our results highlight the complex way in which landscape structure and historical dynamics interact to shape Neotropical moth communities and that saturniid moths respond clearly to the structure of the surrounding landscape, confirming their potential use as an indicator group for environmental monitoring programmes.

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.

BackgroundTraditional biomonitoring approaches have delivered a basic understanding of biodiversity, but they cannot support the large-scale assessments required to manage and protect entire ecosystems. This study employed DNA metabarcoding to assess spatial and temporal variation in species richness and diversity in arthropod communities from 52 protected areas spanning three Canadian ecoregions.ResultsThis study revealed the presence of 26,263 arthropod species in the three ecoregions and indicated that at least another 3,000–5,000 await detection. Results further demonstrate that communities are more similar within than between ecoregions, even after controlling for geographical distance. Overall α-diversity declined from east to west, reflecting a gradient in habitat disturbance. Shifts in species composition were high at every site with turnover greater than nestedness, suggesting the presence of many transient species.ConclusionsDifferences in species composition among their arthropod communities confirm that ecoregions are a useful synoptic for biogeographic patterns and for structuring conservation efforts. The present results also demonstrate that metabarcoding enables large-scale monitoring of shifts in species composition, making it possible to move beyond the biomass measurements that have been the key metric employed in prior efforts to track change in arthropod communities.

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., Measurement(s)DNA • digital imagingTechnology Type(s)taxonomic diversity assessment by targeted gene survey • digital curationFactor Type(s)species • geographic location • habitatSample Characteristic - OrganismMetazoaSample Characteristic - Environmentterrestrial biome • freshwater biome • marine biomeSample Characteristic - LocationCanada Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.10298354

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.

Fastq paired reads for analyzed samples are available as a NCBI Sequence Read Archive (ID#PRJNA606284). Custom script to detect cross-contamination is available from https://github.com/DNAdiversity/UCE-Cross-Contamination-Check.Datasets (morphological matrix, concatenated UCEs) and trees have been uploaded on Zenodo (https://doi.org/10.5281/zenodo.3666638).; International audience; 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.

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. 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 mtDNA barcodes. Out of 184,585 barcode sequences analysed, Rickettsia is observed in approximately 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, with a multilocus analysis of these strains revealing this symbiont commonly shifts between distantly related host taxa. A further targeted PCR screen of 1,612 individuals from 169 terrestrial and aquatic arthropod species identified mostly Torix strains (14/16) and supports the “aquatic hotspot” hypothesis for Torix infection. Furthermore, the analysis of Sequence Read Archive (SRA) deposits indicates Torix infections represent a significant proportion of all Rickettsia symbioses. This combination of methods reveals a broad host diversity associated with Torix Rickettsia including phloem-feeding bugs, parasitoid wasps, forest detritivores and vectors of disease. The unknown host effects and transmission strategies of these endosymbionts makes these newly discovered associations important to inform future directions of investigation involving the understudied Torix Rickettsia.

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.

Three new genera are described:Michener(Proteropinae),Bioalfa(Rogadinae), andHermosomastax(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:Aerophilus paulmarshi,Mesocoelus davidsmithi,Neothlipsis bobkulai,Plesiocoelus vanachterbergi,Pneumagathis erythrogastra(Cameron, 1905),Therophilus bobwhartoni,T. donaldquickei,T. gracewoodae,T. maetoi,T. montywoodi,T. penteadodiasae,Zacremnops brianbrowni,Z. coatlicueSharkey, 1990,Zacremnops cressoni(Cameron, 1887),Z. ekchuahSharkey, 1990,Z. josefernandezi,Zelomorpha sarahmeierottoae. BRACONINAE:Bracon alejandromarini,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,Digonogastra montylloydi,D. montywoodi,D. motohasegawai,D. natwheelwrighti,D. nickgrishini. CHELONINAE:Adelius adrianguadamuzi,A. gauldiShimbori & Shaw, 2019,A. janzeniShimbori & Shaw, 2019,Ascogaster gloriasihezarae,A. grettelvegae,A. guillermopereirai,A. gustavoecheverrii,A. katyvandusenae,A. luisdiegogomezi,Chelonus alejandrozaldivari,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,Leptodrepana alexisae,L. erasmocoronadoi,L. felipechavarriai,L. freddyquesadai,L. gilbertfuentesi,L. manuelriosi,Phanerotoma almasolisae,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,Pseudophanerotoma alanflemingi,Ps. albanjimenezi,Ps. alejandromarini,Ps. alexsmithi,Ps. allisonbrownae,Ps. bobrobbinsi. HOMOLOBINAE:Exasticolus jennyphillipsae,E. randallgarciai,E. robertofernandezi,E. sigifredomarini,E. tomlewinsoni. HORMIINAE:Hormius anamariamongeae,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:Oligoneurus kriskrishtalkai,O. jorgejimenezi,Paroligoneurus elainehoaglandae,P. julianhumphriesi,P. mikeiviei. MACROCENTRINAE:Austrozele jorgecampabadali,A. jorgesoberoni,Dolichozele gravitarsis(Muesebeck, 1938),D. josefernandeztrianai,D. josephinerodriguezae,Hymenochaonia kalevikulli,H. kateperezae,H. katherinebaillieae,H. katherineellisonae,H. katyvandusenae,H. kazumifukunagae,H. keithlangdoni,H. keithwillmotti,H. kenjinishidai,H. kimberleysheldonae,H. krisnorvigae,H. lilianamadrigalae,H. lizlangleyae,Macrocentrus fredsingeri,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:Orgilus amyrossmanae,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,Stantonia billalleni,S. brookejarvisae,S. donwilsoni,S. erikabjorstromae,S. garywolfi,S. henrikekmani,S. luismirandai,S. miriamzunzae,S. quentinwheeleri,S. robinkazmierae,S. ruthtifferae. PROTEROPINAE:Hebichneutes tricolorSharkey & Wharton, 1994,Proterops iangauldi,P. vickifunkae,Michener charlesi. RHYSIPOLINAE:Pseudorhysipolis luisfonsecai,P. mailyngonzalezaeRhysipolis julioquirosi. ROGADINAE:Aleiodes adrianaradulovae,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. ingaShimbori & Shaw, 2020,A. johnchemsaki,A. johnkingsolveri,A. gonodontovorusShimbori & Shaw, 2020,A. manuelzumbadoi,A. mayrabonillae,A. michelledsouzae,A. mikeiviei,A. normwoodleyi,A. pammitchellae,A. pauljohnsoni,A. rosewarnerae,A. steveashei,A. terryerwini,A. willsflowersi,Bioalfa pedroleoni,B. alvarougaldei,B. rodrigogamezi,Choreborogas andydeansi,C. eladiocastroi,C. felipechavarriai,C. frankjoycei,Clinocentrus andywarreni,Cl. angelsolisi,Cystomastax alexhausmanni,Cy. angelagonzalezae,Cy. ayaigarashiae,Hermosomastax clavifemorusQuicke sp. nov.,Heterogamus donstonei,Pseudoyelicones bernsweeneyi,Stiropius bencrairi,S. berndkerni,S. edgargutierrezi,S. edwilsoni,S. ehakernae,Triraphis billfreelandi,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. defectusValerio, 2015,T. danielhubi,T. davidduthiei,T. davidwahli,T. federicomatarritai,T. ferrisjabri,T. mariobozai,T. martindohrni,T. matssegnestami,T. mehrdadhajibabaei,T. ollieflinti,T. tildalauerae,Yelicones dirksteinkei,Y. markmetzi,Y. monserrathvargasae,Y. tricolorQuicke, 1996.Y. woldaiQuicke, 1996.The following new combinations are proposed:Neothlipsis smithi(Ashmead), new combination forMicrodus smithiAshmead, 1894;Neothlipsis pygmaeus(Enderlein), new combination forMicrodus pygmaeusEnderlein, 1920;Neothlipsis unicinctus(Ashmead), new combination forMicrodus unicinctusAshmead, 1894;Therophilus anomalus(Bortoni and Penteado-Dias) new combination forPlesiocoelus anomalusBortoni and Penteado-Dias, 2015;Aerophilus areolatus(Bortoni and Penteado-Dias) new combination forPlesiocoelus areolatusBortoni and Penteado-Dias, 2015;Pneumagathis erythrogastra(Cameron) new combination forAgathis erythrogastraCameron, 1905.Dolichozele citreitarsis(Enderlein), new combination forPaniscozele citreitarsisEnderlein, 1920.Dolichozele fuscivertex(Enderlein) new combination forPaniscozele fuscivertexEnderlein, 1920. Finally,Bassus brooksiSharkey, 1998 is synonymized withAgathis erythrogastraCameron, 1905;Paniscozele griseipesEnderlein, 1920 is synonymized withDolichozele koebeleiViereck, 1911;Paniscozele carinifronsEnderlein, 1920 is synonymized withDolichozele fuscivertex(Enderlein, 1920); andPaniscozele nigricaudaEnderlein,1920 is synonymized withDolichozele quaestor(Fabricius, 1804). (originally described asOphion quaestorFabricius, 1804).

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

The severe acute respiratory syndrome virus, SARS-CoV-2 (hereafter COVID-19), rapidly achieved global pandemic status, provoking large-scale screening programs in many nations. Their activation makes it imperative to identify methods that can deliver a diagnostic result at low cost. This paper describes an approach which employs sequence variation in the gene coding for its envelope protein as the basis for a scalable, inexpensive test for COVID-19. It achieves this by coupling a simple RNA extraction protocol with low-volume RT-PCR, followed by E-Gel screening and sequencing on high-throughput platforms to analyze 10,000 samples in a run. Slight modifications to the protocol could support screening programs for other known viruses and for viral discovery. Just as the $1,000 genome is transforming medicine, a $1 diagnostic test for viral and bacterial pathogens would represent a major advance for public health.

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 $>60$ 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).

Participants in the 7th International Barcode of Life Conference (Kruger National Park, South Africa, 20–24 November 2017) share the latest findings in DNA barcoding research and its increasingly diversified applications. Here, we review prevailing trends synthesized from among 429 invited and contributed abstracts, which are collated in this open-access special issue of Genome. Hosted for the first time on the African continent, the 7th Conference places special emphasis on the evolutionary origins, biogeography, and conservation of African flora and fauna. Within Africa and elsewhere, DNA barcoding and related techniques are being increasingly used for wildlife forensics and for the validation of commercial products, such as medicinal plants and seafood species. A striking trend of the conference is the dramatic rise of studies on environmental DNA (eDNA) and on diverse uses of high-throughput sequencing techniques. Emerging techniques in these areas are opening new avenues for environmental biomonitoring, managing species-at-risk and invasive species, and revealing species interaction networks in unprecedented detail. Contributors call for the development of validated community standards for high-throughput sequence data generation and analysis, to enable the full potential of these methods to be realized for understanding and managing biodiversity on a global scale.

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 five-prime 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 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.

Forensic studies 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 forensic 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 the 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, forensic 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.

Our knowledge of global biodiversity remains incomplete and beset by knowledge shortfalls affecting both the census of species (i.e. the Linnean shortfall) and our understanding of their distributions (i.e. the Wallacean shortfall; Hortal et al. 2015). While alarming rates of species extinction have been reported in most groups of organisms, our capacity to assess extinction threats is limited by these shortfalls and it has become imperative to optimize our use of existing information for the analyses of biodiversity data. There are two major challenges when integrating biodiversity data from heterogeneous sources to ultimately analyzing them: The frequent disparity in taxon names used to refer to the same organisms; Geographical inconsistencies on specimen information. The frequent disparity in taxon names used to refer to the same organisms; Geographical inconsistencies on specimen information. The first refers to disagreements about the taxon concepts attached to names alongside the different interpretations and applications (e.g. gender agreement in taxonomic names) of the existing nomenclatural rules that ensure universality and stability of scientific names. The development of new methods for species delineation, and in particular with the growing integration of genetic data in the practice of taxonomy (e.g. DNA barcoding; Ratnasingham and Hebert 2013), has increased our ability to discriminate closely related species. This enhances the resolution level at which biodiversity is documented, described and analyzed(Goldstein and DeSalle 2011). One frequent outcome is the redefinition of species boundaries; either through merging (synonymy) or splitting of previously recognized species. In understudied groups such as insects, the resulting inflation of names, sometimes provisional, further defies the reconciliation of names used by different sources. The second challenge refers to the completeness and accuracy of geographical information. Specimen records in biodiversity databases often lack GPS coordinates. Consequently we need to accurately infer the latitude and longitude from place names. Other frequent inaccuracies include erroneous georeferencing, imprecision and/or error in the location of a record (Soberón and Peterson 2004). Integration of data on the basis of taxon names and their geographic information is a major challenge that either results in excluding a significant number of records, or in merging incompatible records, leading to erroneous outcomes. Therefore, we have developed WF.ACTIAS, a computational workflow that gathers data from several sources and provides the user with tools to make objective and reproducible decisions to assign records to a consensus species name, while detecting and correcting geographical inconsistencies. Its main objective is to automate a process that can integrate information about nomenclature, taxon concepts and geographical information to reconcile taxon names from different sources. Here, we present the WF.ACTIAS workflow in the context of the analysis of diversity in two sister families of moths – the Saturniidae and Sphingidae. Species boundaries in these insects have been thoroughly and comprehensively revisited through the integration of DNA barcodes and we are tackling the reconciliation of taxon names in ca. 282 000 records of which more than 77 000 have DNA barcodes. The outcome of this data integration is essential to study patterns of biodiversity and distributions and sets the ground to extend this process to other groups of organisms.

Widespread interest in the study of metabarcoding has resulted in data proliferation and the development of a multitude of powerful computational tools. Yet consistent and reproducible interpretation of the data remains challenging. The integration of different data types, software tools, and analytical parameters pose a barrier to scaling research. Further, though the majority of the necessary tools for performing these analyses are already implemented, there is limited support for high throughput analysis due to the requirement for heavy computational capacity. As a result of these complexities, many researchers lack the time, training, or infrastructure to work with larger datasets. mBRAVE, the Multiplex Barcode Research And Visualization Environment, is a cloud-based data storage and analytics platform with standardized pipelines and a sophisticated web interface for transforming raw high-throughput sequencing (HTS) data into biological insights. mBRAVE integrates common analytical methods and links to the Barcode of Life Data (BOLD) System for reference datasets, presenting users with the ability to analyze large volumes of data, without requiring special technical training. mBRAVE's cloud architecture provides centralized and automated storage and compute capacity, thereby reducing the burden on individual researchers. The mBRAVE platform seeks to alleviate the main informatic challenges faced by the metabarcoding research community: the storage and consistent interpretation of HTS data. It is now available for researcher use at www.mbrave.net.

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.

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.

DNA metabarcoding is an attractive approach for monitoring biodiversity. However, it is subject to biases that often impede detection of all species in a sample. In particular, the proportion of sequences recovered from each species depends on its biomass, mitome copy number, and primer set employed for PCR. To examine these variables, we constructed a mock community of terrestrial arthropods comprised of 374 BINs, a species proxy. 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 partitions (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 amplicon 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, other variables did. As expected, the best recovery was obtained from the Single Leg treatment, but the Bulk Abdomen produced a more uniform read abundance than the Bulk Leg or Composite Leg samples. Primer choice also influenced species recovery. Our results reveal how variation in protocols can have substantive impacts on perceived diversity unless sequencing coverage is sufficient to reach an asymptote. Although metabarcoding is a powerful approach, further optimization of analytical protocols is crucial to obtain reproducible results and increase its cost-effectiveness.

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.

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.

Figure S1. Sliding window (5Â bp) analysis showing the distribution of homopolymer regions in the COI gene for the 95 taxa in library #1, the GC composition of the homopolymer regions, the incidence and position of indels in and substitutions in COI sequences for each window in the 99.9% SMRT partition. The incidence of substitutions and indels are shown per base pair. Figure S2. Bivariate plot showing the mean number of indels per CCS for each taxon in the four libraries versus the GC content of a COI amplicon. The number of indels was determined through comparison with the corresponding Sanger sequence for each taxon. Figure S3. Bivariate plot showing the mean number of indels in SMRT sequences for the 95 specimens shared by Libraries #1 and #2. Figure S4. NJ tree of COI sequences for the 945 species in Library #2 showing the correspondence between Sanger and SMRT sequences. Figure S5. Distribution of similarity values between Sanger and SMRT sequences for the 9120 specimens in Library #3 and the 9830 specimens in Library #4 for the three SMRT partitions. Figure S6. Relationship between the number of circular consensus sequences for a COI amplicon and its GC content. The 95 and 950 amplicon datasets (Libraries #1/2) were generated via the PCR1 protocol while the 9120 and 9830 amplicon datasets (Libraries #3/4) were generated via the PCR2 protocol. Figure S7. Incidence of low quality Sanger reads in cases of target and non-target wells for Libraries #3/4. (PDF 4664Â kb)

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. Electronic supplementary material The online version of this article (10.1186/s12864-018-4611-3) contains supplementary material, which is available to authorized users.

SummaryMonitoring 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 20,000 specimens representing 2200 species.These protocols can support arthropod monitoring programs at regional, national, and continental scales.