Estimating aquatic plant diversity and distribution in rivers from Jingjinji region, China, using environmental DNA metabarcoding and a traditional survey method

Environmental DNA metabarcoding reveals the effect of environmental selection on phytoplankton community structure along a subtropical river

The Dongjiang River, a major tributary of the Pearl River system that supplies water to more than 40 million people in Guangdong Province and neighboring regions of China, harbors rich biodiversity, including many endemic and endangered species. However, human activities such as urbanization, agriculture, and industrialization have posed serious threats to its water quality and biodiversity. To assess the status and drivers of phytoplankton diversity, which is a key indicator of aquatic ecosystem health, this study used Environmental DNA (eDNA) metabarcoding combined with machine learning methods to explore spatial variations in the composition and structure of phytoplankton communities along the Dongjiang River, including its estuary. The results showed that phytoplankton diversity exhibited spatial distribution patterns, with higher community structure similarity and lower network complexity in the upstream than in the downstream regions. Environmental selection was the main mechanism shaping phytoplankton community composition, with natural factors driving the dominance of Pyrrophyta, Ochrophyta, and Cryptophyta in the upstream regions and estuaries. In contrast, the downstream regions was influenced by high concentrations of pollutants, resulting in increased abundance of Cryptophyta. The random forest model identified temperature, dissolved oxygen, chlorophyll a, NO2-, and NH4+ as the main factors influencing the primary phytoplankton communities and could be used to predict changes during wet periods. This study provides valuable insights into the factors influencing phytoplankton diversity and community composition in the Dongjiang River, and demonstrates the application value of eDNA metabarcoding technique in large-scale, long-distance river biodiversity monitoring.

Insights in Pharmaceutical Pollution: The Prospective Role of eDNA Metabarcoding

Environmental pollution is a growing threat to natural ecosystems and one of the world's most pressing concerns. The increasing worldwide use of pharmaceuticals has elevated their status as significant emerging contaminants. Pharmaceuticals enter aquatic environments through multiple pathways related to anthropogenic activity. Their high consumption, insufficient waste treatment, and the incapacity of organisms to completely metabolize them contribute to their accumulation in aquatic environments, posing a threat to all life forms. Various analytical methods have been used to quantify pharmaceuticals. Biotechnology advancements based on next-generation sequencing (NGS) techniques, like eDNA metabarcoding, have enabled the development of new methods for assessing and monitoring the ecotoxicological effects of pharmaceuticals. eDNA metabarcoding is a valuable biomonitoring tool for pharmaceutical pollution because it (a) provides an efficient method to assess and predict pollution status, (b) identifies pollution sources, (c) tracks changes in pharmaceutical pollution levels over time, (d) assesses the ecological impact of pharmaceutical pollution, (e) helps prioritize cleanup and mitigation efforts, and (f) offers insights into the diversity and composition of microbial and other bioindicator communities. This review highlights the issue of aquatic pharmaceutical pollution while emphasizing the importance of using modern NGS-based biomonitoring actions to assess its environmental effects more consistently and effectively.

EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation

Nowadays, the detection of environmental microorganism indicators is essential for us to assess the degree of pollution, but the traditional detection methods consume a lot of manpower and material resources. Therefore, it is necessary for us to make microbial data sets to be used in artificial intelligence. The Environmental Microorganism Image Dataset Seventh Version (EMDS-7) is a microscopic image data set that is applied in the field of multi-object detection of artificial intelligence. This method reduces the chemicals, manpower and equipment used in the process of detecting microorganisms. EMDS-7 including the original Environmental Microorganism (EM) images and the corresponding object labeling files in ".XML" format file. The EMDS-7 data set consists of 41 types of EMs, which has a total of 2,65 images and 13,216 labeled objects. The EMDS-7 database mainly focuses on the object detection. In order to prove the effectiveness of EMDS-7, we select the most commonly used deep learning methods (Faster-Region Convolutional Neural Network (Faster-RCNN), YOLOv3, YOLOv4, SSD, and RetinaNet) and evaluation indices for testing and evaluation. EMDS-7 is freely published for non-commercial purpose at: https://figshare.com/articles/dataset/EMDS-7_DataSet/16869571.

Environmental DNA analysis as an emerging non-destructive method for plant biodiversity monitoring: a review

Environmental DNA (eDNA) analysis has recently transformed and modernized biodiversity monitoring. The accurate detection, and to some extent quantification, of organisms (individuals/populations/communities) in environmental samples is galvanizing eDNA as a successful cost and time-efficient biomonitoring technique. Currently, eDNA's application to plants remains more limited in implementation and scope compared to animals and microorganisms. This review evaluates the development of eDNA-based methods for (vascular) plants, comparing its performance and power of detection with that of traditional methods, to critically evaluate and advise best-practices needed to innovate plant biomonitoring. Recent advancements, standardization and field applications of eDNA-based methods have provided enough scope to utilize it in conservation biology for numerous organisms. Despite our review demonstrating only 13% of all eDNA studies focus on plant taxa to date, eDNA has considerable environmental DNA has considerable potential for plants, where successful detection of invasive, endangered and rare species, and community-level interpretations have provided proof-of-concept. Monitoring methods using eDNA were found to be equal or more effective than traditional methods; however, species detection increased when both methods were coupled. Additionally, eDNA methods were found to be effective in studying species interactions, community dynamics and even effects of anthropogenic pressure. Currently, elimination of potential obstacles (e.g. lack of relevant DNA reference libraries for plants) and the development of user-friendly protocols would greatly contribute to comprehensive eDNA-based plant monitoring programs. This is particularly needed in the data-depauperate tropics and for some plant groups (e.g., Bryophytes and Pteridophytes). We further advocate to coupling traditional methods with eDNA approaches, as the former is often cheaper and methodologically more straightforward, while the latter offers non-destructive approaches with increased discrimination ability. Furthermore, to make a global platform for eDNA, governmental and academic-industrial collaborations are essential to make eDNA surveys a broadly adopted and implemented, rapid, cost-effective and non-invasive plant monitoring approach.