Environmental DNA metabarcoding of wild flowers reveals diverse communities of terrestrial arthropods

Terrestrial arthropods comprise the most species‐rich communities on Earth, and grassland flowers provide resources for hundreds of thousands of arthropod species. Diverse grassland ecosystems worldwide are threatened by various types of environmental change, which has led to decline in arthropod diversity. At the same time, monitoring grassland arthropod diversity is time‐consuming and strictly dependent on declining taxonomic expertise. Environmental DNA (eDNA) metabarcoding of complex samples has demonstrated that information on species compositions can be efficiently and non‐invasively obtained. Here, we test the potential of wild flowers as a novel source of arthropod eDNA. We performed eDNA metabarcoding of flowers from several different plant species using two sets of generic primers, targeting the mitochondrial genes 16S rRNA and COI. Our results show that terrestrial arthropod species leave traces of DNA on the flowers that they interact with. We obtained eDNA from at least 135 arthropod species in 67 families and 14 orders, together representing diverse ecological groups including pollinators, parasitoids, gall inducers, predators, and phytophagous species. Arthropod communities clustered together according to plant species. Our data also indicate that this experiment was not exhaustive, and that an even higher arthropod richness could be obtained using this eDNA approach. Overall, our results demonstrate that it is possible to obtain information on diverse communities of insects and other terrestrial arthropods from eDNA metabarcoding of wild flowers. This novel source of eDNA represents a vast potential for addressing fundamental research questions in ecology, obtaining data on cryptic and unknown species of plant‐associated arthropods, as well as applied research on pest management or conservation of endangered species such as wild pollinators.     

Comparing whole‐genome shotgun sequencing and DNA metabarcoding approaches for species identification and quantification of pollen species mixtures

Molecular identification of mixed‐species pollen samples has a range of applications in various fields of research. To date, such molecular identification has primarily been carried out via amplicon sequencing, but whole‐genome shotgun (WGS) sequencing of pollen DNA has potential advantages, including (1) more genetic information per sample and (2) the potential for better quantitative matching. In this study, we tested the performance of WGS sequencing methodology and publicly available reference sequences in identifying species and quantifying their relative abundance in pollen mock communities. Using mock communities previously analyzed with DNA metabarcoding, we sequenced approximately 200Mbp for each sample using Illumina HiSeq and MiSeq. Taxonomic identifications were based on the Kraken k‐mer identification method with reference libraries constructed from full‐genome and short read archive data from the NCBI database. We found WGS to be a reliable method for taxonomic identification of pollen with near 100% identification of species in mixtures but generating higher rates of false positives (reads not identified to the correct taxon at the required taxonomic level) relative to rbcL and ITS2 amplicon sequencing. For quantification of relative species abundance, WGS data provided a stronger correlation between pollen grain proportion and sequence read proportion, but diverged more from a 1:1 relationship, likely due to the higher rate of false positives. Currently, a limitation of WGS‐based pollen identification is the lack of representation of plant diversity in publicly available genome databases. As databases improve and costs drop, we expect that eventually genomics methods will become the methods of choice for species identification and quantification of mixed‐species pollen samples.     

Meta-Fish-Lib: A generalised,dynamic DNA reference library pipeline for metabarcoding of fishes

The accuracy and reliability of DNA metabarcoding analyses depend on the breadth and quality of the reference libraries that underpin them. However, there are limited options available to obtain and curate the huge volumes of sequence data that are available on public repositories such as NCBI and BOLD. Here, we provide a pipeline to download, clean and annotate mitochondrial DNA sequence data for a given list of fish species. Features of this pipeline include (a) support for multiple metabarcode markers; (b) searches on species synonyms and taxonomic name validation; (c) phylogeny assisted quality control for identification and removal of misannotated sequences; (d) automatically generated coverage reports for each new GenBank release update; and (e) citable, versioned DOIs. As an example we provide a ready-to-use curated reference library for the marine and freshwater fishes of the U.K. To augment this reference library for environmental DNA metabarcoding specifically, we generated 241 new MiFish-12S sequences for 88 U.K. marine species, and make available new primer sets useful for sequencing these. This brings the coverage of common U.K. species for the MiFish-12S fragment to 93%, opening new avenues for scaling up fish metabarcoding across wide spatial gradients. The Meta-Fish-Lib reference library and pipeline is hosted at https://github.com/genner-lab/meta-fish-lib .     

环境DNA宏条形码在生物多样性研究与监测中的应用

环境DNA宏条形码(eDNA metabarcoding)技术通过提取水体、土壤、空气中的环境DNA,使用引物PCR扩增与高通量测序,进行物种鉴定与生物多样性评估.作为一种新的监测技术,相比于传统监测技术更加快捷、准确以及对自然环境的破坏小,因此在一定程度上改变了我们调查地球生物多样性的方式.本文综述了环境DNA宏条形...     

DNA宏条形码技术在鸟类食性分析中的运用

鸟类在全球广泛分布,不同鸟类物种利用的食物类群存在很大差异,而食性研究是动物营养学和生态学领域的重要研究内容。本文对一些传统鸟类食性鉴别方式及其不足进行回顾,传统鸟类食性鉴别方式包含扎颈法、剖胃法、粪便收集法、相机记录法等。随着测序技术的高速发展,DNA宏条形码技术出现,并广泛应用于动物食性研究。近些年来,该技术也被应用于鸟类食性研究中。本文综述了DNA条形码和DNA宏条形码的操作原理和条件,对鸟类食性研究中的DNA条形码与引物的选择做了详细介绍。对比传统鉴别方法,DNA宏条形码技术降低了物种鉴定难度,减少了人为影响因素,提高了目标样本中物种的鉴定效率,能对粪便、胃容物等混合或不成型样本进行分析。另一方面,在扩增多物种混合的DNA样品中的目标片段时,可能出现偏离,造成结果的不确定性,并且难以根据结果得出较准确各食物组分的比例。未来在使用宏条形码技术对鸟类食性的分析中,可结合其他方法改善对食物的量化以及食物属性的判断。     

FuzzyID2: A software package for large data set species identification via barcoding and metabarcoding using hidden Markov models and fuzzy set methods

Species identification through DNA barcoding or metabarcoding has become a key approach for biodiversity evaluation and ecological studies. However, the rapid accumulation of barcoding data has created some difficulties: for instance, global enquiries to a large reference library can take a very long time. We here devise a two‐step searching strategy to speed identification procedures of such queries. This firstly uses a Hidden Markov Model (HMM) algorithm to narrow the searching scope to genus level and then determines the corresponding species using minimum genetic distance. Moreover, using a fuzzy membership function, our approach also estimates the credibility of assignment results for each query. To perform this task, we developed a new software pipeline, FuzzyID2, using Python and C++. Performance of the new method was assessed using eight empirical data sets ranging from 70 to 234,535 barcodes. Five data sets (four animal, one plant) deployed the conventional barcode approach, one used metabarcodes, and two were eDNA‐based. The results showed mean accuracies of generic and species identification of 98.60% (with a minimum of 95.00% and a maximum of 100.00%) and 94.17% (with a range of 84.40%–100.00%), respectively. Tests with simulated NGS sequences based on realistic eDNA and metabarcode data demonstrated that FuzzyID2 achieved a significantly higher identification success rate than the commonly used Blast method, and the TIPP method tends to find many fewer species than either FuzztID2 or Blast. Furthermore, data sets with tens of thousands of barcodes need only a few seconds for each query assignment using FuzzyID2. Our approach provides an efficient and accurate species identification protocol for biodiversity‐related projects with large DNA sequence data sets.     

Quantitative and qualitative assessment of pollen DNA metabarcoding using constructed species mixtures

Pollen DNA metabarcoding—marker‐based genetic identification of potentially mixed‐species pollen samples—has applications across a variety of fields. While basic species‐level pollen identification using standard DNA barcode markers is established, the extent to which metabarcoding (a) correctly assigns species identities to mixes (qualitative matching) and (b) generates sequence reads proportionally to their relative abundance in a sample (quantitative matching) is unclear, as these have not been assessed relative to known standards. We tested the quantitative and qualitative robustness of metabarcoding in constructed pollen mixtures varying in species richness (1–9 species), taxonomic relatedness (within genera to across class) and rarity (5%–100% of grains), using Illumina MiSeq with the markers rbcL and ITS2. Qualitatively, species composition determinations were largely correct, but false positives and negatives occurred. False negatives were typically driven by lack of a barcode gap or rarity in a sample. Species richness and taxonomic relatedness, however, did not strongly impact correct determinations. False positives were likely driven by contamination, chimeric sequences and/or misidentification by the bioinformatics pipeline. Quantitatively, the proportion of reads for each species was only weakly correlated with its relative abundance, in contrast to suggestions from some other studies. Quantitative mismatches are not correctable by consistent scaling factors, but instead are context‐dependent on the other species present in a sample. Together, our results show that metabarcoding is largely robust for determining pollen presence/absence but that sequence reads should not be used to infer relative abundance of pollen grains.     

A DNA mini‐barcode for marine macrophytes

Studies focusing on marine macrophyte metabarcoding from environmental samples are scarce, due to the lack of a universal barcode for these taxa, and to their poor representation in DNA databases. Here, we searched for a short barcode able to identify marine macrophytes from tissue samples; then, we created a DNA reference library which was used to identify macrophytes in eDNA from coastal sediments. Barcoding of seagrasses, mangroves and marine macroalgae (Chlorophyta, Rhodophyta and Phaeophyceae) was tested using 18 primer pairs from six barcoding genes: the plant barcodes rbcL, matK and trnL, plus the genes ITS2, COI and 18S. The 18S gene showed the highest universality among marine macrophytes, amplifying 95%–100% of samples; amplification performance of the other barcodes was limited. Taxonomy was assigned using a phylogeny‐based approach to create an 18S DNA reference library. Macrophyte tissue sequences were accurately identified within their phyla (88%), order (76%), genus (71%) and species (23%). Nevertheless, out of 86 macrophytes tested, only 48% and 15% had a reference sequence at genus and at species level, respectively. Identification at these levels can be improved by more inclusive reference libraries. Using the 18S mini‐barcode and the reference library, we recovered eDNA from 21 marine macrophytes in sediments, demonstrating the barcode's ability to trace primary producers that contribute to blue carbon. We expect this barcode to also be useful for other ecological questions, such as tracing macro primary producers in marine food webs.     

DNA metabarcoding and the cytochrome c oxidase subunit I marker: not a perfect match

DNA metabarcoding enables efficient characterization of species composition in environmental DNA or bulk biodiversity samples, and this approach is making significant and unique contributions in the field of ecology. In metabarcoding of animals, the cytochrome c oxidase subunit I (COI) gene is frequently used as the marker of choice because no other genetic region can be found in taxonomically verified databases with sequences covering so many taxa. However, the accuracy of metabarcoding datasets is dependent on recovery of the targeted taxa using conserved amplification primers. We argue that COI does not contain suitably conserved regions for most amplicon-based metabarcoding applications. Marker selection deserves increased scrutiny and available marker choices should be broadened in order to maximize potential in this exciting field of research.     

Environmental DNA methylation of Lymnaea stagnalis varies with age and is hypermethylated compared to tissue DNA

Environmental DNA (eDNA) approaches contributing to species identifications are quickly becoming the new norm in biomonitoring and ecosystem assessments. Yet, information such as age and health state of the population, which is vital to species biomonitoring, has not been accessible from eDNA. DNA methylation has the potential to provide such information on the state of a population. Here, we measured the methylation of eDNA along with tissue DNA (tDNA) of Lymnaea stagnalis at four life stages. We demonstrate that eDNA methylation varies with age and allows distinguishing among age classes. Moreover, eDNA was globally hypermethylated in comparison to tDNA. This difference was age-specific and connected to a limited number of eDNA sites. This differential methylation pattern suggests that eDNA release with age is partially regulated through DNA methylation. Our findings help to understand mechanisms involved in eDNA release and shows the potential of eDNA methylation analysis to assess age classes. Such age class assessments will encourage future eDNA studies to assess fundamental processes of population dynamics and functioning in ecology, biodiversity conservation and impact assessments.     

Environmental DNA metabarcoding primers for freshwater fish detection and quantification: In silico and in tanks

Environmental DNA (eDNA) techniques refer to utilizing the organisms’ DNA extracted from environment samples to genetically identify target species without capturing actual organisms. eDNA metabarcoding via high‐throughput sequencing can simultaneously detect multiple fish species from a single water sample, which is a powerful tool for the qualitative detection and quantitative estimates of multiple fish species. However, sequence counts obtained from eDNA metabarcoding may be influenced by many factors, of which primer bias is one of the foremost causes of methodological error. The performance of 18 primer pairs for COI, cytb, 12S rRNA, and 16S rRNA mitochondrial genes, which are all frequently used in fish eDNA metabarcoding, were evaluated in the current study. The ribosomal gene markers performed better than the protein‐coding gene markers during in silico screening, resulting in higher taxonomic coverage and appropriate barcode lengths. Four primer pairs—AcMDB07, MiFish‐U, Ve16S1, and Ve16S3—designed for various regions of the 12S and 16S rRNA genes were screened for tank metabarcoding in a case study targeting six freshwater fish species. The four primer pairs were able to accurately detect all six species in different tanks, while only MiFish‐U, Ve16S1, and Ve16S3 revealed a significant positive relationship between species biomass and read count for the pooled tank data. The positive relationship could not be found in all species within the tanks. Additionally, primer efficiency differed depending on the species while primer preferential species varied in different fish assemblages. This case study supports the potential for eDNA metabarcoding to assess species diversity in natural ecosystems and provides an alternative strategy to evaluate the performance of candidate primers before application of eDNA metabarcoding in natural ecosystems.     

DNA条形码参考数据集构建和序列分析相关的新兴技术

近年来DNA条形码技术迅速发展, 产生的条形码的数量及其应用范围都呈指数性增长, 现已广泛用于物种鉴定、食性分析、生物多样性评估等方面。本文重点总结并讨论了构建条形码参考数据库和序列聚类相关的信息分析的技术和方法, 包括: 基于高通量测序(high throughput sequencing, HTS)平台以高效并较低的成本获取条形码序列的方法; 同时还介绍了从原始测序序列到分类操作单元(operational taxonomic units, OTUs)过程中的一些计算逻辑以及被广泛采用的软件和技术。这是一个较新并快速发展的领域, 我们希望本文能为读者提供一个梗概, 了解DNA条形码技术在生物多样性研究应用中的方法和手段。     

Rapid degradation of longer DNA fragments enables the improved estimation of distribution and biomass using environmental DNA

The advent of environmental DNA (eDNA) analysis methods has enabled rapid and wide‐range ecological monitoring in aquatic ecosystems, but there is a dearth of information on eDNA degradation. The results of previous studies suggest that the decay rate of eDNA varies depending on the length of DNA fragments. To examine this hypothesis, we compared temporal change in copy number of long eDNA fragments (719 bp) with that of short eDNA fragments (127 bp). First, we isolated rearing water from a target fish species, Japanese Jack Mackerel (Trachurus japonicus), and then quantified the copy number of the long and short eDNA fragments in 1 L water samples after isolating the water from the fish. Long DNA fragments showed a higher decay rate than short fragments. Next, we measured the eDNA copy numbers of long and short DNA fragments using field samples, and compared them with fish biomass as measured by echo intensity. Although a previous study suggested that short eDNA fragments could be overestimated because of nontarget eDNA from a nearby fish market and carcasses, the eDNA concentrations of long fragments were correlated with echo intensity. This suggests that the concentration of longer eDNA fragments reflects fish biomass more accurately than the previous study by removing the effects of the fish market and carcasses. The length‐related differences in eDNA have a substantial potential to improve estimation of species biomass.     

Optimal extraction methods for the simultaneous analysis of DNA from diverse organisms and sample types

Using environmental DNA (eDNA) to assess the distribution of micro‐ and macroorganisms is becoming increasingly popular. However, the comparability and reliability of these studies is not well understood as we lack evidence on how different DNA extraction methods affect the detection of different organisms, and how this varies among sample types. Our aim was to quantify biases associated with six DNA extraction methods and identify one which is optimal for eDNA research targeting multiple organisms and sample types. We assessed each methods’ ability to simultaneously extract bacterial, fungal, plant, animal and fish DNA from soil, leaf litter, stream water, stream sediment, stream biofilm and kick‐net samples, as well as from mock communities. Method choice affected alpha‐diversity for several combinations of taxon and sample type, with the majority of the differences occurring in the bacterial communities. While a single method performed optimally for the extraction of DNA from bacterial, fungal and plant mock communities, different methods performed best for invertebrate and fish mock communities. The consistency of methods, as measured by the similarity of community compositions resulting from replicate extractions, varied and was lowest for the animal communities. Collectively, these data provide the first comprehensive assessment of the biases associated with DNA extraction for both different sample types and taxa types, allowing us to identify DNeasy PowerSoil as a universal DNA extraction method. The adoption of standardized approaches for eDNA extraction will ensure that results can be more reliably compared, and biases quantified, thereby advancing eDNA as an ecological research tool.     

Environmental metabarcodes for insects: in silico PCR reveals potential for taxonomic bias

Studies of insect assemblages are suited to the simultaneous DNA‐based identification of multiple taxa known as metabarcoding. To obtain accurate estimates of diversity, metabarcoding markers ideally possess appropriate taxonomic coverage to avoid PCR‐amplification bias, as well as sufficient sequence divergence to resolve species. We used in silico PCR to compare the taxonomic coverage and resolution of newly designed insect metabarcodes (targeting 16S) with that of existing markers [16S and cytochrome oxidase c subunit I (COI)] and then compared their efficiency in vitro. Existing metabarcoding primers amplified in silico <75% of insect species with complete mitochondrial genomes available, whereas new primers targeting 16S provided >90% coverage. Furthermore, metabarcodes targeting COI appeared to introduce taxonomic PCR‐amplification bias, typically amplifying a greater percentage of Lepidoptera and Diptera species, while failing to amplify certain orders in silico. To test whether bias predicted in silico was observed in vitro, we created an artificial DNA blend containing equal amounts of DNA from 14 species, representing 11 insect orders and one arachnid. We PCR‐amplified the blend using five primer sets, targeting either COI or 16S, with high‐throughput amplicon sequencing yielding more than 6 million reads. In vitro results typically corresponded to in silico PCR predictions, with newly designed 16S primers detecting 11 insect taxa present, thus providing equivalent or better taxonomic coverage than COI metabarcodes. Our results demonstrate that in silico PCR is a useful tool for predicting taxonomic bias in mixed template PCR and that researchers should be wary of potential bias when selecting metabarcoding markers.     

Creating,curating and evaluating a mitogenomic reference database to improve regional species identification using environmental DNA

Species detection using eDNA is revolutionizing global capacity to monitor biodiversity. However, the lack of regional, vouchered, genomic sequence information—especially sequence information that includes intraspecific variation—creates a bottleneck for management agencies wanting to harness the complete power of eDNA to monitor taxa and implement eDNA analyses. eDNA studies depend upon regional databases of mitogenomic sequence information to evaluate the effectiveness of such data to detect and identify taxa. We created the Oregon Biodiversity Genome Project to create a database of complete, nearly error-free mitogenomic sequences for all of Oregon's fishes. We have successfully assembled the complete mitogenomes of 313 specimens of freshwater, anadromous and estuarine fishes representing 24 families, 55 genera and 129 species and lineages. Comparative analyses of these sequences illustrate that many regions of the mitogenome are taxonomically informative, that the short (~150 bp) mitochondrial ‘barcode’ regions typically used for eDNA assays do not consistently diagnose for species and that complete single or multiple genes of the mitogenome are preferable for identifying Oregon's fishes. This project provides a blueprint for other researchers to follow as they build regional databases, illustrates the taxonomic value and limits of complete mitogenomic sequences and offers clues as to how current eDNA assays and environmental genomics methods of the future can best leverage this information.     

Quantification of mesocosm fish and amphibian species diversity via environmental DNA metabarcoding

Freshwater fauna are particularly sensitive to environmental change and disturbance. Management agencies frequently use fish and amphibian biodiversity as indicators of ecosystem health and a way to prioritize and assess management strategies. Traditional aquatic bioassessment that relies on capture of organisms via nets, traps and electrofishing gear typically has low detection probabilities for rare species and can injure individuals of protected species. Our objective was to determine whether environmental DNA (eDNA) sampling and metabarcoding analysis can be used to accurately measure species diversity in aquatic assemblages with differing structures. We manipulated the density and relative abundance of eight fish and one amphibian species in replicated 206‐L mesocosms. Environmental DNA was filtered from water samples, and six mitochondrial gene fragments were Illumina‐sequenced to measure species diversity in each mesocosm. Metabarcoding detected all nine species in all treatment replicates. Additionally, we found a modest, but positive relationship between species abundance and sequencing read abundance. Our results illustrate the potential for eDNA sampling and metabarcoding approaches to improve quantification of aquatic species diversity in natural environments and point the way towards using eDNA metabarcoding as an index of macrofaunal species abundance.     

Quantitative DNA metabarcoding: improved estimates of species proportional biomass using correction factors derived from control material

DNA metabarcoding is a powerful new tool allowing characterization of species assemblages using high‐throughput amplicon sequencing. The utility of DNA metabarcoding for quantifying relative species abundances is currently limited by both biological and technical biases which influence sequence read counts. We tested the idea of sequencing 50/50 mixtures of target species and a control species in order to generate relative correction factors (RCFs) that account for multiple sources of bias and are applicable to field studies. RCFs will be most effective if they are not affected by input mass ratio or co‐occurring species. In a model experiment involving three target fish species and a fixed control, we found RCFs did vary with input ratio but in a consistent fashion, and that 50/50 RCFs applied to DNA sequence counts from various mixtures of the target species still greatly improved relative abundance estimates (e.g. average per species error of 19 ± 8% for uncorrected vs. 3 ± 1% for corrected estimates). To demonstrate the use of correction factors in a field setting, we calculated 50/50 RCFs for 18 harbour seal (Phoca vitulina) prey species (RCFs ranging from 0.68 to 3.68). Applying these corrections to field‐collected seal scats affected species percentages from individual samples (Δ 6.7 ± 6.6%) more than population‐level species estimates (Δ 1.7 ± 1.2%). Our results indicate that the 50/50 RCF approach is an effective tool for evaluating and correcting biases in DNA metabarcoding studies. The decision to apply correction factors will be influenced by the feasibility of creating tissue mixtures for the target species, and the level of accuracy needed to meet research objectives.