Abstract DNA metabarcoding—high‐throughput sequencing of barcode regions from bulk samples—has become a key tool for insect biodiversity assessment. Yet, how methodological choices affect the accuracy of metabarcoding data remains insufficiently explored. In this paper, we ask: (1) How does the lysis method (non‐destructive lysis vs. destructive homogenization) affect community recovery? (2) How comprehensively does metabarcoding capture species richness? (3) To what extent can spike‐ins improve abundance estimates? (4) How accurately can species abundances be estimated? We evaluated the accuracy of insect metabarcoding using 4749 bulk samples from a large‐scale biodiversity survey subjected to mild lysis. Of these samples, 856 were also homogenized, allowing a systematic comparison of the effect of alternative treatments. To potentially improve abundance estimates, we added six biological spike‐ins (i.e. foreign insects) to all samples, and two synthetic spike‐ins (artificial DNA fragments) to the homogenization treatment. In addition, we established the contents of 15 samples by individually barcoding all specimens, enabling direct assessment of occurrence and abundance estimates. Our results revealed consistent differences between destructive and non‐destructive treatments. While both methods reliably detected the majority of species, small and soft‐bodied taxa were more often recovered after mild lysis than after homogenization, while the reverse was true for heavily sclerotized, hairy and large taxa. Using biological spike‐ins for calibration reduced the variance in read numbers per specimen considerably, especially in homogenized samples, while synthetic spike‐ins were less effective. In a Bayesian analysis, where species data were matched to the best‐fitting spike‐in calibration curve, accurate abundance estimates (±1 individual) were obtained for 72.9% of species occurrences. Our results show that it is possible to obtain reasonably accurate abundance estimates from metabarcoding data and that mild lysis and homogenization result in different taxon‐specific biases in terms of occurrence data, with neither method outperforming the other. Abundance accuracy is improved by homogenization rather than mild lysis of samples, and by the use of biological rather than synthetic spike‐ins. Together, these findings provide a major step towards robust, quantitative biodiversity monitoring using DNA‐metabarcoding.

How communities are structured into functional groups and trophic layers is key to understanding ecosystem functioning. Nonetheless, we lack insights about spatiotemporal variation in guild composition of communities and its causes. To investigate spatial and temporal patterns and drivers of variation in insect feeding guilds, we combined data from a nationwide survey of Swedish insects using Malaise traps and DNA metabarcoding with a comprehensive trait database. We assigned species into one of three feeding guilds (phytophages, saprophages, predators) or into one of three associated parasitoid guilds. We then analysed patterns in species richness for each guild. Species richness declined with latitude in all guilds. Beyond this gradient, local variation in species richness matched between hosts and their parasitoids. Yet, hosts and their parasitoids responded differently to habitat. The phenological peak of parasitoid species richness appeared later than the peak of their hosts, but the length of time lags varied among guilds. Spatiotemporal patterns were driven by guild-specific responses to temperature, though much variation remained between seasons and locations even when controlling for temperature. Overall, these patterns suggest that shifts in both climate and land use may alter the synchrony of insect trophic layers, with unknown consequences.

Deep metabarcoding offers an efficient and reproducible approach to biodiversity monitoring, but noisy data and incomplete reference databases challenge accurate diversity estimation and taxonomic annotation. Here, we introduce a novel algorithm, NEEAT, for removing spurious operational taxonomic units (OTUs) originating from nuclear-embedded mitochondrial DNA sequences (NUMTs) or sequencing errors. It integrates 'echo' signals across samples with the identification of unusual evolutionary patterns among similar DNA sequences. We also extensively benchmark current tools for chimera removal, taxonomic annotation and OTU clustering of deep metabarcoding data. The best performing tools/parameter settings are integrated into HAPP, a high-accuracy pipeline for processing deep metabarcoding data. Tests using CO1 data from BOLD and large-scale metabarcoding data on insects demonstrate that HAPP significantly outperforms existing methods, while enabling efficient analysis of extensive datasets by parallelizing computations across taxonomic groups.

To make informed decisions on how to effectively protect biodiversity, we need knowledge of its spatial and temporal dynamics. By combining detailed biodiversity surveys, geospatial data, and machine learning, we can model biodiversity with the aim of gaining insights into how these complex patterns behave. Here, we present a biodiversity modeling approach that utilizes metabarcoding-derived biodiversity data, remote sensing, and convolutional neural networks (CNNs). We apply CNNs to predict the spatial pattern of seasonal arthropod richness across Sweden and compare the results with other statistical models commonly used in spatial modeling. The biodiversity data used to train the models constitutes a state-of-the-art metabarcoding dataset composed of arthropod bulk samples, collected weekly from 198 locations. In addition, we compile 25 environmental features from public spatial data sources, describing each site's conditions. We find that CNN models do not outperform the other models in the applied performance metrics despite their conceptual advantage of incorporating contextual information. Most of the tested models capture the general seasonal diversity trends, resulting in similar performance metrics. However, when more closely inspecting the predicted spatial patterns we find that the CNN predictions yield ecologically more sensible patterns that distinguish different habitat types, as opposed to the other approaches. We conclude that while CNNs offer structural advantages for processing complex spatial data, their predictive performance does not surpass that of less complex statistical models, when applied to biodiversity datasets representing relatively few sites. Nonetheless, as metabarcoding biodiversity datasets continue to grow through large-scale sampling efforts, CNNs constitute a promising modeling approach to capture the complex correlations between biodiversity dynamics and the surrounding multidimensional environmental matrix.

DNA metabarcoding of species-rich taxa is becoming a popular high-throughput method for biodiversity inventories. Unfortunately, its accuracy and efficiency remain unclear, as results mostly pertain to poorly known taxa in underexplored regions. This study evaluates what an extensive sampling effort combined with metabarcoding can tell us about the lepidopteran fauna of Sweden-one of the best-understood insect taxa in one of the most-surveyed countries of the world. We deployed 197 Malaise traps across Sweden for a year, generating 4749 bulk samples for metabarcoding, and compared the results to existing data sources. We detected more than half (1535) of the 2990 known Swedish lepidopteran species and 323 species not reported during the sampling period by other data providers. Full-length barcoding confirmed three new species for the country, substantial range extensions for two species and eight genetically distinct barcode variants potentially representing new species, one of which has since been described. Most new records represented small, inconspicuous species from poorly surveyed regions, highlighting components of the fauna overlooked by traditional surveying. These findings demonstrate that DNA metabarcoding is a highly efficient and accurate biodiversity sampling method, capable of yielding significant new discoveries even for the most well known of insect faunas.

Global change threatens a vast number of species with severe population declines or even extinction. The threat status of an organism is often designated based on geographic range, population size, or declines in either. However, invertebrates, which comprise the bulk of animal diversity, are conspicuously absent from global frameworks that assess extinction risk. Many invertebrates are hard to study, and it has been questioned whether current risk assessments are appropriate for the majority of these organisms. As the majority of invertebrates are rare, we contend that the lack of data for these organisms makes current criteria hard to apply. Using empirical evidence from one of the largest terrestrial arthropod surveys to date, consisting of over 33 000 species collected from over a million hours of survey effort, we demonstrate that estimates of trends based on low sample sizes are associated with major uncertainty and a risk of misclassification under criteria defined by the IUCN. We argue that even the most ambitious monitoring efforts are unlikely to produce enough observations to reliably estimate population sizes and ranges for more than a fraction of species, and there is likely to be substantial uncertainty in assessing risk for the majority of global biodiversity using species-level trends. In response, we discuss the need to focus on metrics we can currently measure when conducting risk assessments for these organisms. We highlight modern statistical methods that allow quantification of metrics that could incorporate observations of rare invertebrates into global conservation frameworks, and suggest how current criteria might be adapted to meet the needs of the majority of global biodiversity.

We present the data from the Insect Biome Atlas project (IBA), characterizing the terrestrial arthropod faunas of Sweden and Madagascar. Over 12 months, Malaise trap samples were collected weekly (biweekly or monthly in the winter, when feasible) at 203 locations within 100 sites in Sweden and weekly at 50 locations within 33 sites in Madagascar; this was complemented by soil and litter samples from each site. The field samples comprise 4,749 Malaise trap, 192 soil and 192 litter samples from Sweden and 2,566 Malaise trap and 190 litter samples from Madagascar. Samples were processed using mild lysis or homogenization, followed by DNA metabarcoding of CO1 (418 bp). The data comprise 698,378 non-chimeric sequence variants from Sweden and 687,866 from Madagascar, representing 33,989 (33,046 Arthropoda) and 77,599 (77,380 Arthropoda) operational taxonomic units, respectively. These are the most comprehensive data presented on these faunas so far, allowing unique analyses of the size, composition, spatial turnover and seasonal dynamics of the sampled communities. They also provide an invaluable baseline against which to gauge future changes.

Obtaining genome-wide data from complex samples, such as environmental material or bulk species collections, is increasingly feasible, yet inferring species presence and population genomic insights remains challenging. We applied metagenomic sequencing to 40 arthropod bulk samples collected with Malaise traps across Sweden and compared results with metabarcoding of the same material. Using a custom genome database, we achieved genus-level classification largely consistent with metabarcoding. While metagenomics detected all genera identified by metabarcoding, conservative filtering thresholds designed to minimise false positives also excluded some true signals, particularly for low-abundance taxa. Taxonomic overlap between methods was further constrained by limited reference database representation. Beyond taxonomic assignment, metagenomic sequencing yielded genome-level information: we inferred haplotype diversity, heterozygosity and geographic population structure for several abundant species, including variable degrees of hybrid origin in red wood ants and the genetic distinctiveness of Gotland bumblebees. Finally, by-catch plant DNA present in the bulk samples revealed plausible arthropod-plant interactions, several of which align with known ecological associations. Together, these results demonstrate the potential of metagenomics for biodiversity monitoring and population genomics, while underscoring the importance of filtering criteria and comprehensive reference databases.

Here we describe a protocol to homogenize and extract DNA for bulk insect catches collected via Malaise traps. By following this protocol you will homogenize the entire catch into an insect soup followed by digestion of the entire insect soup with lysis buffer and proteinase K. After DNA lysis, an aliquot of the homogenate is purified using magnetic beads. The DNA obtained can then be used to amplify target genes for metabarcoding purposes. This protocol assumes you have bulk insect samples that have been drained of ethanol and for which you have recorded the wet biomass of each individual sample. You can obtain that by following steps 1 to 9 from FAVIS protocol: https://www.protocols.io/view/favis-fast-and-versatile-protocol-for-metabarcodin-kqdg36261g25/v2. We have used this protocol to extract DNA from 870 bulk insect samples from the Insect Biome Atlas project (www.insectbiomeatlas.org) that had first been subjected to DNA extraction following the mild lysis FAVIS protocol. To make sure we use all available DNA from each catch we combine the insect soup obtained in this protocol with the lysate obtained from the FAVIS protocol before proceeding with DNA purification with magnetic beads (optional section 4 of this protocol).

The more insects there are, the more food there is for insectivores and the higher the likelihood for insect-associated ecosystem services. Yet, we lack insights into the drivers of insect biomass over space and seasons, for both tropical and temperate zones. We used 245 Malaise traps, managed by 191 volunteers and park guards, to characterize year-round flying insect biomass in a temperate (Sweden) and a tropical (Madagascar) country. Surprisingly, we found that local insect biomass was similar across zones. In Sweden, local insect biomass increased with accumulated heat and varied across habitats, while biomass in Madagascar was unrelated to the environmental predictors measured. Drivers behind seasonality partly converged: In both countries, the seasonality of insect biomass differed between warmer and colder sites, and wetter and drier sites. In Sweden, short-term deviations from expected season-specific biomass were explained by week-to-week fluctuations in accumulated heat, rainfall and soil moisture, whereas in Madagascar, weeks with higher soil moisture had higher insect biomass. Overall, our study identifies key drivers of the seasonal distribution of flying insect biomass in a temperate and a tropical climate. This knowledge is key to understanding the spatial and seasonal availability of insects—as well as predicting future scenarios of insect biomass change.

Any single ecosystem will provide many ecosystem functions. Whether these functions tend to increase in concert or trade off against each other is a question of much current interest. Equally topical are the drivers behind ecosystem function rates. Yet, we lack large-scale systematic studies that investigate how abiotic factors can directly or indirectly — via effects on biodiversity — drive ecosystem functioning. In this study, we assessed the impact of climate, landscape and biotic community on ecosystem functioning and multifunctioning in the temperate and tropical zone, and investigated potential trade-offs among ecosystem functions in both zones. To achieve this, we measured a diverse set of insect-related ecosystem functions — including herbivory, seed dispersal, predation, decomposition and pollination — at 50 sites across Madagascar and 171 sites across Sweden, and characterized the insect community at each site using Malaise traps. We used structural equations models to infer causality of the effects of climate, landscape, and biodiversity on ecosystem functioning. For the temperate zone, we found that abiotic factors were more important than biotic factors in driving ecosystem functioning, while in the tropical zone, effects of biotic drivers were most pronounced. In terms of trade-offs among functions, in the temperate zone, only seed dispersal and predation were positively correlated, while all other functions were uncorrelated. By contrast, in the tropical zone, most ecosystem functions increased in concert, highlighting that tropical ecosystems can simultaneously provide a diverse set of functions. These correlated functions in Madagascar could for the most part be explained by similar responses to local climate, landscape, and biota. Our study suggests that the functioning of temperate and tropical ecosystems differs fundamentally in patterns and drivers. Without a better understanding of these differences, it will be impossible to correctly predict shifts in ecosystem functioning in response to environmental disturbances. To identify global patterns and drivers of ecosystem functioning, we will next need replicate sampling across biomes – as here achieved for two regions, thus paving the road and setting the baseline expectations. ### Competing Interest Statement The authors have declared no competing interest.

Among the most widely used information underpinning international conservation efforts is the IUCN Red List of endangered species. The Red List designates species extinction risk based on geographic range, population size, or declines in either. However, the Red-List has poor representation of invertebrates which comprise the majority of animal diversity, and it has frequently been questioned whether Red List criteria are appropriate for these organisms. Due to their small size, difficulty in identification, and general rarity, many invertebrates are hard to study, making Red List criteria difficult to apply. Here we discuss these criticisms in the context of empirical evidence from one of the largest terrestrial arthropod surveys to date, documenting the abundance and distribution of over 13,000 species in Sweden. Using simple empirical examples from these data, we argue that even the most ambitious monitoring efforts are unlikely to produce enough observations to reliably estimate population sizes and ranges for more than a fraction of species. Thus, there is likely to be substantial uncertainty in classifying most species according to current criteria. In response, we discuss the introduction of potential new IUCN criteria to more accurately capture the conservation needs of invertebrates, and to increase the representation of invertebrates on the IUCN Red List.

Sampling of species-rich taxa followed by DNA metabarcoding is quickly becoming a popular high-throughput method for biodiversity inventories. Unfortunately, we know little about its accuracy and efficiency, as the results mostly pertain to poorly-known organism groups in underexplored environments or regions of the world. Here we ask what an extensive sampling effort based on Malaise trapping and metabarcoding can tell us about the lepidopteran fauna of Sweden - one of the best-understood insect taxa in one of the most-surveyed countries of the world. Specifically, we deployed 197 Malaise traps for a single year across Sweden in a systematic sampling design, then metabarcoded the resulting 4,749 bulk samples, and compared the results to existing data sources. We detected more than half (1,535) of the 2,990 lepidopteran species ever recorded as occurring in Sweden, and 323 species not reported during the sampling period by other data providers. Full-length barcoding of individual specimens confirmed three new species for the country and extensive range extensions for two species. It also corroborated eight genetically distinct COI variants that may represent new species to science, one of which has since been described. Most of the new records are for small and inconspicuous species and poorly surveyed regions, suggesting that they represent previously overlooked components of the fauna. Our findings, corroborated by independent metagenomic analyses, show that DNA metabarcoding can be a highly efficient and accurate method of biodiversity sampling, to the extent that it can generate significant new discoveries even for the most well-known of insect faunas. ### Competing Interest Statement The authors have declared no competing interest.

Insects are diverse and sustain essential ecosystem functions, yet remain understudied. Recent reports about declines in insect abundance and diversity have highlighted a pressing need for comprehensive large-scale monitoring. Metabarcoding (high-throughput bulk sequencing of marker gene amplicons) offers a cost-effective and relatively fast method for characterizing insect community samples. However, the methodology applied varies greatly among studies, thus complicating the design of large-scale and repeatable monitoring schemes. Here we describe a non-destructive metabarcoding protocol that is optimized for high-throughput processing of Malaise trap samples and other bulk insect samples. The protocol details the process from obtaining bulk samples up to submitting libraries for sequencing. It is divided into four sections: 1) Laboratory workspace preparation; 2) Sample processing-decanting ethanol, measuring the wet-weight biomass and the concentration of the preservative ethanol, performing non-destructive lysis and preserving the insect material for future work; 3) DNA extraction and purification; and 4) Library preparation and sequencing. The protocol relies on readily available reagents and materials. For steps that require expensive infrastructure, such as the DNA purification robots, we suggest alternative low-cost solutions. The use of this protocol yields a comprehensive assessment of the number of species present in a given sample, their relative read abundances and the overall insect biomass. To date, we have successfully applied the protocol to more than 7000 Malaise trap samples obtained from Sweden and Madagascar. We demonstrate the data yield from the protocol using a small subset of these samples.

Metabarcoding (high-throughput sequencing of marker gene amplicons) has emerged as a promising and cost-effective method for characterizing insect community samples. Yet, the methodology varies greatly among studies and its performance has not been systematically evaluated to date. In particular, it is unclear how accurately metabarcoding can resolve species communities in terms of presence-absence, abundances, and biomass. Here we use mock community experiments and a simple probabilistic model to evaluate the performance of different metabarcoding protocols. Specifically, we ask four questions: (Q1) How consistent are the recovered community profiles across replicate mock communities?; (Q2) How does the choice of lysis buffer affect the recovery of the original community?; (Q3) How are community estimates affected by differing lysis times and homogenization?; and (Q4) Is it possible to obtain adequate species abundance estimates through the use of biological spike-ins? We show that estimates are quite variable across community replicates. In general, a mild lysis protocol is better at reconstructing species lists and approximate counts, while homogenization is better at retrieving biomass composition. Tiny insects are more likely to be detected in lysates, while some tough species require homogenization to be detected. Results are less consistent across biological replicates for lysates than for homogenates. Some species are associated with strong PCR amplification bias, which complicates the reconstruction of species counts. Yet, with adequate spike-in data, species abundance can be determined with roughly 40% standard error for homogenates, and with roughly 50% standard error for lysates, under ideal conditions. In the latter case, however, this often requires species-specific reference data, while spike-in data generalizes better across species for homogenates. We conclude that a non-destructive, mild lysis approach shows the highest promise for presence/absence description of the community, while also allowing future morphological or molecular work on the material. However, homogenization protocols perform better for characterizing community composition, in particular in terms of biomass.

Insects are the most diverse group of animals on Earth, but their small size and high diversity have always made them challenging to study. Recent technological advances have the potential to revolutionise insect ecology and monitoring. We describe the state of the art of four technologies (computer vision, acoustic monitoring, radar, and molecular methods), and assess their advantages, current limitations, and future potential. We discuss how these technologies can adhere to modern standards of data curation and transparency, their implications for citizen science, and their potential for integration among different monitoring programmes and technologies. We argue that they provide unprecedented possibilities for insect ecology and monitoring, but it will be important to foster international standards via collaboration.

Paleotropical clades with largely disjunct distributions are ideal models for biogeographic reconstructions. The dung beetle genera Grebennikovius Mlambo, Scholtz & Deschodt, Epactoides Olsouffief and Ochicanthon Vaz‐de‐Mello are distributed in Tanzania, Madagascar and Réunion, and the Oriental region, respectively. We combine morphology and molecular dataset to reconstruct the phylogenetic relationships between these taxa. Our analyses corroborate previous hypotheses of monophyly of the group, which is here described as new tribe Epactoidini trib. nov. Grebennikovius is recovered as sister to Epactoides, while Ochicanthon emerges as sister to them both. The disjunct distribution of our focal clade is unusual within the subfamily Scarabaeinae. Bayesian divergence time estimates and ancestral range reconstructions indicate an African origin of the crown group of the tribe Epactoidini trib. nov. in the early mid Eocene, ca. 46 Ma. The divergence between Epactoides and its sister is dated to 32.3 Ma, while the crown age for the genus Ochicanthon is dated to 27 Ma. We investigate the factors that may have shaped the current distribution of the tribe Epactoidini trib. nov. The formation of the Gomphotherium landbridge, along with favourable environmental conditions would have allowed dry‐intolerant organisms, such as Ochicanthon, to disperse out of Africa. Remarkable climatic stability of the Eastern Arc Mountains was critical for the retention of the monotypic genus Grebennikovius. We suggest two subsequent overwater dispersal events: the migration of the most recent common ancestor (MRCA) of Epactoides from Africa to Madagascar (32.3–29.5 Ma); the lately dispersal of the MRCA of the today's extinct Epactoides giganteus Rossini, Vaz‐de‐Mello & Montreuil to Réunion island from Madagascar (3.4 Ma). We suggest that the high potential of dispersal of Epactoidini trib. nov. dung beetles and the strict association to forest habitat might have triggered two major radiations, one in Madagascar and one in the Oriental Region.