Bioinformatic pipelines combining denoising and clustering tools allow for more comprehensive prokaryotic and eukaryotic metabarcoding.

Fungi play essential roles in key ecosystem functions and processes, yet they often occur in inconspicuous, species‐rich, and complex communities that remain difficult to study. Studies of fungal communities based on DNA extracted from environmental samples commonly rely on clustering sequence reads into units of diversity, followed by taxonomic identification and, in some cases, linkage to ecological traits. In this study, we evaluated how two clustering approaches—amplicon sequence variants (ASVs) and operational taxonomic units (OTUs)—affect the characterization of fungal communities. Despite minor differences, both approaches recovered consistent taxonomic patterns and community structure. Although both methods produced a similar total number of sequence clusters, they differed in representation of fungal community composition. All ASV representative sequences matched OTU representative sequences with at least 92.2% similarity, whereas several rare OTUs showed low similarity to ASV reads, suggesting differences in the detection of low‐abundance taxa. However, only a small fraction of OTU reads (< 0.1%) lacked a corresponding ASV, indicating that ASVs captured nearly all OTU‐defined taxa. In contrast, 14% of ASV reads assigned to species hypotheses (SHs) did not match any OTU reads assigned to SHs, whereas only 1.3% of OTU SH‐assigned reads lacked a corresponding ASV match. ASVs generally provided higher resolution than OTUs, as abundant SHs were often represented by multiple ASVs, suggesting that ASVs capture intraspecific diversity. Consequently, ASVs should not be used as direct species proxies but instead require post hoc grouping to reflect species‐level diversity. OTUs‐based community composition aligned more clearly with soil properties, particularly the N:C ratio. Overall, both approaches provided a similar overview of broad‐scale species richness. The choice between two clustering methods depends on the research question and the desired level of taxonomic resolution, and our results provide little support for the claim that ASVs should categorically replace taxonomic units in marker‐gene data analysis.

Environmental DNA (eDNA) metabarcoding has lagged in parasite biodiversity assessments. We implemented this method to examine parasite diversity in sediment and water from 4 physically connected aquatic habitats in coastal South Carolina, USA, as part of a ParasiteBlitz in April 2023. Sediment was collected using a syringe corer, and water was sampled using active filtration and passive collection. Five amplicon libraries, using primers targeting portions of the mitochondrial COI of platyhelminths and 18S ribosomal RNA genes of nematodes, myxozoans, microsporidians, and protists, successfully yielded parasite sequences. Out of >5.8 million sequences, we identified >1,000 parasite amplicon sequence variants (ASVs) corresponding to ~600 parasite operational taxonomic units, from 6 parasite groups. Most diversity was observed among the microsporidians, whose assay demonstrated the highest fidelity. Actively-filtered water samples captured ASVs of all 6 groups, whereas sediment captured only 4, despite yielding 3× as many ASVs. Low DNA yields from passive water samples resulted in fewer, but some unique, ASVs representing 3 parasite groups. The most efficient sampling method varied with respect to parasite group across habitats, and the parasite communities from each habitat were distinct regardless of sampling method. We detected ASVs of 9 named species, 4 of which may represent introductions to the US. The abundance of our results demonstrates the effectiveness and efficiency of eDNA metabarcoding for assessing parasite diversity during short, intensive surveys, and highlights the critical need for more comprehensive sequence databases and the development of primers for those parasite taxa that elude detection using eDNA methods.

To study biogeography and other ecological patterns of microorganisms, including fungi, scientists have been using operational taxonomic units (OTUs) as representations of species or species hypotheses. However, when defined by 97% sequence similarity cutoff at an accepted barcode locus such as 16S in bacteria or ITS in fungi, these OTUs can obscure biogeographic patterns, mask taxonomic diversity, and hinder meta-analyses. Amplicon sequence variants (ASVs) have been proposed to alleviate all of these issues and have been shown to do so in bacteria. Analyzing ASVs is just emerging as a common practice among fungal studies, and it is unclear whether the benefits found in bacterial studies of using such an approach carryover to fungi. Here, we conducted a meta-analysis of Hawaiian fungi by analyzing ITS1 amplicon sequencing data as ASVs and exploring ecological patterns. These surveys spanned three island groups and five ecosystems combined into the first comprehensive Hawaiian Mycobiome ASV Database. Our results show that ASVs can be used to combine fungal ITS surveys, increase reproducibility, and maintain the broad ecological patterns observed with OTUs, including diversity orderings. Additionally, the ASVs that comprise some of the most common OTUs in our database reveals some island specialists, indicating that traditional OTU clustering can obscure important biogeographic patterns. We recommend that future fungal studies, especially those aimed at assessing biogeography, analyze ASVs rather than OTUs. We conclude that similar to bacterial studies, ASVs improve reproducibility and data sharing for fungal studies.

The Amazon basin harbors exceptional fish diversity, with more than 3,500 species reported. However, this biodiversity is increasingly threatened by anthropogenic activities and climate change. The Peruvian Amazon alone is home to nearly 1,000 freshwater fish species - approximately one-third of the entire Amazon - yet significant gaps remain in our understanding of their distribution, ecology, and conservation status. Molecular approaches, particularly environmental DNA (eDNA) metabarcoding, have emerged as promising alternatives for rapid and accurate biodiversity assessment. In this study, a metabarcoding workflow targeting a fragment of the 12S gene to eDNA samples collected from small streams and ponds near Iquitos, Peru, was applied to evaluate the applicability of this approach for local fish community inventories. Water from 12 sites was filtered, and DNA was extracted, amplified, and sequenced using high-throughput Illumina technology. Bioinformatic analyses included MOTU clustering, haplotype identification, and taxonomic assignment using the Lowest Common Ancestor (LCA) algorithm. A total of 226 amplicon sequence variants (ASVs) were identified, 95 of which were assigned to fish taxa and clustered into 44 MOTUs across four orders: Characiformes, Gymnotiformes, Siluriformes, and Cichliformes. The study highlights the effectiveness of eDNA metabarcoding in detecting both common and elusive species, while also highlighting current limitations due to incomplete DNA barcode reference libraries. These findings underscore the need to expand barcode databases to fully harness the potential of eDNA for monitoring Amazonian fish biodiversity and support its use as a valuable tool for inventorying fish communities in sensitive and understudied environments.

Foraminifera are adapted to a wide range of environments, and environmental DNA (eDNA) metabarcoding of foraminifera should facilitate development of new environmental indicators. In this study, we used eDNA metabarcoding to evaluate the discrepancy between planktic and benthic foraminifera molecular communities identified in bottom water and short sediment cores. The molecular community was compared to foraminiferal shells in sediment traps set on the seafloor. Samples were collected in June and August around the Takuyo-Daigo Seamount in the western subtropical Pacific Ocean. Approximately 40% of amplicon sequence variants (ASVs) pertained to unknown foraminiferal lineages in sediment samples, compared with only 22% in bottom water. Bottom water contained benthic foraminifera and taxonomically unassigned lineages, which were attributed to resuspended particles. In bottom water, 100 ASVs were assigned to planktic foraminifera. ASVs assigned to Candeina nitida were most abundant and accounted for 36%–86% of planktic foraminiferal ASVs. In sedimentary DNA, Globigerinita glutinata was the most abundant among 33 ASVs of planktic foraminifera. However, transparent shells in sediment traps contained more spinose species, such as Globigerinoides ruber, whereas C. nitida was not found and few G. glutinata were detected. This discrepancy between the three samples may be due to the species-specific preservation, to polymerase chain reaction biases, and/or to low abundance of planktic foraminifers. In sedimentary DNA, 893 ASVs were assigned to high-level foraminiferal taxa. Among benthic foraminiferal lineages, monothalamids were most abundant, as reported in other deep-sea regions. Molecular communities formed one cluster above the boundary at which ASVs sharply decrease across the three cores. Our results suggest that depth within the sediment core can affect foraminiferal ASVs, but the distance between sites up to 200 m did not strongly affect ASVs of sedimentary DNA at least above the boundary at which ASVs sharply decrease. Sequences of foraminiferal DNA in sediment decreased linearly in core PC02-A1, but exponentially in core PC03-B3. The decline of foraminiferal ASVs may reflect both the decreases in numbers of living foraminifera and degradation of DNA in sediment, related to the particle mixing depth.

Environmental DNA (eDNA) metabarcoding has become a powerful, non-invasive method for detecting aquatic organisms. However, optimal sampling strategies for benthic taxa in lentic ecosystems remain unclear. This study evaluated the effectiveness of eDNA metabarcoding in detecting freshwater Unionidae mussels in lake water columns and examined their spatial and seasonal distribution patterns. We validated a mini-barcode primer targeting the mitochondrial 16S rDNA of unionid mussels through controlled laboratory experiments and field tests, confirming reliable amplification and accurate taxonomic assignment of freshwater bivalve DNA. Field surveys were conducted in four lakes within the Nakdong River basin, where eDNA samples were collected from littoral zones and from surface, mid-, and bottom layers of central lake areas during autumn and winter. Metabarcoding analysis identified 79 amplicon sequence variants (ASVs) representing four unionid taxa, with Cristaria plicata and Sinanodonta lauta comprising the majority of reads and ASVs. Overall, the number of Unionidae eDNA reads showed no significant seasonal differences, but there was notable spatial variation among sampling locations. Read numbers were significantly lower in littoral zones compared to central lake areas, with no significant differences detected among depth layers within the central zones. Species-specific analyses revealed contrasting spatial patterns: C. plicata had higher read abundance in mid- and bottom layers, while S. lauta was more frequently detected in surface and littoral samples. These findings suggest that the distribution of freshwater mussel eDNA in lakes is primarily influenced by spatial factors related to habitat preference and hydrodynamic mixing, rather than by seasonal variation during stable periods. This study offers practical insights for designing effective eDNA sampling strategies for benthic invertebrates in lentic ecosystems.

The microbiome is increasingly regarded as a key component of human health, and analysis of microbiome data can aid in the development of precision medicine. Due to the high cost of shotgun metagenomic sequencing (SM-seq), microbiome analyses can be done cost-effectively in two phases: Phase 1-sequencing of 16S ribosomal RNA, and Phase 2-SM-seq of an informative subsample. Existing research suggests strategies to select the subsample based on biological diversity and dissimilarity metrics calculated using operational taxonomic units (OTUs). However, the microbiome field has progressed towards amplicon sequencing variants (ASVs), as they provide more precise microbe identification and sample diversity information. The aim of this work is to compare the subsampling strategies for two-phase metagenomic studies when using ASVs instead of OTUs, and to propose data driven strategies for subsample selection through dimension reduction techniques. We used 199 samples of infant-gut microbiome data from the DIABIMMUNE project to generate ASVs and OTUs, then generated subsamples based on five existing biologically driven subsampling methods and two data driven methods. Linear discriminant analysis Effect Size (LEfSe) was used to assess differential representation of taxa between the subsamples and the overall sample. The use of ASVs showed a 50-93% agreement in the subsample selection with the use of OTUs for the subsampling methods evaluated, and showed a similar bacterial representation across all methods. Although sampling using ASVs and OTUs typically lead to similar results for each subsample, ASVs had more clades that differed in expression levels between allergic and non-allergic individuals across all sample sizes compared to OTUs, and led to more biomarkers discovered at Phase 2-SM-seq level.

Environmental DNA studies targeting multiple taxa using metabarcoding provide remarkable insights into levels of species diversity in any habitat. The main drawbacks are the presence of primer bias and difficulty in identifying rare species. We tested a DNA sequence-capture method in parallel with the metabarcoding approach to reveal possible advantages of one method over the other. Both approaches were performed using the same eDNA samples and the same 18S and COI regions, followed by high throughput sequencing. Metabarcoded eDNA libraries were PCR amplified with one primer pair from 18S and COI genes. DNA sequence-capture libraries were enriched with 3,639 baits targeting the same gene regions. We tested amplicon sequence variants (ASVs) and operational taxonomic units (OTUs) in silico approaches for both markers and methods, using for this purpose the metabarcoding data set. ASVs methods uncovered more species for the COI gene, whereas the opposite occurred for the 18S gene, suggesting that clustering reads into OTUs could bias diversity richness especially using 18S with relaxed thresholds. Additionally, metabarcoding and DNA sequence-capture recovered 80%-90% of the control sample species. DNA sequence-capture was 8x more expensive, nonetheless it identified 1.5x more species for COI and 13x more genera for 18S than metabarcoding. Both approaches offer reliable results, sharing ca. 40% species and 72% families and retrieve more taxa when nuclear and mitochondrial markers are combined. eDNA metabarcoding is quite well established and low-cost, whereas DNA-sequence capture for biodiversity assessment is still in its infancy, is more time-consuming but provides more taxonomic assignments.

The Itaipu Hydroelectric Power Plant is the second largest in the world in power generation. The artificial barrier created by its dam imposes an obstacle for fish migration. Thus, in 2002, a fish pass system, named Piracema Channel, was built to allow fish to access areas upstream of the reservoir. We tested the potential of environmental DNA metabarcoding to monitor the impact of both the dam and associated fish pass system in the Paraná River fish communities and to compare it with traditional monitoring methods. Using a fragment of the 12S gene, we characterized richness and community composition based on amplicon sequence variants, operational taxonomic units, and zero-radius OTUs. We combined GenBank and in-house data for taxonomic assignment. We found that different bioinformatics approaches showed similar results. Also, we found a decrease in fish diversity from 2019 to 2020 probably due to the recent extreme drought experienced in southeastern Brazil. The highest alpha diversity was recorded in the mouth of the fish pass system, located in a protected valley with the highest environmental heterogeneity. Despite the clear indication that the reference databases need to be continuously improved, our results demonstrate the analytical efficiency of the metabarcoding to monitor fish species.

Environmental DNA (eDNA) metabarcoding has been commonly used in recent years (Jeunen et al. 2019) for the identification of the species composition of environmental samples. By making use of genetic markers anchored in conserved gene regions, universally present acrooss the species of large taxonomy groups, eDNA metabarcoding exploits both extra- and intra-cellular DNA fragments for biodiversity assessment. However, there is not a truly “universal” marker gene that is capable of amplifying all species across different taxa (Kress et al. 2015). The mitochondrial cytochrome C oxidase subunit I gene (COI) has many of the desirable properties of a “universal" marker and has been widely used for assessing species identity in Eukaryotes, especially metazoans (Andjar et al. 2018). However, a great number of COI Operational Taxonomic Units (OTUs) or/and Amplicon Sequence Variants (ASVs) retrieved from such studies do not match reference sequences and are often referred to as “dark matter” (Deagle et al. 2014). The aim of this study was to discover the origins and identities of these COI dark matter sequences. We built a reference phylogenetic tree that included as many COI-sequence-related information across the tree of life as possible. An overview of the steps followed is presented in Fig. 1a. Briefly, the Midori reference 2 database was used to retrieve eukaryotes sequences (183,330 species). In addition, the API of the BOLD database was used as source for the corresponding Bacteria (559 genera) and Archaea (41 genera) sequences. Consensus sequences at the family level were constructed from each of these three initial COI datasets. The COI-oriented reference phylogenetic tree of life was then built by using 1,240 consensus sequences with more than 80% of those coming from eukaryotic taxa. Phylogeny-based taxonomic assignment was then used to place query sequences. The a) total number of sequences, b) sequences assigned to Eukaryotes and c) unassigned subsets of OTUs, from marine and freshwater samples, retrieved during in-house metabarcoding experiments, were placed in the reference tree (Fig. 1b). It is clear that a large proportion of sequences targeting the COI region of Eukaryotes actually represents bacterial branches in the phylogenetic tree (Fig. 1b). We conclude that COI metabarcoding studies targeting Eukaryotes may come with a great bias derived from amplification and sequencing of bacterial taxa, depending on the primer pair used. However, for the time being, publicly available bacterial COI sequences are far too few to represent the bacterial variability; thus, a reliable taxonomic identification of them is not possible. We suggest that bacterial COI sequences should be included in the reference databases used for the taxonomy assignment of OTUs/ASVs in COI-based eukaryote metabarcoding studies to allow for bacterial sequences that were amplified to be excluded enabling researchers to exclude non-target sequences. Further, the approach presented here allows researchers to better understand the unknown unknowns and shed light on the dark matter of their metabarcoding sequence data.

Harmonized coexistence of intragenomic variations in diatom Skeletonema strains

The mutualism between the green alga Oophila amblystomatis (“Oophila”) and egg masses of the spotted salamander Ambystoma maculatum involves acquisition of algal cells from water, followed by their population growth within individual egg capsules of the egg mass. The host range spans over 20 degrees latitude and longitude. High site fidelity and fine scale genetic structure of host populations has been resolved, but genetic diversity of green algal symbionts across this range has not been investigated. We sampled egg capsule fluid harvested from five locales in North America, each separated by ~2° latitude and used both PacBio and Illumina sequencing of the 18S rRNA gene to investigate (i) whether algal symbionts are restricted to the Oophila clade and (ii) examine geographic variation in Oophila diversity. We also re-analyzed 18S (V4) reads from previous work using contemporary denoising protocols, which, along with current data, produced 16.37 million reads. We detected no chlorophytes outside Oophila clade B and detected cercozoan protists and rhizophydialean fungi in some capsules. There was a mean of 10.14 ± 3.40 amplicon sequence variants (ASVs) per sample corresponding to O. amblystomatis, ranging in prevalence from single ponds within a locale to all samples. We observed among-locale variation in unique ASVs, but not in a latitudinal pattern. However, we detected two highly prevalent ASVs (ASVs A and B), which varied in abundance, relative to each other, as a function of latitude. Pairwise alignments of ASVs from the V4 region revealed that, on average, more divergent ASVs were rarer. Haplotype network analysis revealed ASV A as the ancestral variant and amplicon sequence analysis from newly isolated cultures of O. amblystomatis demonstrate that ASVs A and B are intragenomic variants that can segregate. We conclude that, at the scale of the host range, partner specificity in this algal-salamander mutualism is very high and reveal an intriguing effect of latitude on the relative abundance of intragenomic 18S rRNA gene variants.

To understand the relative influences of operational taxonomic units (OTUs) and amplicon sequence variants (ASVs) on patterns of marine microbial diversity and community composition, we examined bacterial diversity and community composition of seawater from 12 sites in the North Atlantic Ocean and Canadian Arctic and sediment from two sites in the North Atlantic. For the seawater analyses, we included samples from three to six zones in the water column of each site. For the sediment analyses, we included over 20 sediment horizons at each of two sites. For all samples, we amplified the V4–V5 hypervariable region of the 16S ribosomal RNA (rRNA) gene. We analyzed each sample in two different ways: (i) by clustering its reads into 97%-similar OTUs and (ii) by assigning sequences to unique ASVs. OTU richness is much higher than ASV richness for every sample, but both OTUs and ASVs exhibit similar vertical patterns of relative diversity in both the water column and the sediment. Bacterial richness is highest just below the photic zone in the water column and at the seafloor in the sediment. For both OTUs and ASVs, richness estimates depend on the number of sequences analyzed. Both methods yield broadly similar community compositions for each sample at the taxonomic levels of phyla to families. While the two methods yield different richness values, broad-scale patterns of relative richness and community composition are similar with both methods.

In metabarcoding studies, Linnaean taxonomy assignments of Operational Taxonomic Units (OTUs) or Amplicon Sequence Variants (ASVs) underpin many downstream bioinformatics analyses and ecological interpretations of environmental DNA (eDNA) datasets. However, public molecular databases (i.e., SILVA, EUKARYOME, BOLD) for most microbial metazoan phyla (nematodes, tardigrades, kinorhynchs, etc.) are sparsely populated, negatively impacting our ability to assign ecologically meaningful taxonomy to these understudied groups. Additionally, the choice of bioinformatics parameters and computational algorithms can further impact the accuracy of eDNA taxonomy assignments. Here, we use two in-silico datasets to show that taxonomy assignments using the 18S rRNA gene can be dramatically improved by curating Linnaean taxonomy strings associated with each reference sequence and closing phylogenetic gaps by improving taxon sampling. Using free-living nematodes as a case study, we applied two commonly used taxonomy assignment algorithms (BLAST+ and the QIIME2 Naïve Bayes classifier) across six iterations of the SILVA 138 reference database to evaluate the precision and accuracy of taxonomy assignments. The BLAST+ top hit with a 90% sequence similarity cutoff often returned the highest percentage of correctly assigned taxonomy at the genus level, and the QIIME2 Naïve Bayes classifier performed similarly well when paired with a reference database containing corrected taxonomy strings. Our results highlight the urgent need for phylogenetically-informed expansions of public reference databases (encompassing both genomes and common gene markers), focused on poorly sampled lineages which are now robustly recovered via eDNA metabarcoding approaches. Additional taxonomy curation efforts should be applied to popular reference databases such as SILVA, and taxon sampling could be rapidly improved by more frequent incorporation of newly published GenBank sequences linked to genus and/or species level identifications.

Understanding microbial community composition and the factors influencing it is essential for effective ecological surveillance and biomonitoring. Advancements in non‐invasive sampling and metabarcoding have improved the understanding of soil fungal distribution and composition; however, seasonal variations in fungal composition across climate zones and the influence of environmental factors on community composition remain underexplored. We utilized environmental DNA (eDNA) metabarcoding to assess fungal diversity across equatorial, tropical, arid, and savanna climate zones along a 12‐degree latitudinal gradient, at four time points from 2023 to 2024 in the Northern Territory, Australia. On 88 soil samples, we applied two DNA extraction methods (kit‐based and non‐kit based) and two sequence clustering approaches (Operational Taxonomic Units (OTUs) and Amplicon Sequence Variants (ASVs)) to determine the influence of methodology in soil fungal microbiome assessment. ASVs from the kit‐based extraction method showed a higher alpha‐diversity. Fungal diversity was greater in the tropical climate zone than in savanna, arid, and equatorial zones. Across all climate zones, diversity peaked in August–September, coinciding with the high humidity “build‐up” season. Fungal communities in tropical and equatorial zones were more similar to each other than to those in savanna and arid zones. Temperature, precipitation, and climate zones were the primary drivers of fungal composition change, while vegetation type and soil type had smaller effects. The most abundant fungal families were Aspergillaceae, Ascomycota (family Incertae sedis ), Herpotrichiellaceae, Malasseziaceae, and Chaetomiaceae. Significant temporal change was observed in the relative abundance of Aspergillaceae, Ascomycota (family Incertae sedis ), and Chaetomiaceae, with the highest levels recorded in the monsoon season. Differences across extraction and sequence grouping methods suggest that using multiple approaches improves the assessment of fungal diversity and composition. Our study provides insights into soil fungal composition and abundance across diverse ecosystems, generating baseline data to support biosecurity surveillance and monitoring.