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Identifying the factors that structure host–parasite interactions is fundamental to understand the drivers of species distributions and to predict novel cross-species transmission events. More phylogenetically related host species tend to have more similar parasite associations, but parasite specificity may vary as a function of transmission mode, parasite taxonomy or life history. Accordingly, analyses that attempt to infer host−parasite associations using combined data on different parasite groups may perform quite differently relative to analyses on each parasite subset. In essence, are more data always better when predicting host−parasite associations, or does parasite taxonomic resolution matter? Here, we explore how taxonomic resolution affects predictive models of host−parasite associations using the London Natural History Museum's database of host–helminth interactions. Using boosted regression trees, we demonstrate that taxon-specific models (i.e. of Acanthocephalans, Nematodes and Platyhelminthes) consistently outperform full models in predicting mammal-helminth associations. At finer spatial resolutions, full and taxon-specific model performance does not vary, suggesting tradeoffs between phylogenetic and spatial scales of analysis. Although all models identify similar host and parasite covariates as important to such patterns, our results emphasize the importance of phylogenetic scale in the study of host–parasite interactions and suggest that using taxonomic subsets of data may improve predictions of parasite distributions and cross-species transmission. Predictive models of host–pathogen interactions should thus attempt to encompass the spatial resolution and phylogenetic scale desired for inference and prediction and potentially use model averaging or ensemble models to combine predictions from separately trained models.

Food webs are a major focus and organizing theme of ecology, but the data used to assemble them are deficient. Early debates over food-web data focused on taxonomic resolution and completeness, lack of which had produced spurious inferences. Recent data are widely believed to be much better and are used extensively in theoretical and meta-analytic research on network ecology. Confidence in these data rests on the assumptions ( a) that empiricists correctly identified consumers and their foods and ( b) that sampling methods were adequate to detect a near-comprehensive fraction of the trophic interactions between species. Abundant evidence indicates that these assumptions are often invalid, suggesting that most topological food-web data may remain unreliable for inferences about network structure and underlying ecological and evolutionary processes. Morphologically cryptic species are ubiquitous across taxa and regions, and many trophic interactions routinely evade detection by conventional methods. Molecular methods have diagnosed the severity of these problems and are a necessary part of the cure.

The spirotrichean ciliate Stylonychia notophora has previously been recorded in India although the descriptions are lacking in detail. It has been suggested several times that the Indian population, S. notophora sensu Sapra and Dass, 1970 collected along the Delhi stretch of the River Yamuna, is identical to Tetmemena pustulata, but this has never been confirmed due to insufficient data for the former. The present study includes detailed descriptions (classical and molecular) of populations of Tetmemena isolated from six locations along the River Yamuna, India. These include four from the Delhi stretch including that from which Sapra and Dass, 1970 isolated their population of S. notophora. Due to the lack of a sufficiently detailed description, the taxonomic status of S. notophora sensu Sapra and Dass, 1970 was not clear. Comparisons among the populations isolated in the present study with previous descriptions of T. pustulata and S. notophora sensu Sapra and Dass, 1970 show only minor differences in morphometry, morphogenesis and in 18S rDNA sequences. The 18S rDNA sequences of all six populations had 99% similarity to both T. pustulata and S. notophora. These findings support the contention that S. notophora sensu Sapra and Dass, 1970 was misidentified and is a population of T. pustulata. This study supports the need for adopting an integrative approach based on morphological, morphogenetic and molecular data in order to understand species delimitation in ciliated protists.

Significance Too often, ecological monitoring studies are designed without understanding whether they have sufficient statistical power to detect changes beyond natural variability. The Peace–Athabasca Delta is North America’s largest inland delta, within a World Heritage area, and is currently threatened by human development. Using multispecies occupancy models we show that the wetland macroinvertebrate community is highly diverse, and spatial and temporal turnover are so high that composition is nearly random, emphasizing stochastic processes of assembly. Using DNA metabarcoding, our study detected more taxa, both overall and per sample, than traditional morphology-based sample processing, increasing our power to detect ecosystem change. Improving data quality and quantifying error are key to delivering effective monitoring and understanding the dynamic structure of the metacommunity., 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.

Ecologists are increasingly interested in plant–pollinator networks that synthesize in a single object the species and the interactions linking them within their ecological context. Numerous indices have been developed to describe the structural properties and resilience of these networks, but currently, these indices are calculated for a network resolved to the species level, thus preventing the full exploitation of numerous datasets with a lower taxonomic resolution. Here, we used datasets from the literature to study whether taxonomic resolution has an impact on the properties of plant–pollinator networks.For a set of 41 plant–pollinator networks from the literature, we calculated nine network index values at three different taxonomic resolutions: species, genus, and family. We used nine common indices assessing the structural properties or resilience of networks: nestedness (estimated using the nestedness index based on overlap and decreasing fill [NODF], weighted NODF, discrepancy [BR], and spectral radius [SR]), connectance, modularity, robustness to species loss, motifs frequencies, and normalized degree.We observed that modifying the taxonomic resolution of these networks significantly changes the absolute values of the indices that describe their properties, except for the spectral radius and robustness. After the standardization of indices measuring nestedness with the Z‐score, three indices—NODF, BR, and SR for binary matrices—are not significantly different at different taxonomic resolutions. Finally, the relative values of all indices are strongly conserved at different taxonomic resolutions.We conclude that it is possible to meaningfully estimate the properties of plant–pollinator interaction networks with a taxonomic resolution lower than the species level. We would advise using either the SR or robustness on untransformed data, or the NODF, discrepancy, or SR (for weighted networks only) on Z‐scores. Additionally, connectance and modularity can be compared between low taxonomic resolution networks using the rank instead of the absolute values., Using plant–pollinator networks from the literature, we show that commonly used network metrics can be meaningful on networks with lower‐than‐species resolution. Spectral radius and robustness to taxa loss can be used at species, genus, and family levels. The relative values of all metrics studied are strongly conserved at different taxonomic resolutions.

Phytoplankton are aquatic, microscopically small primary producers, accounting for almost half of the worldwide carbon fixation. As early indicators of environmental change, they play a crucial role in water quality management. Human activities like climate change, eutrophication, or international shipping traffic strongly impact diversity of these organisms. Phytoplankton monitoring is a crucial step in the recognition of changes in community composition. The common standard for monitoring programs is manual microscopic counting, which strongly limits sample number and sampling frequency. In contrast, high-throughput technologies like standard flow cytometry (FCM) are restricted to a low taxonomic resolution, which makes them unsuitable for the identification of indicator species. Imaging flow cytometers (IFC) could overcome these limitations as they combine microscopy and high-throughput analysis. In comparison to single fluorescence values, image information not only allows for a wide variety of possibilities to characterize different species as well as immediate and fast measurements but also provides an archivable data output. Taxonomic resolution of IFC (ImageStream X Mk II) was proven comparable to standard FCM (FACSAria II) by the help of numerical evaluations. This is demonstrated on different levels of taxonomic differentiation of laboratory grown cultures in this study. Phytoplankton species discrimination by an imaging flow cytometer could be useful as supportive tool to make machine-learning classifications more robust, reliable, and flexible. Furthermore, this study provides examples, demonstrating the possibility of discrimination between species with similar fluorescence properties, strains, and even subpopulations. In contrast to standard FCM, each cell is not only represented as a dot in a cytogram but is also linked to microscopic brightfield and the author presents a new way to visualize this as image-based cytograms. The source code is supplied and could be useful for all kind of IFC data in general. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Most stream bioassessment and biodiversity surveys are currently based on morphological identification of communities. However, DNA metabarcoding is emerging as a fast and cost-effective alternative for species identification. We compared both methods in a survey of benthic macroinvertebrate communities across 36 stream sites in northern Finland. We identified 291 taxa of which 62% were identified only by DNA metabarcoding. DNA metabarcoding produced extensive species level inventories for groups (Oligochaeta, Chironomidae, Simuliidae, Limoniidae and Limnephilidae), for which morphological identification was not feasible due to the high level of expertise needed. Metabarcoding also provided more insightful taxonomic information on the occurrence of three red-listed vulnerable or data deficient species, the discovery of two likely cryptic and potentially new species to Finland and species information of insect genera at an early larval stage that could not be separated morphologically. However, it systematically failed to reliably detect the occurrence of gastropods that were easily identified morphologically. The impact of mining on community structure could only be shown using DNA metabarcoding data which suggests that the finer taxonomic detail can improve detection of subtle impacts. Both methods generally exhibited similar strength of community-environment relationships, but DNA metabarcoding showed better performance with presence/absence data than with relative DNA sequence abundances. Our results suggest that DNA metabarcoding holds a promise for future anthropogenic impact assessments, although, in our case, the performance did not improve much from the morphological species identification. The key advantage of DNA metabarcoding lies in efficient biodiversity surveys, taxonomical studies and applications in conservation biology.

Hospital-acquired infections (HAIs) pose a serious threat to patients, and hospitals spend billions of dollars each year to reduce and treat these infections. Many HAIs are due to contamination from workers’ hands and contact with high-touch surfaces. Therefore, we set out to test the efficacy of a new preventative technology, AIONX® Antimicrobial Technologies, Inc’s cleanSURFACES®, which is designed to complement daily chemical cleaning events by continuously preventing re-colonization of surfaces. To that end, we swabbed surfaces before (Baseline) and after (Post) application of the cleanSURFACES® at various time points (Day 1, Day 7, Day 14, and Day 28). To circumvent limitations associated with culture-based and 16S rRNA gene amplicon sequencing methodologies, these surface swabs were processed using metatranscriptomic (RNA) analysis to allow for comprehensive taxonomic resolution and the detection of active microorganisms. Overall, there was a significant (P < 0.05) global reduction of microbial diversity in Post-intervention samples. Additionally, Post sample microbial communities clustered together much more closely than Baseline samples based on pairwise distances calculated with the weighted Jaccard distance metric, suggesting a defined shift after product application. This shift was characterized by a general depletion of several microbes among Post samples, with multiple phyla also being reduced over the duration of the study. Notably, specific clinically relevant microbes, including Staphylococcus aureus, Clostridioides difficile and Streptococcus spp., were depleted Post-intervention. Taken together, these findings suggest that chemical cleaning events used jointly with cleanSURFACES® have the potential to reduce colonization of surfaces by a wide variety of microbes, including many clinically relevant pathogens.

Characterization of the bacterial composition and functional repertoires of microbiome samples is the most common application of metagenomics. Although deep whole-metagenome shotgun sequencing (WMS) provides high taxonomic resolution, it is generally cost-prohibitive for large longitudinal investigations. Until now, 16S rRNA gene amplicon sequencing (16S) has been the most widely used approach and usually cooperates with WMS to achieve cost-efficiency. However, the accuracy of 16S results and its consistency with WMS data have not been fully elaborated, especially by complicated microbiomes with defined compositional information. Here, we constructed two complex artificial microbiomes, which comprised more than 60 human gut bacterial species with even or varied abundance. Utilizing real fecal samples and mock communities, we provided solid evidence demonstrating that 16S results were of poor consistency with WMS data, and its accuracy was not satisfactory. In contrast, shallow whole-metagenome shotgun sequencing (shallow WMS, S-WMS) with a sequencing depth of 1 Gb provided outputs that highly resembled WMS data at both genus and species levels and presented much higher accuracy taxonomic assignments and functional predictions than 16S, thereby representing a better and cost-efficient alternative to 16S for large-scale microbiome studies.

Environmental surveillance is a critical tool for combatting public health threats represented by the global COVID-19 pandemic and the continuous increase of antibiotic resistance in pathogens. With its power to detect entire microbial communities, metagenomics-based methods stand out in addressing the need. However, several hurdles remain to be overcome in order to generate actionable interpretations from metagenomic sequencing data for infection prevention. Conceptually and technically, we focus on viability assessment, taxonomic resolution, and quantitative metagenomics, and discuss their current advancements, necessary precautions and directions to further development. We highlight the importance of building solid conceptual frameworks and identifying rational limits to facilitate the application of techniques. We also propose the usage of internal standards as a promising approach to overcome analytical bottlenecks introduced by low biomass samples and the inherent lack of quantitation in metagenomics. Taken together, we hope this perspective will contribute to bringing accurate and consistent metagenomics-based environmental surveillance to the ground.

Meat makes up a complex environment, home to an extensive bacterial diversity that is heavily influenced by environmental conditions during processing and storage. The current review focuses on the use of next-generation sequencing methods to unravel the complex microbial communities that are present in meat environments in greater detail. Recent applications of these methods in amplicon-sequencing approaches targeting other phylogenetic marker genes than the 16S rRNA gene allowed for a better assessment of species richness in meat and the detection of many minor communities that might have previously gone underreported. With the increasing base quality of the single-molecule sequencing methods, an even improved resolution is to be expected, as high-throughput sequencing of full-length phylogenetic marker genes is on the rise.

Abtract As DNA metabarcoding has become an emerging tool for surveying biodiversity, including its application in legally binding assessments, reliable and efficient barcodes are requested, especially for the highly diverse group of zooplankton. This study focuses on comparing the efficiency of two mitochondrial COI barcodes based on the internal primers mlCOIintF and mlCOIintR utilizing mesozooplankton samples collected in a Mediterranean lagoon. Our results indicate that after a slight adjustment, the mlCOIintR primer performs in combination with jdgLCO1490 (herein) very comparably to the much more widely used primer system mlCOIintF/jgHCO2198+dgHCO2198, in terms of level of taxonomic resolution, species detection and their relative abundance in terms of numbers of reads. As for some groups, like Ctenophora, this barcode is not suitable; a combination of them may be the best option to rely on the Folmer region in its entirety without the risk of losing information for a limited primer match.

Traveler’s diarrhea (TD) is a recurrent and significant issue for many travelers including the military. While many known enteric pathogens exist that are causative agents of diarrhea, our gut microbiome may also play a role in travelers’ diarrhea susceptibility. To this end we conducted a pilot study of the microbiome of warfighters prior to- and after deployment overseas to identify marker taxa relevant to traveler’s diarrhea. This initial study utilized full-length 16S rRNA gene sequencing to provide additional taxonomic resolution towards identifying predictive taxa.16S rRNA analyses of pre- and post-deployment fecal samples identified multiple marker taxa as significantly differentially abundant in subjects that reported diarrhea, including Weissella, Butyrivibrio, Corynebacterium, uncultivated Erysipelotrichaceae, Jeotgallibaca, unclassified Ktedonobacteriaceae, Leptolinea, and uncultivated Ruminiococcaceae. The ability to identify TD risk prior to travel will inform prevention and mitigation strategies to influence diarrhea susceptibility while traveling.

Purpose of review: there has been an exponential increase in research intoinfant microbiome evolution, and it appears that pharyngeal microbiota are associated with clinical phenotypes(e.g. infection and asthma). While broad consensus views are emerging, significant challenges and uncertainties remain.Recent findings: infant pharyngeal microbiome research is limited by low biomass, high temporal diversity, and lack of agreed standards for sampling, DNA sequencing, and taxonomic reporting. Analysis of amplicon sequence variants and improvedcost and availability of whole genome sequencing are promising options for improving taxonomic resolution of such studies. Infant respiratory microbiomes arise, at least in part, from maternal flora (e.g. the respiratory tract and breastmilk), and are associated with environmental and clinical factors (e.g.mode of feeding and delivery, siblings, daycare attendance, birth season, and antibiotic usage). Interventional research to modify the infant pharyngeal microbiota hasrecentlybeen reported, using dietary supplements. Summary: further work is needed to improve characterisation ofthe infant pharyngeal microbiomes, including routes of bacterial acquisition, role of environmental factors, and associations with disease phenotypes. Methodological standards are desirable to facilitate more reproducible, comparable research. Improved understanding may enable manipulation of infant pharyngeal microbiota to improve clinical outcomes.

Insight into the microbial species diversity of fermented meats is not only paramount to gain control over quality development, but also to better understand the link with processing technology and geographical origin. To study the composition of the microbial communities, the use of culture-independent methods is increasingly popular but often still suffers from drawbacks, such as a limited taxonomic resolution. This study aimed to apply a previously developed high-throughput amplicon sequencing (HTS) method targeting the 16S rRNA and tuf genes to characterize the bacterial communities in European fermented meats in greater detail. The data obtained broadened the view on the microbial communities that were associated with the various products examined, revealing the presence of previously underreported subdominant species. Moreover, the composition of these communities could be linked to the specificities of individual products, in particular pH, salt content, and geographical origin. In contrast, no clear links were found between the volatile organic compound profiles of the different products and the country of origin, distinct processing conditions, or microbial communities. Future application of the HTS method offers the potential to further unravel complex microbial communities in fermented meats, as well as to assess the impact of different processing conditions on microbial consortia.

Summary The term metagenomics refers to the use of sequencing methods to simultaneously identify genomic material from all organisms present in a sample, with the advantage of greater taxonomic resolution than culture or other methods. Applications include pathogen detection and discovery, species characterisation, antimicrobial resistance detection, virulence profiling, and study of the microbiome and microecological factors affecting health. However, metagenomics involves complex and multistep processes and there are important technical and methodological challenges that require careful consideration to support valid inference. We co-ordinated a multidisciplinary, international expert group to establish reporting guidelines that address specimen processing, nucleic acid extraction, sequencing platforms, bioinformatics considerations, quality assurance, limits of detection, power and sample size, confirmatory testing, causality criteria, cost, and ethical issues. The guidance recognises that metagenomics research requires pragmatism and caution in interpretation, and that this field is rapidly evolving.

We developed a novel method for accurate cataloguing of bacterial communities at (sub)species level involving amplification of the internal transcribed spacer (ITS) region through optimized primers, followed by next-generation sequencing and taxonomic classification of amplicons by means of a comprehensive database of bacterial ITS sequences. Host-associated, food, and environmental matrices were employed to test the performance of the microbial ITS profiling pipeline. Moreover, mucosal biopsy samples from colorectal cancer patients were analyzed to demonstrate the scientific relevance of this profiling approach in a clinical setting through identification of putative novel biomarkers. The results indicate that the ITS-based profiling pipeline proposed here represents a key metagenomic tool with major relevance for research, industrial, and clinical settings., 16S small-subunit (SSU) rRNA gene-based bacterial profiling is the gold standard for cost-effective taxonomic reconstruction of complex bacterial populations down to the genus level. However, it has been proven ineffective in clinical and research settings requiring higher taxonomic resolution. We therefore developed a bacterial profiling method based on the internal transcribed spacer (ITS) region employing optimized primers and a comprehensive ITS database for accurate cataloguing of bacterial communities at (sub)species resolution. Performance of the microbial ITS profiling pipeline was tested through analysis of host-associated, food, and environmental matrices, while its efficacy in clinical settings was assessed through analysis of mucosal biopsy specimens of colorectal cancer, leading to the identification of putative novel biomarkers. The data collected indicate that the proposed pipeline represents a major step forward in cost-effective identification and screening of microbial biomarkers at (sub)species level, with relevant impact in research, industrial, and clinical settings. IMPORTANCE We developed a novel method for accurate cataloguing of bacterial communities at (sub)species level involving amplification of the internal transcribed spacer (ITS) region through optimized primers, followed by next-generation sequencing and taxonomic classification of amplicons by means of a comprehensive database of bacterial ITS sequences. Host-associated, food, and environmental matrices were employed to test the performance of the microbial ITS profiling pipeline. Moreover, mucosal biopsy samples from colorectal cancer patients were analyzed to demonstrate the scientific relevance of this profiling approach in a clinical setting through identification of putative novel biomarkers. The results indicate that the ITS-based profiling pipeline proposed here represents a key metagenomic tool with major relevance for research, industrial, and clinical settings.

Summary The complexity of host-associated microbial ecosystems requires host-specific reference catalogs to survey the functions and diversity of these communities. We generate a comprehensive resource, the integrated mouse gut metagenome catalog (iMGMC), comprising 4.6 million unique genes and 660 metagenome-assembled genomes (MAGs), many (485 MAGs, 73%) of which are linked to reconstructed full-length 16S rRNA gene sequences. iMGMC enables unprecedented coverage and taxonomic resolution of the mouse gut microbiota; i.e., more than 92% of MAGs lack species-level representatives in public repositories (, Graphical Abstract, Highlights • Large-scale metagenomic assembly uncovers hundreds of mouse microbiome species • Most microbes found in the mouse gut are unique to the ecosystem • Integration of gene catalog and microbial genomes creates a comprehensive resource • Linking of 16S rRNA genes with metagenome-assembled genomes allows new applications, Gene catalogs and genome references facilitate taxonomic and functional annotation of sequencing data. Through the combination of data from laboratory and wild mice, Lesker et al. create a comprehensive resource, the integrated mouse gut metagenome catalog, to characterize the microbial ecosystem in the murine gut.

Protecting plants from new pathogen incursions requires effective surveillance practices. Environmental DNA (eDNA) metabarcoding shows considerable promise for detecting invasive organisms in terrestrial and aquatic ecosystems. Metabarcoding is widely used to characterise plant microbiotas and is beginning to be applied to plant biosecurity. However, diagnostic assays for biosecurity must fit within internationally agreed frameworks. Development of new gene targets to improve taxonomic resolution, improved reference databases and simplified bioinformatics platforms are required before phytopathogenic bacteria and fungi can be routinely detected by metabarcoding. Building biodiversity maps from accumulated metabarcoding data represents an important opportunity to define organism presence/absence in New Zealand and Australia and thus to enhance biosecurity decision making. Advances in sequencing technologies and infrastructure promise the creation of eDNA biosecurity surveillance networks from substrates including soil, trapped spores and insects.

Complex microbial communities shape the dynamics of various environments, ranging from the mammalian gastrointestinal tract to the soil. Advances in DNA sequencing technologies and data analysis have provided drastic improvements in microbiome analyses, for example, in taxonomic resolution, false discovery rate control and other properties, over earlier methods. In this Review, we discuss the best practices for performing a microbiome study, including experimental design, choice of molecular analysis technology, methods for data analysis and the integration of multiple omics data sets. We focus on recent findings that suggest that operational taxonomic unit-based analyses should be replaced with new methods that are based on exact sequence variants, methods for integrating metagenomic and metabolomic data, and issues surrounding compositional data analysis, where advances have been particularly rapid. We note that although some of these approaches are new, it is important to keep sight of the classic issues that arise during experimental design and relate to research reproducibility. We describe how keeping these issues in mind allows researchers to obtain more insight from their microbiome data sets.

Assessing diet variability is of main importance to better understand the biology of bats and design conservation strategies. Although the advent of metabarcoding has facilitated such analyses, this approach does not come without challenges. Biases may occur throughout the whole experiment, from fieldwork to biostatistics, resulting in the detection of false negatives, false positives or low taxonomic resolution. We detail a rigorous metabarcoding approach based on a short COI minibarcode and two-step PCR protocol enabling the ‘all at once’ taxonomic identification of bats and their arthropod preys for several hundreds of samples. Our study includes faecal pellets collected in France from 357 bats representing 16 species, as well as insect mock communities that mimic bat meals of known composition, negative and positive controls. All samples were analysed using three replicates. We compare the efficiency of DNA extraction methods and we evaluate the effectiveness of our protocol using identification success, taxonomic resolution, sensitivity, and amplification biases. Our parallel identification strategy of predators and preys reduces the risk of mis-assigning preys to wrong predators and decreases the number of molecular steps. Controls and replicates enable to filter the data and limit the risk of false positives, hence guaranteeing high confidence results for both prey occurrence and bat species identification. We validate 551 COI variants from arthropod including 18 orders, 117 family, 282 genus and 290 species. Our method therefore provides a rapid, resolutive and cost-effective screening tool for addressing evolutionary ecological issues or developing ‘chirosurveillance’ and conservation strategies.

Background One of the most crucial steps in high-throughput sequence-based microbiome studies is the taxonomic assignment of sequences belonging to operational taxonomic units (OTUs). Without taxonomic classification, functional and biological information of microbial communities cannot be inferred or interpreted. The internal transcribed spacer (ITS) region of the ribosomal DNA is the conventional marker region for fungal community studies. While bioinformatics pipelines that cluster reads into OTUs have received much attention in the literature, less attention has been given to the taxonomic classification of these sequences, upon which biological inference is dependent. Results Here we compare how three common fungal OTU taxonomic assignment tools (RDP Classifier, UTAX, and SINTAX) handle ITS fungal sequence data. The classification power, defined as the proportion of assigned OTUs at a given taxonomic rank, varied among the classifiers. Classifiers were generally consistent (assignment of the same taxonomy to a given OTU) across datasets and ranks; a small number of OTUs were assigned unique classifications across programs. We developed CONSTAX (CONSensus TAXonomy), a Python tool that compares taxonomic classifications of the three programs and merges them into an improved consensus taxonomy. This tool also produces summary classification outputs that are useful for downstream analyses. Conclusions Our results demonstrate that independent taxonomy assignment tools classify unique members of the fungal community, and greater classification power is realized by generating consensus taxonomy of available classifiers with CONSTAX. Electronic supplementary material The online version of this article (10.1186/s12859-017-1952-x) contains supplementary material, which is available to authorized users.

Phytoplankton and bacterioplankton are integral components of aquatic food webs and play essential roles in the structure and function of freshwater ecosystems. However, little is known about how phyto- and bacterioplankton may respond synchronously to changing environmental conditions. Thus, we analyzed simultaneously the composition and structure of phyto- and bacterioplankton on a monthly basis over 12 months in cyanobacteria-dominated areas of Lake Taihu and compared their responses to changes in environmental factors. Metric multi-dimensional scaling (mMDS) revealed that the temporal variations of phyto- and bacterioplankton were significant. Time lag analysis (TLA) indicated that the temporal pattern of phytoplankton tended to exhibit convergent dynamics while bacterioplankton showed highly stable or stochastic variation. A significant directional change was found for bacterioplankton at the genus level and the slopes (rate of change) and regression R2 (low stochasticity or stability) were greater if Cyanobacteria were included, suggesting a higher level of instability in the bacterial community at lower taxonomy level. Consequently, phytoplankton responded more rapidly to the change in environmental conditions than bacterioplankton when analyzed at the phylum level, while bacterioplankton were more sensitive at the finer taxonomic resolution in Lake Taihu. Redundancy analysis (RDA) results showed that environmental variables collectively explained 51.0% variance of phytoplankton and 46.7% variance of bacterioplankton, suggesting that environmental conditions have a significant influence on the temporal variations of phyto- and bacterioplankton. Furthermore, variance partitioning indicated that the bacterial community structure was largely explained by water temperature and nitrogen, suggesting that these factors were the primary drivers shaping bacterioplankton.

Summary DNA metabarcoding holds great promise for the assessment of macroinvertebrates in stream ecosystems. However, few large-scale studies have compared the performance of DNA metabarcoding with that of routine morphological identification. We performed metabarcoding using four primer sets on macroinvertebrate samples from 18 stream sites across Finland. The samples were collected in 2013 and identified based on morphology as part of a Finnish stream monitoring program. Specimens were morphologically classified, following standardised protocols, to the lowest taxonomic level for which identification was feasible in the routine national monitoring. DNA metabarcoding identified more than twice the number of taxa than the morphology-based protocol, and also yielded a higher taxonomic resolution. For each sample, we detected more taxa by metabarcoding than by the morphological method, and all four primer sets exhibited comparably good performance. Sequence read abundance and the number of specimens per taxon (a proxy for biomass) were significantly correlated in each sample, although the adjusted R2 values were low. With a few exceptions, the ecological status assessment metrics calculated from morphological and DNA metabarcoding datasets were similar. Given the recent reduction in sequencing costs, metabarcoding is currently approximately as expensive as morphology-based identification. Using samples obtained in the field, we demonstrated that DNA metabarcoding can achieve comparable assessment results to current protocols relying on morphological identification. Thus, metabarcoding represents a feasible and reliable method to identify macroinvertebrates in stream bioassessment, and offers powerful advantage over morphological identification in providing identification for taxonomic groups that are unfeasible to identify in routine protocols. To unlock the full potential of DNA metabarcoding for ecosystem assessment, however, it will be necessary to address key problems with current laboratory protocols and reference databases.

Soil microbiota plays a key role in suppressing soil-borne plant pathogens improving the natural soil suppressiveness. Microbiome disturbance triggers specific perturbation to change and shape the soil microbial communities’ network for increasing suppression against phytopathogens and related diseases. Very important goals have been reached in manipulation of soil microbiota through agronomical practices based on soil pre-fumigation, organic amendment, crop rotation and intercropping. Nevertheless, to limit inconsistencies, drawbacks and failures related to soil microbiota disturbance, a detailed understanding of the microbiome shifts during its manipulation is needed under the light of the microbiome-assisted strategies. Next-generation sequencing often offers a better overview of the soil microbial communities during microbiomes manipulation, but sometime it does not provide information related to the highest taxonomic resolution of the soil microbial communities. This review work reports and discusses the most reliable findings in relation to a comprehensive understanding of soil microbiota and how its manipulation can improve suppression against soil-borne diseases in organic farming systems. Role and functionality of the soil microbiota in suppressing soil-borne pathogens affecting crops have been basically described in the first section of the paper. Characterization of the soil microbiomes network by high-throughput sequencing has been introduced in the second section. Some relevant findings by which soil microbiota manipulation can address the design of novel sustainable cropping systems to sustain crops’ health without use (or reduced use) of synthetic fungicides and fumigants have been extensively presented and discussed in the third and fourth sections, respectively, under the light of the new microbiome-assisted strategies. Critical comparisons on the next-generation sequencing have been provided in the fifth section. Concluding remarks have been drawn in the last section.

Metabarcoding is often presented as an alternative identification tool to compensate for coarse taxonomic resolution and misidentification encountered with traditional morphological approaches. However, metabarcoding comes with two major impediments which slow down its adoption. First, the picking and destruction of organisms for DNA extraction are time and cost consuming and do not allow organism conservation for further evaluations. Second, current metabarcoding protocols include a PCR enrichment step which induces errors in the estimation of species diversity and relative biomasses. In this study, we first evaluated the capacity of capture enrichment to replace PCR enrichment using controlled freshwater macrozoobenthos mock communities. Then, we tested if DNA extracted from the fixative ethanol (etDNA) of the same mock communities can be used as an alternative to DNA extracted from pools of whole organisms (bulk DNA). We show that capture enrichment provides more reliable and accurate representation of species occurrences and relative biomasses in comparison with PCR enrichment for bulk DNA. While etDNA does not permit to estimate relative biomasses, etDNA and bulk DNA provide equivalent species detection rates. Thanks to its robustness to mismatches, capture enrichment is already an efficient alternative to PCR enrichment for metabarcoding and, if coupled to etDNA, is a time-saver option in studies where presence information only is sufficient.

An ongoing challenge for ecological studies has been the collection of data with high precision and accuracy at a suitable scale to detect and manage critical global change processes. A major hurdle has been the time-consuming and challenging process of sorting and identification of organisms, but the rapid development of DNA metabarcoding as a biodiversity observation tool provides a potential solution. As high-throughput sequencing becomes more rapid and cost-effective, a “big data” revolution is anticipated, based on higher and more accurate taxonomic resolution, more efficient detection, and greater sample processing capacity. These advances have the potential to amplify the power of ecological studies to detect change and diagnose its cause, through a methodology termed “Biomonitoring 2.0.” Despite its promise, the unfamiliar terminology and pace of development in high-throughput sequencing technologies has contributed to a growing concern that an unproven technology is supplanting tried and tested approaches, lowering trust among potential users, and reducing uptake by ecologists and environmental management practitioners. While it is reasonable to exercise caution, we argue that any criticism of new methods must also acknowledge the shortcomings and lower capacity of current observation methods. Broader understanding of the statistical properties of metabarcoding data will help ecologists to design, test and review evidence for new hypotheses. We highlight the uncertainties and challenges underlying DNA metabarcoding and traditional methods for compositional analysis, specifically comparing the interpretation of otherwise identical bulk-community samples of freshwater benthic invertebrates. We explore how taxonomic resolution, sample similarity, taxon misidentification, and taxon abundance affect the statistical properties of these samples, but recognize these issues are relevant to applications across all ecosystem types. In conclusion, metabarcoding has the capacity to improve the quality and utility of ecological data, and consequently the quality of new research and efficacy of management responses.

The phylogenetic depth at which arbuscular mycorrhizal (AM) fungi harbor a coherent ecological niche is unknown, which has consequences for operational taxonomic unit (OTU) delineation from sequence data and the study of their biogeography. We tested how changes in AM fungi community composition across habitats (beta diversity) vary with OTU phylogenetic resolution. We inferred exact sequence variants (ESVs) to resolve phylotypes at resolutions finer than provided by traditional sequence clustering and analyzed beta diversity profiles up to order-level sequence clusters. At the ESV level, we detected the environmental predictors revealed with traditional OTUs or at higher genetic distances. However, the correlation between environmental predictors and community turnover steeply increased at a genetic distance of c. 0.03 substitutions per site. Furthermore, we observed a turnover of either closely or distantly related taxa (respectively at or above 0.03 substitutions per site) along different environmental gradients. This study suggests that different axes of AM fungal ecological niche are conserved at different phylogenetic depths. Delineating AM fungal phylotypes using DNA sequences should screen different phylogenetic resolutions to better elucidate the factors that shape communities and predict the fate of AM symbioses in a changing environment.

Environmental DNA (eDNA) is one of the most promising approaches to meet the demand for the fast and frequent monitoring of ecosystems needed to tackle the current decline in biodiversity. However, before eDNA can establish itself as a robust alternative for mammal monitoring, comparison with existing approaches is necessary, yet has not been done. Moreover, much is unknown regarding the nature, spread and persistence of DNA shed by animals into terrestrial environments, or the optimal experimental design for understanding these potential biases.To address some of these challenges, we compared the detection of terrestrial mammals using eDNA analysis of soil samples against confirmed species observations from a long-term (∼9-yr) camera trapping study. At the same time, we considered multiple experimental parameters, including two sampling designs, two DNA extraction kits and two metabarcodes of different sizes.All mammals consistently recorded with cameras were detected in eDNA. In addition, eDNA reported many small mammals not recorded by camera traps, but whose presence in the study area is otherwise documented. A long metabarcode (≈220bp) offering a high taxonomic resolution, achieved a similar efficiency as a shorter one (≈70bp) and a phosphate buffer-based extraction gave similar results as a total DNA extraction method for a fraction of the price. Our results support that eDNA-based monitoring should become a valuable part of terrestrial mammal surveys. Yet, the lack of coverage of mammal mitochondrial genomes in public databases must be addressed before eDNA can be used to its full potential.

Inflammation and its resolution is a tenuous balance that is under constant contest. Though several regulatory mechanisms are employed to maintain homeostasis, disruptions in the regulation of inflammation can lead to detrimental effects for the host. Of note, the gut and microbial dysbiosis are implicated in the pathology of systemic chronic low-grade inflammation which has been linked to several metabolic diseases. What remains to be described is the extent to which dietary fat and concomitant changes in the gut microbiota contribute to, or arise from, the onset of metabolic disorders. The present review will highlight the role of microorganisms in host energy regulation and several mechanisms that contribute to inflammatory pathways. This review will also discuss the immunomodulatory effects of the endocannabinoid system and its link with the gut microbiota. Finally, a brief discussion arguing for improved taxonomic resolution (at the species and strain level) is needed to deepen our current knowledge of the microbiota and host inflammatory state.

Studies have demonstrated that many cosmopolitan species actually consist of divergent clades that present high levels of morphological stasis throughout their evolutionary histories.Phallus indusiatuss.l. has been described as a circum-tropical species. However, this distribution may actually reflect the lack of taxonomic resolution due to the small number of diagnostic morphological characters, which leads to the identification of new records as populations ofP. indusiatus. Here, we examine the diversity ofP. indusiatus-like species in Brazilian Amazonia. We show a clear congruence between detailed morphological data and ITS, nuc-LSU andatp6 based phylogenetic analyses and three new species are described within the Brazilian indusiate clade. These results highlight the importance of more detailed investigation, with the inclusion of molecular information, in Neotropical fungi.

Microscopic organisms are the dominant and most diverse organisms on Earth. Nematodes, as part of this microscopic diversity, are by far the most abundant animals and their diversity is equally high. Molecular metabarcoding is often applied to study the diversity of microorganisms, but has yet to become the standard to determine nematode communities. As such, the information metabarcoding provides, such as in terms of species coverage, taxonomic resolution and especially if sequence reads can be linked to the abundance or biomass of nematodes in a sample, has yet to be determined. Here, we applied metabarcoding using three primer sets located within ribosomal rRNA gene regions to target assembled mock-communities consisting of 18 different nematode species that we established in 9 different compositions. We determined abundances and biomass of all species added to examine if relative sequence abundance or biomass can be linked to relative sequence reads. We found that nematode communities are not equally represented by the three different primer sets and we found that relative read abundances almost perfectly correlated positively with relative species biomass for two of the primer sets. This strong biomass-read number correlation suggests that metabarcoding reads can reveal biomass information even amongst more complex nematode communities as present in the environment and possibly can be transferred to better study other groups of organisms. This biomass-read link is of particular importance for more reliably assessing nutrient flow through food-webs, as well as adjusting biogeochemical models through user-friendly and easily obtainable metabarcoding data.

International audience; Extensive studies on the taxonomic resolution required for bioassessment purposes have determined that resolution above species level (genus, family) is sufficient for their use as indicators of relevant environmental pressures. The high‐throughput se - quencing (HTS) and meta‐barcoding methods now used for bioassessment tradition - ally employ an arbitrary sequence similarity threshold (SST) around 95% or 97% to cluster sequences into operational taxonomic units, which is considered descriptive of species‐level resolution. In this study, we analyzed the effect of the SST on the resulting diatom‐based ecological quality index, which is based on OTU abundance distribution along a defined environmental gradient, ideally avoiding taxonomic as - signments that could result in high rates of unclassified OTUs and biased final values. A total of 90 biofilm samples were collected in 2014 and 2015 from 51 stream sites on Mayotte Island in parallel with measures of relevant physical and chemical param - eters. HTS sequencing was performed on the biofilms using the rbcL region as the genetic marker and diatom‐specific primers. Hierarchical clustering was used to group sequences into OTUs using 20 experimental SST levels (80%–99%). An OTU‐ based quality index (Idx OTU ) was developed based on a weighted average equation using the abundance profiles of the OTUs. The developed Idx OTU revealed significant correlations between the Idx OTU values and the reference pressure gradient, which reached maximal performance using an SST of 90% (well above species level delimi - tation). We observed an interesting and important trade‐off with the power to dis - criminate between sampling sites and index stability that will greatly inform future applications of the index. Taken together, the results from this study detail a thor - oughly optimized and validated approach to generating robust, reproducible, and complete indexes that will greatly facilitate effective and efficient environmental monitoring

Colombia holds one of the most spectacular biodiversity of the world. Yet, vast aspects of this biodiversity are still poorly inventoried. One of the least known aspects of Colombia's biodiversity is the sound produced by its animals, even for the most conspicuous ones, the vertebrates. Here we reviewed and compiled the sound records available for the Department of Santander, a region in the North-East of Colombia, gathering the sound records of birds, anurans, mammals, and fishes. By conducting a detailed review in the environmental sound collection of the Humboldt Institute, the Macaulay Library of the Cornell Lab of Ornithology and Xeno-canto platform of the Naturalis Biodiversity Center, we present the first dataset of vertebrate sounds information from the Santander department. We selected recordings with a taxonomic resolution up to species and complete metadata information. Using latitude and longitude information, we assigned each recording to one of the six biotic units reported for Santander. We found a total of 1499 recordings, which belong to six biotic units: Guane-Yariguíes (597), Middle Magdalena Valley and Mompox Depression (484), High Andes Eastern Cordillera (167), Nechí-San Lucas (150), Middle Magdalena Eastern Cordillera (95), Catatumbo (6). This dataset can have a wide scope of applications, from basic scientific questions, to analyses made by decision makers regarding conservation strategies, to support biodiversity-based economies such as ecotourism. Keywords: Colombia, Natural history audio archives, Sound recordings, Sound collection

Microbial communities drive ecosystem processes, but microbial community composition analyses using 16S rRNA gene amplicon data sets are limited by the lack of fine-resolution taxonomy classifications. Coarse taxonomic groupings at the phylum, class, and order levels lump ecologically distinct organisms together. To avoid this, many researchers define operational taxonomic units (OTUs) based on clustered sequences, sequence variants, or unique sequences. These fine-resolution groupings are more ecologically relevant, but OTU definitions are data set dependent and cannot be compared between data sets. Microbial ecologists studying freshwater have curated a small, ecosystem-specific taxonomy database to provide consistent and up-to-date terminology. We created TaxAss, a workflow that leverages this database to assign taxonomy. We found that TaxAss improves fine-resolution taxonomic classifications (family, genus, and species). Fine taxonomic groupings are more ecologically relevant, so they provide an alternative to OTU-based analyses that is consistent and comparable between data sets., Taxonomy assignment of freshwater microbial communities is limited by the minimally curated phylogenies used for large taxonomy databases. Here we introduce TaxAss, a taxonomy assignment workflow that classifies 16S rRNA gene amplicon data using two taxonomy reference databases: a large comprehensive database and a small ecosystem-specific database rigorously curated by scientists within a field. We applied TaxAss to five different freshwater data sets using the comprehensive SILVA database and the freshwater-specific FreshTrain database. TaxAss increased the percentage of the data set classified compared to using only SILVA, especially at fine-resolution family to species taxon levels, while across the freshwater test data sets classifications increased by as much as 11 to 40% of total reads. A similar increase in classifications was not observed in a control mouse gut data set, which was not expected to contain freshwater bacteria. TaxAss also maintained taxonomic richness compared to using only the FreshTrain across all taxon levels from phylum to species. Without TaxAss, most organisms not represented in the FreshTrain were unclassified, but at fine taxon levels, incorrect classifications became significant. We validated TaxAss using simulated amplicon data derived from full-length clone libraries and found that 96 to 99% of test sequences were correctly classified at fine resolution. TaxAss splits a data set’s sequences into two groups based on their percent identity to reference sequences in the ecosystem-specific database. Sequences with high similarity to sequences in the ecosystem-specific database are classified using that database, and the others are classified using the comprehensive database. TaxAss is free and open source and is available at https://www.github.com/McMahonLab/TaxAss. IMPORTANCE Microbial communities drive ecosystem processes, but microbial community composition analyses using 16S rRNA gene amplicon data sets are limited by the lack of fine-resolution taxonomy classifications. Coarse taxonomic groupings at the phylum, class, and order levels lump ecologically distinct organisms together. To avoid this, many researchers define operational taxonomic units (OTUs) based on clustered sequences, sequence variants, or unique sequences. These fine-resolution groupings are more ecologically relevant, but OTU definitions are data set dependent and cannot be compared between data sets. Microbial ecologists studying freshwater have curated a small, ecosystem-specific taxonomy database to provide consistent and up-to-date terminology. We created TaxAss, a workflow that leverages this database to assign taxonomy. We found that TaxAss improves fine-resolution taxonomic classifications (family, genus, and species). Fine taxonomic groupings are more ecologically relevant, so they provide an alternative to OTU-based analyses that is consistent and comparable between data sets.

Background: Chironomids, or non-biting midges, often dominate stream invertebrate communities in terms of biomass, abundance, and species richness and play an important role in riverine food webs. Despite these clear facts, the insect family Chironomidae is often treated as a single family in ecological studies or bioassessments given the difficulty to determine specimens further. We investigated stressor responses of single chironomid operational taxonomic units (OTUs) to three globally important stressors (increased salinity, fine sediment and reduced water flow velocity) in a highly replicated mesocosm experiment using a full-factorial design (eight treatment combinations with eight replicates each). Results: In total, 183 chironomid OTUs (97% similarity) were obtained by applying a quantitative DNA metabarcoding approach. Whereas on the typically applied family level, chironomids responded positively to added fine sediment and reduced water velocity in the streambed and negatively to reduced velocity in the leaf litter, an OTU-level analysis revealed a total of 15 different response patterns among the 35 most common OTUs only. The response patterns ranged from (a) insensitivity to any experimental manipulation over (b) highly specific sensitivities to only one stressor to (c) additive multiple-stressor effects and even (d) complex interactions. Conclusion: Even though most OTUs (> 85%) could not be assigned to a formally described species due to a lack of accurate reference data bases at present, the results indicate increased explanatory power with higher taxonomic resolution. Thus, our results highlight the potential of DNA-based approaches when studying environmental impacts, especially for this ecologically important taxon and in the context of multiple stressors. CA Beermann

Accurate taxonomic resolution in light microscopy analyses of microalgae is essential to achieve high quality, comparable results in both floristic analyses and biomonitoring studies. A number of closely related diatom taxa have been detected to date co-occurring within benthic diatom assemblages, sharing many morphological, morphometrical and ecological characteristics. In this contribution, we analysed the hypothesis that, where a large sample size (number of individuals) is available, common morphometrical parameters (valve length, width and stria density) are sufficient to achieve a correct identification to the species level. We focused on some common diatom taxa belonging to the genusGomphonema. More than 400 valves and frustules were photographed in valve view and measured using Fiji software. Several statistical tools (mixture and discriminant analysis, k-means clustering, classification trees, etc.) were explored to test whether mere morphometry, independently of other valve features, leads to correct identifications, when compared to identifications made by experts. In view of the results obtained, morphometry-based determination in diatom taxonomy is discouraged.

Background Illumina paired-end sequencing has been increasingly popular for 16S rRNA gene-based microbiota profiling. It provides higher phylogenetic resolution than single-end reads due to a longer read length. However, the reverse read (R2) often has significant low base quality, and a large proportion of R2s will be discarded after quality control, resulting in a mixture of paired-end and single-end reads. A typical 16S analysis pipeline usually processes either paired-end or single-end reads but not a mixture. Thus, the quantification accuracy and statistical power will be reduced due to the loss of a large amount of reads. As a result, rare taxa may not be detectable with the paired-end approach, or low taxonomic resolution will result in a single-end approach. Results To have both the higher phylogenetic resolution provided by paired-end reads and the higher sequence coverage by single-end reads, we propose a novel OTU-picking pipeline, hybrid-denovo, that can process a hybrid of single-end and paired-end reads. Using high-quality paired-end reads as a gold standard, we show that hybrid-denovo achieved the highest correlation with the gold standard and performed better than the approaches based on paired-end or single-end reads in terms of quantifying the microbial diversity and taxonomic abundances. By applying our method to a rheumatoid arthritis (RA) data set, we demonstrated that hybrid-denovo captured more microbial diversity and identified more RA-associated taxa than a paired-end or single-end approach. Conclusions Hybrid-denovo utilizes both paired-end and single-end 16S sequencing reads and is recommended for 16S rRNA gene targeted paired-end sequencing data.

Summary Antibiotics alter microbiota composition and increase infection susceptibility. However, the generalizable effects of antibiotics on and the contribution of environmental variables to gut commensals remain unclear. To address this, we tracked microbiota dynamics with high temporal and taxonomic resolution during antibiotic treatment in a controlled murine system by isolating variables such as diet, treatment history, and housing co-inhabitants. Human microbiotas were remarkably resilient and recovered during antibiotic treatment, with transient dominance of resistant Bacteroides and taxa-asymmetric diversity reduction. In certain cases, in vitro sensitivities were not predictive of in vivo responses, underscoring the significance of host and community context. A fiber-deficient diet exacerbated microbiota collapse and delayed recovery. Species replacement through cross housing after ciprofloxacin treatment established resilience to a second treatment. Single housing drastically disrupted recovery, highlighting the importance of environmental reservoirs. Our findings highlight deterministic microbiota adaptations to perturbations and the translational potential for modulating diet, sanitation, and microbiota composition during antibiotics.

Compelling research over the past decade identified a fundamental role of the intestinal microbiome on human health. Compositional and functional changes of this microbial ecosystem are correlated with a variety of human pathologies. Metagenomic resolution and bioinformatic tools considerably improved, allowing even strain-level analysis. However, the search for microbial risk patterns in human cohorts is often confounded by environmental factors (eg, medication) and host status (eg, disease relapse), questioning the prognostic and therapeutic value of the currently available information. In addition to a better stratification of human phenotypes, the implementation of standardized protocols for sampling and analysis is needed to improve the reproducibility and comparability of microbiome signatures at a meaningful taxonomic resolution. At the level of mechanistic understanding, the molecular integration of pleiotropic signals coming from this complex and dynamically changing ecosystem is one of the biggest challenges in this field. The first successful attempts to apply reverse genetics based on the available metagenomic information yielded identification of small molecules and metabolites with functional relevance for microbe-host interactions. Further expansion on the isolation of bacteria from the "unculturable biomass" will help characterize microbiome signatures in model systems, finally aiming at the development of clinically relevant synthetic consortia with safe and functionally well-defined strains. In conclusion and beyond reasonable enthusiasm, the mechanistic implementation and clinical relevance of microbiome alterations on disease susceptibility is still in its infancy, but the integration of all the above-mentioned strategies will help overcome the correlation era in microbiome research and lead to a rational evaluation of clinical strategies relevant for targeted microbial intervention.

This work was supported by the Swedish Research Council (grant no. 2013-D0568401), SciLifeLab Stockholm and the Carl Triggers’ Foundation (grant no. 14:371) to L.P., and the Research Council of Norway to I.G.A. (grant no. 213692/F20). Recent advances in sequencing technologies now permit the analyses of plant DNA from fossil samples (ancient plant DNA, plant aDNA), and thus enable the molecular reconstruction of palaeofloras. Hitherto, ancient frozen soils have proved excellent in preserving DNA molecules, and have thus been the most commonly used source of plant aDNA. However, DNA from soil mainly represents taxa growing a few metres from the sampling point. Lakes have larger catchment areas and recent studies have suggested that plant aDNA from lake sediments is a more powerful tool for palaeofloristic reconstruction. Furthermore, lakes can be found globally in nearly all environments, and are therefore not limited to perennially frozen areas. Here, we review the latest approaches and methods for the study of plant aDNA from lake sediments and discuss the progress made up to the present. We argue that aDNA analyses add new and additional perspectives for the study of ancient plant populations and, in time, will provide higher taxonomic resolution and more precise estimation of abundance. Despite this, key questions and challenges remain for such plant aDNA studies. Finally, we provide guidelines on technical issues, including lake selection, and we suggest directions for future research on plant aDNA studies in lake sediments. Postprint

Premise of the study: To study pollination networks in a changing environment, we need accurate, high-throughput methods. Previous studies have shown that more highly resolved networks can be constructed by studying pollen loads taken from bees, relative to field observations. DNA metabarcoding potentially allows for faster and finer-scale taxonomic resolution of pollen compared to traditional approaches (e.g., light microscopy), but has not been applied to pollination networks. Methods: We sampled pollen from 38 bee species collected in Florida from sites differing in forest management. We isolated DNA from pollen mixtures and sequenced rbcL and ITS2 gene regions from all mixtures in a single run on the Illumina MiSeq platform. We identified species from sequence data using comprehensive rbcL and ITS2 databases. Results: We successfully built a proof-of-concept quantitative pollination network using pollen metabarcoding. Discussion: Our work underscores that pollen metabarcoding is not quantitative but that quantitative networks can be constructed based on the number of interacting individuals. Due to the frequency of contamination and false positive reads, isolation and PCR negative controls should be used in every reaction. DNA metabarcoding has advantages in efficiency and resolution over microscopic identification of pollen, and we expect that it will have broad utility for future studies of plant–pollinator interactions.

Identification of the species origin of pollen has many applications, including assessment of plant–pollinator networks, reconstruction of ancient plant communities, product authentication, allergen monitoring, and forensics. Such applications, however, have previously been limited by microscopy-based identification of pollen, which is slow, has low taxonomic resolution, and has few expert practitioners. One alternative is pollen DNA barcoding, which could overcome these issues. Recent studies demonstrate that both chloroplast and nuclear barcoding markers can be amplified from pollen. These recent validations of pollen metabarcoding indicate that now is the time for researchers in various fields to consider applying these methods to their research programs. In this paper, we review the nascent field of pollen DNA barcoding and discuss potential new applications of this technology, highlighting existing limitations and future research developments that will improve its utility in a wide range of applications.

Metabarcoding is becoming an increasingly valuable alternative approach to biodiversity assessment, due to the combination of extreme sensitivity and potential for the highest taxonomic resolution in a cost- and time-effective methodology. To evaluate the capacity of metabarcoding for estuarine plankton monitoring, a comparison between the results obtained with this approach were compared with those based on traditional taxonomic analysis (microscopy). Database incompleteness, one of the main limitations of metabarcoding, was somewhat overcome by the addition of DNA sequences for local species, which increased the taxonomic assignment success from 23.7 to 50.5 %. When the communities were studied along with environmental variables, similar spatial and temporal trends of taxonomic diversity were observed for metabarcoding and microscopic studies of zooplankton, but not for phytoplankton. This is most likely attributable to the lack of representative sequences for phytoplankton species in current databases. In addition, there was high correspondence in community composition when comparing abundances estimated from metabarcoding and microscopy, suggesting semiquantitative potential for metabarcoding. Furthermore, metabarcoding allowed the detection and identification of two non-indigenous species (NIS) found in the study area at abundances hardly detectable by microscopy. Overall, our results indicate that metabarcoding is a powerful approach with excellent possibilities for use in plankton monitoring, early detection of NIS and plankton biodiversity shifts.

Environmental sequencing regularly recovers fungi that cannot be classified to any meaningful taxonomic level beyond "Fungi". There are several examples where evidence of such lineages has been sitting in public sequence databases for up to ten years before receiving scientific attention and formal recognition. In order to highlight these unidentified lineages for taxonomic scrutiny, a search function is presented that produces updated lists of approximately genus-level clusters of fungal ITS sequences that remain unidentified at the phylum, class, and order levels, respectively. The search function (https://unite.ut.ee/top50.php) is implemented in the UNITE database for molecular identification of fungi, such that the underlying sequences and fungal lineages are open to third-party annotation. We invite researchers to examine these enigmatic fungal lineages in the hope that their taxonomic resolution will not have to wait another ten years or more. Copyright R. Henrik Nilsson et al.

Matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) has been shown to be a rapid, effective, and sensitive tool for characterizing diverse bacteria. Historically, the primary application of this technology has been to identify bacteria at the genus and species levels in a few minutes. The most commonly used identification method involves matching mass spectra of unknown bacteria to reference spectra in commercially available databases with the assistance of manufacturer-created algorithms. Recently, there has been keen interest in enhancing the taxonomic resolution of MALDI to characterize bacteria at the strain and subspecies levels. Often, categorization of strains sharing similar traits, differentiation of closely related strains, and/or identification of a single strain are desired. Strain-level characterization is important in the development of counter-bioterrorism strategies, prevention of food contamination, and rapid diagnosis of bacterial infections. This chapter focuses on factors, including culture medium, sample preparation, data acquisition, and data analysis that affect strain-level characterization. In addition, the chapter highlights techniques and treatments that offer promise to improve the taxonomic resolution of MALDI-TOF MS-enabled approaches to bacterial characterization.

Bar-coded pyrosequencing has been increasingly used due to its fine taxonomic resolution and high throughput. Yet, concerns arise regarding the reproducibility of bar-coded pyrosequencing. We evaluated the run-to-run variation of barcoded pyrosequencing in detecting bacterial community shifts and taxa dynamics. Our results demonstrate that pyrosequencing is reproducible in evaluating community shifts within a run, but not between runs. Also, the reproducibility of pyrosequencing in detecting individual taxa increased as a function of taxa abundance. Based on our findings: (1) for studies with modest sequencing depth, it is doubtful that data from different pyrosequencing runs can be considered comparable; (2) if multiple pyrosequencing runs are needed to increase the sequencing depth, additional sequencing efforts should be applied to all samples, rather than to selected samples; (3) if pyrosequencing is used for estimating bacterial population dynamics, only the abundant taxa should be considered; (4) for less-abundant taxa, the sequencing depth should be increased to ensure an accurate evaluation of taxon variation trends across samples. Copyright: © 2014 Ge et al.

The advent of next-generation sequencing has allowed huge amounts of DNA sequence data to be produced, advancing the capabilities of microbial ecosystem studies. The current challenge is to identify from which microorganisms and genes the DNA originated. Several tools and databases are available for annotating DNA sequences. The tools, databases and parameters used can have a significant impact on the results: naïve choice of these factors can result in a false representation of community composition and function. We use a simulated metagenome to show how different parameters affect annotation accuracy by evaluating the sequence annotation performances of MEGAN, MG-RAST, One Codex and Megablast. This simulated metagenome allowed the recovery of known organism and function abundances to be quantitatively evaluated, which is not possible for environmental metagenomes. The performance of each program and database varied, e.g. One Codex correctly annotated many sequences at the genus level, whereas MG-RAST RefSeq produced many false positive annotations. This effect decreased as the taxonomic level investigated increased. Selecting more stringent parameters decreases the annotation sensitivity, but increases precision. Ultimately, there is a trade-off between taxonomic resolution and annotation accuracy. These results should be considered when annotating metagenomes and interpreting results from previous studies., The sequence annotation accuracies of metagenomic analysis tools were evaluated using a simulated metagenome.

The present study examined the suitability of matrix assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) for the rapid grouping of bacterial isolates, i.e. dereplication. Dereplication is important in large-scale isolation campaigns and screening programs since it can significantly reduce labor intensity, time and costs in further downstream analyses. Still, current dereplication techniques are time consuming and costly. MALDI-TOF MS is an attractive tool since it performs fast and cheap analyses with the potential of automation. However, its taxonomic resolution for a broad diversity of bacteria remains largely unknown. To verify the suitability of MALDI-TOF MS for dereplication, a total of 249 unidentified bacterial isolates retrieved from the rhizosphere of potato plants, were analyzed with both MALDI-TOF MS and repetitive element sequence based polymerase chain reaction (rep-PCR). The latter technique was used as a benchmark. Cluster analysis and inspection of the profiles showed that for 204 isolates (82%) the taxonomic resolution of both techniques was comparable, while for 45 isolates (18%) one of both techniques had a higher taxonomic resolution. Additionally, 16S rRNA gene sequence analysis was performed on all members of each delineated cluster to gain insight in the identity and sequence similarity between members in each cluster. MALDI-TOF MS proved to have higher reproducibility than rep-PCR and seemed to be more promising with respect to high-throughput analyses, automation, and time and cost efficiency. Its taxonomic resolution was situated at the species to strain level. The present study demonstrated that MALDI-TOF MS is a powerful tool for dereplication.

Insect metabarcoding has been mainly based on PCR amplification of short fragments within the ‘barcoding region’ of the gene COI. However, because of the variability of this gene, it has been difficult to design good universal PCR primers. Most primers used today are associated with gaps in the taxonomic coverage or amplification biases that make the results less reliable and impede the detection of species that are present in the sample. We identify new primers for insect metabarcoding using computational approaches (ECOPRIMERS and DEGEPRIME) applied to the most comprehensive reference databases of mitochondrial genomes of Hexapoda assembled to date. New primers are evaluated in silico against previously published primers in terms of taxonomic coverage and resolution of the corresponding amplicons. For the latter criterion, we propose a new index, exclusive taxonomic resolution, which is a more biologically meaningful measure than the standard index used today. Our results show that the best markers are found in the ribosomal RNA genes (12S and 16S); they resolve about 90% of the genetically distinct species in the reference database. Some markers in protein-coding genes provide similar performance but only at much higher levels of primer degeneracy. Combining two of the best individual markers improves the effective taxonomic resolution with up to 10%. The resolution is strongly dependent on insect taxon: COI primers detect 40% of Hymenoptera, while 12S primers detect 12% of Collembola. Our results indicate that amplicon-based metabarcoding of insect samples can be improved by choosing other primers than those commonly used today.