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.

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.

Biotic signals associated with benthic impacts of salmon farms from eDNA metabarcoding of sediments.

Environmental DNA (eDNA) metabarcoding can rapidly characterize the composition and diversity of benthic communities, thus it has high potential utility for routine assessments of benthic impacts of marine finfish farming. In this study, 126 sediment grab samples from 42 stations were collected at six salmon farms in British Columbia, Canada. Benthic community changes were assessed by both eDNA metabarcoding of metazoans and macrofaunal polychaete surveys. The latter was done by analysing 11,466 individuals using a combination of morphology-based taxonomy and DNA barcoding. Study objectives were to: (i) compare biotic signals associated with benthic impacts of salmon farming in the two data sources, and (ii) identify potential eDNA indicators to facilitate monitoring in Canada. Alpha diversity parameters were consistently reduced near fish cage edge and negatively correlated with pore-water sulphide concentration, with coefficients ranging from -0.62 to -0.48. Although Polychaeta are a common indicator group, the negative correlation with pore-water sulphide concentration was much stronger for Nematoda OTU richness (correlation coefficient: -0.86) than for Polychaeta (correlation coefficient: -0.38). Presence/absence of Capitella generally agreed well between the two methods despite that they differed in the volume of sediments sampled and the molecular marker used. Multiple approaches were used to identify OTUs related to organic enrichment statuses. We demonstrate that eDNA metabarcoding generates biotic signals that could be leveraged for environmental assessment of benthic impacts of fish farms in multiple ways: both alpha diversity and Nematoda OTU richness could be used to assess the spatial extent of impact, and OTUs related to organic enrichment could be used to develop local biotic indices.

Using DNA-barcoded Malaise trap samples to measure impact of a geothermal energy project on the biodiversity of a Costa Rican old-growth rain forest.

We report one year (2013-2014) of biomonitoring an insect community in a tropical old-growth rain forest, during construction of an industrial-level geothermal electricity project. This is the first-year reaction by the species-rich insect biodiversity; six subsequent years are being analyzed now. The site is on the margin of a UNESCO Natural World Heritage Site, Área de Conservación Guanacaste (ACG), in northwestern Costa Rica. This biomonitoring is part of Costa Rica's ongoing efforts to sustainably retain its wild biodiversity through biodevelopmental integration with its societies. Essential tools are geothermal engineering needs, entomological knowledge, insect species-rich forest, government-NGO integration, common sense, DNA barcoding for species-level identification, and Malaise traps. This research is tailored for integration with its society at the product level. We combine an academic view with on-site engineering decisions. This biomonitoring requires alpha-level DNA barcoding combined with centuries of morphology-based entomological taxonomy and ecology. Not all desired insect community analyses are performed; they are for data from subsequent years combined with this year. We provide enough analysis to be used by both guilds now. This biomonitoring has shown, for the first year, that the geothermal project impacts only the biodiversity within a zone less than 50 m from the project margin.

Nuclear genomes distinguish cryptic species suggested by their DNA barcodes and ecology.

DNA sequencing brings another dimension to exploration of biodiversity, and large-scale mitochondrial DNA cytochrome oxidase I barcoding has exposed many potential new cryptic species. Here, we add complete nuclear genome sequencing to DNA barcoding, ecological distribution, natural history, and subtleties of adult color pattern and size to show that a widespread neotropical skipper butterfly known as Udranomia kikkawai (Weeks) comprises three different species in Costa Rica. Full-length barcodes obtained from all three century-old Venezuelan syntypes of U. kikkawai show that it is a rainforest species occurring from Costa Rica to Brazil. The two new species are Udranomia sallydaleyae Burns, a dry forest denizen occurring from Costa Rica to Mexico, and Udranomia tomdaleyi Burns, which occupies the junction between the rainforest and dry forest and currently is known only from Costa Rica. Whereas the three species are cryptic, differing but slightly in appearance, their complete nuclear genomes totaling 15 million aligned positions reveal significant differences consistent with their 0.00065-Mbp (million base pair) mitochondrial barcodes and their ecological diversification. DNA barcoding of tropical insects reared by a massive inventory suggests that the presence of cryptic species is a widespread phenomenon and that further studies will substantially increase current estimates of insect species richness.

Bacterial diversity in the waterholes of the Kruger National Park: an eDNA metabarcoding approach 1.

Bacteria are essential components of natural environments. They contribute to ecosystem functioning through roles as mutualists and pathogens for larger species, and as key components of food webs and nutrient cycles. Bacterial communities respond to environmental disturbances, and the tracking of these communities across space and time may serve as indicators of ecosystem health in areas of conservation concern. Recent advances in DNA sequencing of environmental samples allow for rapid and culture-free characterization of bacterial communities. Here we conduct the first metabarcoding survey of bacterial diversity in the waterholes of the Kruger National Park, South Africa. We show that eDNA can be amplified from waterholes and find strongly structured microbial communities, likely reflecting local abiotic conditions, animal ecology, and anthropogenic disturbance. Over timescales from days to weeks we find increased turnover in community composition, indicating bacteria may represent host-associated taxa of large vertebrates visiting the waterholes. Through taxonomic annotation we also identify pathogenic taxa, demonstrating the utility of eDNA metabarcoding for surveillance of infectious diseases. These samples serve as a baseline survey of bacterial diversity in the Kruger National Park, and in the future, spatially distinct microbial communities may be used as markers of ecosystem disturbance, or biotic homogenization across the park.

Determinants of Soil Bacterial and Fungal Community Composition Toward Carbon-Use Efficiency Across Primary and Secondary Forests in a Costa Rican Conservation Area.

Tropical secondary forests currently represent over half of the world's remaining tropical forests and are critical candidates for maintaining global biodiversity and enhancing potential carbon-use efficiency (CUE) and, thus, carbon sequestration. However, these ecosystems can exhibit multiple successional pathways, which have hindered our understanding of the soil microbial drivers that facilitate improved CUE. To begin to address this, we examined soil % C; % N; C:N ratio; soil microbial biomass C (Cmic); NO3-; NH4+; pH; % moisture; % sand, silt, and clay; and elevation, along with soil bacterial and fungal community composition, and determined which soil abiotic properties structure the soil Cmic and the soil bacterial and fungal communities across a primary forest, 33-year-old secondary forest, and 22-year-old young secondary in the Northern Zone of Costa Rica. We provide evidence that soil microbial communities were mostly distinct across the habitat types and that these habitats appear to have affected the soil ectomycorrhizal fungi and the soil microbial groups associated with the degradation of complex carbon compounds. We found that soil Cmic levels increased along the management gradient from young, to old secondary, to primary forest. In addition, the changes in soil Cmic and soil fungal community structure were significantly related to levels of soil NO3-. Our analyses showed that even after 33 years of natural forest regrowth, the clearing of tropical forests can have persistent effects on soil microbial communities and that it may take a longer time than we realized for secondary forests to develop carbon-utilization efficiencies similar to that of a primary forest. Our results also indicated that forms of inorganic N may be an important factor in structuring soil Cmic and the soil microbial communities, leading to improved CUE in regenerating secondary forests. This study is the first in the region to highlight some of the factors which appear to be structuring the soil Cmic and soil microbial communities such that they are more conducive for enhanced CUE in secondary forests.

Scaling up: A guide to high-throughput genomic approaches for biodiversity analysis.

The purpose of this review is to present the most common and emerging DNA-based methods used to generate data for biodiversity and biomonitoring studies. As environmental assessment and monitoring programmes may require biodiversity information at multiple levels, we pay particular attention to the DNA metabarcoding method and discuss a number of bioinformatic tools and considerations for producing DNA-based indicators using operational taxonomic units (OTUs), taxa at a variety of ranks and community composition. By developing the capacity to harness the advantages provided by the newest technologies, investigators can "scale up" by increasing the number of samples and replicates processed, the frequency of sampling over time and space, and even the depth of sampling such as by sequencing more reads per sample or more markers per sample. The ability to scale up is made possible by the reduced hands-on time and cost per sample provided by the newest kits, platforms and software tools. Results gleaned from broad-scale monitoring will provide opportunities to address key scientific questions linked to biodiversity and its dynamics across time and space as well as being more relevant for policymakers, enabling science-based decision-making, and provide a greater socio-economic impact. As genomic approaches are continually evolving, we provide this guide to methods used in biodiversity genomics.

Using metagenomics to show the efficacy of forest restoration in the New Jersey Pine Barrens.

The Franklin Parker Preserve within the New Jersey Pine Barrens contains 5000 acres of wetlands habitat, including old-growth Atlantic white cedar (or AWC; Chamaecyparis thyoides) swamps, cranberry bogs, and former cranberry bogs undergoing restoration into AWC forests. This study showed that the C-use efficiency was greater in the old-growth AWC soils than in soils from 8-year-old mid-stage restored AWC stands, which were greater than found in soil from 4-year-old AWC stands-the latter two stands being restored from long-term cranberry bogs. A metagenomic analysis of eDNA extracted from these soils showed that the C-cycle trends were associated with increases in the relative numbers of DNA sequences from several copiotrophic bacterial groups (Bacteroidetes and Proteobacteria), complex C-decomposing fungal groups (Sordiomycetes, Mortierellales, and Thelephorales), and collembolan and formicid invertebrates. All groups are indicators of successionally more advanced soils, and critical for soil C-cycle activities. These data suggest that the restoration activities studied are enhancing critical guilds of soil biota, and increasing C-use efficiency in the soils of restored habitats, and that the use of metagenomic analysis of soil eDNA can be used in the development of assessment models for soil recovery of wetlands following restoration.

Simultaneous assessment of the macrobiome and microbiome in a bulk sample of tropical arthropods through DNA metasystematics.

Conventional assessments of ecosystem sample composition are based on morphology-based or DNA barcode identification of individuals. Both approaches are costly and time-consuming, especially when applied to the large number of specimens and taxa commonly included in ecological investigations. Next-generation sequencing approaches can overcome the bottleneck of individual specimen isolation and identification by simultaneously sequencing specimens of all taxa in a bulk mixture. Here we apply multiple parallel amplification primers, multiple DNA barcode markers, 454-pyrosequencing, and Illumina MiSeq sequencing to the same sample to maximize recovery of the arthropod macrobiome and the bacterial and other microbial microbiome of a bulk arthropod sample. We validate this method with a complex sample containing 1,066 morphologically distinguishable arthropods from a tropical terrestrial ecosystem with high taxonomic diversity. Multiamplicon next-generation DNA barcoding was able to recover sequences corresponding to 91% of the distinguishable individuals in a bulk environmental sample, as well as many species present as undistinguishable tissue. 454-pyrosequencing was able to recover 10 more families of arthropods and 30 more species than did conventional Sanger sequencing of each individual specimen. The use of other loci (16S and 18S ribosomal DNA gene regions) also added the detection of species of microbes associated with these terrestrial arthropods. This method greatly decreases the time and money necessary to perform DNA-based comparisons of biodiversity among ecosystem samples. This methodology opens the door to much cheaper and increased capacity for ecological and evolutionary studies applicable to a wide range of socio-economic issues, as well as a basic understanding of how the world works.

The golden age of DNA metasystematics.

The convergence of next-generation sequencing and DNA barcoding has sparked a golden age of 'DNA metasystematics', allowing researchers to understand the biodiversity of an entire ecosystem solely through DNA information, and transforming the way we view the living world around us.

Naphthenic Acid Mixtures from Oil Sands Process-Affected Water Enhance Differentiation of Mouse Embryonic Stem Cells and Affect Development of the Heart.

Extraction of petrochemicals from the surface mining of oil sand deposits results in generation of large volumes of oil sands process-affected water (OSPW). Naphthenic acids (NA) are generally considered to be among the most toxic components of OSPW. Previous studies have shown that NAs are toxic to aquatic organisms, however knowledge of their effects on mammalian health and development is limited. In the present study, we evaluated the developmental effects of an NA extract prepared from fresh OSPW on differentiating mouse embryonic stem cells (ESC). We found that treatment of differentiating cells with the NA extract at noncytotoxic concentrations alters expression of various lineage specification markers and development of the heart. Notably, expression of cardiac specific markers such as Nkx2.5, Gata4, and Mef2c were significantly up-regulated. Moreover, exposure to the NA extract enhanced differentiation of embryoid bodies and resulted in the early appearance of spontaneously beating clusters. Interestingly, exposure of undifferentiated mouse ESCs to the NA extract did not change the expression level of pluripotency markers (i.e., Oct4, Nanog, and Sox2). Altogether, these data identify some of the molecular pathways affected by components within this NA extract during differentiation of mammalian cells.

Mitochondrial and nuclear phylogenetic analysis with Sanger and next-generation sequencing shows that, in Área de Conservación Guanacaste, northwestern Costa Rica, the skipper butterfly named Urbanus belli (family Hesperiidae) comprises three morphologically cryptic species.

BACKGROUND: Skipper butterflies (Hesperiidae) are a relatively well-studied family of Lepidoptera. However, a combination of DNA barcodes, morphology, and natural history data has revealed several cryptic species complexes within them. Here, we investigate three DNA barcode lineages of what has been identified as Urbanus belli (Hesperiidae, Eudaminae) in Área de Conservación Guanacaste (ACG), northwestern Costa Rica. RESULTS: Although no morphological traits appear to distinguish among the three, congruent nuclear and mitochondrial lineage patterns show that "Urbanus belli" in ACG is a complex of three sympatric species. A single strain of Wolbachia present in two of the three cryptic species indicates that Urbanus segnestami Burns (formerly Urbanus belliDHJ01), Urbanus bernikerni Burns (formerly Urbanus belliDHJ02), and Urbanus ehakernae Burns (formerly Urbanus belliDHJ03) may be biologically separated by Wolbachia, as well as by their genetics. Use of parallel sequencing through 454-pyrosequencing improved the utility of ITS2 as a phylogenetic marker and permitted examination of the intra- and interlineage relationships of ITS2 variants within the species complex. Interlineage, intralineage and intragenomic compensatory base pair changes were discovered in the secondary structure of ITS2. CONCLUSION: These findings corroborate the existence of three cryptic species. Our confirmation of a novel cryptic species complex, initially suggested by DNA barcode lineages, argues for using a multi-marker approach coupled with next-generation sequencing for exploration of other suspected species complexes.

A striking new genus and species of tiger-moth (Lepidoptera: Erebidae, Arctiinae, Arctiini) from the Caribbean, with molecular and morphological analysis of its systematic placement.

Westindia Vincent, a new genus, is proposed for W. haxairei Vincent, a new species of Neotropical tiger-moth described from Dominican Republic. Habitus, male and female genitalia are described and figured. The systematic position of the new genus within Arctiinae is discussed in light of a comparative morphology and a molecular phylogeny derived from the DNA barcode fragment of the mitochondrial COI gene and of the D2 region of the 28S rDNA gene.

Sequence signatures within the genome of SARS-CoV-2 can be used to predict host source.

We conducted an in silico analysis to better understand the potential factors impacting host adaptation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in white-tailed deer, humans, and mink due to the strong evidence of sustained transmission within these hosts. Classification models trained on single nucleotide and amino acid differences between samples effectively identified white-tailed deer-, human-, and mink-derived SARS-CoV-2. For example, the balanced accuracy score of Extremely Randomized Trees classifiers was 0.984 ± 0.006. Eighty-eight commonly identified predictive mutations are found at sites under strong positive and negative selective pressure. A large fraction of sites under selection (86.9%) or identified by machine learning (87.1%) are found in genes other than the spike. Some locations encoded by these gene regions are predicted to be B- and T-cell epitopes or are implicated in modulating the immune response suggesting that host adaptation may involve the evasion of the host immune system, modulation of the class-I major-histocompatibility complex, and the diminished recognition of immune epitopes by CD8+ T cells. Our selection and machine learning analysis also identified that silent mutations, such as C7303T and C9430T, play an important role in discriminating deer-derived samples across multiple clades. Finally, our investigation into the origin of the B.1.641 lineage from white-tailed deer in Canada discovered an additional human sequence from Michigan related to the B.1.641 lineage sampled near the emergence of this lineage. These findings demonstrate that machine-learning approaches can be used in combination with evolutionary genomics to identify factors possibly involved in the cross-species transmission of viruses and the emergence of novel viral lineages.IMPORTANCESevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmissible virus capable of infecting and establishing itself in human and wildlife populations, such as white-tailed deer. This fact highlights the importance of developing novel ways to identify genetic factors that contribute to its spread and adaptation to new host species. This is especially important since these populations can serve as reservoirs that potentially facilitate the re-introduction of new variants into human populations. In this study, we apply machine learning and phylogenetic methods to uncover biomarkers of SARS-CoV-2 adaptation in mink and white-tailed deer. We find evidence demonstrating that both non-synonymous and silent mutations can be used to differentiate animal-derived sequences from human-derived ones and each other. This evidence also suggests that host adaptation involves the evasion of the immune system and the suppression of antigen presentation. Finally, the methods developed here are general and can be used to investigate host adaptation in viruses other than SARS-CoV-2.

Looking for the sponge loop: analyses of detritus on a Caribbean forereef using stable isotope and eDNA metabarcoding techniques.

Coral reefs are biodiverse ecosystems that rely on trophodynamic transfers from primary producers to consumers through the detrital pathway. The sponge loop hypothesis proposes that sponges consume dissolved organic carbon (DOC) and produce large quantities of detritus on coral reefs, with this turn-over approaching the daily gross primary production of the reef ecosystem. In this study, we collected samples of detritus in the epilithic algal matrix (EAM) and samples from potential sources of detritus over two seasons from the forereef at Carrie Bow Cay, Belize. We chose this location to maximize the likelihood of finding support for the sponge loop hypothesis because Caribbean reefs have higher sponge abundances than other tropical reefs worldwide and the Mesoamerican barrier reef is an archetypal coral reef ecosystem. We used stable isotope analyses and eDNA metabarcoding to determine the composition of the detritus. We determined that the EAM detritus was derived from a variety of benthic and pelagic sources, with primary producers (micro- and macroalgae) as major contributors and metazoans (Arthropoda, Porifera, Cnidaria, Mollusca) as minor contributors. None of the sponge species that reportedly produce detritus were present in EAM detritus. The cnidarian signature in EAM detritus was dominated by octocorals, with a scarcity of hard corals. The composition of detritus also varied seasonally. The negligible contribution of sponges to reef detritus contrasts with the detrital pathway originally proposed in the sponge loop hypothesis. The findings indicate a mix of pelagic and benthic sources in the calmer summer and primarily benthic sources in the more turbulent spring.

Global arthropod beta-diversity is spatially and temporally structured by latitude.

Global biodiversity gradients are generally expected to reflect greater species replacement closer to the equator. However, empirical validation of global biodiversity gradients largely relies on vertebrates, plants, and other less diverse taxa. Here we assess the temporal and spatial dynamics of global arthropod biodiversity dynamics using a beta-diversity framework. Sampling includes 129 sampling sites whereby malaise traps are deployed to monitor temporal changes in arthropod communities. Overall, we encountered more than 150,000 unique barcode index numbers (BINs) (i.e. species proxies). We assess between site differences in community diversity using beta-diversity and the partitioned components of species replacement and richness difference. Global total beta-diversity (dissimilarity) increases with decreasing latitude, greater spatial distance and greater temporal distance. Species replacement and richness difference patterns vary across biogeographic regions. Our findings support long-standing, general expectations of global biodiversity patterns. However, we also show that the underlying processes driving patterns may be regionally linked.

Decision Tree Ensembles Utilizing Multivariate Splits Are Effective at Investigating Beta Diversity in Medically Relevant 16S Amplicon Sequencing Data.

Developing an understanding of how microbial communities vary across conditions is an important analytical step. We used 16S rRNA data isolated from human stool samples to investigate whether learned dissimilarities, such as those produced using unsupervised decision tree ensembles, can be used to improve the analysis of the composition of bacterial communities in patients suffering from Crohn's disease and adenomas/colorectal cancers. We also introduce a workflow capable of learning dissimilarities, projecting them into a lower dimensional space, and identifying features that impact the location of samples in the projections. For example, when used with the centered log ratio transformation, our new workflow (TreeOrdination) could identify differences in the microbial communities of Crohn's disease patients and healthy controls. Further investigation of our models elucidated the global impact amplicon sequence variants (ASVs) had on the locations of samples in the projected space and how each ASV impacted individual samples in this space. Furthermore, this approach can be used to integrate patient data easily into the model and results in models that generalize well to unseen data. Models employing multivariate splits can improve the analysis of complex high-throughput sequencing data sets because they are better able to learn about the underlying structure of the data set. IMPORTANCE There is an ever-increasing level of interest in accurately modeling and understanding the roles that commensal organisms play in human health and disease. We show that learned representations can be used to create informative ordinations. We also demonstrate that the application of modern model introspection algorithms can be used to investigate and quantify the impacts of taxa in these ordinations, and that the taxa identified by these approaches have been associated with immune-mediated inflammatory diseases and colorectal cancer.

Influence of plant species, mycorrhizal inoculant, and soil phosphorus level on arbuscular mycorrhizal communities in onion and carrot roots.

Arbuscular mycorrhizal fungi (AMF) are ancient and ecologically important symbionts that colonize plant roots. These symbionts assist in the uptake of water and nutrients, particularly phosphorus, from the soil. This important role has led to the development of AMF inoculants for use as biofertilizers in agriculture. Commercial mycorrhizal inoculants are increasingly popular to produce onion and carrot, but their specific effects on native mycorrhizal communities under field conditions are not known. Furthermore, adequate availability of nutrients in soils, specifically phosphorus, can reduce the diversity and abundance of AMF communities in the roots. The type of crop grown can also influence the composition of AMF communities colonizing the plant roots. This study aimed to investigate how AMF inoculants, soil phosphorus levels, and plant species influence the diversity of AMF communities that colonize the roots of onion and carrot plants. Field trials were conducted on high organic matter (muck) soil in the Holland Marsh, Ontario, Canada. The treatments included AMF-coated seeds (three to five propagules of Rhizophagus irregularis per seed) and non-treated onion and carrot seeds grown in soil with low (~46 ppm) and high (~78 ppm) phosphorus levels. The mycorrhizal communities colonizing the onion and carrot roots were identified by Illumina sequencing. Five genera, Diversispora, Claroideoglomus, Funneliformis, Rhizophagus, and Glomus, were identified in roots of both plant species. AMF communities colonizing carrot roots were more diverse and richer than those colonizing onion roots. Diversispora and Funneliformis had a 1.3-fold and 2.9-fold greater abundance, respectively, in onion roots compared to carrots. Claroideoglomus was 1.4-fold more abundant in carrot roots than in onions. Inoculation with R. irregularis increased the abundance and richness of Rhizophagus in AMF communities of onion roots but not in carrot roots. The soil phosphorus level had no effect on the richness and diversity of AMF in the roots of either crop. In summary, AMF inoculant and soil phosphorus levels influenced the composition of AMF communities colonizing the roots of onion and carrot plants, but the effects varied between plant species.

Comparative analysis of fish environmental DNA reveals higher sensitivity achieved through targeted sequence-based metabarcoding.

Environmental DNA (eDNA)-based methods of species detection are enabling various applications in ecology and conservation including large-scale biomonitoring efforts. qPCR is widely used as the standard approach for species-specific detection, often targeting a fish species of interest from aquatic eDNA. However, DNA metabarcoding has the potential to displace qPCR in certain eDNA applications. In this study, we compare the sensitivity of the latest Illumina NovaSeq 6000 NGS platform to qPCR TaqMan assays by measuring limits of detection and by analysing eDNA from water samples collected from Churchill River and Lake Melville, NL, Canada. Species-specific, targeted next generation sequencing (NGS) assays had significantly higher sensitivity than qPCR, with limits of detection 14- to 29-fold lower. For example, when analysing eDNA, qPCR detected Gadus ogac (Greenland cod) in 21% of samples, but targeted NGS detected this species in 29% of samples. General NGS assays were as sensitive as qPCR, while simultaneously detecting 15 fish species from eDNA samples. With over 34,000 fish species on the planet, parallel and sensitive methods such as NGS will be required to support effective biomonitoring at both regional and global scales.