Environmental DNA (eDNA) refers to DNA fragments that organisms leave behind in their surrounding environment (such as soil, sediment and water). eDNA technology sequences these DNA fragments and can provide information on taxonomic composition of benthic macroinvertebrate communities. Compared with traditional biological survey methods, eDNA technology is more sensitive, efficient and noninvasive. As a novel method for surveying aquatic organisms, eDNA techniques have been widely used in biodiversity assessments of aquatic organisms, including monitoring of endangered, rare and invasive species. In this review, we summarize recent developments in eDNA technology and focus primarily on the operational procedure and its application for freshwater benthic macroinvertebrate analyses. Finally, we discuss the advantages and potential caveats of current eDNA practices.

Biological invasion is a major threat to multiple ecosystems across the globe, causing severe damages to ecological integrity, loss of biodiversity, economic and social development and even human health. With the rapid development in aquaculture, shipping and aquarium and ornamental trades in the past several decades, China has become one of the countries most influenced by invasive species. Studies have clearly shown that the development and application of robust early monitoring and warning is one of the most effective ways to prevent and possibly control invasive species in aquatic ecosystems. Compared to terrestrial ecosystems, there remain several technical difficulties for developing early monitoring and warning in aquatic habitats. The technical challenges are mainly due to several features of aquatic biological communities such as high biodiversity and complex structure, a large number of microscopic species, extremely low population density and lack of available taxonomic keys for species identification. With the rapid development of high-throughput sequencing techniques, environmental DNA (eDNA)-metabarcoding has become the top priority method for developing the early monitoring and warning programs in aquatic ecosystems. In this review, we aim to synthesize the research progress on eDNA-metabarcoding and its application to early monitoring and warning of invasive species in aquatic ecosystems. In addition, we briefly discuss the technological advantages of eDNA-metabarcoding for the early monitoring and warning programs. Finally, we propose research perspectives for solutions to technical issues for false positive and false negative errors in the eDNA-metabarcoding process.

Symbiotic microorganisms colonize external or internal surfaces of a host depending on environmental factors, and may supply the host with special functions. More and more researchers have proven that symbiotic gut microorganisms are related to a diverse range of physiological functions of a host including immunity, nutrition, metabolism and even mental health. Thus, gut microorganisms comprise an important “microbial organ” in humans. Since the early days of microbiota research, animal models have been used frequently for their microbiota, contributing greatly to new research in this field. This review provides an overview of animals used as models in symbiotic microorganism studies, including zebrafish (Danio rerio), mice (Mus musculus), pigs (Sus scrofa domesticus), and monkeys (Macaca mulatta). We provide insight into the development and characteristics of these model animals, highlighting the advantages and disadvantages of each model, as well as any outstanding scientific achievements based on their use. We also note that honey bee (Apis), fruit fly (Drosophila) and nematode (Caenorhabditis elegans) models are emerging as more prevalent in recent gut microbiota studies. This paper will contribute to better understanding the similarities and differences between the microbiota of model animals and humans, while providing useful information for effectively implementing these animal models in future research.

Pollinator bees are providers of an important ecosystem service, and their survival relies completely on the landscape. Now with the landscape dominated by agriculture, bee diversity has been significantly reduced. Studies suggest that bee populations decline as agricultural land-use increases due to increased exposure to detrimental pesticides. Further, the protein content of pollen is highly important for the growth and development of a bee, and different landscapes provide distinct sources of nutrition. Although many studies have demonstrated the apparent impacts of landscape change on the population dynamics and individual survival of the bees, the underpinning mechanisms remain largely unknown. On the other hand, an increasing body of literature has shown that bee gut symbionts are of great importance to the health of the host bees in absorbing nutrients and resisting pathogens. When foraging, pollinator bees are exposed to particular microbes from pollen and nectar which have been suggested to be a source of some bee gut symbionts and could be either probiotics or pathogens. Together with landscape-related nutrition and pesticides, environmental microbes have been reported to affect bee microbiomes significantly. A number of pilot studies suggest that landscape change could affect bee microbiota, thereby influencing host health. An important linkage, however, is missing between environmental microbiota, especially those associated with the flowers, and that of the bee gut in a changing habitat. It is worth exploring how gut microbiomes respond to landscape changes. This will hopefully help us identify landscape types that are friendly to bees, so proper land-use can be implemented to protect the bees.

DNA barcoding has been growing exponentially in terms of the number of barcode generated as well as its applications, e.g. as conservation tools in: species identification for damaged specimens, diet analysis from gut content and feces, biodiversity assessment from environmental DNA (eDNA), bulk arthropod samples or invertebrate-derived DNA (iDNA). These applications often require coupling with high throughput sequencing (HTS) technologies, and when done so are referred to as metabarcoding. Here, we discuss the methods used to generate reference barcodes using cost-efficient HTS platforms, and introduce several rules-of-thumb and some widely-used tools to conduct data quality control, denoising, and Operational Taxonomic Units (OTUs) clustering. We hope this review will help readers better understand how these emerging technologies can be implemented alongside existing technologies to accelerate biodiversity assessments in an accurate and efficient way.

Microbes are ubiquitous in human life. In years past, the study of microbes has only focused on single-bacteria cultures and qualitative analyses. The development of sequencing technology has greatly enhanced progress in microbiology research and more and more evidence shows that human symbiotic microbes, especially intestinal microbes, are closely related to human health. Second-generation sequencing technology is currently mainstream in microbiology research because of its high throughput, high accuracy and low cost. However, with the deepening complexity of research, the disadvantages of second-generation technology, i.e. short read length (< 450 bp), lead to subsequent challenges in data analysis and genome assembly, and limit the applicability to future research. In this context, the third-generation sequencing technology comes into being. The third-generation of sequencing (TGS) technology is also called single molecule sequencing. It directly carries out real-time sequencing of single DNA molecules without PCR amplifications. TGS technology significantly increases read length up to 2-10 kb or even 2.2 Mb. Because of its advantages of long read and no preference for GC, TGS provides a new method for full-length gene sequencing that facilitates the assembly of complete and reliable genome maps in microbes and that further reveals the diversity of microbial structures and functions. This review summarizes the technical characteristics and principles of TGS, and then mainly analyzes its applications and progress in 16S/18S rRNA gene sequencing, complete bacterial genome mapping and metagenomics research.

Large carnivores play an important role in the regulation of food-web structure and ecosystem functioning. However, large carnivores face serious threats that have caused declines in their populations and geographic ranges due to habitat loss and degradation, hunting, human disturbance and pathogen transmission. Conservation of large carnivore species richness and population size has become a pressing issue and an important research focus of conservation biology. The western Sichuan Plateau, located at the intersection of the mountains of southwest China and the eastern margin of the Tibetan Plateau, is a global biodiversity hotspot and has high carnivore species richness. However, increasing human activities may exacerbate the destruction of local flora and fauna, thereby threatening the survival of wild carnivores. Information on species composition and dietary habits can improve our understanding of the structure and function of the ecosystem and food-web relationships in the study area. In addition, species composition and dietary habits are of great significance for understanding multi-species coexistence mechanisms and preserving biodiversity. This study collected carnivore fecal samples from Xinlong and Shiqu counties in the Ganzi Tibetan Autonomous Prefecture, Sichuan Province. DNA was then extracted from the samples and the species was identified based on DNA sequences and DNA barcoding techniques. Seven carnivores were identified, including five large carnivores (Canis lupus, Ursus arctos, Panthera pardus, P. uncia and Canis lupus familiaris) and two medium and small-sized carnivores (Prionailurus bengalensis and Vulpes vulpes). Using fecal DNA, high-throughput sequencing and metabarcoding, we conducted diet analysis for the seven carnivores and found 28 different food molecular operational taxonomic units (MOTUs), including 19 mammals, eight birds and one fish species. The predominant prey categories of wolves, dogs and brown bears were ungulates. The domestic yak (Bos grunniens) was the most frequently identified prey species. Small mammals such as rodents and lagomorphs accounted for a significant proportion in the diets of leopard cats and red foxes, The most frequent prey of this category of carnivore were the Chinese scrub vole (Neodon irene) and plateau pika (Ochotona curzoniae). In addition, leopards and snow leopards mainly fed on the Chinese goral (Naemorhedus griseus) and blue sheep (Pseudois nayaur), respectively. Our study highlights the utility of fecal DNA and metabarcoding technique in fast carnivore surveys and high-throughput diet analysis, and provides a technical reference and guidance for future biodiversity surveys and food-web studies.

Honey and bumble bees are import pollinators, playing significant roles in the agricultural industry and maintaining the bio-ecosystem balance. Recently, it was found that the bees harbor a simple, yet specific gut microbiota. The normal bee gut microbiota makes essential contributions to host growth, endocrine signaling, and pathogen resistance. With the development of high through-put sequencing technology, researchers can now quickly identify the gut community structure for a low cost. This is helpful for biodiversity, conservation and bee health studies. However, the currently-used 16S rRNA databases are not specific enough to classify the bee gut microbiota properly. Many of the specific bacteria that enrich the gut of Apis cerana are in the genus Apibacter. Here, we isolated Apibacter species from A. cerana collected in five provinces of China, and added them to the current SILVA database. We also curated the nomenclature of some existing sequences and re-classified them in the updated database. Based on the analysis of the 16S rRNA sequencing data from one A. cerana and one Apis mellifera sample, our Bee Gut Microbiota-Database (BGM-Db) offers a more accurate classification of bee gut microbiota at a higher resolution than either the SILVA or Ribosomal Database Project (RDP) database.

Metabarcoding helps to quickly assess biodiversity. In this study, we discuss popular metabarcoding analytical tools and parameter settings. We also develop a metabarcoding bioinformatics pipeline, EPPS, to process data from quality control of raw reads to biodiversity comparisons between samples using a pipeline building program, Nextflow. The EPPS pipeline can summarize the time and memory cost of each process in the pipeline. We also apply the pipeline on a test dataset and a public dataset from a previous study. The result suggests that this pipeline can reliably analyze metabarcoding data and facilitate pipeline sharing of metabarcoding studies.