Carmelo Andújar

High‐throughput sequencing (HTS) allows large‐scale DNA barcoding of individually tagged specimens (‘megabarcoding’), but deep amplicon sequencing produces a mixture of authentic mitochondrial sequences together with nuclear pseudogenes (NUMTs), environmental and cross‐sample contaminants, and sequencing artefacts. Standard approaches relying on read clustering or dominant‐read selection often fail to classify these types, leading to incorrect taxonomic identifications and species counts. We developed an authentication framework by integrating abundance filtering, phylogenetic placement and taxonomic congruence. The workflow was applied to 18,533 morphospecies of tropical beetles (Coleoptera) from multiple biogeographic regions, which were imaged for family‐level identification, prior to individual Illumina barcoding. Sequencing yielded > 36 million reads and 64,544 unique ASVs, which were evaluated against a reference phylogeny of > 13,000 mitogenomes. Authentication succeeded for 86.5% of quality‐passing specimens (15,901 ASVs). Non‐authentic sequences were technical artefacts (58.0%), environmental contamination including prey DNA (14.2%), intra‐individual variants (NUMTs, heteroplasmy; 11.3%) and cross‐sample contamination (7.5%). Authentication success and the proportions of failure categories varied markedly across trap types, sampling campaigns, taxonomic groups and sequencing runs. We identified 930 confirmed NUMTs based on consistent co‐occurrence patterns and phylogenetic proximity to authenticated haplotypes. Single‐specimen HTS data contain substantial biological and technical complexity not resolved by standard filtering methods. Our pipeline‐agnostic, phylogenetically informed authentication framework achieves robust recovery of validated barcodes while retaining informative secondary variants, improving the accuracy of molecular ASV data to a standard sufficient for inclusion in barcode reference databases and the phylogenetically informed DNA barcoding of tropical insects.

Animal COI (mitochondrial cytochrome oxidase I) metabarcoding of environmental DNA (eDNA) is increasingly used to assess biodiversity in complex substrates such as soil. However, due to read-length constraints of second-generation sequencing platforms, mini-barcodes have been used instead of the full barcode region. Long-read sequencing technologies now enable the recovery of full-length barcode sequences, and are more commonly applied for studying microbes, but their use for metabarcoding the full-length standard COI barcoding region in animals remains limited. In this study, we compared three COI amplicon sets — 313 bp, 660 bp, and 1,256 bp — amplified from soil eDNA samples and sequenced using Illumina and PacBio platforms to evaluate their overall concurrence, the effectiveness of identifying nuclear mitochondrial DNA segments (NUMTs) and chimeras, as well as their respective taxonomic resolution. The long-read datasets exhibited a higher identification rate of NUMTs and true chimeras, suggesting that longer sequences improve the detection of noise in COI metabarcoding data, thereby reducing the occurrence of spurious taxa. Taxonomy assignment confidence was similar between the 313 bp and 660 bp datasets, whereas extending the amplicon beyond the standard COI barcode region (1,256 bp) reduced confidence, likely because longer reads extend into regions poorly represented in barcode reference databases. Despite substantially lower sequencing depth in the 660 bp dataset, per-sample OTU richness did not differ significantly from that recovered with the Illumina 313 bp amplicon set. Similarly, the relationships between samples were strongly correlated across the detected OTU communities, indicating consistent ecological interpretations between short and long amplicons. We conclude that the standard ~658 bp COI barcode is an optimal marker for soil animal metabarcoding from eDNA, balancing target recovery, artifact detection, taxonomic assignment and ecological interpretability. As COI eDNA metabarcoding becomes increasingly used in biodiversity assessment and is increasingly adopted in large-scale monitoring initiatives, this study provides methodological guidance for improving the robustness of soil animal community biomonitoring.

Persistent biodiversity data shortfalls undermine our capacity to detect species, map their distributions and characterize their spatial genetic structure, limiting robust biogeographic analyses and the development of effective conservation strategies. This particularly affects hyperdiverse invertebrate groups where hidden diversity remains largely undocumented. This study develops and demonstrates the potential of an integrated high‐throughput sequencing (HTS) framework to improve the representation of hidden diversity in regional species inventories and to help close critical gaps in our understanding of species distributions and genetic diversity from a conservation biogeography perspective. Focusing on the Canary Islands (Spain), the workflow combines megabarcoding of more than 4000 mesofauna specimens to generate a curated species‐level molecular reference library with community DNA metabarcoding of 168 soil samples. This approach enables consistent taxonomic assignment across insular landscapes and increases the spatial and genetic resolution of occurrence data. We identified 145 species of mites and springtails, including 49 species newly recorded for the archipelago and numerous genetically distinct lineages likely representing undescribed taxa, highlighting all the biodiversity that remains to be described. Integration of the barcode library with metabarcoding data produced 1440 species occurrences, revealing extensive distributional gaps, multiple range expansions and strong within‐island phylogeographic structuring, indicating prevalent diversification at fine spatial scales. These results highlight a deep, taxonomically broad underestimation of soil biodiversity and demonstrate that this integrative approach provides a transferable model for advancing the biogeography, evolutionary understanding and conservation of dark and cryptic taxa across broad taxonomic and conservation‐relevant contexts.

Deep‐soil arthropods remain one of the least studied components of terrestrial biodiversity, and we present, as an exciting discovery, a new endogean beetle collected by floating deep soil samples in Chile. We analysed a 4203‐ortholog dataset using both maximum likelihood (ML) and coalescent methods to infer its relationships. Regardless of the analytical setup, this lineage was recovered as sister to the clade consisting of Lycidae, Cantharidae, Elateridae and the lampyroids. The distant position of this new taxon relative to previously described elateroid families is confirmed by additional analyses of differently sampled, although less phylogenetically informative, 66‐gene and mitogenome datasets. As a result, we describe Badmaater chilensis sp. and gen. nov. in Badmaateridae fam. nov. (Coleoptera: Elateroidea). Badmaater resembles other clicking elateroids but has a three‐segmented antennal club, slender palpi and globular metacoxae that are situated close to each other. It is blind, wingless and extremely small‐bodied. We date the origin of Badmaateridae to the Triassic. The ancient origin of Badmaateridae suggests that this lineage has probably survived all disturbances shaping modern life, from the Triassic/Jurassic volcanic activity to the end‐Cretaceous asteroid impact and subsequent Cenozoic and Quaternary climatic fluctuations. However, Badmaateridae is now one of the few beetle families with only a single species restricted to a narrow range. Deep‐soil dwelling Badmaater survived in a relatively high‐latitude area periodically affected by close glaciers and separated from the tropics by an arid zone. Badmaater , as a wingless deep‐soil beetle occurring in a restricted area, has a limited dispersal propensity and consequently faces an inherent risk during turbulent environmental changes.

DNA metabarcoding is fast becoming a method of choice for the inventory and monitoring of community‐level arthropod biodiversity, with growing interest in the recovery of haplotype‐level variation within species from metabarcoding reads. Denoising tools have been established as the standard for metabarcode processing, with the promise to provide accurate sequence data at the haplotype level. Despite their widespread use, the accuracy of denoising methods relative to the elimination of non‐target sequences and retention of target sequences has received limited attention. Recent studies based on whole organism community DNA (wocDNA) metabarcoding suggest that misleading estimates of species richness and diversity may be obtained after denoising. Understanding and evaluating how denoising pipelines perform and how they can be improved is crucial for broadening the applicability of metabarcoding to community ecology, metaphylogeography and biodiversity inventory and monitoring. To evaluate the performance of the most popularly used denoising tools, we use (i) complex communities from malaise traps subjected to wocDNA metabarcoding (representing the information yielded by metabarcoding) and (ii) additional sequencing of each individual specimen (representing the ground truth of sample contents). To these data, we apply the metabarcoding evaluation rationale described in Andújar et al. (2021), complemented by an additional layer of clustering and filtering with LULU and metaMATE. This study confirms that denoising tools alone, but also in combination with clustering, retain a high number of non‐target amplicon sequence variants (ASVs) and operational taxonomic units (OTUs). Both LULU and metaMATE removed large amounts of non‐target sequences and noise. This, however, came at the cost of the collateral removal of target sequences. We recommend a workflow including one of the better performing denoising tools: DADA2 or UNOISE3, in combination with LULU or metaMATE as a customizable option for evaluating pipeline performance.

Aim: Globally, arthropod biodiversity is under threat, with increased risk of species‐level extinctions, and this threat is particularly acute on oceanic islands. A fundamental first step towards understanding extinction risk is to understand genetic connectivity among the constituent populations of a species. Our aim is to develop and implement a protocol to characterise genetic connectivity among island populations within arthropod species to reveal otherwise hidden range size drivers of extinction.Location: Canary Islands, Spain.Methods: We implement a protocol based on mtDNA sequence data for the delimitation of evolutionary significant units (ESUs) to evaluate extinction risk among species of beetle and spider distributed across multiple islands.Results: Our results reveal that more than half of the species analysed are comprised of two or more ESUs. We also find that low dispersal ability was a significant predictor of ESUs within species of Coleoptera, but with no significant difference for Araneae.Main Conclusions: Most ESUs are consistent with early stage differentiation or incipient speciation, with some exceeding a conservative interspecific threshold, thus indicative of cryptic species. We suggest that extending our approach with the integration of other species‐level traits may provide for a more refined predictive framework for understanding extinction risks across island arthropod species.

The tribe Raymondionymini has long been neglected in phylogenetic studies. The tribe is characterized by uncertain monophyly, fluctuating taxonomic status, and a composition prone to instability. All raymondionymine weevils are wingless and have eyes either completely absent or, rarely, consisting of a single ommatidium. With body lengths predominantly below three millimeters, they inhabit deep soil environments and are infrequently collected. The core of this tribe comprises nine genera distributed in Europe and around the Mediterranean region and encompassing 76 species, while six additional genera include 17 species distributed in USA (California), Mexico, Ecuador, Venezuela, Russian Far East, and Madagascar. Here, we present eight new mitogenomes, complemented by one publicly available, encompassing all but two Mediterranean genera of raymondionymine weevils. We used publicly available Curculionoidea mitogenomes to compile an all-inclusive dataset with 391 terminals and a reduced dataset with 61 terminals representing main families of Curculionoidea and subfamilies within Curculionidae. Our maximum likelihood and Bayesian phylogenetic analyses, employing both DNA and amino acids datasets under alternative partition schemes, consistently produced congruent phylogenies. Our results show that the Mediterranean raymondionymines form a strongly supported clade, and their easternmost and morphologically distinct genus Ubychia is sister to the rest of them. Most notably, our results consistently recover a sister relationship between the clade of Mediterranean raymondionymine weevils and a clade encompassing all remaining Curculionidae. Consequently, we propose a revision of weevil taxonomy: (i) Our target group is removed from the non-monophyletic subfamily Brachycerinae; (ii) this clade is resurrected to its former subfamily level within Curculionidae, as the subfamily Raymondionyminaestat. rev; (iii) the nine Mediterranean genera Alaocephala, Alaocyba, Coiffaitiella, Derosasius, Ferreria, Raymondiellus, Raymondionymus, Tarattostichus, and Ubychia compose Raymondionyminaestat. rev; (iv) and non-Mediterranean genera Alaocybites, Bordoniola, Gilbertiola, Homosomus, Neoubychia, and Schizomicrus are considered as “incertae sedis” pending further phylogenetic corroboration. We hypothesize that the remaining Brachycerinae and the non-Mediterranean representatives within Raymondionyminae constitute a series of species-poor early-diverging lineages representing currently unrecognized subfamilies of Curculionidae.

Soil arthropod diversity contributes to a high proportion of the total biodiversity on Earth. However, most soil arthropods are still undescribed, hindering our understanding of soil functioning and global biodiversity estimations. Inventorying soil arthropods using conventional taxonomical approaches is particularly difficult and costly due to the great species richness, abundance and local-scale heterogeneity of mesofauna communities and the poor taxonomic background knowledge of most lineages. To alleviate this situation, we have designed and implemented a molecular barcoding framework adapted to soil fauna. This pipeline includes different steps, starting with a morphology-based selection of specimens which are imaged. Then, DNA is extracted non-destructively. Both images and voucher specimens are used to assign a taxonomic identification, based on morphology that is further checked for consistency with molecular information. Using this procedure, we studied 239 specimens of mites from the Canary Islands including representatives of Mesostigmata, Sarcoptiformes and Trombidiformes, of which we recovered barcode sequences for 168 specimens that were morphologically identified to 49 species, with nine specimens that could only be identified at the genus or family levels. Multiple species delimitation analyses were run to compare molecular delimitations with morphological identifications, including ASAP, mlPTP, BINs and 3% and 8% genetic distance thresholds. Additionally, a species-level search was carried out at the Biodiversity Databank of the Canary Islands (BIOTA) to evaluate the number of species in our dataset that were not previously recorded in the archipelago. In parallel, a sequence-level search of our sequences was performed against BOLD Systems. Our results reveal that multiple morphologically identified species correspond to different molecular lineages, which points to significant levels of unknown cryptic diversity within the archipelago. In addition, we evidenced that multiple species in our dataset constituted new records for the Canary Islands fauna and that the information for these lineages within online genetic repositories is very incomplete. Our study represents the first systematic effort to catalogue the soil arthropod mesofauna of the Canary Islands and establishes the basis for the Canary Islands Soil Biodiversity barcode database. This resource will constitute a step forward in the knowledge of these arthropods in a region of special interest.

Understanding the relative contributions of ecological and evolutionary processes to the structuring of ecological communities is needed to improve our ability to predict how communities may respond to future changes in an increasingly human‐modified world. Metabarcoding methods make it possible to gather population genetic data for all species within a community, unlocking a new axis of data to potentially unveil the origins and maintenance of biodiversity at local scales. Here, we present a new eco‐evolutionary simulation model for investigating community assembly dynamics using metabarcoding data. The model makes joint predictions of species abundance, genetic variation, trait distributions and phylogenetic relationships under a wide range of parameter settings (e.g. high speciation/low dispersal or vice versa) and across a range of community states, from pristine and unmodified to heavily disturbed. We first demonstrate that parameters governing metacommunity and local community processes leave detectable signatures in simulated biodiversity data axes. Next, using a simulation‐based machine learning approach we show that neutral and non‐neutral models are distinguishable and that reasonable estimates of several model parameters within the local community can be obtained using only community‐scale genetic data, while phylogenetic information is required to estimate those describing metacommunity dynamics. Finally, we apply the model to soil microarthropod metabarcoding data from the Troodos mountains of Cyprus, where we find that communities in widespread forest habitats are structured by neutral processes, while high‐elevation and isolated habitats act as an abiotic filter generating non‐neutral community structure. We implement our model within the ibiogen R package, a package dedicated to the investigation of island, and more generally community‐scale, biodiversity using community‐scale genetic data.