Recent declines in insect abundances, especially populations of wild pollinators, pose a threat to many natural and agricultural ecosystems. Traditional species monitoring relies on morphological character identification and is inadequate for efficient and standardized surveys. DNA barcoding has become a standard approach for molecular identification of organisms, aiming to overcome the shortcomings of traditional species monitoring. However, its efficacy depends on the completeness of reference databases. Large DNA barcoding efforts are (almost entirely) lacking in many European countries, and such patchy data limit Europe-wide analyses of precisely how to apply DNA barcoding in wild bee identification. Here, we advance towards an effective molecular identification of European wild bees. We conducted a high-effort survey of wild bees in Slovenia, a country where central Europe meets the Balkan peninsula, and DNA barcoded all collected morpho-species. For global analyses, we complemented our DNA barcode dataset with all relevant European species and conducted a global analyses of species delimitation, general and genus-specific barcoding gaps, and examined the error-rate in DNA data repositories. We found that i) a sixth of all specimens from Slovenia could not be reliably identified, ii) species delimitation methods show numerous systematic problems, iii) there is no general barcoding gap across all bees, iv) the barcoding gap is genus-specific, but only after curating for errors in DNA data repositories. Intense sampling and barcoding efforts in underrepresented regions and strict curation of DNA barcode repositories are needed to enhance the use of DNA barcoding for identification of wild bees.

Heterozygosity is frequently used to describe variation in genetic diversity amongst populations and is often estimated using single nucleotide polymorphisms (SNPs). However, methods of calculating heterozygosity from SNPs have been shown to be affected study design and filtering parameters, reducing their utility and comparability across studies. Though solutions have been proposed to account for identified problems, in our own data, we continued to see inconsistent results. Here, we aimed to further improve methods of reducing inconsistency in these results, specifically by investigating how sample size and missing data thresholds influenced autosomal estimates of heterozygosity (heterozygosity calculated from across the genome, i.e., both fixed and variable sites). We also investigated how the exclusion of tri- and tetra-allelic sites, which is generally standard practice in such studies, could affect eventual estimates of heterozygosity. Across three distinct taxa (a frog, Litoria rubella; a tree, Eucalyptus microcarpa; and a grasshopper, Keyacris scurra) we found autosomal heterozygosity estimates to be affected by samples size when missing data is not allowed and show that this is partly due to the exclusion of tri- and tetra-allelic loci. We also show that the biases introduced by these factors are not consistent between species, or even populations, with higher levels of actual heterozygosity tending to result in larger adverse effects. We propose a modified framework for calculating heterozygosity to reduce these inherent issues and highlight the need for further development in methods such that tri- and tetra-allelic sites can be included in the calculation of population genomics statistics.

There is growing interest in uncovering genetic kinship patterns in past societies using low-coverage paleogenomes. Here, we benchmark four tools for kinship estimation with such data: lcMLkin, NgsRelate, KIN, and READ, which differ in their input, IBD estimation methods, and statistical approaches. We used pedigree and ancient genome sequence simulations to evaluate these tools when only a limited number (1K to 50K) of shared SNPs (with minor allele frequency ≥0.01) are available. The performance of all four tools was comparable using ≥20K SNPs. We found that first-degree related pairs can be accurately classified even with 1K SNPs, with 85% F1 scores using READ and 96% using NgsRelate or lcMLkin. Distinguishing third-degree relatives from unrelated pairs or second-degree relatives was also possible with high accuracy (F1 >90%) with 5K SNPs using NgsRelate and lcMLkin, while READ and KIN showed lower success (69% and 79%, respectively). Meanwhile, noise in population allele frequencies and inbreeding (first cousin mating) led to deviations in kinship coefficients, with different sensitivities across tools. We conclude that using multiple tools in parallel might be an effective approach to achieve robust estimates on ultra-low coverage genomes.

Amplicon sequencing is an effective and increasingly applied method for studying viral communities in the environment. Here, we present vAMPirus, a user-friendly, comprehensive, and versatile DNA and RNA virus amplicon sequence analysis program, designed to support investigators in exploring virus amplicon sequencing data and running informed, reproducible analyses. vAMPirus intakes raw virus amplicon libraries and, by default, performs nucleotide- and amino acid-based analyses to produce results such as sequence abundance information, taxonomic classifications, phylogenies, and community diversity metrics. The vAMPirus analytical framework leverages 16 different opensource tools and provides optional approaches that can increase the ratio of biological signal-to-noise and thereby reveal patterns that would have otherwise been masked. Here, we validate the vAMPirus analytical framework and illustrate its implementation as a general virus amplicon sequencing workflow by recapitulating findings from two previously published double-stranded DNA virus datasets. As a case study, we also apply the program to explore the diversity and distribution of a coral reef-associated RNA virus. vAMPirus is streamlined within Nextflow, offering straightforward scalability, standardization, and communication of virus lineage-specific analyses. The vAMPirus framework is designed to be adaptable; community-driven analytical standards will continue to be incorporated as the field advances. vAMPirus supports researchers in revealing patterns of virus diversity and population dynamics in nature, while promoting study reproducibility and comparability.

The ITS-2-rRNA has been particularly useful for nematode metabarcoding. but does not resolve all phylogenetic relationships and reference sequences are not available for many nematode species. This is a particular issue when metabarcoding complex communities such as wildlife parasites or terrestrial and aquatic free-living nematode communities. We have used markerDB to produce four databases of distinct regions of the rRNA cistron; the 18S rRNA gene, the 28S rRNA gene, the ITS-1 intergenic spacer and the region spanning ITS-1_5.8S_ITS-2. These databases comprise 2645, 254, 13461, and 10107 unique full-length sequences representing 1391, 204, 1837, and 1322 nematode species, respectively. The comparative analysis illustrates the complementary value but also reveals a better representation of Clade III, IV, and V than Clade I and Clade II nematodes in each case. Although the ITS-1 database includes the largest number of unique full-length sequences, the 18S rRNA database provides the widest taxonomic coverage. We also developed, PrimerTC; a tool to assess primer sequence conservation across any reference sequence database and have applied it to evaluate a large number of previously published rRNA cistron primers. We identified sets of primers that currently provide the broadest taxonomic coverage for each rRNA marker across the nematode phylum. These new resources will facilitate more comprehensive metabarcoding of nematode communities using either short-read or long-read sequencing platforms. Further, PrimerTC is available as a simple WebApp to guide or assess PCR primer design for any genetic marker and/or taxonomic group beyond the nematode phylum.

With increasingly large genomic datasets, even routine bioinformatic tasks can be arduous, computationally demanding, and time-consuming. Additionally, most bioinformatic programs do not have a graphical user interface (GUI) and thus, require users to be minimally competent in command-line. These impediments present significant economic and technological barriers for practitioners who do not have access to advanced computational resources and support. In this issue of Molecular Ecology Resources, Handika and Esselstyn (2024) developed an ultrafast and memory-efficient bioinformatic tool, SEGUL, that performs common manipulations and calculations of summary statistics on phylogenomic datasets. SEGUL has two main features that distinguish it from other bioinformatic programs: (1) it is based on the recently emergent, high-performance programming language Rust, and (2) it has a GUI written using Flutter, a cross-platform programming framework that also supports mobile operating systems (mobile iOS, iPadOS, Android). By leveraging and combining the power of Rust and Flutter, SEGUL achieves significantly faster computation times and lower memory usage across different platforms and CPU architectures compared to similar programs. The unique and innovative features of SEGUL pave the way for a new era of bioinformatics that can be more energy-efficient, inclusive, and available to a broader swathe of users.

Marine sponges have recently emerged as efficient natural environmental DNA (eDNA) samplers. The ability of sponges to accumulate eDNA provides an exciting opportunity to reconstruct contemporary communities and ecosystems with high temporal and spatial precision. However, the use of historical eDNA (heDNA), trapped within the vast number of specimens stored in scientific collections, opens up the opportunity to begin to reconstruct the communities and ecosystems of the past. Here, using a variety of Antarctic sponge specimens stored in an extensive marine invertebrate collection, we were able to recover information on Antarctic fish biodiversity from specimens up to 20 years old. We successfully recovered 64 fish heDNA signals from 27 sponge specimens. Alpha diversity measures did not differ among preservation methods, but sponges stored frozen had a significantly different fish community composition compared to those stored dry or in ethanol. Our results show that we were consistently and reliably able to extract the heDNA trapped within marine sponge specimens, thereby enabling the reconstruction and investigation of communities and ecosystems of the recent past with a spatial and temporal resolution previously unattainable. Future research into heDNA extraction from other preservation methods, as well as the impact of specimen age and collection method will strengthen and expand the opportunities for this novel resource to access new knowledge on ecological change during the last century.

Molecular techniques like metabarcoding, while promising for exploring diversity of communities, are often impeded by the lack of reference DNA sequences available for taxonomic annotation. Our study explores the benefits of combining targeted DNA barcoding and morphological taxonomy to improve metabarcoding efficiency, using beach meiofauna as a case study. Beaches are globally important ecosystems and are inhabited by meiofauna, microscopic animals living in the interstitial space between the sand grains, which play a key role in coastal biodiversity and ecosystem dynamics. However, research on meiofauna faces challenges due to limited taxonomic expertise and sparse sampling. We generated 775 new cytochrome c oxidase I DNA barcodes from meiofauna specimens collected along the Netherlands’ west coast and combined them with the NCBI GenBank database. We analysed alpha and beta diversity in 561 metabarcoding samples from 24 North Sea beaches, a region extensively studied for meiofauna, using both the enriched reference database and the NCBI database without the additional reference barcodes. Our results show a 2.5-fold increase in sequence annotation and a doubling of species-level Operational Taxonomic Units (OTUs) identification when annotating the metabarcoding data with the enhanced database. Additionally, our analyses revealed a bell-shaped curve of OTU richness across the intertidal zone, aligning more closely with morphological analysis patterns, and more defined community dissimilarity patterns between supralittoral and intertidal sites. Our research highlights the importance of expanding molecular reference databases and combining morphological taxonomy with molecular techniques for biodiversity assessments, ultimately improving our understanding of coastal ecosystems.