Molluscs, characterised by various shapes and sizes, represent the most diverse group of animals after arthropods. This morphological disparity has historically complicated the establishment of phylogenetic relationships among the different classes of molluscs and subclasses of gastropods and their positions within the lophotrochozoans. In recent decades, ultraconserved elements (UCEs) have emerged as an effective technique for capturing large numbers of genes from preserved museum specimens across various animal groups. This study evaluates the efficiency of UCE capture across all classes of molluscs using a probe set designed for heterobranch gastropods. We analysed the effects of missing data using three levels of the GBLOCKS masker, ZORRO, and by generating various occupancy matrices. Our analyses successfully captured 2,119 out of 2,259 possible UCEs, recovering many genes from all molluscan classes and gastropod subclasses (144–1,604). As expected, efficiency decreases in groups more distantly related to gastropods, particularly heterobranchs. The more stringent maskers recovered shorter sequences with less missing data, yet fewer informative sites. Thus, our primary analyses focused on the use of more relaxed masking settings and the 50% occupancy matrix. These analyses resolved the well-established Conchifera and Aculifera hypothesis and further recovered a close relationship between Bivalvia and Scaphopoda (Diasoma). Moreover, with a wider taxon sampling, the interrelationships within, e.g., Gastropoda and Solenogastres are well resolved, highlighting the true universality of the probe set and encouraging its use for future research on any mollusc class.

Next-generation sequencing (NGS) has the potential to transform mosquito-borne disease surveillance but remains under-utilized. This study introduces a comprehensive multi-loci metabarcoding-based MX (molecular xenomonitoring) approach to mosquito and arbovirus surveillance, enabling parallel identification of mosquito vectors, circulating arboviruses, and vertebrate hosts from bulk mosquito collections. The feasibility of this approach was demonstrated through its application to a large set (n=134) of bulk field collections. This set was complemented by a number (n=28) of single-species mosquito pools that had previously been screened for viruses using quantitative reverse transcription PCR (RT-qPCR) and metatranscriptomics. Universal alphavirus and flavivirus primer sets were used to screen for arboviruses in the resulting metabarcoding library. Viral amplicons were then indexed and combined with mosquito-specific (ITS2), universal invertebrate (COI), and vertebrate (Cyt b) barcode amplicons prior to sequencing. This approach confirmed the presence of all previously identified mosquito species, as well as those commonly misidentified morphologically, and enabled a degree of quantification regarding their relative physical abundance in each collection. Additionally, the developed approach identified a diverse vertebrate host community (18 species), demonstrating its potential for defining host preferences and, in tandem with the viral screens and associated vector data, understanding disease transmission pathways. Importantly, metabarcoding detected a diversity of regionally prevalent arboviruses and insect-specific viruses, with all three viral diagnostics demonstrating a similar sensitivity and specificity in detecting Ross River virus and Barmah Forest virus, Australia’s commonest arboviruses. In summary, multi-loci metabarcoding is an affordable and efficient MX tool that enables complete mosquito-borne disease surveillance.

Environmental DNA (eDNA) has emerged as a crucial tool for monitoring terrestrial animals, yet numerous variables can influence species detection. Among these, the choice of sampling substrate and method is paramount and should align with the specific research objectives. This review provides a thorough overview of eDNA sampling methods and substrates, focusing on their effectiveness in detecting species across various spatiotemporal scales. We categorize substrates into three groups: abiotic (soil, water, air), biotic (invertebrate, spiderweb, plant tissue, plant swab), and trace (scat, material with trace, material from artificial setup). Additionally, we address key processes leading to false-negatives and -positives due to spatial and temporal mismatches between the eDNA source and the sampling. Practical considerations for designing sampling plans are also discussed, including strategies to enhance detection accuracy and considerations tailored to specific taxa and habitats.

Both environmental DNA (eDNA) and environmental RNA (eRNA) have widely adopted for biodiversity assessment. While eDNA often persists longer in environments, eRNA offers a more current view of biological activities. In eRNA metabarcoding, extracted eRNA is reverse transcribed into complementary DNA (cDNA) for metabarcoding. However, the efficacy of various reverse transcription strategies has not been evaluated. Here we compared the biodiversity recovery efficiency of three common reverse transcription strategies: random priming with hexamers, oligo(dT) priming, and taxa-specific priming using Mifish-U for fish in both high- and low-biodiversity regions. Our results demonstrate that reverse transcription strategies significantly impact biodiversity recovery. Random hexamer priming consistently detected the highest number of taxa in both low- and high-biodiversity regions. In low-biodiversity areas, oligo(dT) performed comparably to random hexamers; however, in high-biodiversity regions, random hexamers outperformed oligo(dT), particularly in recovering rare taxa. While taxa-specific priming was comparative to the other strategies for high-abundance taxa, it was less effective for rare taxa, limiting its utility for comprehensive biodiversity assessment. These differences are largely due to the multiple binding sites for random hexamers compared to the fewer or absent sites with oligo(dT) under high eRNA degradation. Combining random hexamers and oligo(dT) significantly improved taxa recovery, especially for low-abundance species, supporting its best practice in eukaryotes. For prokaryotes or genes lacking polyadenylation, random priming is favored over taxa- or gene-specific priming. Collectively, these findings underscore the critical importance of selecting appropriate reverse transcription strategies in eRNA metabarcoding, with significant implications for effective biodiversity monitoring and conservation efforts.

Insect decline necessitates the development and use of standardized protocols for regular monitoring. These methods have to be rapid, efficient, and cost-effective to allow for large-scale implementation. Many insect sampling and molecular methods have been developed. This includes Malaise trapping, high-throughput DNA barcoding (“megabarcoding”), and metabarcoding. The latter allows for assessing the species diversity in whole samples using few steps, but sample heterogeneity in terms of body size remains a challenge since large insects contribute disproportionately more mtDNA than small ones, potentially overwhelming the template DNA from small species that then go undetected. Size-sorting can mitigate this problem, but no satisfying automated, rapid and non-destructive solutions are available. We introduce the EntoSieve, a low-cost and DIY motorized instrument that disentangles and sorts abundant insect bulk samples into several body-size fractions while minimizing damage to specimens thus reducing the risk of DNA contamination across size fractions (e.g., legs of large specimens in small body-size fraction). EntoSieve utilizes readily available components, 3D-printed parts and customizable meshes, thus enabling parallelization at low cost. We here show the efficiency of the EntoSieve for three samples with more than 10,000 specimens using three sieving protocols and assess the impact on specimen integrity. Efficiency ranged from 92-99% and achieved within 18-60 minutes and specimen damage was not significant for subsamples. By facilitating rapid pre-processing, the device contributes to producing morphologically valuable vouchers for megabarcoding and is likely to improve compositional diversity accuracy across size classes when using metabarcoding.

Escalating concern regarding the impacts of reduced genetic diversity on the conservation of endangered species has spurred efforts to obtain chromosome-level genomes through consortia such as the Vertebrate Genomes Project. However, assembling reference genomes for many threatened species remains challenging due to difficulties obtaining optimal input samples (e.g., fresh tissue, cell lines) that can characterize long-term conservation collections. Here, we present a pipeline that leverages genome synteny to construct high-quality genomes for species of conservation concern despite less-than-optimal samples and/or sequencing data, demonstrating its use on Hector’s and Māui dolphins. These endemic New Zealand dolphins are threatened by human activities due to their coastal habitat and small population sizes. Hector’s dolphins are classified as endangered by the IUCN, while the Māui dolphin is among the most critically endangered marine mammals. To assemble reference genomes for these dolphins, we created a pipeline combining de novo assembly tools with reference-guided techniques, utilizing chromosome-level genomes of closely related species. The pipeline assembled highly contiguous chromosome-level genomes (scaffold N50: 110 MB, scaffold L50: 9, miniBUSCO completeness scores >96.35%), despite non-optimal input tissue samples. We demonstrate that these genomes can provide insights relevant for conservation, including historical demography revealing long-term small population sizes, with subspecies divergence occurring ~20 kya, potentially linked to the Last Glacial Maximum. Māui dolphin heterozygosity was 40% lower than Hector’s and comparable to other cetacean species noted for reduced genetic diversity. Through these exemplar genomes, we demonstrate that our pipeline can provide high-quality genomic resources to facilitate ongoing conservation genomics research.

Environmental DNA (eDNA) analysis is effective for non-invasive biodiversity monitoring, as it reveals species distribution and abundance without ecosystem disruption. Concentration, extraction, and preservation are three essential steps in the eDNA analysis process. Among these, the concentration of eDNA has attracted significant research interest, particularly due to the variability of water samples used in studies. To date, various methods for eDNA concentration have been developed, including glass fiber filtration, Sterivex filters, and passive samplers; however, no single method is universally applicable because of the variabilities of eDNA presence and water characteristics including turbidity levels. Therefore, the development of alternative eDNA concentration methods is crucial for advancing eDNA research. This study introduces QuickConc, a novel nucleic acid capture method that combines benzalkonium chloride (BAC) with dispersed glass fibers. Our results indicate that this approach enhances eDNA capture sensitivity by likely improving the interaction between silica and eDNA. QuickConc was tested in three environments, using metabarcoding and qPCR. Species-specific qPCR results showed that QuickConc detected 2 to 3 times higher copy numbers compared to the glass fiber filter and Sterivex methods. Metabarcoding analyses using the MiFish method revealed that the number of fish species detected in river water was higher with QuickConc, compared to other methods, while in sea water, the number of fish species was at a similar level compared to other methods. QuickConc offers new options for eDNA analysis, providing a more sensitive and easily deployable approach to biodiversity monitoring and conservation strategies.

Short-read metabarcoding analysis is the gold standard to access to partial 16S and ITS genes with high read quality. With the advent of long-read sequencing, the amplification of full-length target genes is possible but with low read accuracy. Moreover, the amplification of 16S rDNA genes in seaweed or plant samples results in a large proportion of plastid reads, which are directly or indirectly derived from cyanobacteria. Primers designed not to amplify plastid sequences are available for short-read sequencing, while Oxford Nanopore Technology offers adaptive sampling, a unique way to remove reads in real-time. In this study, we compare three options to address the plastid read issue: deleting plastid reads with adaptative sampling, using optimized primers with Illumina MiSeq technology, and sequencing large numbers of reads with Illumina NovaSeq technology with universal primers. We showed that adaptive sampling using default settings of the MinKNOW software was ineffective for plastid depletion. We also demonstrated with a mock community that the SAMBA workflow provided the most accurate taxonomic assignment at the bacterial genus level compared to the IDTAXA and KRAKEN2 pipelines, but many false positives were generated at species level. Although NovaSeq sequencing with universal primer stood out for studying the algal bacterial community due to its deep coverage, the inclusion of eukaryotes and bacteria in the same sequencing run, and the low error rate. The combination of Illumina and ONT sequencing helped us explore the fungal diversity and allowed for the retrieval taxonomic information for genera poorly represented in the sequence databases.

Flower-derived eDNA holds great promise as a rapid and non-invasive tool for monitoring pollinators and their plant-associations. However, pollinators often only briefly interact with a plant and leave little eDNA, making them particularly challenging to detect. In addition, taxonomic biases in eDNA deposition and PCR amplification prevent quantitative analysis of pollinator diversity. These limitations have so far precluded the widespread use of eDNA in pollinator monitoring. Comparing flower-derived eDNA with conventional monitoring in flower strips, we here explore the utility of eDNA to detect community diversity, species abundances and ecological specificity of plant-associated arthropods. We show that read abundances are a bad predictor of true abundances at the community level. Instead, the occupancy of individual species in replicated flower eDNA samples provides reliable quantitative estimates of pollinator biodiversity and detects their ecological specificity very well. Also, we find that pollinator eDNA can be collected non-invasively, by washing off from flowers in the field. Our work highlights eDNA analysis as a powerful tool for the rapid future monitoring of plant-arthropod interactions and plant-pollinator networks.

Marker gene sequencing or “metabarcoding” is the primary sequencing approach currently used for molecular biodiversity surveys, but this approach is taxonomically limited and hampered by amplification biases. Shotgun metagenomes offer a PCR–free approach to environmental DNA (eDNA) sequencing, theoretically capturing the full taxonomic breadth of the eDNA pool. However, eukaryotic DNA is often a small component of metagenomes and it has seen limited use for metazoan biodiversity surveys. Here, we compare metabarcoding and shotgun metagenomes on a large (>200 sample size) set of marine water column eDNA samples and show that metagenomes can provide biodiversity information comparable to that of metabarcoding surveys. Moreover, as a result of biases in reference database composition, shotgun sequencing can outperform marker genes for certain taxa. Taxonomic database gaps remain an obstacle to accurate and comprehensive biodiversity surveys for both metabarcoding and shotgun metagenomes. We provide examples of taxa that may benefit from one approach over another and highlight cases of metagenomic utility.

javascript:void(0) The boll weevil, Anthonomus grandis grandis Boheman, and thurberia weevil, Anthonomus grandis thurberiae Pierce (Coleoptera: Curculionidae), together comprise a species complex that ranges throughout Mexico, the southwestern regions of the United States, and South America. The boll weevil is a historically damaging and contemporaneously threatening pest to commercial Upland cotton, Gossypium hirsutum L. (Malvales: Malvaceae), whereas the thurberia weevil is regarded as an innocuous non-pest subspecies that is mostly found on non-cultivated Gossypium species, e.g. Thurber’s or Arizona cotton, G. thurberi, throughout its native range in western parts of Mexico and the southwestern US. Recent independent analyses using mitochondrial COI and whole genome ddRADseq have suggested the independent evolution of these lineages is largely attributable to geographic isolation and not to host plant preference. We furthered this investigation by employing comparative genomic, population genetic, and pangenome methodologies to identify large and small polymorphisms within this complex and described their role in demography and adaptation. We also leveraged genetic differences to identify nearly 40,000 diagnostic loci between the subspecies, find genes under selection, and model the subspecies’ shared and unique evolutionary history. Interestingly, structural variations capture a large proportion of genes at the population level and demographic reconstruction suggests a split between these subspecies that coincides with cotton cultivation in the southern U.S. in the late 1800s. Observed polymorphisms are enriched for reproductive, regulatory, and metabolic genes which may be attributed to the boll weevil’s rapid expansion onto commercial cotton.

not-yet-known not-yet-known not-yet-known unknown The analysis of environmental DNA (eDNA) has become a non-invasive, cost-efficient and universal biomonitoring tool, widely applied across the globe. Most eDNA research focuses on aquatic organisms in freshwater and marine environments. eDNA shedding rates are key to interpretating eDNA-based results, such as for abundance estimations or detection probabilities. Shedding rates have been estimated for several species and life stages, however virtually all of them are aquatic. As eDNA-based biomonitoring expands to terrestrial systems, waterborne eDNA from freshwater is increasingly used to assess species presence of terrestrial mammals. While interacting with the water, terrestrial mammals deposit their DNA into the water body, with the amount deposited presumably heavily depending on the interaction type. Here we quantify eDNA shedding rates from domestic dogs during various interactions with water bodies, including ”passing by”, ”drinking”, ”crossing through”, ”standing still” and ”defecating.” “Crossing through” and “defecating” had the highest DNA shedding rates (both approx. 4x107 pg/h/ind). Since scat largely remains in the water once deposited, and the active movement of terrestrial mammals through the water is brief, we conclude that defecation is the major source of terrestrial mammal DNA in surface waters where direct defecation into the water is possible. This has important implications for interpretations of eDNA-based data from such water bodies. We also highlight the patchiness of eDNA particle distribution in such systems, which needs to be accounted for when designing eDNA sampling schemes.

Inferring community composition from shotgun sequencing of environmental DNA is highly dependent on the completeness of reference databases used to assign taxonomic information as well as the pipeline used. While the number of complete, fully assembled reference genomes is increasing rapidly, their taxonomic coverage is generally too sparse to use them to build complete reference databases that span all or most of the target taxa. Low-coverage, whole genome sequencing, or skimming, provides a cost-effective and scalable alternative source of genome-wide information in the interim. Without enough coverage to assemble large contigs of nuclear DNA, much of the utility of a genome skim in the context of taxonomic annotation is found in its short read form. However, previous methods have not been able to fully leverage the data in this format. We demonstrate the utility of wholeskim, a pipeline for the indexing of k-mers present in genome skims and subsequent querying of these indices with short DNA reads. Wholeskim expands on the functionality of kmindex, a software which utilizes Bloom filters to efficiently index and query billions of k-mers. Using a collection of thousands of plant genome skims, wholeskim is the only software that is able to index and query the skims in their unassembled form. We also explore the effects of taxonomic and genomic completeness of the reference database on the accuracy and sensitivity of read assignment.

Biodiversity monitoring based on DNA metabarcoding depends on primer performance. Here, we develop a new metabarcoding primer pair that targets a ~ 318 bp fragment of the 28S rRNA gene. We validate the primer pair in assessing sponges, a notoriously challenging group for coral reef metabarcoding studies, by using mock and natural complex reef communities to examine its performance in species detection, amplification efficiency, and quantitative potential. Mock community experiments revealed a high number of sponge species (n = 94) spanning a broad taxonomic scope (15 orders), limited taxon-specific primer biases (only a single species exceeded a two-fold deviation from the expected number of reads), and its suitability for quantitative metabarcoding – there was a significant relationship between read abundance and visual percent coverage of sponge taxa (R = 0.76). In the natural complex coral reef community experiments, commonly used COI metabarcoding primers detected only 30.9% of sponge species, while the new 28S primer increased detection to 79.4%. These new 28S primers detect a broader taxonomic array of species across phyla and classes within the complex cryptobiome of coral reef communities than the Leray-Geller COI primers. As biodiversity assessments using metabarcoding tools are increasingly being leveraged for environmental monitoring and guide policymaking, these new 28S rRNA primers can improve biodiversity assessments for complex ecological coral reef communities.

Numerous specimens stored in natural history collections have been involuntarily preserved together with their associated microbiomes. We propose exploiting centuries-old soils occasionally found on the roots of herbarium plants to assess the diversity of ancient soil microbial communities originally associated with these plants. We validated this approach extracting and sequencing DNA from rhizospheric soils and roots of four plant species preserved in herbaria for more than 120 years. Extracted DNA displayed typical features of ancient DNA, with cytosine deamination at the ends of fragments predominantly shorter than 50 bp. When compared to extant microbiomes, herbarium microbial communities clustered with soil communities and were distinct from communities from other environments. Herbarium communities also displayed biodiversity features and assembly rules typical of soil and plant-associated ones. Soil communities were richer than root-associated ones with which they shared most taxa. Regarding community turnover, we detected collection site, soil versus root, and also plant species effects. Eukaryotic taxa that displayed a higher abundance in roots were mostly plant pathogens or obligate symbionts that were not identified among soil-enriched ones. Conservation of these biodiversity features and assembly rules in herbarium-associated microbial communities indicates that herbarium-extracted DNA could reflect the composition of the original plant-associated microbial communities and that preservation in herbaria seemingly did not alter these characteristics. Through the use of this approach, it should be possible to investigate historical soils and herbarium plant roots to explore the diversity and temporal dynamics of soil microbial communities.

Missing genotypes in DNA sequence data are an issue in many evolutionary genomic studies, especially of non-model organisms. It can be addressed using genotype imputation. However, algorithms that do not require additional genotype data as reference for imputation, which is often not available for non-model taxa, and are able to work with large whole-genome data sets are scarce. Therefore, we developed a new algorithm called GenoPop-Impute, which imputes the whole genome in separate batches and employs a random forest algorithm for imputation of correlated data sets. The batch-wise approach utilizes linkage disequilibrium to increase imputation accuracy and allows computational parallelization and thus efficiency. Tests on simulated data demonstrate that linkage disequilibrium between SNPs has a positive effect on imputation accuracy, due to correlation that originated in a shared evolutionary history. In comparison to two alternative algorithms, GenoPop-Impute is more accurate and is the only one computationally applicable to data sets of whole genomes. In addition, we found that GenoPop-Impute also increases the accuracy of commonly estimated population genomic metrics and mitigates biases due to missing data in demographic modeling experiments. We conclude that genotype imputation can be a valuable tool for evolutionary genomic studies of non-model taxa and that GenoPop-Impute is a highly suitable algorithm for this.

The increasing climate-driven replacement of kelp forests by turf algae highlights the need for efficient biodiversity monitoring. Traditionally, monitoring turf communities involves species identification based on morphology, which is challenging due to their reduced dimensions and highly variable morphology. Molecular methods promise to revolutionize this field, but their real-world effectiveness needs to be evaluated. Here, we evaluate the performance of DNA metabarcoding (COI and rbcL markers) and morphological identification (in situ and photoquadrat identifications) to describe intertidal turf communities along the Portuguese coast. When comparing metabarcoding with in situ and photoquadrat identification, it was found that both COI and rbcL markers detected more taxa than the other two (277 and 140 vs 28 and 34 taxa, respectively). Metabarcoding also showed greater discrimination of turf communities between shores and regions, matching our knowledge of the geographical and climatic patterns for the region. However, certain taxa that were identified by in situ and photoquadrat approaches were not detected through metabarcoding, likely due to lack of reference barcodes or taxonomic resolution. Our multi-marker metabarcoding approach was more efficient than morphology-based methods in characterizing turf communities along the Portuguese coast, differentiating morphologically similar species, and detecting unicellular organisms. Additionally, although not the primary focus, the COI marker identified metazoans, which can be used in future ecological studies on species co-occurrence and algae-animal interactions. Metabarcoding emerges as a valuable tool for monitoring these communities, particularly in long-term programs requiring accuracy, speed, and reproducibility.

Comparative genomic studies of closely related taxa are important for our understanding of the causes of divergence on a changing Earth. This being said, the genomic resources available for marine intertidal molluscs are limited and currently, there are few publicly available high-quality annotated genomes for intertidal habitats and for molluscs in general. Here we report transcriptome assemblies for six species of Patellogastropoda and genome assemblies and annotations for three of these species (Scurria scurra, Scurria viridula, and Scurria zebrina). Comparative analysis using these genomic resources suggest that there was a large gene family contraction during the early evolutionary history of Patellogastropoda (140-170 Mya) and recently diverging lineages (10-20 Mya) have experienced similar amounts of contractions and expansions but across different gene families. Furthermore, differences among recently diverged species are reflected in variation in the amount of coding and noncoding material in genomes, such as amount of repetitive elements and lengths of transcripts and introns and exons. Additionally, functional ontologies of species-specific and duplicated genes together with demographic inference support the finding that recent divergence among members of the genus Scurria aligns with their unique ecological characteristics. Overall, the resources presented here will be extremely valuable for future studies of adaptation in molluscs and in intertidal habitats as a whole.