Environmental filtering of macroinvertebrate traits influences ecosystem functioning in a large river floodplain

Article describes how the biodiversity–ecosystem function hypothesis postulates that higher biodiversity is correlated with faster ecosystem process rates and increased ecosystem stability in fluctuating environments. The authors examined linkages among floodplain wetland habitats, invertebrate communities and their associated traits, and ecosystem function across 60 sites within the floodplain wetlands of the lower Wolastoq, Saint John River, New Brunswick

DNA metabarcoding reveals metacommunity dynamics in a threatened boreal wetland wilderness

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

Ancient Pathogen Dna in Archaeological Samples Detected With a Microbial Detection Array

Ancient human remains of paleopathological interest typically contain highly degraded DNA in which pathogenic taxa are often minority components, making sequence-based metagenomic characterization costly. Microarrays may hold a potential solution to these challenges, offering a rapid, affordable and highly informative snapshot of microbial diversity in complex samples without the lengthy analysis and/or high cost associated with high-throughput sequencing. Their versatility is well established for modern clinical specimens, but they have yet to be applied to ancient remains. Here we report bacterial profiles of archaeological and historical human remains using the Lawrence Livermore Microbial Detection Array (LLMDA). The array successfully identified previously-verified bacterial human pathogens, including Vibrio cholerae (cholera) in a 19th century intestinal specimen and Yersinia pestis (“Black Death” plague) in a medieval tooth, which represented only minute fractions (0.03% and 0.08% alignable high-throughput shotgun sequencing reads) of their respective DNA content. This demonstrates that the LLMDA can identify primary and/or co-infecting bacterial pathogens in ancient samples, thereby serving as a rapid and inexpensive paleopathological screening tool to study health across both space and time

Factors That Affect Large Subunit Ribosomal DNA Amplicon Sequencing Studies of Fungal Communities: Classification Method, Primer Choice, and Error

Nuclear large subunit ribosomal DNA is widely used in fungal phylogenetics and to an increasing extent also amplicon-based environmental sequencing. The relatively short reads produced by next-generation sequencing, however, makes primer choice and sequence error important variables for obtaining accurate taxonomic classifications. In this simulation study we tested the performance of three classification methods: 1) a similarity-based method (BLAST + Metagenomic Analyzer, MEGAN); 2) a composition-based method (Ribosomal Database Project naïve Bayesian classifier, NBC); and, 3) a phylogeny-based method (Statistical Assignment Package, SAP). We also tested the effects of sequence length, primer choice, and sequence error on classification accuracy and perceived community composition. Using a leave-one-out cross validation approach, results for classifications to the genus rank were as follows: BLAST + MEGAN had the lowest error rate and was particularly robust to sequence error; SAP accuracy was highest when long LSU query sequences were classified; and, NBC runs significantly faster than the other tested methods. All methods performed poorly with the shortest 50–100 bp sequences. Increasing simulated sequence error reduced classification accuracy. Community shifts were detected due to sequence error and primer selection even though there was no change in the underlying community composition. Short read datasets from individual primers, as well as pooled datasets, appear to only approximate the true community composition. We hope this work informs investigators of some of the factors that affect the quality and interpretation of their environmental gene surveys

Methods Are similarity-or phylogeny-based methods more appropriate for classifying internal transcribed spacer (ITS) metagenomic amplicons?

Summary • The internal transcribed spacer (ITS) of the nuclear ribosomal DNA region is a widely used species marker for plants and fungi. Recent metagenomic studies using next-generation sequencing, however, generate only partial ITS sequences. Here we compare the performance of partial and full-length ITS sequences with several classification methods. • We compiled a full-length ITS data set and created short fragments to simulate the read lengths commonly recovered from current next-generation sequencing platforms. We compared recovery, erroneous recovery, and coverage for the following methods: best BLAST hit classification, MEGAN classification, and automated phylogenetic assignment using the Statistical Assignment Program (SAP). • We found that summarizing results with more inclusive taxonomic ranks increased recovery and reduced erroneous recovery. The similarity-based methods BLAST and MEGAN performed consistently across most fragment lengths. Using a phylogeny-based method, SAP runs with queries 400 bp or longer worked best. Overall, BLAST had the highest recovery rates and MEGAN had the lowest erroneous recovery rates. • A high-throughput ITS classification method should be selected, taking into consideration read length, an acceptable tradeoff between maximizing the total number of classifications and minimizing the number of erroneous classifications, and the computational speed of the assignment method

terrimporter/MetaWorks: MetaWorks v1.13.0

Added support for the mitochondrial 16S vertebrate classifier

MetaWorks workflow to produce taxonomically assigned exact sequence variants.

To aid reproducibility, a Conda environment is provided. Although multiple Snakemake workflows are provided in MetaWorks, here we show the main workflow that generates taxonomically assigned ESVs. Input files are shown in the first panel (green), the ESV workflow is shown in the centre panel (blue), and outfiles are shown in the last panel (orange). The input files in white boxes are required by snakemake to run the appropriate workflow. The input files in green need to be supplied by the user. Note that only custom-trained classifiers such as for COI need to be supplied by the user whereas classifiers built-in to the RDP classifier are used automatically to process prokaryote 16S assignments, for example. The denoising step shown here includes the removal of rare clusters, sequences with putative errors, as well as chimeric sequences. The results are provided in a comma-separated value (CSV) file and shows each ESV per sample with read counts and taxonomic assignments. Abbreviations: Demultiplexed Illumina paired-end reads (R1 + R2), internal transcribed spacer (ITS) region, open reading frame sequences (ORFs).</p

RDP-trained reference sets that can be used with MetaWorks.

RDP-trained reference sets that can be used with MetaWorks.</p