Implementing high-throughput insect barcoding in microbiome studies: impact of non-destructive DNA extraction on microbiome reconstruction.

Background: Symbiotic relationships with diverse microorganisms are crucial for many aspects of insect biology. However, while our understanding of insect taxonomic diversity and the distribution of insect species in natural communities is limited, we know much less about their microbiota. In the era of rapid biodiversity declines, as researchers increasingly turn towards DNA-based monitoring, developing and broadly implementing approaches for high-throughput and cost-effective characterization of both insect and insect-associated microbial diversity is essential. We need to verify whether approaches such as high-throughput barcoding, a powerful tool for identifying wild insects, would permit subsequent microbiota reconstruction in these specimens. Methods: High-throughput barcoding ("megabarcoding") methods often rely on non-destructive approaches for obtaining template DNA for PCR amplification by leaching DNA out of insect specimens using alkaline buffers such as HotSHOT. This study investigated the impact of HotSHOT on microbial abundance estimates and the reconstructed bacterial community profiles. We addressed this question by comparing quantitative 16S rRNA amplicon sequencing data for HotSHOT-treated or untreated specimens of 16 insect species representing six orders and selected based on the expectation of limited variation among individuals. Results: We find that in 13 species, the treatment significantly reduced microbial abundance estimates, corresponding to an estimated 15-fold decrease in amplifiable 16S rRNA template on average. On the other hand, HotSHOT pre-treatment had a limited effect on microbial community composition. The reconstructed presence of abundant bacteria with known significant effects was not affected. On the other hand, we observed changes in the presence of low-abundance microbes, those close to the reliable detection threshold. Alpha and beta diversity analyses showed compositional differences in only a few species. Conclusion: Our results indicate that HotSHOT pre-treated specimens remain suitable for microbial community composition reconstruction, even if abundance may be hard to estimate. These results indicate that we can cost-effectively combine barcoding with the study of microbiota across wild insect communities. Thus, the voucher specimens obtained using megabarcoding studies targeted at characterizing insect communities can be used for microbiome characterizations. This can substantially aid in speeding up the accumulation of knowledge on the microbiomes of abundant and hyperdiverse insect species.

Towards global insect biomonitoring with frugal methods.

None of the global targets for protecting nature are currently met, although humanity is critically dependent on biodiversity. A significant issue is the lack of data for most biodiverse regions of the planet where the use of frugal methods for biomonitoring would be particularly important because the available funding for monitoring is insufficient, especially in low-income countries. We here discuss how three approaches to insect biomonitoring (computer vision, lidar, DNA sequences) could be made more frugal and urge that all biomonitoring techniques should be evaluated for global suitability before becoming the default in high-income countries. This requires that techniques popular in high-income countries should undergo a phase of 'innovation through simplification' before they are implemented more broadly. We predict that techniques that acquire raw data at low cost and are suitable for analysis with AI (e.g. images, lidar-signals) will be particularly suitable for global biomonitoring, while techniques that rely heavily on patented technologies may be less promising (e.g. DNA sequences). We conclude the opinion piece by pointing out that the widespread use of AI for data analysis will require a global strategy for providing the necessary computational resources and training. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Towards holistic insect monitoring: species discovery, description, identification and traits for all insects.

Holistic insect monitoring needs scalable techniques to overcome taxon biases, determine species abundances, and gather functional traits for all species. This requires that we address taxonomic impediments and the paucity of data on abundance, biomass and functional traits. We here outline how these data deficiencies could be addressed at scale. The workflow starts with large-scale barcoding (megabarcoding) of all specimens from mass samples obtained at biomonitoring sites. The barcodes are then used to group the specimens into molecular operational taxonomic units that are subsequently tested/validated as species with a second data source (e.g. morphology). New species are described using barcodes, images and short diagnoses, and abundance data are collected for both new and described species. The specimen images used for species discovery then become the raw material for training artificial intelligence identification algorithms and collecting trait data such as body size, biomass and feeding modes. Additional trait data can be obtained from vouchers by using genomic tools developed by molecular ecologists. Applying this pipeline to a few samples per site will lead to greatly improved insect monitoring regardless of whether the species composition of a sample is determined with images, metabarcoding or megabarcoding. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Scalable, Cost-Effective, and Decentralized DNA Barcoding with Oxford Nanopore Sequencing.

DNA barcodes are useful in biodiversity research, but sequencing barcodes with dye termination methods ("Sanger sequencing") has been so time-consuming and expensive that DNA barcodes are not as widely used as they should be. Fortunately, MinION sequencers from Oxford Nanopore Technologies have recently emerged as a cost-effective and efficient alternative for barcoding. MinION barcodes are now suitable for large-scale species discovery and enable specimen identification when the target species are represented in barcode databases. With a MinION, it is possible to obtain 10,000 barcodes from a single flow cell at a cost of less than 0.10 USD per specimen. Additionally, a Flongle flow cell can be used for small projects requiring up to 300 barcodes (0.50 USD per specimen). We here describe a cost-effective laboratory workflow for obtaining tagged amplicons, preparing ONT libraries, sequencing amplicon pools, and analyzing the MinION reads with the software ONTbarcoder. This workflow has been shown to yield highly accurate barcodes that are 99.99% identical to Sanger barcodes. Overall, we propose that the use of MinION for DNA barcoding is an attractive option for all researchers in need of a cost-effective and efficient solution for large-scale species discovery and specimen identification.

Express barcoding with NextGenPCR and MinION for species-level sorting of ecological samples.

The use of DNA barcoding is well established for specimen identification and large-scale biodiversity discovery, but remains underutilized for time-sensitive applications such as rapid species discovery in field stations, identifying pests, citizen science projects, and authenticating food. The main reason is that existing express barcoding workflows are either too expensive or can only be used in very well-equipped laboratories by highly-trained staff. We here show an alternative workflow combining rapid DNA extraction with HotSHOT, amplicon production with NextGenPCR thermocyclers, and sequencing with low-cost MinION sequencers. We demonstrate the power of the approach by generating 250 barcodes for 285 specimens within 6 h including specimen identification through BLAST. The workflow required only the following major equipment that easily fits onto a lab bench: Thermocycler, NextGenPCR, microplate sealer, Qubit, and MinION. Based on our results, we argue that simplified barcoding workflows for species-level sorting are now faster, more accurate, and sufficiently cost-effective to replace traditional morpho-species sorting in many projects.

ONTbarcoder 2.0: rapid species discovery and identification with real-time barcoding facilitated by Oxford Nanopore R10.4.

Most arthropod species are undescribed and hidden in specimen-rich samples that are difficult to sort to species using morphological characters. For such samples, sorting to putative species with DNA barcodes is an attractive alternative, but needs cost-effective techniques that are suitable for use in many laboratories around the world. Barcoding using the portable and inexpensive MinION sequencer produced by Oxford Nanopore Technologies (ONT) could be useful for presorting specimen-rich samples with DNA barcodes because it requires little space and is inexpensive. However, similarly important is user-friendly and reliable software for analysis of the ONT data. It is here provided in the form of ONTbarcoder 2.0 that is suitable for all commonly used operating systems and includes a Graphical User Interface (GUI). Compared with an earlier version, ONTbarcoder 2.0 has three key improvements related to the higher read quality obtained with ONT's latest flow cells (R10.4), chemistry (V14 kits) and basecalling model (super-accuracy model). First, the improved read quality of ONT's latest flow cells (R10.4) allows for the use of primers with shorter indices than those previously needed (9 bp vs. 12-13 bp). This decreases the primer cost and can potentially improve PCR success rates. Second, ONTbarcoder now delivers real-time barcoding to complement ONT's real-time sequencing. This means that the first barcodes are obtained within minutes of starting a sequencing run; i.e. flow cell use can be optimized by terminating sequencing runs when most barcodes have already been obtained. The only input needed by ONTbarcoder 2.0 is a demultiplexing sheet and sequencing data (raw or basecalled) generated by either a Mk1B or a Mk1C. Thirdly, we demonstrate that the availability of R10.4 chemistry for the low-cost Flongle flow cell is an attractive option for users who require only 200-250 barcodes at a time.

Two centuries of biodiversity discovery and loss in Singapore.

There is an urgent need for reliable data on the impacts of deforestation on tropical biodiversity. The city-state of Singapore has one of the most detailed biodiversity records in the tropics, dating back to the turn of the 19th century. In 1819, Singapore was almost entirely covered in primary forest, but this has since been largely cleared. We compiled more than 200 y of records for 10 major taxonomic groups in Singapore (>50,000 individual records; >3,000 species), and we estimated extinction rates using recently developed and novel statistical models that account for "dark extinctions," i.e., extinctions of undiscovered species. The estimated overall extinction rate was 37% (95% CI [31 to 42%]). Extrapolating our Singapore observations to a future business-as-usual deforestation scenario for Southeast Asia suggests that 18% (95% CI [16 to 22%]) of species will be lost regionally by 2100. Our extinction estimates for Singapore and Southeast Asia are a factor of two lower than previous estimates that also attempted to account for dark extinctions. However, we caution that particular groups such as large mammals, forest-dependent birds, orchids, and butterflies are disproportionately vulnerable.

Global determinants of insect mitochondrial genetic diversity.

Understanding global patterns of genetic diversity is essential for describing, monitoring, and preserving life on Earth. To date, efforts to map macrogenetic patterns have been restricted to vertebrates, which comprise only a small fraction of Earth's biodiversity. Here, we construct a global map of predicted insect mitochondrial genetic diversity from cytochrome c oxidase subunit 1 sequences, derived from open data. We calculate the mitochondrial genetic diversity mean and genetic diversity evenness of insect assemblages across the globe, identify their environmental correlates, and make predictions of mitochondrial genetic diversity levels in unsampled areas based on environmental data. Using a large single-locus genetic dataset of over 2 million globally distributed and georeferenced mtDNA sequences, we find that mitochondrial genetic diversity evenness follows a quadratic latitudinal gradient peaking in the subtropics. Both mitochondrial genetic diversity mean and evenness positively correlate with seasonally hot temperatures, as well as climate stability since the last glacial maximum. Our models explain 27.9% and 24.0% of the observed variation in mitochondrial genetic diversity mean and evenness in insects, respectively, making an important step towards understanding global biodiversity patterns in the most diverse animal taxon.

Convergence of dominance and neglect in flying insect diversity.

Most of arthropod biodiversity is unknown to science. Consequently, it has been unclear whether insect communities around the world are dominated by the same or different taxa. This question can be answered through standardized sampling of biodiversity followed by estimation of species diversity and community composition with DNA barcodes. Here this approach is applied to flying insects sampled by 39 Malaise traps placed in five biogeographic regions, eight countries and numerous habitats (>225,000 specimens belonging to >25,000 species in 458 families). We find that 20 insect families (10 belonging to Diptera) account for >50% of local species diversity regardless of clade age, continent, climatic region and habitat type. Consistent differences in family-level dominance explain two-thirds of variation in community composition despite massive levels of species turnover, with most species (>97%) in the top 20 families encountered at a single site only. Alarmingly, the same families that dominate insect diversity are 'dark taxa' in that they suffer from extreme taxonomic neglect, with little signs of increasing activities in recent years. Taxonomic neglect tends to increase with diversity and decrease with body size. Identifying and tackling the diversity of 'dark taxa' with scalable techniques emerge as urgent priorities in biodiversity science.

Size rather than complexity of sexual ornaments prolongs male metamorphosis and explains sexual size dimorphism in sepsid flies.

Male sexual ornaments often evolve rapidly and are thought to be costly, thus contributing to sexual size dimorphism. However, little is known about their developmental costs, and even less about costs associated with structural complexity. Here, we quantified the size and complexity of three morphologically elaborate sexually dimorphic male ornaments that starkly differ across sepsid fly species (Diptera: Sepsidae): (i) male forelegs range from being unmodified, like in most females, to being adorned with spines and large cuticular protrusions; (ii) the fourth abdominal sternites are either unmodified or are converted into complex de novo appendages; and (iii) male genital claspers range from small and simple to large and complex (e.g. bifurcated). We tracked the development of 18 sepsid species from egg to adult to determine larval feeding and pupal metamorphosis times of both sexes. We then statistically explored whether pupal and adult body size, ornament size and/or ornament complexity are correlated with sex-specific development times. Larval growth and foraging periods of male and female larvae did not differ, but the time spent in the pupal stage was ca 5% longer for sepsid males despite emerging 9% smaller than females on average. Surprisingly, we found no evidence that sexual trait complexity prolongs pupal development beyond some effects of trait size. Evolving more complex traits thus does not incur developmental costs at least in this system.

Future of DNA-based insect monitoring.

Insects are crucial for ecosystem health but climate change and pesticide use are driving massive insect decline. To mitigate this loss, we need new and effective monitoring techniques. Over the past decade there has been a shift to DNA-based techniques. We describe key emerging techniques for sample collection. We suggest that the selection of tools should be broadened, and that DNA-based insect monitoring data need to be integrated more rapidly into policymaking. We argue that there are four key areas for advancement, including the generation of more complete DNA barcode databases to interpret molecular data, standardisation of molecular methods, scaling up of monitoring efforts, and integrating molecular tools with other technologies that allow continuous, passive monitoring based on images and/or laser imaging, detection, and ranging (LIDAR).

Multispecies environmental DNA metabarcoding sheds light on annual coral spawning events.

Synchronous multispecific coral spawning generally occurs annually and forms an integral part of the coral life cycle. Apart from spawning times and species participation, however, much else remains unknown. Here, we applied environmental DNA (eDNA) metabarcoding to study two tropical reef sites of contrasting coral cover before, during and after coral spawning. Using coral-ITS2 and vertebrate-12S markers, we evaluated eDNA as an alternative monitoring tool by assessing its capabilities in detecting spawning species and tracking relative abundances of coral and fish eDNA. Over 3 years, elevated eDNA coral signals during the event (proportional read increase of up to five-fold) were observed, detecting a total of 38 coral and 133 fish species with all but one of the coral species visually observed to be spawning. This is also the first demonstration that eDNA metabarcoding can be used to infer the diurnal partitioning of night- and day-time spawning, spawning in coral species overlooked by visual surveys, and the associated changes in fish trophic structures as an indicator of spawning events. Our study paves the way for applied quantitative eDNA metabarcoding approaches to better study ephemeral and important biological events.

Network analysis with either Illumina or MinION reveals that detecting vertebrate species requires metabarcoding of iDNA from a diverse fly community.

DNA obtained from invertebrates (iDNA) can be metabarcoded in order to survey vertebrate communities. However, little attention has been paid to the interaction between the invertebrate and vertebrate species. Here, we tested for specialization by sampling the dung and carrion fly community of a swamp forest remnant along a disturbance gradient (10 sites: 80-310 m from a road). Approximately, 60% of the baited 407 flies yielded 294 vertebrate identifications based on two COI fragments and 16S. A bipartite network analysis found no statistically significant specialization in the interactions between fly and vertebrate species, but uncommon fly species can carry the signal for vertebrate species that are otherwise difficult to detect with iDNA. A spatial analysis revealed that most of the 20 vertebrate species reported in this study could be detected within 150 m of the road (18 spp.) and that the fly community sourced for iDNA was unexpectedly rich (24 species, 3 families). They carried DNA for rare and common species inhabiting different layers of the forest (ground-dwelling: wild boar, Sunda pangolin, skinks, rats; arboreal: long-tailed macaque, Raffles' banded langur; flying: pin-striped tit-babbler, olive-winged bulbul). All our results were obtained with a new, greatly simplified iDNA protocol that eliminates DNA extraction by obtaining template directly through dissolving fly faeces and regurgitates with water. Lastly, we show that MinION- and Illumina-based metabarcoding yield similar results. We conclude by urging more studies that use different baits and involve experiments that are capable of revealing the dispersal capabilities of the flies carrying the iDNA.

Collective and harmonized high throughput barcoding of insular arthropod biodiversity: Toward a Genomic Observatories Network for islands.

Current understanding of ecological and evolutionary processes underlying island biodiversity is heavily shaped by empirical data from plants and birds, although arthropods comprise the overwhelming majority of known animal species, and as such can provide key insights into processes governing biodiversity. Novel high throughput sequencing (HTS) approaches are now emerging as powerful tools to overcome limitations in the availability of arthropod biodiversity data, and hence provide insights into these processes. Here, we explored how these tools might be most effectively exploited for comprehensive and comparable inventory and monitoring of insular arthropod biodiversity. We first reviewed the strengths, limitations and potential synergies among existing approaches of high throughput barcode sequencing. We considered how this could be complemented with deep learning approaches applied to image analysis to study arthropod biodiversity. We then explored how these approaches could be implemented within the framework of an island Genomic Observatories Network (iGON) for the advancement of fundamental and applied understanding of island biodiversity. To this end, we identified seven island biology themes at the interface of ecology, evolution and conservation biology, within which collective and harmonized efforts in HTS arthropod inventory could yield significant advances in island biodiversity research.

Phylogenetic resolution of the fly superfamily Ephydroidea-Molecular systematics of the enigmatic and diverse relatives of Drosophilidae.

The schizophoran superfamily Ephydroidea (Diptera: Cyclorrhapha) includes eight families, ranging from the well-known vinegar flies (Drosophilidae) and shore flies (Ephydridae), to several small, relatively unusual groups, the phylogenetic placement of which has been particularly challenging for systematists. An extraordinary diversity in life histories, feeding habits and morphology are a hallmark of fly biology, and the Ephydroidea are no exception. Extreme specialization can lead to "orphaned" taxa with no clear evidence for their phylogenetic position. To resolve relationships among a diverse sample of Ephydroidea, including the highly modified flies in the families Braulidae and Mormotomyiidae, we conducted phylogenomic sampling. Using exon capture from Anchored Hybrid Enrichment and transcriptomics to obtain 320 orthologous nuclear genes sampled for 32 species of Ephydroidea and 11 outgroups, we evaluate a new phylogenetic hypothesis for representatives of the superfamily. These data strongly support monophyly of Ephydroidea with Ephydridae as an early branching radiation and the placement of Mormotomyiidae as a family-level lineage sister to all remaining families. We confirm placement of Cryptochetidae as sister taxon to a large clade containing both Drosophilidae and Braulidae-the latter a family of honeybee ectoparasites. Our results reaffirm that sampling of both taxa and characters is critical in hyperdiverse clades and that these factors have a major influence on phylogenomic reconstruction of the history of the schizophoran fly radiation.

Towards Large-Scale Integrative Taxonomy (LIT): Resolving the Data Conundrum for Dark Taxa.

New, rapid, accurate, scalable, and cost-effective species discovery and delimitation methods are needed for tackling "dark taxa," here defined as groups for which $<$10$\%$ of all species are described and the estimated diversity exceeds 1,000 species. Species delimitation for these taxa should be based on multiple data sources ("integrative taxonomy") but collecting multiple types of data risks impeding a discovery process that is already too slow. We here develop large-scale integrative taxonomy (LIT), an explicit method where preliminary species hypotheses are generated based on inexpensive data that can be obtained quickly and cost-effectively. These hypotheses are then evaluated based on a more expensive type of "validation data" that is only obtained for specimens selected based on objective criteria applied to the preliminary species hypotheses. We here use this approach to sort 18,000 scuttle flies (Diptera: Phoridae) into 315 preliminary species hypotheses based on next-generation sequencing barcode (313 bp) clusters (using objective clustering [OC] with a 3$\%$ threshold). These clusters are then evaluated with morphology as the validation data. We develop quantitative indicators for predicting which barcode clusters are likely to be incongruent with morphospecies by randomly selecting 100 clusters for in-depth validation with morphology. A linear model demonstrates that the best predictors for incongruence between barcode clusters and morphology are maximum p-distance within the cluster and a newly proposed index that measures cluster stability across different clustering thresholds. A test of these indicators using the 215 remaining clusters reveals that these predictors correctly identify all clusters that are incongruent with morphology. In our study, all morphospecies are true or disjoint subsets of the initial barcode clusters so that all incongruence can be eliminated by varying clustering thresholds. This leads to a discussion of when a third data source is needed to resolve incongruent grouping statements. The morphological validation step in our study involved 1,039 specimens (5.8$\%$ of the total). The formal LIT protocol we propose would only have required the study of 915 (5.1$\%$: 2.5 specimens per species), as we show that clusters without signatures of incongruence can be validated by only studying two specimens representing the most divergent haplotypes. To test the generality of our results across different barcode clustering techniques, we establish that the levels of incongruence are similar across OC, Automatic Barcode Gap Discovery (ABGD), Poisson Tree Processes (PTP), and Refined Single Linkage (RESL) (used by Barcode of Life Data System to assign Barcode Index Numbers [BINs]). OC and ABGD achieved a maximum congruence score with the morphology of 89$\%$ while PTP was slightly less effective (84$\%$). RESL could only be tested for a subset of the specimens because the algorithm is not public. BINs based on 277 of the original 1,714 haplotypes were 86$\%$ congruent with morphology while the values were 89$\%$ for OC, 74$\%$ for PTP, and 72$\%$ for ABGD. [Biodiversity discovery; dark taxa; DNA barcodes; integrative taxonomy.].

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.

Halting biodiversity decline is one of the most critical challenges for humanity, but monitoring biodiversity is hampered by taxonomic impediments. One impediment is the large number of undescribed species (here called "dark taxon impediment") whereas another is caused by the large number of superficial species descriptions, that can only be resolved by consulting type specimens ("superficial description impediment"). Recently, Sharkey et al. (2021) proposed to address the dark taxon impediment for Costa Rican braconid wasps by describing 403 species based on COI barcode clusters ("BINs") computed by BOLD Systems. More than 99% of the BINs (387 of 390) were converted into species by assigning binominal names (e.g. BIN "BOLD:ACM9419" becomes Bracon federicomatarritai) and adding a minimal diagnosis (consisting only of a consensus barcode for most species). We here show that many of Sharkey et al.'s species are unstable when the underlying data are analyzed using different species delimitation algorithms. Add the insufficiently informative diagnoses, and many of these species will become the next "superficial description impediment" for braconid taxonomy because they will have to be tested and redescribed after obtaining sufficient evidence for confidently delimiting species. We furthermore show that Sharkey et al.'s approach of using consensus barcodes as diagnoses is not functional because it cannot be applied consistently. Lastly, we reiterate that COI alone is not suitable for delimiting and describing species, and voice concerns over Sharkey et al.'s uncritical use of BINs because they are calculated by a proprietary algorithm (RESL) that uses a mixture of public and private data. We urge authors, reviewers and editors to maintain high standards in taxonomy by only publishing new species that are rigorously delimited with open-access tools and supported by publicly available evidence.

DiversityScanner: Robotic handling of small invertebrates with machine learning methods.

Invertebrate biodiversity remains poorly understood although it comprises much of the terrestrial animal biomass, most species and supplies many ecosystem services. The main obstacle is specimen-rich samples obtained with quantitative sampling techniques (e.g., Malaise trapping). Traditional sorting requires manual handling, while molecular techniques based on metabarcoding lose the association between individual specimens and sequences and thus struggle with obtaining precise abundance information. Here we present a sorting robot that prepares specimens from bulk samples for barcoding. It detects, images and measures individual specimens from a sample and then moves them into the wells of a 96-well microplate. We show that the images can be used to train convolutional neural networks (CNNs) that are capable of assigning the specimens to 14 insect taxa (usually families) that are particularly common in Malaise trap samples. The average assignment precision for all taxa is 91.4% (75%-100%). This ability of the robot to identify common taxa then allows for taxon-specific subsampling, because the robot can be instructed to only pick a prespecified number of specimens for abundant taxa. To obtain biomass information, the images are also used to measure specimen length and estimate body volume. We outline how the DiversityScanner can be a key component for tackling and monitoring invertebrate diversity by combining molecular and morphological tools: the images generated by the robot become training images for machine learning once they are labelled with taxonomic information from DNA barcodes. We suggest that a combination of automation, machine learning and DNA barcoding has the potential to tackle invertebrate diversity at an unprecedented scale.

Monophyletic blowflies revealed by phylogenomics.

BACKGROUND: Blowflies are ubiquitous insects, often shiny and metallic, and the larvae of many species provide important ecosystem services (e.g., recycling carrion) and are used in forensics and debridement therapy. Yet, the taxon has repeatedly been recovered to be para- or polyphyletic, and the lack of a well-corroborated phylogeny has prevented a robust classification. RESULTS: We here resolve the relationships between the different blowfly subclades by including all recognized subfamilies in a phylogenomic analysis using 2221 single-copy nuclear protein-coding genes of Diptera. Maximum likelihood (ML), maximum parsimony (MP), and coalescent-based phylogeny reconstructions all support the same relationships for the full data set. Based on this backbone phylogeny, blowflies are redefined as the most inclusive monophylum within the superfamily Oestroidea not containing Mesembrinellidae, Mystacinobiidae, Oestridae, Polleniidae, Sarcophagidae, Tachinidae, and Ulurumyiidae. The constituent subfamilies are re-classified as Ameniinae (including the Helicoboscinae, syn. nov.), Bengaliinae, Calliphorinae (including Aphyssurinae, syn. nov., Melanomyinae, syn. nov., and Toxotarsinae, syn. nov.), Chrysomyinae, Luciliinae, Phumosiinae, Rhiniinae stat. rev., and Rhinophorinae stat. rev. Metallic coloration in the adult is shown to be widespread but does not emerge as the most likely ground plan feature. CONCLUSIONS: Our study provides the first phylogeny of oestroid calyptrates including all blowfly subfamilies. This allows settling a long-lasting controversy in Diptera by redefining blowflies as a well-supported monophylum, and blowfly classification is adjusted accordingly. The archetypical blowfly trait of carrion-feeding maggots most likely evolved twice, and the metallic color may not belong to the blowfly ground plan.

Mangroves are an overlooked hotspot of insect diversity despite low plant diversity.

BACKGROUND: The world's fast disappearing mangrove forests have low plant diversity and are often assumed to also have a species-poor insect fauna. We here compare the tropical arthropod fauna across a freshwater swamp and six different forest types (rain-, swamp, dry-coastal, urban, freshwater swamp, mangroves) based on 140,000 barcoded specimens belonging to ca. 8500 species. RESULTS: We find that the globally imperiled habitat "mangroves" is an overlooked hotspot for insect diversity. Our study reveals a species-rich mangrove insect fauna (>3000 species in Singapore alone) that is distinct (>50% of species are mangrove-specific) and has high species turnover across Southeast and East Asia. For most habitats, plant diversity is a good predictor of insect diversity, but mangroves are an exception and compensate for a comparatively low number of phytophagous and fungivorous insect species by supporting an unusually rich community of predators whose larvae feed in the productive mudflats. For the remaining tropical habitats, the insect communities have diversity patterns that are largely congruent across guilds. CONCLUSIONS: The discovery of such a sizeable and distinct insect fauna in a globally threatened habitat underlines how little is known about global insect biodiversity. We here show how such knowledge gaps can be closed quickly with new cost-effective NGS barcoding techniques.