Automated high throughput animal CO1 metabarcode classification

Contamination Survey of Insect Genomic and Transcriptomic Data

The rapid advancement of high-throughput sequencing has led to a great increase in sequencing data, resulting in a significant accumulation of contamination, for example, sequences from non-target species may be present in the target species' sequencing data. Insecta, the most diverse group within Arthropoda, still lacks a comprehensive evaluation of contamination prevalence in public databases and an analysis of potential contamination causes. In this study, COI barcodes were used to investigate contamination from insects and mammals in GenBank's genomic and transcriptomic data across four insect orders. Among the 2796 WGS and 1382 TSA assemblies analyzed, contamination was detected in 32 (1.14%) WGS and 152 (11.0%) TSA assemblies. Key findings from this study include the following: (1) TSA data exhibited more severe contamination than WGS data; (2) contamination levels varied significantly among the four orders, with Hemiptera showing 9.22%, Coleoptera 3.48%, Hymenoptera 7.66%, and Diptera 1.89% contamination rates; (3) possible causes of contamination, such as food, parasitism, sample collection, and cross-contamination, were analyzed. Overall, this study proposes a workflow for checking the existence of contamination in WGS and TSA data and some suggestions to mitigate it.

Ecosystem transplant from a healthy reef boosts coral health at a degraded reef

Organismal communities associated with coral reefs, particularly invertebrates and microbes, play crucial roles in ecosystem maintenance and coral health. Here, we characterized the organismal composition of a healthy, non-urbanized reef (Site A) and a degraded, urbanized reef (Site B) in the Gulf of Eilat/Aqaba, Red Sea to assess its impact on coral health and physiology. Biomimetically designed terracotta tiles were conditioned for 6 months at both sites, then reciprocally transplanted, and scleractinian coral species, Acropora eurystoma and Stylophora pistillata, were attached for an additional 6 months. After 12 months, tiles from Site A transplanted to Site B exhibited greater invertebrate richness and diversity than Site B's original tiles (via Cytochrome c. Oxidase subunit I metabarcoding). Key bacteria from the healthy reef were more prevalent on Site A tiles and on the tiles transplanted to Site B (via 16S rRNA gene sequencing). Corals originally from Site B attached to transplanted healthy tiles (Site A) showed higher photochemical capacity, increased endosymbionts, and reduced physiological stress, measured by total antioxidant capacity and an integrated biomarker response. Our findings demonstrate the successful transfer of organismal communities between reefs, highlighting the potential benefits of healthy reef-associated invertebrates and microbes on coral physiology and their implications for reef restoration strategies.

Most soil and litter arthropods are unidentifiable based on current DNA barcode reference libraries

We are far from knowing all species living on the planet. Understanding biodiversity is demanding and requires time and expertise. Most groups are understudied given problems of identifying and delimiting species. DNA barcoding emerged to overcome some of the difficulties in identifying species. Its limitations derive from incomplete taxonomic knowledge and the lack of comprehensive DNA barcode libraries for so many taxonomic groups. Here, we evaluate how useful barcoding is for identifying arthropods from highly diverse leaf litter communities in the southern Appalachian Mountains (USA). We used 3 reference databases and several automated classification methods on a data set including several arthropod groups. Acari, Araneae, Collembola, Coleoptera, Diptera, and Hymenoptera were well represented, showing different performances across methods and databases. Spiders performed the best, with correct identification rates to species and genus levels of ~50% across databases. Springtails performed poorly, no barcodes were identified to species or genus. Other groups showed poor to mediocre performance, from around 3% (mites) to 20% (beetles) correctly identified barcodes to species, but also with some false identifications. In general, BOLD-based identification offered the best identification results but, in all cases except spiders, performance is poor, with less than a fifth of specimens correctly identified to genus or species. Our results indicate that the soil arthropod fauna is still insufficiently documented, with many species unrepresented in DNA barcode libraries. More effort toward integrative taxonomic characterization is needed to complete our reference libraries before we can rely on DNA barcoding as a universally applicable identification method.

Comparing modern identification methods for wild bees: Metabarcoding and image-based morphological taxonomic assignment

With the decline of bee populations worldwide, studies determining current wild bee distributions and diversity are increasingly important. Wild bee identification is often completed by experienced taxonomists or by genetic analysis. The current study was designed to compare two methods of identification including: (1) morphological identification by experienced taxonomists using images of field-collected wild bees and (2) genetic analysis of composite bee legs (multiple taxa) using metabarcoding. Bees were collected from conservation grasslands in eastern Iowa in summer 2019 and identified to the lowest taxonomic unit using both methods. Sanger sequencing of individual wild bee legs was used as a positive control for metabarcoding. Morphological identification of bees using images resulted in 36 unique taxa among 22 genera, and >80% of Bombus specimens were identified to species. Metabarcoding was limited to genus-level assignments among 18 genera but resolved some morphologically similar genera. Metabarcoding did not consistently detect all genera in the composite samples, including kleptoparasitic bees. Sanger sequencing showed similar presence or absence detection results as metabarcoding but provided species-level identifications for cryptic species (i.e., Lasioglossum). Genus-specific detections were more frequent with morphological identification than metabarcoding, but certain genera such as Ceratina and Halictus were identified equally well with metabarcoding and morphology. Genera with proportionately less tissue in a composite sample were less likely to be detected using metabarcoding. Image-based methods were limited by image quality and visible morphological features, while genetic methods were limited by databases, primers, and amplification at target loci. This study shows how an image-based identification method compares with genetic techniques, and how in combination, the methods provide valuable genus- and species-level information for wild bees while preserving tissue for other analyses. These methods could be improved and transferred to a field setting to advance our understanding of wild bee distributions and to expedite conservation research.

taxalogue: a toolkit to create comprehensive CO1 reference databases

Background

Taxonomic identification through DNA barcodes gained considerable traction through the invention of next-generation sequencing and DNA metabarcoding. Metabarcoding allows for the simultaneous identification of thousands of organisms from bulk samples with high taxonomic resolution. However, reliable identifications can only be achieved with comprehensive and curated reference databases. Therefore, custom reference databases are often created to meet the needs of specific research questions. Due to taxonomic inconsistencies, formatting issues, and technical difficulties, building a custom reference database requires tremendous effort. Here, we present taxalogue, an easy-to-use software for creating comprehensive and customized reference databases that provide clean and taxonomically harmonized records. In combination with extensive geographical filtering options, taxalogue opens up new possibilities for generating and testing evolutionary hypotheses.

Methods

taxalogue collects DNA sequences from several online sources and combines them into a reference database. Taxonomic incongruencies between the different data sources can be harmonized according to available taxonomies. Dereplication and various filtering options are available regarding sequence quality or metadata information. taxalogue is implemented in the open-source Ruby programming language, and the source code is available at https://github.com/nwnoll/taxalogue. We benchmark four reference databases by sequence identity against eight queries from different localities and trapping devices. Subsamples from each reference database were used to compare how well another one is covered.

Results

taxalogue produces reference databases with the best coverage at high identities for most tested queries, enabling more accurate, reliable predictions with higher certainty than the other benchmarked reference databases. Additionally, the performance of taxalogue is more consistent while providing good coverage for a variety of habitats, regions, and sampling methods. taxalogue simplifies the creation of reference databases and makes the process reproducible and transparent. Multiple available output formats for commonly used downstream applications facilitate the easy adoption of taxalogue in many different software pipelines. The resulting reference databases improve the taxonomic classification accuracy through high coverage of the query sequences at high identities.

Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests

Tropical forest recovery is fundamental to addressing the intertwined climate and biodiversity loss crises. While regenerating trees sequester carbon relatively quickly, the pace of biodiversity recovery remains contentious. Here, we use bioacoustics and metabarcoding to measure forest recovery post-agriculture in a global biodiversity hotspot in Ecuador. We show that the community composition, and not species richness, of vocalizing vertebrates identified by experts reflects the restoration gradient. Two automated measures - an acoustic index model and a bird community composition derived from an independently developed Convolutional Neural Network - correlated well with restoration (adj-R² = 0.62 and 0.69, respectively). Importantly, both measures reflected composition of non-vocalizing nocturnal insects identified via metabarcoding. We show that such automated monitoring tools, based on new technologies, can effectively monitor the success of forest recovery, using robust and reproducible data.

Rapid and accurate taxonomic classification of cpn60 amplicon sequence variants

The "universal target" region of the gene encoding the 60 kDa chaperonin protein (cpn60, also known as groEL or hsp60) is a proven sequence barcode for bacteria and a useful target for marker gene amplicon-based studies of complex microbial communities. To date, identification of cpn60 sequence variants from microbiome studies has been accomplished by alignment of queries to a reference database. Naïve Bayesian classifiers offer an alternative identification method that provides variable rank classification and shorter analysis times. We curated a set of cpn60 barcode sequences to train the RDP classifier and tested its performance on data from previous human microbiome studies. Results showed that sequences accounting for 79%, 86% and 92% of the observations (read counts) in saliva, vagina and infant stool microbiome data sets were classified to the species rank. We also trained the QIIME 2 q2-feature-classifier on cpn60 sequence data and demonstrated that it gives results consistent with the standalone RDP classifier. Successful implementation of a naïve Bayesian classifier for cpn60 sequences will facilitate future microbiome studies and open opportunities to integrate cpn60 amplicon sequence identification into existing analysis pipelines.

Phylogenetic Triage and Risk Assessment: How to Predict Emerging Phytoplasma Diseases

Phytoplasma diseases pose a substantial threat to diverse crops of agricultural importance. Management measures are usually implemented only after the disease has already occurred. Early detection of such phytopathogens, prior to disease outbreak, has rarely been attempted, but would be highly beneficial for phytosanitary risk assessment, disease prevention and mitigation. In this study, we present the implementation of a recently proposed proactive disease management protocol (DAMA: Document, Assess, Monitor, Act) for a group of vector-borne phytopathogens. We used insect samples collected during a recent biomonitoring program in southern Germany to screen for the presence of phytoplasmas. Insects were collected with malaise traps in different agricultural settings. DNA was extracted from these mass trap samples and subjected to PCR-based phytoplasma detection and mitochondrial cytochrome c oxidase subunit I (COI) metabarcoding. Phytoplasma DNA was detected in two out of the 152 insect samples analyzed. Phytoplasma identification was performed using iPhyClassifier based on 16S rRNA gene sequence and the detected phytoplasmas were assigned to 'Candidatus Phytoplasma asteris'-related strains. Insect species in the sample were identified by DNA metabarcoding. By using established databases, checklists, and archives, we documented historical associations and records of phytoplasmas and its hosts in the study region. For the assessment in the DAMA protocol, phylogenetic triage was performed in order to determine the risk for tri-trophic interactions (plant-insect-phytoplasma) and associated disease outbreaks in the study region. A phylogenetic heat map constitutes the basis for risk assessment and was used here to identify a minimum number of seven leafhopper species suggested to be monitored by stakeholders in this region. A proactive stance in monitoring changing patterns of association between hosts and pathogens can be a cornerstone in capabilities to prevent future phytoplasma disease outbreaks. To the best of our knowledge, this is the first time that the DAMA protocol has been applied in the field of phytopathology and vector-borne plant diseases.

All boreal forest successional stages needed to maintain the full suite of soil biodiversity, community composition, and function following wildfire

Wildfire is a natural disturbance in boreal forest systems that has been predicted to increase in frequency, intensity, and extent due to climate change. Most studies tend to assess the recovery of one component of the community at a time but here we use DNA metabarcoding to simultaneously monitor soil bacteria, fungi, and arthropods along an 85-year chronosequence following wildfire in jack pine-dominated ecosites. We describe soil successional and community assembly processes to better inform sustainable forest management practices. Soil taxa showed different recovery trajectories following wildfire. Bacteria shared a large core community across stand development stages (~ 95-97% of their unique sequences) and appeared to recover relatively quickly by crown closure. By comparison fungi and arthropods shared smaller core communities (64-77% and 68-69%, respectively) and each stage appeared to support unique biodiversity. We show the importance of maintaining a mosaic ecosystem that represents each stand development stage to maintain the full suite of biodiversity in soils following wildfire, especially for fungi and arthropods. These results will provide a useful baseline for comparison when assessing the effects of human disturbance such as harvest or for assessing the effects of more frequent wildfire events due to climate change.

Assessing arthropod diversity metrics derived from stream environmental DNA: spatiotemporal variation and paired comparisons with manual sampling

Background

Benthic invertebrate (BI) surveys have been widely used to characterize freshwater environmental quality but can be challenging to implement at desired spatial scales and frequency. Environmental DNA (eDNA) allows an alternative BI survey approach, one that can potentially be implemented more rapidly and cheaply than traditional methods.

Methods

We evaluated eDNA analogs of BI metrics in the Potomac River watershed of the eastern United States. We first compared arthropod diversity detected with primers targeting mitochondrial 16S (mt16S) and cytochrome c oxidase 1 (cox1 or COI) loci to that detected by manual surveys conducted in parallel. We then evaluated spatial and temporal variation in arthropod diversity metrics with repeated sampling in three focal parks. We also investigated technical factors such as filter type used to capture eDNA and PCR inhibition treatment.

Results

Our results indicate that genus-level assessment of eDNA compositions is achievable at both loci with modest technical noise, although database gaps remain substantial at mt16S for regional taxa. While the specific taxa identified by eDNA did not strongly overlap with paired manual surveys, some metrics derived from eDNA compositions were rank-correlated with previously derived biological indices of environmental quality. Repeated sampling revealed statistical differences between high- and low-quality sites based on taxonomic diversity, functional diversity, and tolerance scores weighted by taxon proportions in transformed counts. We conclude that eDNA compositions are efficient and informative of stream condition. Further development and validation of scoring schemes analogous to commonly used biological indices should allow increased application of the approach to management needs.

Maximizing the reliability and the number of species assignments in metabarcoding studies using a curated regional library and a public repository

Biodiversity assessments relying on DNA have increased rapidly over the last decade. However, the reliability of taxonomic assignments in metabarcoding studies is variable and affected by the reference databases and the assignment methods used. Species level assignments are usually considered as reliable using regional libraries but unreliable using public repositories. In this study, we aimed to test this assumption for metazoan species detected in the Gulf of St. Lawrence in the Northwest Atlantic. We first created a regional library (GSL-rl) by data mining COI barcode sequences from BOLD, and included a reliability ranking system for species assignments. We then estimated 1) the accuracy and precision of the public repository NCBI-nt for species assignments using sequences from the regional library and 2) compared the detection and reliability of species assignments of a metabarcoding dataset using either NCBI-nt or the regional library and popular assignment methods. With NCBI-nt and sequences from the regional library, the BLAST-LCA (least common ancestor) method was the most precise method for species assignments, but the accuracy was higher with the BLAST-TopHit method (>80% over all taxa, between 70% and 90% amongst taxonomic groups). With the metabarcoding dataset, the reliability of species assignments was greater using GSL-rl compared to NCBI-nt. However, we also observed that the total number of reliable species assignments could be maximized using both GSL-rl and NCBI-nt with different optimized assignment methods. The use of a two-step approach for species assignments, i.e., using a regional library and a public repository, could improve the reliability and the number of detected species in metabarcoding studies.

Be positive: customized reference databases and new, local barcodes balance false taxonomic assignments in metabarcoding studies

Background

In metabarcoding analyses, the taxonomic assignment is crucial to place sequencing data in biological and ecological contexts. This fundamental step depends on a reference database, which should have a good taxonomic coverage to avoid unassigned sequences. However, this goal is rarely achieved in many geographic regions and for several taxonomic groups. On the other hand, more is not necessarily better, as sequences in reference databases belonging to taxonomic groups out of the studied region/environment context might lead to false assignments.

Methods

We investigated the effect of using several subsets of a cytochrome c oxidase subunit I (COI) reference database on taxonomic assignment. Published metabarcoding sequences from the Mediterranean Sea were assigned to taxa using COInr, which is a comprehensive, non-redundant and recent database of COI sequences obtained both from BOLD and NCBI, and two of its subsets: (i) all sequences except insects (COInr-WO-Insecta), which represent the overwhelming majority of COInr database, but are irrelevant for marine samples, and (ii) all sequences from taxonomic families present in the Mediterranean Sea (COInr-Med). Four different algorithms for taxonomic assignment were employed in parallel to evaluate differences in their output and data consistency.

Results

The reduction of the database to more specific custom subsets increased the number of unassigned sequences. Nevertheless, since most of them were incorrectly assigned by the less specific databases, this is a positive outcome. Moreover, the taxonomic resolution (the lowest taxonomic level to which a sequence is attributed) of several sequences tended to increase when using customized databases. These findings clearly indicated the need for customized databases adapted to each study. However, the very high proportion of unassigned sequences points to the need to enrich the local database with new barcodes specifically obtained from the studied region and/or taxonomic group. Including novel local barcodes to the COI database proved to be very profitable: by adding only 116 new barcodes sequenced in our laboratory, thus increasing the reference database by only 0.04%, we were able to improve the resolution for ca. 0.6-1% of the Amplicon Sequence Variants (ASVs).

Designing a surveillance program for early detection of alien plants and insects in Norway

Naturalized species of alien plants and animals comprise < 3% of biodiversity recorded in Norway but have had major impacts on natural ecosystems through displacement of native species. Encroachment of alien species has been especially problematic for coastal sites close to transport facilities and urban areas with high density housing. The goal of our field project was to design and test a surveillance program for early detection of alien species of vascular plants and terrestrial insects at the first phase of establishment in natural areas. In our 3-year project (2018–2020), we sampled 60 study plots in three counties in the Oslofjord region of southern Norway. Study plots (6.25 ha) were selected by two criteria: manual selection based on expert opinion (27 plots) or by random selection based on weights from a hotspot analysis of occurrence of alien species (33 plots). Vascular plants were surveyed by two experienced botanists who found a total of 239 alien species of vascular plants in 95 rounds of surveys. Insects and other invertebrates were captured with a single Malaise trap per site, with 3–4 rounds of repeated sampling. We used DNA-metabarcoding to identify invertebrates based on DNA extractions from crushed insects or from the preservative media. Over 3500 invertebrate taxa were detected in 255 rounds of sampling. We recorded 20 alien species of known risk, and 115 species that were new to Norway, including several ‘doorknocker’ species identified by previous risk assessments. We modeled the probabilities of occupancy (ψ) and detection (p) with occupancy models with repeated visits by multiple observers (vascular plants) or multiple rounds of sampling (insects). The two probabilities covaried with risk category for alien organisms and both were low for species categorized as no known or low risk (range = 0.052–0.326) but were higher for species categorized as severe risk (range = 0.318–0.651). Selecting sites at random or manually did not improve the probability of finding novel alien species, but occupancy had a weak positive relationship with housing density for some categories of alien plants and insects. We used our empirical estimates to test alternative sampling designs that would minimize the combined variance of occupancy and detection (A-optimality criterion). Sampling designs with 8–10 visits per site were best for surveillance of new alien species if the probabilities of occupancy and detection were both low, and provided low conditional probabilities of site occupancy ($$\hat{\psi }_{condl}$$ ψ ^ condl ≤ 0.032) and a high probabilities of cumulative detection ($$\hat{p}*$$ p ^ ∗  ≥ 0.943). Our field results demonstrate that early detection is feasible as a key component of a national surveillance program based on early detection and rapid response.

Taxonomically and Functionally Distinct Ciliophora Assemblages Inhabiting Baltic Sea Ice

Ciliophora is a phylum of unicellular eukaryotes that are common and have pivotal roles in aquatic environments. Sea ice is a marine habitat, which is composed of a matrix of solid ice and pockets of saline water in which Ciliophora thrive. Here, we used phylogenetic placement to identify Ciliophora 18S ribosomal RNA reads obtained from wintertime water and sea ice, and assigned functions to the reads based on this taxonomic information. Based on our results, sea-ice Ciliophora assemblages are poorer in taxonomic and functional richness than under-ice water and water-column assemblages. Ciliophora diversity stayed stable throughout the ice-covered season both in sea ice and in water, although the assemblages changed during the course of our sampling. Under-ice water and the water column were distinctly predominated by planktonic orders Choreotrichida and Oligotrichida, which led to significantly lower taxonomic and functional evenness in water than in sea ice. In addition to planktonic Ciliophora, assemblages in sea ice included a set of moderately abundant surface-oriented species. Omnivory (feeding on bacteria and unicellular eukaryotes) was the most common feeding type but was not as predominant in sea ice as in water. Sea ice included cytotrophic (feeding on unicellular eukaryotes), bacterivorous and parasitic Ciliophora in addition to the predominant omnivorous Ciliophora. Potentially mixotrophic Ciliophora predominated the water column and heterotrophic Ciliophora sea ice. Our results highlight sea ice as an environment that creates a set of variable habitats, which may be threatened by the diminishing extent of sea ice due to changing climate.

MetaWorks: A flexible, scalable bioinformatic pipeline for high-throughput multi-marker biodiversity assessments

Multi-marker metabarcoding is increasingly being used to generate biodiversity information across different domains of life from microbes to fungi to animals such as for molecular ecology and biomonitoring applications in different sectors from academic research to regulatory agencies and industry. Current popular bioinformatic pipelines support microbial and fungal marker analysis, while ad hoc methods are often used to process animal metabarcode markers from the same study. MetaWorks provides a harmonized processing environment, pipeline, and taxonomic assignment approach for demultiplexed Illumina reads for all biota using a wide range of metabarcoding markers such as 16S, ITS, and COI. A Conda environment is provided to quickly gather most of the programs and dependencies for the pipeline. Several workflows are provided such as: taxonomically assigning exact sequence variants, provides an option to generate operational taxonomic units, and facilitates single-read processing. Pipelines are automated using Snakemake to minimize user intervention and facilitate scalability. All pipelines use the RDP classifier to provide taxonomic assignments with confidence measures. We extend the functionality of the RDP classifier for taxonomically assigning 16S (bacteria), ITS (fungi), and 28S (fungi), to also support COI (eukaryotes), rbcL (eukaryotes, land plants, diatoms), 12S (fish, vertebrates), 18S (eukaryotes, diatoms) and ITS (fungi, plants). MetaWorks properly handles ITS by trimming flanking conserved rRNA gene regions as well as protein coding genes by providing two options for removing obvious pseudogenes. MetaWorks can be downloaded from https://github.com/terrimporter/MetaWorks and quickstart instructions, pipeline details, and a tutorial for new users can be found at https://terrimporter.github.io/MetaWorksSite.

Metabarcoding of Fish Larvae in the Merbok River Reveals Species Diversity and Distribution Along its Mangrove Environment

The Merbok River (north-west of Peninsular Malaysia) is a mangrove estuary that provides habitat for over 100 species of fish, which are economically and ecologically important. Threats such as habitat loss and overfishing are becoming a great concern for fisheries conservation and management. The identification of larval fish in this estuarine system is important to complement information on the adults. This is because the data could inform the spawning behaviour, reproductive biology, selection of nursery grounds and migration route of fish. Such information is invaluable for fisheries and aquatic environmental monitoring, and thus for their conservation and management. However, identifying fish larvae is a challenging task based only on morphology and even traditional DNA barcoding. To address this, DNA metabarcoding was utilised to detect the diversity of fish in the Merbok River. To complete the study, the fish larvae were collected at six sampling sites of the river. The extracted larval DNA was amplified for the Cytochrome Oxidase subunit 1 (COI) and 12S ribosomal RNA (12S rRNA) genes based on the metabarcoding approach using shotgun sequencing on the next-generation sequencing (NGS) Illumina MiSeq platform. Eighty-nine species from 65 genera and 41 families were detected, with Oryzias javanicus, Oryzias dancena, Lutjanus argentimaculatus and Lutjanus malabaricus among the most common species. The lower diversity observed from previous morphological studies is suggested to be mainly due to seasonal variation over the sampling period between the two methods and limited 12S rRNA sequences in current databases. The metabarcode data and a validation Sanger sequencing step using 15 species-specific primer pairs detected three species in common: Oryzias javanicus, Decapterus maruadsi and Pennahia macrocephalus. Several discrepancies observed between the two molecular approaches could be attributed to contaminants during sampling and DNA extraction, which could mask the presence of target species, especially when DNA from the contaminants is more abundant than the target organisms. In conclusion, this rapid and cost-effective identification method using DNA metabarcoding allowed the detection of numerous fish species from bulk larval samples in the Merbok River. This method can be applied to other sites and other organisms of interest.

Comparison of traditional and DNA metabarcoding samples for monitoring tropical soil arthropods (Formicidae, Collembola and Isoptera)

The soil fauna of the tropics remains one of the least known components of the biosphere. Long-term monitoring of this fauna is hampered by the lack of taxonomic expertise and funding. These obstacles may potentially be lifted with DNA metabarcoding. To validate this approach, we studied the ants, springtails and termites of 100 paired soil samples from Barro Colorado Island, Panama. The fauna was extracted with Berlese-Tullgren funnels and then either sorted with traditional taxonomy and known, individual DNA barcodes ("traditional samples") or processed with metabarcoding ("metabarcoding samples"). We detected 49 ant, 37 springtail and 34 termite species with 3.46 million reads of the COI gene, at a mean sequence length of 233 bp. Traditional identification yielded 80, 111 and 15 species of ants, springtails and termites, respectively; 98%, 37% and 100% of these species had a Barcode Index Number (BIN) allowing for direct comparison with metabarcoding. Ants were best surveyed through traditional methods, termites were better detected by metabarcoding, and springtails were equally well detected by both techniques. Species richness was underestimated, and faunal composition was different in metabarcoding samples, mostly because 37% of ant species were not detected. The prevalence of species in metabarcoding samples increased with their abundance in traditional samples, and seasonal shifts in species prevalence and faunal composition were similar between traditional and metabarcoding samples. Probable false positive and negative species records were reasonably low (13-18% of common species). We conclude that metabarcoding of samples extracted with Berlese-Tullgren funnels appear suitable for the long-term monitoring of termites and springtails in tropical rainforests. For ants, metabarcoding schemes should be complemented by additional samples of alates from Malaise or light traps.

Multi-marker DNA metabarcoding detects suites of environmental gradients from an urban harbour

There is increasing need for biodiversity monitoring, especially in places where potential anthropogenic disturbance may significantly impact ecosystem health. We employed a combination of traditional morphological and bulk macroinvertebrate metabarcoding analyses to benthic samples collected from Toronto Harbour (Ontario, Canada) to compare taxonomic and functional diversity of macroinvertebrates and their responses to environmental gradients. At the species rank, sites assessed using COI metabarcoding showed more variation than sites assessed using morphological methods. Depending on the assessment method, we detected gradients in magnesium (morphological taxa), ammonia (morphological taxa, COI sequence variants), pH (18S sequence variants) as well as gradients in contaminants such as metals (COI & 18S sequence variants) and organochlorines (COI sequence variants). Observed responses to contaminants such as aromatic hydrocarbons and metals align with known patchy distributions in harbour sediments. We determined that the morphological approach may limit the detection of macroinvertebrate responses to lake environmental conditions due to the effort needed to obtain fine level taxonomic assignments necessary to investigate responses. DNA metabarcoding, however, need not be limited to macroinvertebrates, can be automated, and taxonomic assignments are associated with a certain level of accuracy from sequence variants to named taxonomic groups. The capacity to detect change using a scalable approach such as metabarcoding is critical for addressing challenges associated with biodiversity monitoring and ecological investigations.

The cost of living in larger primate groups includes higher fly densities

Flies are implicated in carrying and mechanically transmitting many primate pathogens. We investigated how fly associations vary across six monkey species (Cercopithecus ascanius, Cercopithecus mitis, Colobus guereza, Lophocebus albigena, Papio anubis, and Piliocolobus tephrosceles) and whether monkey group size impacts fly densities. Fly densities were generally higher inside groups than outside them, and considering data from these primate species together revealed that larger groups harbored more flies. Within species, this pattern was strongest for colobine monkeys, and we speculate this might be due to their smaller home ranges, suggesting that movement patterns may influence fly-primate associations. Fly associations increase with group sizes and may thus represent a cost to sociality.

The bacterial hitchhiker’s guide to COI: Universal primer-based COI capture probes fail to exclude bacterial DNA, but 16S capture leaves metazoa behind

Environmental DNA (eDNA) metabarcoding from water samples has, in recent years, shown great promise for biodiversity monitoring. However, universal primers targeting the cytochrome oxidase I (COI) marker gene popular in metazoan studies have displayed high levels of nontarget amplification. To date, enrichment methods bypassing amplification have not been able to match the detection levels of conventional metabarcoding. This study evaluated the use of universal metabarcoding primers as capture probes to either isolate target DNA or to remove nontarget DNA, prior to amplification, by using biotinylated versions of universal metazoan and bacterial barcoding primers, namely metazoan COI (mlCOIintF) and bacterial 16S (515F). Additionally, each step of the protocol was assessed by amplifying for both metazoan COI (mlCOIintF/jgHCO2198) and bacterial 16S (515F/806R) to investigate the effect on the metazoan and bacterial communities. Bacterial read abundance increased significantly in response to the captures (COI library), while the quality of the captured DNA was also improved. The metazoan-oriented probe captured bacterial DNA in a range that was also amplifiable with the 16S primers, demonstrating the ability of capture probes to isolate fragments of DNA spanning over a longer distance than perhaps expected, from eDNA. Although the use of the tested COI probe cannot be recommended for metazoan enrichment, based on the experimental results, the concept of capturing these longer fragments could be applied to metazoan metabarcoding. By using a truly conserved site without a high-level taxonomic resolution as a target for capture, it may be possible to isolate DNA fragments large enough to span over a nearby barcoding region (e.g., COI), which can then be processed through a conventional metabarcoding-by-amplification protocol.

LANDMark: an ensemble approach to the supervised selection of biomarkers in high-throughput sequencing data

Background

Identification of biomarkers, which are measurable characteristics of biological datasets, can be challenging. Although amplicon sequence variants (ASVs) can be considered potential biomarkers, identifying important ASVs in high-throughput sequencing datasets is challenging. Noise, algorithmic failures to account for specific distributional properties, and feature interactions can complicate the discovery of ASV biomarkers. In addition, these issues can impact the replicability of various models and elevate false-discovery rates. Contemporary machine learning approaches can be leveraged to address these issues. Ensembles of decision trees are particularly effective at classifying the types of data commonly generated in high-throughput sequencing (HTS) studies due to their robustness when the number of features in the training data is orders of magnitude larger than the number of samples. In addition, when combined with appropriate model introspection algorithms, machine learning algorithms can also be used to discover and select potential biomarkers. However, the construction of these models could introduce various biases which potentially obfuscate feature discovery.

Results

We developed a decision tree ensemble, LANDMark, which uses oblique and non-linear cuts at each node. In synthetic and toy tests LANDMark consistently ranked as the best classifier and often outperformed the Random Forest classifier. When trained on the full metabarcoding dataset obtained from Canada’s Wood Buffalo National Park, LANDMark was able to create highly predictive models and achieved an overall balanced accuracy score of 0.96 ± 0.06. The use of recursive feature elimination did not impact LANDMark’s generalization performance and, when trained on data from the BE amplicon, it was able to outperform the Linear Support Vector Machine, Logistic Regression models, and Stochastic Gradient Descent models (p ≤ 0.05). Finally, LANDMark distinguishes itself due to its ability to learn smoother non-linear decision boundaries.

Conclusions

Our work introduces LANDMark, a meta-classifier which blends the characteristics of several machine learning models into a decision tree and ensemble learning framework. To our knowledge, this is the first study to apply this type of ensemble approach to amplicon sequencing data and we have shown that analyzing these datasets using LANDMark can produce highly predictive and consistent models.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04631-z.

Forest soil biotic communities show few responses to wood ash applications at multiple sites across Canada

There is interest in utilizing wood ash as an amendment in forestry operations as a mechanism to return nutrients to soils that are removed during harvesting, with the added benefit of diverting this bioenergy waste material from landfill sites. Existing studies have not arrived at a consensus on what the effects of wood ash amendments are on soil biota. We collected forest soil samples from studies in managed forests across Canada that were amended with wood ash to evaluate the effects on arthropod, bacterial and fungal communities using metabarcoding of F230, 16S, 18S and ITS2 sequences as well as enzyme analyses to assess its effects on soil biotic function. Ash amendment did not result in consistent effects across sites, and those effects that were detected were small. Overall, this study suggests that ash amendment applied to managed forest systems in amounts (up to 20 Mg ha-1) applied across the 8 study sties had little to no detectable effects on soil biotic community structure or function. When effects were detected, they were small, and site-specific. These non-results support the application of wood ash to harvested forest sites to replace macronutrients (e.g., calcium) removed by logging operations, thereby diverting it from landfill sites, and potentially increasing stand productivity.

Assessment of benthic invertebrate diversity and river ecological status along an urbanized gradient using environmental DNA metabarcoding and a traditional survey method

Benthic invertebrate diversity is one of the most commonly used bioindicators for assessing aquatic ecosystem health in river systems. Although an increasing number of studies have focused on assessing benthic invertebrate diversity using environmental DNA metabarcoding and traditional survey methods, benthic invertebrate diversity and ecological status assessments performed across different landscapes within river systems have not been well documented. Here, the diversity and ecological status of benthic invertebrates and the influence of water quality on the invertebrate assemblage distribution along an urbanization gradient in rivers from the Jingjinji (JJJ) region, China, were investigated using eDNA metabarcoding and the traditional method. With the combination of the two methods, 395 benthic invertebrates from 6 phyla, 27 orders, 94 families, and 222 genera were identified. The species richness of the benthic invertebrate community in the mountain area was significantly higher than that in the urban and agricultural areas. Compared to the traditional results, eDNA metabarcoding obtained a significantly greater number of species from every sampling site (P = 0.000) and detected a notably higher abundance in Annelida (P = 0.000). Furthermore, the nonmetric multidimensional scaling (NMDS) and permutational multivariate analysis of variance (PERMANOVA) based on the Bray-Curtis dissimilarity index indicated that the benthic invertebrate communities from the different habitats were discriminated more accurately and easily using eDNA metabarcoding (P = 0.038) than with the traditional method (P = 0.829). Additionally, the assemblages identified by eDNA metabarcoding were more closely linked to water quality and could be realistically used to assess the ecological status of rivers. Our findings highlight that eDNA metabarcoding could represent a rapid and reliable method for estimating benthic invertebrate diversity and ecological status in river systems.

Development and evaluation of a meat mitochondrial metagenomic (3MG) method for composition determination of meat from fifteen mammalian and avian species

BACKGROUND

Bioassessment and biomonitoring of meat products are aimed at identifying and quantifying adulterants and contaminants, such as meat from unexpected sources and microbes. Several methods for determining the biological composition of mixed samples have been used, including metabarcoding, metagenomics and mitochondrial metagenomics. In this study, we aimed to develop a method based on next-generation DNA sequencing to estimate samples that might contain meat from 15 mammalian and avian species that are commonly related to meat bioassessment and biomonitoring.

RESULTS

In this project, we found the meat composition from 15 species could not be identified with the metabarcoding approach because of the lack of universal primers or insufficient discrimination power. Consequently, we developed and evaluated a meat mitochondrial metagenomics (3MG) method. The 3MG method has four steps: (1) extraction of sequencing reads from mitochondrial genomes (mitogenomes); (2) assembly of mitogenomes; (3) mapping of mitochondrial reads to the assembled mitogenomes; and (4) biomass estimation based on the number of uniquely mapped reads. The method was implemented in a python script called 3MG. The analysis of simulated datasets showed that the method can determine contaminant composition at a proportion of 2% and the relative error was < 5%. To evaluate the performance of 3MG, we constructed and analysed mixed samples derived from 15 animal species in equal mass. Then, we constructed and analysed mixed samples derived from two animal species (pork and chicken) in different ratios. DNAs were extracted and used in constructing 21 libraries for next-generation sequencing. The analysis of the 15 species mix with the method showed the successful identification of 12 of the 15 (80%) animal species tested. The analysis of the mixed samples of the two species revealed correlation coefficients of 0.98 for pork and 0.98 for chicken between the number of uniquely mapped reads and the mass proportion.

CONCLUSION

To the best of our knowledge, this study is the first to demonstrate the potential of the non-targeted 3MG method as a tool for accurately estimating biomass in meat mix samples. The method has potential broad applications in meat product safety.

Do the evolutionary interactions between moths and bats promote niche partitioning between bats and birds?

Ecological theory suggests that the coexistence of species is promoted by the partitioning of available resources, as in dietary niche partitioning where predators partition prey. Yet, the mechanisms underlying dietary niche partitioning are not always clear. We used fecal DNA metabarcoding to investigate the diets of seven nocturnal insectivorous bird and bat species. Low diet overlap (2%-22%) supported resource partitioning among all species. Differences in diet corresponded with species identity, prey detection method, and foraging behavior of predators. Insects with ultrasonic hearing capabilities were consumed significantly more often by birds than bats, consistent with an evolved avoidance of echolocating strategies. In turn, bats consumed a greater proportion of noneared insects such as spruce budworms. Overall, our results suggest that evolutionary interactions among bats and moths translate to dietary niche partitioning and coexistence among bats and nocturnal birds.

RESCRIPt: Reproducible sequence taxonomy reference database management

Nucleotide sequence and taxonomy reference databases are critical resources for widespread applications including marker-gene and metagenome sequencing for microbiome analysis, diet metabarcoding, and environmental DNA (eDNA) surveys. Reproducibly generating, managing, using, and evaluating nucleotide sequence and taxonomy reference databases creates a significant bottleneck for researchers aiming to generate custom sequence databases. Furthermore, database composition drastically influences results, and lack of standardization limits cross-study comparisons. To address these challenges, we developed RESCRIPt, a Python 3 software package and QIIME 2 plugin for reproducible generation and management of reference sequence taxonomy databases, including dedicated functions that streamline creating databases from popular sources, and functions for evaluating, comparing, and interactively exploring qualitative and quantitative characteristics across reference databases. To highlight the breadth and capabilities of RESCRIPt, we provide several examples for working with popular databases for microbiome profiling (SILVA, Greengenes, NCBI-RefSeq, GTDB), eDNA and diet metabarcoding surveys (BOLD, GenBank), as well as for genome comparison. We show that bigger is not always better, and reference databases with standardized taxonomies and those that focus on type strains have quantitative advantages, though may not be appropriate for all use cases. Most databases appear to benefit from some curation (quality filtering), though sequence clustering appears detrimental to database quality. Finally, we demonstrate the breadth and extensibility of RESCRIPt for reproducible workflows with a comparison of global hepatitis genomes. RESCRIPt provides tools to democratize the process of reference database acquisition and management, enabling researchers to reproducibly and transparently create reference materials for diverse research applications. RESCRIPt is released under a permissive BSD-3 license at https://github.com/bokulich-lab/RESCRIPt.

Generating and managing sequence and taxonomy reference data presents a bottleneck to many researchers, whether they are generating custom databases or attempting to format existing, curated reference databases for use with standard sequence analysis tools. Evaluating database quality and choosing the “best” database can be an equally formidable challenge. We developed RESCRIPt to alleviate this bottleneck, supporting reproducible, streamlined generation, curation, and evaluation of reference sequence databases. RESCRIPt uses QIIME 2 artifact file formats, which store all processing steps as data provenance within each file, allowing researchers to retrace the computational steps used to generate any given file. We used RESCRIPt to benchmark several commonly used marker-gene sequence databases for 16S rRNA genes, ITS, and COI sequences, demonstrating both the utility of RESCRIPt to streamline use of these databases, but also to evaluate several qualitative and quantitative characteristics of each database. We show that larger databases are not always best, and curation steps to reduce redundancy and filter out noisy sequences may be beneficial for some applications. We anticipate that RESCRIPt will streamline the use, management, and evaluation/selection of reference database materials for microbiomics, diet metabarcoding, eDNA, and other diverse applications.

Highly comparable metabarcoding results from MGI-Tech and Illumina sequencing platforms

With the developments in DNA nanoball sequencing technologies and the emergence of new platforms, there is an increasing interest in their performance in comparison with the widely used sequencing-by-synthesis methods. Here, we test the consistency of metabarcoding results from DNBSEQ-G400RS (DNA nanoball sequencing platform by MGI-Tech) and NovaSeq 6000 (sequencing-by-synthesis platform by Illumina) platforms using technical replicates of DNA libraries that consist of COI gene amplicons from 120 soil DNA samples. By subjecting raw sequencing data from both platforms to a uniform bioinformatics processing, we found that the proportion of high-quality reads passing through the filtering steps was similar in both datasets. Per-sample operational taxonomic unit (OTU) and amplicon sequence variant (ASV) richness patterns were highly correlated, but sequencing data from DNBSEQ-G400RS harbored a higher number of OTUs. This may be related to the lower dominance of most common OTUs in DNBSEQ data set (thus revealing higher richness by detecting rare taxa) and/or to a lower effective read quality leading to generation of spurious OTUs. However, there was no statistical difference in the ASV and post-clustered ASV richness between platforms, suggesting that additional denoising step in the ASV workflow had effectively removed the 'noisy' reads. Both OTU-based and ASV-based composition were strongly correlated between the sequencing platforms, with essentially interchangeable results. Therefore, we conclude that DNBSEQ-G400RS and NovaSeq 6000 are both equally efficient high-throughput sequencing platforms to be utilized in studies aiming to apply the metabarcoding approach, but the main benefit of the former is related to lower sequencing cost.

DNA barcodes enable higher taxonomic assignments in the Acari

Although mites (Acari) are abundant in many terrestrial and freshwater ecosystems, their diversity is poorly understood. Since most mite species can be distinguished by variation in the DNA barcode region of cytochrome c oxidase I, the Barcode Index Number (BIN) system provides a reliable species proxy that facilitates large-scale surveys. Such analysis reveals many new BINs that can only be identified as Acari until they are examined by a taxonomic specialist. This study demonstrates that the Barcode of Life Datasystem's identification engine (BOLD ID) generally delivers correct ordinal and family assignments from both full-length DNA barcodes and their truncated versions gathered in metabarcoding studies. This result was demonstrated by examining BOLD ID's capacity to assign 7021 mite BINs to their correct order (4) and family (189). Identification success improved with sequence length and taxon coverage but varied among orders indicating the need for lineage-specific thresholds. A strict sequence similarity threshold (86.6%) prevented all ordinal misassignments and allowed the identification of 78.6% of the 7021 BINs. However, higher thresholds were required to eliminate family misassignments for Sarcoptiformes (89.9%), and Trombidiformes (91.4%), consequently reducing the proportion of BINs identified to 68.6%. Lineages with low barcode coverage in the reference library should be prioritized for barcode library expansion to improve assignment success.

Profile hidden Markov model sequence analysis can help remove putative pseudogenes from DNA barcoding and metabarcoding datasets

BACKGROUND

Pseudogenes are non-functional copies of protein coding genes that typically follow a different molecular evolutionary path as compared to functional genes. The inclusion of pseudogene sequences in DNA barcoding and metabarcoding analysis can lead to misleading results. None of the most widely used bioinformatic pipelines used to process marker gene (metabarcode) high throughput sequencing data specifically accounts for the presence of pseudogenes in protein-coding marker genes. The purpose of this study is to develop a method to screen for nuclear mitochondrial DNA segments (nuMTs) in large COI datasets. We do this by: (1) describing gene and nuMT characteristics from an artificial COI barcode dataset, (2) show the impact of two different pseudogene removal methods on perturbed community datasets with simulated nuMTs, and (3) incorporate a pseudogene filtering step in a bioinformatic pipeline that can be used to process Illumina paired-end COI metabarcode sequences. Open reading frame length and sequence bit scores from hidden Markov model (HMM) profile analysis were used to detect pseudogenes.

RESULTS

Our simulations showed that it was more difficult to identify nuMTs from shorter amplicon sequences such as those typically used in metabarcoding compared with full length DNA barcodes that are used in the construction of barcode libraries. It was also more difficult to identify nuMTs in datasets where there is a high percentage of nuMTs. Existing bioinformatic pipelines used to process metabarcode sequences already remove some nuMTs, especially in the rare sequence removal step, but the addition of a pseudogene filtering step can remove up to 5% of sequences even when other filtering steps are in place.

CONCLUSIONS

Open reading frame length filtering alone or combined with hidden Markov model profile analysis can be used to effectively screen out apparent pseudogenes from large datasets. There is more to learn from COI nuMTs such as their frequency in DNA barcoding and metabarcoding studies, their taxonomic distribution, and evolution. Thus, we encourage the submission of verified COI nuMTs to public databases to facilitate future studies.

Land-use intensity affects the potential for apparent competition within and between habitats

Arthropod communities dwelling in adjacent habitats are able to impact one another via shared natural enemies. In agricultural landscapes, drastic differences in resource availability between crop and non-crop habitats cause variation in insect herbivore densities over short distances, potentially driving inter-habitat effects. Moreover, the composition of the landscape in which the habitats are embedded likely affects realised attack rates from natural enemies via impacts on local arthropod community structure. Here, we examine indirect effects between herbivore species within and between habitat types by calculating the potential for apparent competition between multiple populations. Firstly, we aim to determine how disparities in resource availability impact the strength of the potential for apparent competition occurring between habitats, secondly to examine the impact of landscape composition upon these effects, and finally to couch these observations in reality by investigating the link between the potential for apparent competition and realised attack rates. We used DNA metabarcoding to characterise host-parasitoid interactions within two habitat types (with divergent nutrient inputs) at 11 locations with variable landscape composition within an agroecosystem context. We then used these interaction networks to estimate the potential for apparent competition between each host pair and to compare expected versus realised attack rates across the system. Shared natural enemies were found to structure host herbivore communities within and across habitat boundaries. The size of this effect was related to the resource availability of habitats, such that the habitat with high nutrient input exerted a stronger effect. The overall potential for apparent competition declined with increasing land-use intensity in the surrounding landscape and exhibited a discernible impact on realised attack rates upon herbivore species. Thus, our results suggest that increasing the proportion of perennial habitat in agroecosystems could increase the prevalence of indirect effects such as apparent competition among insect herbivore communities, potentially leading to enhanced population regulation via increased attack rates from natural enemies like parasitoid wasps.

Animals, protists and bacteria share marine biogeographic patterns

Over millennia, ecological and evolutionary mechanisms have shaped macroecological patterns across the tree of life. Research describing these patterns at both regional and global scales has traditionally focused on the study of metazoan species. Consequently, there is a limited understanding of cross-phylum biogeographic structuring and an escalating need to understand the macroecology of both microscopic and macroscopic organisms. Here we used environmental DNA (eDNA) metabarcoding to explore the biodiversity of marine metazoans, protists and bacteria along an extensive and highly heterogeneous coastline. Our results showed remarkably consistent biogeographic structure across the kingdoms of life despite billions of years of evolution. Analyses investigating the drivers of these patterns for each taxonomic kingdom found that environmental conditions (such as temperature) and, to a lesser extent, anthropogenic stressors (such as fishing pressure and pollution) explained some of the observed variation. Additionally, metazoans displayed biogeographic patterns that suggested regional biotic homogenization. Against the backdrop of global pervasive anthropogenic environmental change, our work highlights the importance of considering multiple domains of life to understand the maintenance and drivers of biodiversity patterns across broad taxonomic, ecological and geographical scales.

Developing a non-destructive metabarcoding protocol for detection of pest insects in bulk trap catches

Metabarcoding has the potential to revolutionise insect surveillance by providing high-throughput and cost-effective species identification of all specimens within mixed trap catches. Nevertheless, incorporation of metabarcoding into insect diagnostic laboratories will first require the development and evaluation of protocols that adhere to the specialised regulatory requirements of invasive species surveillance. In this study, we develop a multi-locus non-destructive metabarcoding protocol that allows sensitive detection of agricultural pests, and subsequent confirmation using traditional diagnostic techniques. We validate this protocol for the detection of tomato potato psyllid (Bactericera cockerelli) and Russian wheat aphid (Diuraphis noxia) within mock communities and field survey traps. We find that metabarcoding can reliably detect target insects within mixed community samples, including specimens that morphological identification did not initially detect, but sensitivity appears inversely related to community size and is impacted by primer biases, target loci, and sample indexing strategy. While our multi-locus approach allowed independent validation of target detection, lack of reference sequences for 18S and 12S restricted its usefulness for estimating diversity in field samples. The non-destructive DNA extraction proved invaluable for resolving inconsistencies between morphological and metabarcoding identification results, and post-extraction specimens were suitable for both morphological re-examination and DNA re-extraction for confirmatory barcoding.

Exploring the Use of Environmental DNA (eDNA) to Detect Animal Taxa in the Mesopelagic Zone

Animal biodiversity in the ocean’s vast mesopelagic zone is relatively poorly studied due to technological and logistical challenges. Environmental DNA (eDNA) analyses show great promise for efficiently characterizing biodiversity and could provide new insight into the presence of mesopelagic species, including those that are missed by traditional net sampling. Here, we explore the utility of eDNA for identifying animal taxa. We describe the results from an August 2018 cruise in Slope Water off the northeast United States. Samples for eDNA analysis were collected using Niskin bottles during five CTD casts. Sampling depths along each cast were selected based on the presence of biomass as indicated by the shipboard Simrad EK60 echosounder. Metabarcoding of the 18S V9 gene region was used to assess taxonomic diversity. eDNA metabarcoding results were compared with those from net-collected (MOCNESS) plankton samples. We found that the MOCNESS sampling recovered more animal taxa, but the number of taxa detected per liter of water sampled was significantly higher in the eDNA samples. eDNA was especially useful for detecting delicate gelatinous animals which are undersampled by nets. We also detected eDNA changes in community composition with depth, but not with sample collection time (day vs. night). We provide recommendations for applying eDNA-based methods in the mesopelagic including the need for studies enabling interpretation of eDNA signals and improvement of barcode reference databases.

Lord of the Diptera (and Moths and a Spider): Molecular Diet Analyses and Foraging Ecology of Indiana Bats in Illinois

Effective management of endangered or threatened wildlife requires an understanding of how foraging habitats are used by those populations. Molecular diet analysis of fecal samples offers a cost-effective and non-invasive method to investigate how diets of wild populations vary with respect to spatial and temporal factors. For the federally endangered Indiana bat (Myotis sodalis), documenting its preferred food sources can provide critical information to promote effective conservation of this federally endangered species. Using cytochrome oxidase I amplicon sequence data from Indiana bat guano samples collected at two roosting areas in Cypress Creek National Wildlife Refuge, we found that dipteran taxa (i.e., flies) associated with riparian habitats were the most frequently detected taxon and represented the majority of the sequence diversity among the arthropods sampled. A select few arthropods from other taxa—especially spiders—are also likely important to Indiana bat diets in this refuge. A supervised learning analysis of diet components suggest only a small fraction of the frequently detected taxa are important contributors to spatial and temporal variation. Overall, these data depict the Indiana bat as a generalist consumer whose diet includes some prey items associated with particular seasonal or spatial components, along with other taxa repeatedly consumed throughout the entire foraging season. These molecular diet analyses suggest that protecting foraging resources specifically associated with the riparian habitat of Cypress Creek National Wildlife Refuge is essential to promote effective Indiana bat conservation.

Metabarcoding From Microbes to Mammals: Comprehensive Bioassessment on a Global Scale

Global biodiversity loss is unprecedented, and threats to existing biodiversity are growing. Given pervasive global change, a major challenge facing resource managers is a lack of scalable tools to rapidly and consistently measure Earth's biodiversity. Environmental genomic tools provide some hope in the face of this crisis, and DNA metabarcoding, in particular, is a powerful approach for biodiversity assessment at large spatial scales. However, metabarcoding studies are variable in their taxonomic, temporal, or spatial scope, investigating individual species, specific taxonomic groups, or targeted communities at local or regional scales. With the advent of modern, ultra-high throughput sequencing platforms, conducting deep sequencing metabarcoding surveys with multiple DNA markers will enhance the breadth of biodiversity coverage, enabling comprehensive, rapid bioassessment of all the organisms in a sample. Here, we report on a systematic literature review of 1,563 articles published about DNA metabarcoding and summarize how this approach is rapidly revolutionizing global bioassessment efforts. Specifically, we quantify the stakeholders using DNA metabarcoding, the dominant applications of this technology, and the taxonomic groups assessed in these studies. We show that while DNA metabarcoding has reached global coverage, few studies deliver on its promise of near-comprehensive biodiversity assessment. We then outline how DNA metabarcoding can help us move toward real-time, global bioassessment, illustrating how different stakeholders could benefit from DNA metabarcoding. Next, we address barriers to widespread adoption of DNA metabarcoding, highlighting the need for standardized sampling protocols, experts and computational resources to handle the deluge of genomic data, and standardized, open-source bioinformatic pipelines. Finally, we explore how technological and scientific advances will realize the promise of total biodiversity assessment in a sample—from microbes to mammals—and unlock the rich information genomics exposes, opening new possibilities for merging whole-system DNA metabarcoding with (1) abundance and biomass quantification, (2) advanced modeling, such as species occupancy models, to improve species detection, (3) population genetics, (4) phylogenetics, and (5) food web and functional gene analysis. While many challenges need to be addressed to facilitate widespread adoption of environmental genomic approaches, concurrent scientific and technological advances will usher in methods to supplement existing bioassessment tools reliant on morphological and abiotic data. This expanded toolbox will help ensure that the best tool is used for the job and enable exciting integrative techniques that capitalize on multiple tools. Collectively, these new approaches will aid in addressing the global biodiversity crisis we now face.

PLANiTS: a curated sequence reference dataset for plant ITS DNA metabarcoding

DNA metabarcoding combines DNA barcoding with high-throughput sequencing to identify different taxa within environmental communities. The ITS has already been proposed and widely used as universal barcode marker for plants, but a comprehensive, updated and accurate reference dataset of plant ITS sequences has not been available so far. Here, we constructed reference datasets of Viridiplantae ITS1, ITS2 and entire ITS sequences including both Chlorophyta and Streptophyta. The sequences were retrieved from NCBI, and the ITS region was extracted. The sequences underwent identity check to remove misidentified records and were clustered at 99% identity to reduce redundancy and computational effort. For this step, we developed a script called 'better clustering for QIIME' (bc4q) to ensure that the representative sequences are chosen according to the composition of the cluster at a different taxonomic level. The three datasets obtained with the bc4q script are PLANiTS1 (100 224 sequences), PLANiTS2 (96 771 sequences) and PLANiTS (97 550 sequences), and all are pre-formatted for QIIME, being this the most used bioinformatic pipeline for metabarcoding analysis. Being curated and updated reference databases, PLANiTS1, PLANiTS2 and PLANiTS are proposed as a reliable, pivotal first step for a general standardization of plant DNA metabarcoding studies. The bc4q script is presented as a new tool useful in each research dealing with sequences clustering. Database URL: https://github.com/apallavicini/bc4q; https://github.com/apallavicini/PLANiTS.

Drivers of tropical soil invertebrate community composition and richness across tropical secondary forests using DNA metasystematics

Tropical forests are fundamental ecosystems, essential for providing terrestrial primary productivity, global nutrient cycling, and biodiversity. Despite their importance, tropical forests are currently threatened by deforestation and associated activities. Moreover, tropical regions are now mostly represented by secondary forest regrowth, with half of the remaining tropical forests as secondary forest. Soil invertebrates are an important component to the functioning and biodiversity of these soil ecosystems. However, it remains unclear how these past land-use activities and subsequent secondary forest developments have altered the soil invertebrate communities and any potential ecological consequences associated with this. DNA metabarcoding offers an effective approach to rapidly monitor soil invertebrate communities under different land-use practices and within secondary forests. In this study, we used DNA metabarcoding to detect community-based patterns of soil invertebrate composition across a primary forest, a 23-year-old secondary forest, and a 33-year-old secondary forest and the associated soil environmental drivers of the soil invertebrate community structure in the Maquenque National Wildlife Refuge of Costa Rica (MNWR). We also used a species contribution analysis (SIMPER) to determine which soil invertebrate groups may be an indication of these soils reaching a pre-disturbed state such as a primary forest. We found that the soil invertebrate community composition at class, order, family, and ESV level were mostly significantly different across that habitats. We also found that the primary forest had a greater richness of soil invertebrates compared to the 23-year-old and 33-year-old secondary forest. Moreover, a redundancy analysis indicated that soil moisture influenced soil invertebrate community structure and explained up to 22% of the total variation observed in the community composition across the habitats; whereas soil invertebrate richness was structured by soil microbial biomass carbon (C) (Cmic) and explained up to 52% of the invertebrate richness across the primary and secondary forests. Lastly, the SIMPER analysis revealed that Naididae, Entomobryidae, and Elateridae could be important indicators of soil and forest recuperation in the MNWR. This study adds to the increasing evidence that soil invertebrates are intimately linked with the soil microbial biomass carbon (Cmic) and that even after 33 years of natural regrowth of a forest, these land use activities can still have persisting effects on the overall composition and richness of the soil invertebrate communities.

A total crapshoot? Evaluating bioinformatic decisions in animal diet metabarcoding analyses

Metabarcoding studies provide a powerful approach to estimate the diversity and abundance of organisms in mixed communities in nature. While strategies exist for optimizing sample and sequence library preparation, best practices for bioinformatic processing of amplicon sequence data are lacking in animal diet studies. Here we evaluate how decisions made in core bioinformatic processes, including sequence filtering, database design, and classification, can influence animal metabarcoding results. We show that denoising methods have lower error rates compared to traditional clustering methods, although these differences are largely mitigated by removing low-abundance sequence variants. We also found that available reference datasets from GenBank and BOLD for the animal marker gene cytochrome oxidase I (COI) can be complementary, and we discuss methods to improve existing databases to include versioned releases. Taxonomic classification methods can dramatically affect results. For example, the commonly used Barcode of Life Database (BOLD) Classification API assigned fewer names to samples from order through species levels using both a mock community and bat guano samples compared to all other classifiers (vsearch-SINTAX and q2-feature-classifier's BLAST + LCA, VSEARCH + LCA, and Naive Bayes classifiers). The lack of consensus on bioinformatics best practices limits comparisons among studies and may introduce biases. Our work suggests that biological mock communities offer a useful standard to evaluate the myriad computational decisions impacting animal metabarcoding accuracy. Further, these comparisons highlight the need for continual evaluations as new tools are adopted to ensure that the inferences drawn reflect meaningful biology instead of digital artifacts.

Humic-acid-driven escape from eye parasites revealed by RNA-seq and target-specific metabarcoding

Background

Next generation sequencing (NGS) technologies are extensively used to dissect the molecular mechanisms of host-parasite interactions in human pathogens. However, ecological studies have yet to fully exploit the power of NGS as a rich source for formulating and testing new hypotheses.

Methods

We studied Eurasian perch (Perca fluviatilis) and its eye parasite (Trematoda, Diplostomidae) communities in 14 lakes that differed in humic content in order to explore host-parasite-environment interactions. We hypothesised that high humic content along with low pH would decrease the abundance of the intermediate hosts (gastropods), thus limiting the occurrence of diplostomid parasites in humic lakes. This hypothesis was initially invoked by whole eye RNA-seq data analysis and subsequently tested using PCR-based detection and a novel targeted metabarcoding approach.

Results

Whole eye transcriptome results revealed overexpression of immune-related genes and the presence of eye parasite sequences in RNA-seq data obtained from perch living in clear-water lakes. Both PCR-based and targeted-metabarcoding approach showed that perch from humic lakes were completely free from diplostomid parasites, while the prevalence of eye flukes in clear-water lakes that contain low amounts of humic substances was close to 100%, with the majority of NGS reads assigned to Tylodelphys clavata.

Conclusions

High intraspecific diversity of T. clavata indicates that massively parallel sequencing of naturally pooled samples represents an efficient and powerful strategy for shedding light on cryptic diversity of eye parasites. Our results demonstrate that perch populations in clear-water lakes experience contrasting eye parasite pressure compared to those from humic lakes, which is reflected by prevalent differences in the expression of immune-related genes in the eye. This study highlights the utility of NGS to discover novel host-parasite-environment interactions and provide unprecedented power to characterize the molecular diversity of cryptic parasites.

Rarity is a more reliable indicator of land-use impacts on soil invertebrate communities than other diversity metrics

The effects of land use on soil invertebrates - an important ecosystem component - are poorly understood. We investigated land-use impacts on a comprehensive range of soil invertebrates across New Zealand, measured using DNA metabarcoding and six biodiversity metrics. Rarity and phylogenetic rarity - direct measures of the number of species or the portion of a phylogeny unique to a site - showed stronger, more consistent responses across taxa to land use than widely used metrics of species richness, effective species numbers, and phylogenetic diversity. Overall, phylogenetic rarity explained the highest proportion of land use-related variance. Rarity declined from natural forest to planted forest, grassland, and perennial cropland for most soil invertebrate taxa, demonstrating pervasive impacts of agricultural land use on soil invertebrate communities. Commonly used diversity metrics may underestimate the impacts of land use on soil invertebrates, whereas rarity provides clearer and more consistent evidence of these impacts.

DNA metabarcoding reveals metacommunity dynamics in a threatened boreal wetland wilderness

The complexity and natural variability of ecosystems present a challenge for reliable detection of change due to anthropogenic influences. This issue is exacerbated by necessary trade-offs that reduce the quality and resolution of survey data for assessments at large scales. The Peace-Athabasca Delta (PAD) is a large inland wetland complex in northern Alberta, Canada. Despite its geographic isolation, the PAD is threatened by encroachment of oil sands mining in the Athabasca watershed and hydroelectric dams in the Peace watershed. Methods capable of reliably detecting changes in ecosystem health are needed to evaluate and manage risks. Between 2011 and 2016, aquatic macroinvertebrates were sampled across a gradient of wetland flood frequency, applying both microscope-based morphological identification and DNA metabarcoding. By using multispecies occupancy models, we demonstrate that DNA metabarcoding detected a much broader range of taxa and more taxa per sample compared to traditional morphological identification and was essential to identifying significant responses to flood and thermal regimes. We show that family-level occupancy masks high variation among genera and quantify the bias of barcoding primers on the probability of detection in a natural community. Interestingly, patterns of community assembly were nearly random, suggesting a strong role of stochasticity in the dynamics of the metacommunity. This variability seriously compromises effective monitoring at local scales but also reflects resilience to hydrological and thermal variability. Nevertheless, simulations showed the greater efficiency of metabarcoding, particularly at a finer taxonomic resolution, provided the statistical power needed to detect change at the landscape scale.

Validating metabarcoding-based biodiversity assessments with multi-species occupancy models: A case study using coastal marine eDNA

Environmental DNA (eDNA) metabarcoding is an increasingly popular method for rapid biodiversity assessment. As with any ecological survey, false negatives can arise during sampling and, if unaccounted for, lead to biased results and potentially misdiagnosed environmental assessments. We developed a multi-scale, multi-species occupancy model for the analysis of community biodiversity data resulting from eDNA metabarcoding; this model accounts for imperfect detection and additional sources of environmental and experimental variation. We present methods for model assessment and model comparison and demonstrate how these tools improve the inferential power of eDNA metabarcoding data using a case study in a coastal, marine environment. Using occupancy models to account for factors often overlooked in the analysis of eDNA metabarcoding data will dramatically improve ecological inference, sampling design, and methodologies, empowering practitioners with an approach to wield the high-resolution biodiversity data of next-generation sequencing platforms.

From metabarcoding to metaphylogeography: separating the wheat from the chaff

Metabarcoding is by now a well-established method for biodiversity assessment in terrestrial, freshwater, and marine environments. Metabarcoding data sets are usually used for α- and β-diversity estimates, that is, interspecies (or inter-MOTU [molecular operational taxonomic unit]) patterns. However, the use of hypervariable metabarcoding markers may provide an enormous amount of intraspecies (intra-MOTU) information-mostly untapped so far. The use of cytochrome oxidase (COI) amplicons is gaining momentum in metabarcoding studies targeting eukaryote richness. COI has been for a long time the marker of choice in population genetics and phylogeographic studies. Therefore, COI metabarcoding data sets may be used to study intraspecies patterns and phylogeographic features for hundreds of species simultaneously, opening a new field that we suggest to name metaphylogeography. The main challenge for the implementation of this approach is the separation of erroneous sequences from true intra-MOTU variation. Here, we develop a cleaning protocol based on changes in entropy of the different codon positions of the COI sequence, together with co-occurrence patterns of sequences. Using a data set of community DNA from several benthic littoral communities in the Mediterranean and Atlantic seas, we first tested by simulation on a subset of sequences a two-step cleaning approach consisting of a denoising step followed by a minimal abundance filtering. The procedure was then applied to the whole data set. We obtained a total of 563 MOTUs that were usable for phylogeographic inference. We used semiquantitative rank data instead of read abundances to perform AMOVAs and haplotype networks. Genetic variability was mainly concentrated within samples, but with an important between seas component as well. There were intergroup differences in the amount of variability between and within communities in each sea. For two species, the results could be compared with traditional Sanger sequence data available for the same zones, giving similar patterns. Our study shows that metabarcoding data can be used to infer intra- and interpopulation genetic variability of many species at a time, providing a new method with great potential for basic biogeography, connectivity and dispersal studies, and for the more applied fields of conservation genetics, invasion genetics, and design of protected areas.

Towards conserving natural diversity: A biotic inventory by observations, specimens, DNA barcoding and high-throughput sequencing methods

The Kenai National Wildlife Refuge has been given a broad conservation mandate to conserve natural diversity. A prerequisite for fulfilling this purpose is to be able to identify the species and communities that make up that biodiversity. We tested a set of varied methods for inventory and monitoring of plants, birds and terrestrial invertebrates on a grid of 40 sites in a 938 ha study area in the Slikok Creek watershed, Kenai Peninsula, Alaska. We sampled plants and lichens through observation and specimen-based methods. We surveyed birds using bird call surveys on variable circular plots. We sampled terrestrial arthropods by sweep net sampling, processing samples with High Throughput Sequencing methods. We surveyed for earthworms, using the hot mustard extraction method and identified worm specimens by morphology and DNA barcoding. We examined community membership using clustering methods and Nonmetric Multidimensional Scaling. We documented a total of 4,764 occurrences of 984 species and molecular operational taxonomic units: 87 vascular plants, 51 mosses, 12 liverworts, 111 lichens, 43 vertebrates, 663 arthropods, 9 molluscs and 8 annelid worms. Amongst these records, 102 of the arthropod species appeared to be new records for Alaska. We found three non-native species: Deroceras agreste (Linnaeus, 1758) (Stylommatophora: Agriolimacidae), Dendrobaena octaedra (Savigny, 1826) (Crassiclitellata: Lumbricidae) and Heterarthrus nemoratus (Fallén, 1808) (Hymenoptera: Tenthredinidae). Both D. octaedra and H. nemoratus were found at sites distant from obvious human disturbance. The 40 sites were grouped into five community groups: upland mixed forest, black spruce forest, open deciduous forest, shrub-sedge bog and willow. We demonstrated that, at least for a subset of species that could be detected using these methods, we were able to document current species distributions and assemblages in a way that could be efficiently repeated for the purposes of biomonitoring. While our methods could be improved and additional methods and groups could be added, our combination of techniques yielded a substantial portion of the data necessary for fulfilling Kenai National Wildlife Refuge's broad conservation purposes.

Beyond the visual: using metabarcoding to characterize the hidden reef cryptobiome

In an era of coral reef degradation, our knowledge of ecological patterns in reefs is biased towards large conspicuous organisms. The majority of biodiversity, however, inhabits small cryptic spaces within the framework of the reef. To assess this biodiverse community, which we term the ‘reef cryptobiome’, we deployed 87 autonomous reef monitoring structures (ARMS), on 22 reefs across 16 degrees latitude of the Red Sea. Combining ARMS with metabarcoding of the mitochondrial cytochrome oxidase I gene, we reveal a rich community, including the identification of 14 metazoan phyla within 10 416 operational taxonomic units (OTUs). While mobile and sessile subsets were similarly structured along the basin, the main environmental driver was different (particulate organic matter and sea surface temperature, respectively). Distribution patterns of OTUs showed that only 1.5% were present in all reefs, while over half were present in a single reef. On both local and regional scales, the majority of OTUs were rare. The high heterogeneity in community patterns of the reef cryptobiome has implications for reef conservation. Understanding the biodiversity patterns of this critical component of reef functioning will enable a sound knowledge of how coral reefs will respond to future anthropogenic impacts.

Increased performance of DNA metabarcoding of macroinvertebrates by taxonomic sorting

DNA-based identification through the use of metabarcoding has been proposed as the next step in the monitoring of biological communities, such as those assessed under the Water Framework Directive (WFD). Advances have been made in the field of metabarcoding, but challenges remain when using complex samples. Uneven biomass distributions, preferential amplification and reference database deficiencies can all lead to discrepancies between morphological and DNA-based taxa lists. The effects of different taxonomic groups on these issues remain understudied. By metabarcoding WFD monitoring samples, we analyzed six different taxonomic groups of freshwater organisms, both separately and combined. Identifications based on metabarcoding data were compared directly to morphological assessments performed under the WFD. The diversity of taxa for both morphological and DNA-based assessments was similar, although large differences were observed in some samples. The overlap between the two taxon lists was 56.8% on average across all taxa, and was highest for Crustacea, Heteroptera, and Coleoptera, and lowest for Annelida and Mollusca. Taxonomic sorting in six basic groups before DNA extraction and amplification improved taxon recovery by 46.5%. The impact on ecological quality ratio (EQR) scoring was considerable when replacing morphology with DNA-based identifications, but there was a high correlation when only replacing a single taxonomic group with molecular data. Different taxonomic groups provide their own challenges and benefits. Some groups might benefit from a more consistent and robust method of identification. Others present difficulties in molecular processing, due to uneven biomass distributions, large genetic diversity or shortcomings of the reference database. Sorting samples into basic taxonomic groups that require little taxonomic knowledge greatly improves the recovery of taxa with metabarcoding. Current standards for EQR monitoring may not be easily replaced completely with molecular strategies, but the effectiveness of molecular methods opens up the way for a paradigm shift in biomonitoring.