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.

Fish ear bones, known as otoliths, are often collected by fisheries to assist in management, and are a common sample type in museum and national archives. Beyond their utility for aging, morphological and trace element analysis, otoliths are a repository of valuable genomic information. Previous work has shown that DNA can be extracted from the trace quantities of tissue remaining on the surface of otoliths, despite the fact that they are often stored dry at room temperature. However, much of this work has used reduced-representation sequencing methods in clean lab conditions, to achieve adequate yields of DNA, libraries, and ultimately single-nucleotide polymorphisms (SNPs). Here, we pioneer the use of small-scale (spike-in) sequencing to screen otolith samples prepared in regular molecular biology laboratories for contamination and quality levels, submitting for whole-genome resequencing only samples above a defined endogenous DNA threshold. Despite the typically low quality and quantity of DNA extracted from otoliths, we are able to produce whole-genome libraries and ultimately sets of filtered, unlinked and even adaptive SNPs of ample size for downstream uses in population, climate, and conservation genomics. By comparing with a set of tissue samples from the same species, we are able to highlight the quality and efficacy of otolith DNA samples from DNA extraction and library preparation, to bioinformatic preprocessing and SNP calling. We provide detailed schematics, protocols, and scripts of our approach, such that it can be adopted widely by the community, improving the use of otoliths as a source of valuable genomic data.

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.

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.

1. The use of environmental DNA (eDNA) as a new method of ecological monitoring is widely applied. Although eDNA can provide important information on the distribution and biomass of particular taxa, an organism’s DNA sequences remain unaltered throughout its existence, which complicates identifying crucial events, including reproduction, with high accuracy. We thus examined DNA methylation as a novel source of information from eDNA, considering that methylation patterns of eggs and sperm released during reproduction differ from those of somatic tissues. 2. Despite its potential applications, little is known about eDNA methylation, including its stability and methods for detection and quantification. Therefore, we conducted tank experiments and performed methylation analysis targeting 18S rDNA through bisulfite amplicon sequencing. 3. Methylation of eDNA was not affected by degradation and was equivalent to the rate of genomic DNA from somatic tissues. Unmethylated DNA, which is abundant in the ovary, was detected in eDNA during reproductive activity of fish. 4. These results indicate that eDNA methylation is a stable signal reflecting genomic methylation and demonstrate that germ cell-specific methylation patterns can be used as markers for detecting reproductive activity.

Chironomidae, so-called non-biting midges, are considered key bioindicators of aquatic ecosystem variability. Data derived from morphologically identifying their chitinous remains in sediments documents chironomid larvae assemblages, which are studied to reconstruct ecosystem changes over time. Recent developments in sedimentary DNA (sedDNA) research have demonstrated that molecular techniques are suitable for determining past and present occurrences of organisms. Nevertheless, sedDNA records documenting alterations in chironomid assemblages remain largely unexplored. To close this gap, we examined the applicability of sedDNA metabarcoding to identify Chironomidae assemblages in lake sediments by sampling and processing three 21 – 35 cm long sediment cores from Lake Sempach in Switzerland. With a focus on developing analytical approaches, we compared an invertebrate-universal (FWH) and a newly designed Chironomidae-specific metabarcoding primer set (CH) to assess their performance in detecting Chironomidae DNA. We isolated and identified chitinous larval remains and compared the morphotype assemblages with the data derived from sedDNA metabarcoding. Results showed a good overall agreement of the morphotype assemblage-specific clustering among the chitinous remains and the metabarcoding datasets. Both methods indicated higher chironomid assemblage similarity between the two littoral cores in contrast to the deep lake core. Moreover, we experienced a pronounced primer bias effect resulting in more Chironomidae detections with the CH primer combination compared to the FWH combination. Overall, we conclude that sedDNA metabarcoding can supplement traditional remain identifications and potentially provide independent reconstructions of past chironomid assemblage changes. Furthermore, it has the potential of more efficient workflows, better sample standardisation and species-level resolution datasets.

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.

Diapause, a form of dormancy to delay or halt the reproductive development during unfavourable seasons, has evolved in many insect species. One example is aestivation, a summer adult-stage diapause, enhancing malaria vectors’ survival during the unfavourable dry season (DS) and their re-establishment in the next rainy season (RS). This work develops a novel genetic approach to estimate the number or proportion of individuals undergoing diapause, as well as the breeding sizes of the two seasons, using signals from temporal allele frequency dynamics. Using Anopheles coluzzii as an example, our modelling shows the magnitude of drift is dampened at early RS when previously aestivating individuals reappear. Aestivation severely biases the temporal effective population size (N_e), leading to overestimation of the DS breeding size by 1/(1-α)^2 across one year, where α is the aestivating proportion. We find sampling breeding individuals in three consecutive seasons starting from a RS is sufficient for parameter estimation, and perform extensive simulations to verify our derivations. This method does not require sampling individuals in the dormant state, the biggest challenge in most studies. We apply the method to a published An. coluzzii dataset from Thierola, Mali, and the estimated aestivating proportions were 39%-79%. These results will inform the development of genetic approaches to vector control. Beyond mosquitoes, our method and the expected evolutionary implications are applicable to any species in which a fraction of the population diapauses for more than one generation, and are difficult or impossible to sample during that stage.

We present a complete portable pipeline for sequencing and analysis of environmental metagenomes in less than a day. This unprecedented development was possible due to the conjunction of state-of-the art experimental and computational advances: a portable laboratory suitable for DNA extraction and sequencing with nanopore technology.The powerful metagenomic analysis pipeline SqueezeMeta, capable to provide a complete analysis in a few hours and using scarce computational resources. Finally, tools for the automatic inspection of the results via a graphical user interface, that can be coupled to a web server to allow remote visualization of data (SQMtools and SQMxplore). We tested the feasibility of our approach in the sequencing of the microbiota associated to volcanic rocks in La Palma, Canary Islands. Also, we did a two-day sampling campaign of marine waters in which the results obtained the first day guided the experimental design of the second day. We demonstrate that it is possible to generate metagenomic information in less than one day, making it feasible to obtain taxonomic and functional profiles fast and efficiently, even in field conditions. This capacity can be used in the further to perform real-time functional and taxonomic profiling of microbial communities in remote areas

Mesozooplankton is a very diverse group of small animals ranging in size from 0.2 to 20 mm not able to swim against ocean currents. It is a key component of pelagic ecosystems through its roles in the trophic networks and the biological carbon pump. Traditionally studied through microscopes, recent methods have been however developed to rapidly acquire large amounts of data (morphological, molecular) at the individual scale, making it possible to study mesozooplankton using a trait-based approach. Here, combining quantitative imaging with metabarcoding time-series data obtained in the Sargasso Sea at the Bermuda Atlantic Time-series Study (BATS) site, we showed that organisms’ transparency might be an important trait to also consider regarding mesozooplankton impact on carbon export, contrary to the common assumption that just size is the master trait directing most mesozooplankton-linked processes. Three distinct communities were defined based on taxonomic composition, and succeeded one another throughout the study period, with changing levels of transparency among the community. A co-occurrences’ network was built from metabarcoding data revealing six groups of taxa. These were related to changes in the functioning of the ecosystem and/or in the community’s morphology. The importance of Diel Vertical Migration at BATS was confirmed by the existence of a group made of taxa known to be strong migrators. Finally, we assessed if metabarcoding can provide a quantitative approach to biomass and/or abundance of certain taxa. Knowing more about mesozooplankton diversity and its impact on ecosystem functioning would allow to better represent them in biogeochemical models.

Environmental DNA is an effective tool for describing fish biodiversity in lotic environments, but the downstream transport of eDNA released by organisms makes it difficult to interpret species detection at the local scale. In addition to biophysical degradation and exchanges at the water-sediment interface, hydrological conditions control the transport distance. We have developed an eDNA transport model that considers downstream retention and degradation processes in combination with hydraulic conditions and assumes that the sedimentation rate of very fine particles is a correct estimate of the eDNA deposition rate. Based on meta-analyses of available studies, we successively modelled the particle size distribution of fish eDNA (PSD), the relationship between the sedimentation rate and the size of very fine particles in suspension, and the influence of temperature on the degradation rate of fish eDNA. After combining the results in a mechanistic-based model, we correctly simulated the eDNA uptake distances observed in a compilation of previous experimental studies. eDNA degradation is negligible at low flow and temperature but has a comparable influence to background transfer when hydraulic conditions allow a long uptake distance. The wide prediction intervals associated with the simulations reflect the complexity of the processes acting on eDNA after shedding. This model can be useful for estimating eDNA detection distance downstream from a source point and discussing the possibility of false positive detection in eDNA samples, as shown in an example.

Autopolyploidy is quite common in most clades of eukaryotes. The emergence of sequence-based genotyping methods with individual and marker tags enables now confident allele dosage, overcoming the main obstacle to the democratization of the population genetic approaches when studying ecology and evolution of autopolyploid populations and species. Reproductive modes, including clonality, selfing and allogamy, have deep consequences on the ecology and evolution of population and species. Analysing genetic diversity and its dynamics over generations is one efficient way to infer the relative importance of clonality, selfing and allogamy in populations. GENAPOPOP is a user-friendly solution to compute the specific corpus of population genetic indices, including indices about genotypic diversity, needed to analyse partially clonal, selfed and allogamous polysomic populations genotyped with confident allele dosage. It also easily provides the posterior probabilities of quantitative reproductive modes in autopolyploid populations genotyped at two-time steps and a graphical representation of the minimum spanning trees of the genetic distances between polyploid individuals, facilitating the interpretation of the genetic coancestry between individuals in hierarchically structured populations. GENAPOPOP complements the previously existing solutions, including SPAGEDI and POLYGENE, to use genotypings to study the ecology and evolution of autopolyploid populations. It was specially developed with a simple graphical interface and workflow, and comes with a simulator to facilitate practical course and teaching of population genetics for autopolyploid populations.

Biomonitoring of marine life has been enhanced in recent years by the integration of innovative DNA-based approaches, which offer advantages over more laborious conventional techniques (e.g. direct capture) and greater taxonomic resolution especially in complex life cycles and early life stages. However, tradeoffs between throughput, sensitivity and quantitative measurements must be made when choosing between the prevailing molecular methodologies (i.e. metabarcoding or qPCR/dPCR). Thus, the aim of the present study was to demonstrate the utility of a microfluidic-enabled High Throughput quantitative PCR platform (HT-qPCR) for the rapid and cost-effective development and validation of a DNA-based multi-species biomonitoring toolkit, using larvae of 24 commercially targeted bivalve and crustacean species as a case study. The workflow was divided into three main phases: definition of target taxa and establishment of reference databases (PHASE 1); in silico selection/development and in vitro assessment of molecular assays (PHASE 2); and protocol optimization and field validation (PHASE 3). Of a total of 85 assays in silico, 42 were eventually chosen and validated in vitro. Genetic signal showed good correlation with direct visual counts by microscopy, but also showed the ability to provide quantitative data at the highest taxonomic resolution (species level) in a time- and cost-effective fashion. This study developed a biomonitoring toolkit, demonstrating the considerable advantages of this state-of-the-art technology in boosting the development and application of panels of molecular assays for the monitoring and management of natural resources that can be applied to a range of monitoring programmes. Keywords: DNA, High Throughput, qPCR, biomonitoring, shellfish

Age is necessary information for the study of life history of wild animals. A general method to estimate the age of odontocetes is counting dental growth layer groups (GLGs). However, this method is highly invasive as it requires the capture and handling of individuals to collect their teeth. Recently, the development of DNA-based age estimation methods has been actively studied as an alternative to such invasive methods, of which many have used biopsy samples. However, if DNA-based age estimation can be developed from fecal samples, age estimation can be performed without touching or disrupting individuals, thus establishing an entirely non-invasive method. We developed an age estimation model using the methylation rate of two gene regions, GRIA2 and CDKN2A, measured through methylation-sensitive high-resolution melting (MS-HRM) from fecal samples of wild Indo-Pacific bottlenose dolphins (Tursiops aduncus). The age of individuals was known through conducting longitudinal individual identification surveys underwater. Methylation rates were quantified from 36 samples. Both gene regions showed a significant correlation between age and methylation rate. The age estimation model was constructed based on the methylation rates of both genes which achieved sufficient accuracy (after LOOCV: MAE = 5.08, R2 = 0.34) for the ecological studies of the Indo-Pacific bottlenose dolphins, with a lifespan of 40-50 years. This is the first study to report the use of non-invasive fecal samples to estimate the age of marine mammals.

Phylogenetic studies now routinely require manipulating and summarizing thousands of data files. For most of these tasks, currently available software requires considerable computing resources and substantial knowledge of command-line applications. We develop ultrafast and memory-efficient software that performs over a dozen common phylogenomic manipulations and calculates statistics summarizing essential data features. Our software is available as standalone command-line (CLI) and graphical user interface (GUI) applications, and as a programming language library for Rust, R, and Python, with possible support of other languages. The CLI and library versions, SEGUL, run native on Windows, Linux, and macOS, including Apple ARM Macs. The GUI version extends support to include mobile iOS and Android operating systems. SEGUL offer fast execution times and low memory footprints regardless of dataset size and platform choice. The inclusion of a GUI minimizes bioinformatics barriers to phylogenomics while SEGUL’s efficiency reduces economic barriers by enabling analysis on inexpensive hardware. Our support for mobile operating systems further enables teaching phylogenomics where access to computing power is limited.

Our limited knowledge about the ecological drivers of global arthropod decline highlights the urgent need for more effective biodiversity monitoring approaches. Monitoring of arthropods is commonly performed using passive trapping devices, which reliably recover diverse communities, but provide little ecological information on the sampled taxa. Especially the manifold interactions of arthropods with plants are barely understood. A promising strategy to overcome this shortfall is environmental DNA (eDNA) metabarcoding of arthropods from plant material they have interacted with. However, the accuracy of this approach has not been sufficiently tested. In four experiments, we exhaustively test the comparative performance of plant-derived eDNA from surface washes of plants and homogenized plant material against traditional monitoring approaches. We show that the recovered communities of plant-derived eDNA and traditional approaches only partly overlap, with eDNA recovering various additional cryptic taxa. This suggests eDNA as a useful complementary tool to traditional monitoring. Despite the differences in recovered taxa, estimates of community α- and β-diversity between both approaches are well correlated, highlighting the utility of eDNA as a broad scale tool for community monitoring. Last, eDNA outperforms traditional approaches in the recovery of plant-specific arthropod communities. Unlike traditional monitoring, eDNA revealed fine-scaled community differentiation between individual plants and even within plant compartments. Especially specialized herbivores are better recovered with eDNA. Our results highlight the value of plant derived eDNA analysis for large-scale biodiversity assessments that include information about community level interactions.

Whole genome sequencing data allow survey of variation from across the genome, reducing the constraint of balancing genome sub-sampling with recombination rates and linkage between sampled markers and target loci. As sequencing costs decrease, low coverage whole genome sequencing of pooled or indexed-individual samples is commonly utilized to identify loci associated with phenotypes or environmental axes in non-model organisms. There are, however, relatively few publicly available bioinformatic pipelines designed explicitly to analyze these types of data, and fewer still that process the raw sequencing data, provide useful metrics of quality control, and then execute analyses. Here, we present an updated version of a bioinformatics pipeline called POOLPARTY2 that can effectively handle either pooled or indexed DNA samples and includes new features to improve computational efficiency. Using simulated data, we demonstrate the ability of our pipeline to recover segregating variants, estimate their allele frequencies accurately, and identify genomic regions harboring loci under selection. Based on the simulated data set, we benchmark the efficacy of our pipeline with another bioinformatic suite, ANGSD, and illustrate the compatibility and complementarity of these suites by using ANGSD to generate genotype likelihoods as input for identifying linkage outlier regions using alignment files and variants provided by POOLPARTY2. Finally, we apply our updated pipeline to an empirical dataset of low coverage whole genomic data from uncurated population samples of Columbia River steelhead trout (Oncorhynchus mykiss), results from which demonstrate the genomic impacts of decades of artificial selection in a prominent hatchery stock.

Using high-throughput sequencing for precise genotyping of multi-locus gene families, such as the Major Histocompatibility Complex (MHC), remains challenging, due to the complexity of the data and difficulties in distinguishing genuine from erroneous variants. Several dedicated genotyping pipelines for data from high-throughput sequencing, such as next-generation sequencing (NGS), have been developed to tackle the ensuing risk of artificially inflated diversity. Here, we thoroughly assess three such multi-locus genotyping pipelines for NGS data, using MHC class IIβ datasets of three-spined stickleback gDNA, cDNA, and “artificial” plasmid samples with known allelic diversity. We show that genotyping of gDNA and plasmid samples at optimal pipeline parameters was highly accurate and reproducible across methods. However, for cDNA data, the same configuration yielded decreased overall genotyping precision and consistency between pipelines. Further adjustments of key clustering parameters were required tο account for higher error rates and larger variation in sequencing depth per allele, highlighting the importance of template-specific pipeline optimization for reliable genotyping of multi-locus gene families. Through accurate paired gDNA-cDNA genotyping and MHC-II haplotype inference, we show that MHC-II allele-specific expression levels correlate negatively with allele number across haplotypes. Lastly, sibship-assisted cDNA genotyping of MHC-I revealed novel variants and haplotype-based allelic segregation with a higher-than-previously-reported individual allelic diversity for MHC-I in sticklebacks. In conclusion, we here provide novel genotyping protocols for MHC-I and -II genes of the three-spined stickleback, but also evaluate the performance of popular NGS-genotyping pipelines and highlight the need for template-specific optimization for reliable multi-locus genotyping.