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Article

Research Trends and Hotspots in eDNA-Based Surveys of Macroinvertebrates: A Bibliometric Analysis

by
Xinyu Ge
,
Junyu Zhang
,
Ziming Shao
,
Lu Chai
,
Jiaxin Nie
,
Dan Yin
,
Haoran Zhang
,
Wenbin Liu
* and
Chuncai Yan
*
Tianjin Key Laboratory of Conservation and Utilization of Animal Diversity, College of Life Sciences, Tianjin Normal University, Tianjin 300387, China
*
Authors to whom correspondence should be addressed.
Diversity 2025, 17(6), 402; https://doi.org/10.3390/d17060402
Submission received: 29 April 2025 / Revised: 22 May 2025 / Accepted: 4 June 2025 / Published: 6 June 2025
(This article belongs to the Section Animal Diversity)
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Abstract

Environmental DNA metabarcoding offers an efficient and non-invasive solution for aquatic biomonitoring surveys, particularly demonstrating remarkable potential in macroinvertebrate research. In this study, we systematically analyzed 322 articles in the Web of Science Core Collection from 2010 to 2024 using bibliometric methods to reveal the research trends and technological advances in the field of macroinvertebrate surveys based on eDNA metabarcoding technology. The results showed that the number of annual publications has increased rapidly since 2012, with the United States (n = 58), France (n = 43), and Canada (n = 39) as the main contributing countries, and the most intensive collaboration network was observed among Europe countries. A keyword analysis shows that, in the past five years, the research direction has shifted to novel methodologies including multi-marker approaches, quantitative PCR, digital PCR, and other molecular detection platforms and high-throughput sequencing technology, with the integration of machine-learning and deep-learning architecture significantly improving the taxonomic resolution of data analysis. Despite the advantages of eDNA metabarcoding technology in terms of sensitivity and efficiency, persistent challenges including false positives/negatives in detection and incomplete reference databases are still the main challenges. This study provides methodological evidence for standardizing eDNA protocols in benthic assessments while identifying critical knowledge gaps.

1. Introduction

Macroinvertebrates are animals without backbones that are large enough to be seen without a microscope, with adults typically exceeding 0.5 mm in size. They inhabit various environments, particularly aquatic ecosystems [1]. Characterized by species richness, diverse life cycles, sedentary habitats, and differential environmental sensitivities, these organisms are frequently employed as ecological indicators that provide biological documentation of ecosystem changes [2]. As an important basis for the biological evaluation of water quality and aquatic ecosystem integrity, the distribution and community structure of macroinvertebrates in various aquatic environments are of particular interest [3]. In recent years, significant advancements have been documented in the water quality assessment of macroinvertebrates in marine, rivers, and wetlands [4,5,6]. However, these studies are based on traditional survey methods, which typically involve using tools such as D-shaped nets, grab samplers, and sediment corers to collect macroinvertebrates. The samples are then manually sorted, selected, and identified. However, the monitoring of macroinvertebrates using these traditional field survey methods is often limited by the complex environmental conditions of the environment and the diverse life histories of various species. Sampling in deep water can be more challenging and may require additional effort or specialized equipment. Although, in general, collecting aquatic macroinvertebrates is not particularly difficult, the ability to conduct comprehensive monitoring of regional species diversity within a short timeframe can be constrained by limited resources, such as manpower or equipment availability, and the need for sufficient time to adequately assess the diversity of species in the region [7].
Environmental DNA (eDNA) is a complex mixture of genomic DNA from different organisms extracted from environmental samples (e.g., soil, sediment, water, air, etc.) and consists of organisms (including viable individuals at the time of collection), fragments of organisms, and extracellular DNA (genetic material shed by organisms or biologically active propagule) [8]. Free eDNA is susceptible to degradation into short fragments or adsorption onto particulate matter under environmental influence [9]. With the development of sequencing technology and advances in computational technology, environmental DNA metabarcoding (eDNA metabarcoding), which integrates high-throughput sequencing platforms, has emerged. This method allows for the simultaneous rapid identification of multiple species or taxonomic units. The eDNA metabarcoding technology not only compresses the processing time but also enables the rapid and effective monitoring of species diversity in communities. Furthermore, relying on well-established databases enhances the efficiency of species identification and characterization, leading to more accurate and comprehensive biodiversity assessments. Although the species can only be identified if reference databases exist and there is sufficient DNA in the sample for detection, this can be challenging during the water filtration process in eDNA analysis, as the DNA content in the sample may sometimes be too low for detection. Nevertheless, due to its potential to detect a wide range of taxa, it has been referred to as ‘biomonitoring 2.0’ in ecosystem monitoring [10]. Sources of eDNA include DNA produced by feces, saliva, urine, roots, leaves, fruits, and shed cells of animals living in the aquatic environment [11], as well as DNA from animals that also visit the environment (e.g., birds and mammals that visit the aquatic environment to drink) [9]. The first study of freshwater samples was conducted in 2008 on the invasive species American bullfrog. Tanks with different densities of American bullfrog tadpoles were reared in controlled environments and water samples were tested for DNA [12]. With the continuous development of molecular biology techniques, the field of eDNA research is moving towards multiple taxa (microorganisms [13], eukaryotic zooplankton [14], macroinvertebrates [15], plants [16], fishes [17], etc.) and multiple environments (air [18], soil [19], freshwater [20], marine [21], etc.). The field is currently transitioning into a standardization phase for eDNA metabarcoding protocols. In recent years, documents and specifications on the standardization of environmental DNA monitoring technology have been issued both domestically and internationally, and there are mature programs and assessment standards for sampling protocols, sample handling methods, DNA extraction, and data processing methods [22,23].
eDNA metabarcoding technology has become an indispensable tool for macroinvertebrate biomonitoring, demonstrating particular value in three key applications: biodiversity assessments, invasive species detection, and the conservation of rare/endangered species. In biodiversity monitoring, this approach enhances the scalable efficiency for community profiling across extensive sampling networks, compared to conventional morphological surveys, while still providing taxonomically comprehensive data [24]. In both invasive species surveillance and endangered species conservation, the technology’s high sensitivity enables the reliable detection of the target taxa even at exceptionally low population densities [25].
Currently, the mainstream benthic research using the eDNA method involves collecting eDNA samples from the natural environment (usually water or sediment samples), concentrating the DNA from the environmental samples, removing PCR inhibitors (e.g., humic acid, etc.), and using primers (single- or dual-indexed) for PCR amplification, followed by high-throughput sequencing. Finally, publicly available databases such as NCBI and BOLD, or a more refined local comparison database constructed based on these resources, are used for comparison and a bioinformatic analysis [26]. At the same time, the emergence and development of new technologies are constantly pushing the development of eDNA research [27]. The use of quantitative PCR (qPCR) and digital PCR (dPCR) techniques has enabled a shift in eDNA metabarcoding technology from primarily qualitative to include potential quantitative analyses. The development of primers for specific taxa is expanding the range of taxa that can be covered by PCR amplification in multi-class surveys. It also enables a more specific amplification for targeted taxa. At the same time, sequencing errors in high-throughput sequencing are being gradually reduced, and databases are continuously improving. This results in more accurate data comparisons.
Although the application of eDNA barcode technology in biological monitoring research has increased exponentially since 2010, its application to macroinvertebrates is still limited. In addition, eDNA research also faces problems such as the coextraction of inhibitory substances, such as humic acid, proteins, and heavy metal salt ions, inaccurate primer construction, and incomplete databases, which require further research and development. A statistical analysis of a large number of publications in this field of study can help researchers understand the current hot spots and trends in laboratory research, and, simultaneously, further guide the scientific work to better serve practical applications.
This study synthesizes and analyzes the research findings on macroinvertebrate biomonitoring using environmental DNA (eDNA) metabarcoding technology. We employed a bibliometric approach to summarize the advancements, trends, and research priorities in this field over a 15-year period (2010–2024), based on publications indexed in the Web of Science Core Collection (WoSCC).

2. Materials and Methods

2.1. Data Sources and Search Strategies

Relevant data from Web of Science (core collection) were searched for the years 2010–2024. We also searched for data prior to 2010, but did not retrieve articles in the targeted research areas. The language was limited to English. Because keywords in benthos-related eDNA research literature are scattered, and many articles focus solely on individual species as their analytical object, using “Macroinvertebrates” as a keyword alone would not capture all relevant articles. Therefore, “eDNA” and “Environmental DNA” were selected as keywords for the search on WoSCC. This search yielded over 200,000 articles, from which spreadsheet-formatted data were obtained. Through an initial reading and organization of the retrieved articles, a controlled vocabulary was developed for matching terms in the titles, keywords, and abstracts of the papers. This vocabulary includes the primary terms used for benthic species, major benthic taxonomic groups, sampling methods, and the orders and families associated with benthic organisms. Due to space constraints, the full list of family names is not fully expanded here (Table 1). We retrieve article data using a Python v3.11 script based on the contents of the vocabulary. The Python code used for the retrieval work is provided in Supplementary Materials Code S1. Finally, after two independent pairs of researchers cross-verified the code-filtered results, the target articles were systematically consolidated.

2.2. Data Analysis

After confirming the target article, first, we export the Plain Text File from WoSCC and import this file into VOSviewer v1.6.20 for basic analysis [28]. This software can analyze key metrics for countries, journals, and authors, such as extracting rankings, publication counts, and citation counts, as well as keyword information.
When analyzing countries, journals, and authors, the h-index and CPP index are used for evaluation [29]. Current Paper Performance value (CPP) measures the average number of citations a paper receives in the two years following publication, indicating its immediate impact and acceptance within academia. And the h-index quantifies both productivity and citation impact of a researcher’s publications, defined as the maximum value h such that the given author/journal has published h papers that have each been cited at least h times. When conducting journal analysis, the journal’s IF (Impact Factor) index and JIF (Journal Impact Factor) quartile are also evaluated. Impact Factor (IF) and journal quartiles are derived from Web of Science’s 2023 Journal Citation Report. The IF is a key indicator of a journal’s scholarly impact, representing the average number of citations to articles published by the journal in the past two years [30].
When conducting cooperation network analysis between countries, both VOSviewer and SCImago Graphica v1.0.49 software are utilized [31]. Collaborative data between countries are first processed in VOSviewer, and the processed results are exported as a “.gml” file compatible with SCImago Graphica. This file is then imported into SCImago Graphica to generate the country collaboration network visualization. During the mapping process, special attention should be paid to country and region names, as some national designations exported from VOSviewer might not be recognized by SCImago Graphica and required manual adjustment.
Keyword network analysis is performed using VOSviewer, Pajek v5.19, and SCImago Graphica [32]. First, the data exported from Web of Science (WoSCC) is imported into VOSviewer for initial parsing, where a keyword list is generated and manually categorized based on thematic relevance. The categorized keyword list is then reimported into VOSviewer and saved in a Pajek-compatible file format (e.g., .net). Using Pajek, the network layout is refined by adjusting node positions and cluster separation according to predefined categories. The adjusted data is reimported into VOSviewer for final formatting and style optimization. Subsequently, the processed data is exported to SCImago Graphica to generate the keyword network visualization. Special attention is required to ensure compatibility between software tools, including verifying file formats (e.g., .gml, and .net), resolving unrecognized labels (e.g., manual correction of country/region names), and maintaining consistent formatting during transitions. Manual categorization and layout adjustments are critical to enhancing network clarity and thematic alignment, particularly for highlighting clusters and minimizing visual overlaps.
Keyword burst analysis is performed using CiteSpace v6.3.R1for analysis and visualization [33]. The Plain Text File exported from WoSCC is imported into the software, and its relevant modules are utilized to conduct the analysis and generate the keyword burst visualization map.
In research trend analysis, the initial step involves reading and organizing the content of articles to identify research directions and trend information. The Sankey diagram is primarily created using Python pyecharts package, which generates an HTML-formatted image [34]. This image is then manually adjusted to obtain the final visualization. The Python code used for the Sankey diagram is provided in Supplementary Materials Code S2.

3. Results

3.1. Publication Model

We searched the WoSCC for articles related to macroinvertebrate studies that employed eDNA methods. Following an automated screening and subsequent manual review, the filtering process yielded 322 articles for final inclusion. These comprised 273 original research articles, 15 review articles, 33 methodological articles, and 1 editorial material.
This study analyzed 322 publications encompassing contributions from 1573 authors across 67 countries and 722 organizations, including both freshwater and marine environments. These works were published in 107 journals, collectively citing 13,686 references from 4047 distinct source journals. From 2010 to 2024, the annual and cumulative publication output of benthic eDNA research shows a distinct temporal progression. After 2012, the research output in this field significantly accelerated, with a rapid increase in the number of annual publications. Notably, a marked acceleration in macroinvertebrate eDNA research output emerged post-2012, culminating in 2014 as a pivotal milestone when annual publications first reached double digits (12 articles). The complete trajectory of this growth pattern, along with detailed temporal dynamics, is systematically visualized in Figure 1. The annual output exceeded 100 publications in 2019, marking the transition to triple-digit productivity. This represents a considerable expansion of eDNA research on macroinvertebrates in recent years and suggests a promising trajectory for the future.

3.2. Cooperation of Countries

Contributions from different countries were assessed based on the affiliation of at least one of the authors of the published articles. This means that an article was considered a contribution from a country if at least one of its authors was affiliated with an institution in that country. The total number of articles, independently published articles, and coauthored articles were used as indicators to assess the research performance of each country. The results showed that the top 15 countries published at least 17 articles, ranked by the total number of articles (Table S1). When assessed by the total number of articles, the United States contributed the most publications (58 articles), followed by France (43 articles) and Canada (39 articles).
To visualize the global distribution of research related to macroinvertebrate surveys based on eDNA metabarcoding technology, we have mapped collaborative networks (Figure 2). The extensive international collaboration network highlights Europe as the main region of collaboration, with France emerging as the primary hub (36 collaborative articles involving 37 countries), followed by the United States (26 articles involving 39 countries) and Spain (26 articles involving 34 countries).

3.3. High-Impact Journals, Productive Authors, and Highly Cited Articles

The 322 publications were distributed across 107 journals: the top 15 journals with the highest number of articles published accounted for 52.94% of the total number of articles published, while the remaining 92 journals accounted for 47.06% of the total number of articles published. The top 15 institutions ranked by the number of publications have all published at least 6 articles.
Of the top 15 most productive journals in the direction of benthic-related research based on eDNA metabarcoding technology, the 3 journals with the top IF include SCIENCE OF THE TOTAL ENVIRONMENT (IF = 8.2), ECOLOGICAL INDICATORS (IF = 7.0), and MARINE POLLUTION BULLETIN (IF = 5.3). We analyzed the citation of the journals, CPP value, and h-index to reflect the quality of the publication of the journals. MOLECULAR ECOLOGY, PLOS ONE, and FRESHWATER SCIENCE ranked in the top three in terms of CPP, at 93.20, 74.25, and 52.11, respectively. This suggests that articles published in these journals have received much attention in the field of macroinvertebrate eDNA research. The journal PLOS ONE ranks high in terms of the number of articles published, number of citations, CPP value, and h-index, indicating that the journal has an important role in this research field (Table S2). Like the analysis method used for the journals, we also analyzed the information of the top 17 authors, which can be found in Table S3.
The citation frequency of articles can reflect the research hotspots and trends in a particular field. Among the 322 articles, the 15 most cited publications (each cited over 132 times) are listed in Table 2, with contributions primarily from the United States (3 articles), France (2 articles), Germany (2 articles), Switzerland (2 articles), Wales (2 articles), Australia (1 article), Canada (1 article), Denmark (1 article), and Spain (1 article). Notably, 6 of these highly cited articles resulted from international collaborations.
As mentioned earlier, the most cited paper is the 2012 article by Thomsen, Philip Francis in MOLECULAR ECOLOGY entitled “Monitoring endangered freshwater biodiversity using eDNA”. The authors obtained the DNA directly from small water samples from lakes, ponds, and streams to detect and quantify amphibians, fish, mammals, insects, and crustaceans. The authors also conducted mid-universe experiments to investigate the degradation rate of the DNA in the environment—it has been cited 783 times. The second most cited paper is a 2015 article in PLOS ONE by Elbrecht, Vasco titled “Can DNA-Based Ecosystem Assessments Quantify Species Abundance? Testing Primer Bias and Biomass Sequence Relationships with an Innovative Metabarcoding Protocol”: the authors investigated the means of detection by developing and testing a DNA metabarcoding protocol that utilizes standard mitochondrial cytochrome c oxidase subunit I (mtCOI) barcode fragments to detect freshwater macroinvertebrate taxa. DNA was extracted, amplified, and purified in a single-step PCR. This article has been cited 461 times since publication. The rest of the literature also explores the application of environmental DNA (eDNA) metabarcoding technology in biodiversity monitoring and its potential. It is also discussed separately in terms of species dispersal distance, a comparison with traditional methods and competitive advantages, specific primer design, DNA extraction from ethanol preservations of macroinvertebrate, multiplex PCR, and the use of machine learning to construct predictive models for macroinvertebrate monitoring [35,36,37,38,39].

3.4. Keyword Network Analysis

Keywords condense the core and essence of a paper, and research hotspots in a scientific field can be identified by a keyword cooccurrence analysis [40]. From 322 publications, 55 keywords mentioned more than 10 times were selected to construct a co-occurrence network, where larger circular nodes indicate a higher keyword frequency and stronger representation of research hotspots. This network highlights the core themes in macroinvertebrate surveys using eDNA metabarcoding technology.
The central terms of the keyword network are eDNA, Environmental DNA, Biodiversity, Diversity, and Metabarcoding. The keywords were classified into 4 categories based on the linkage of the keywords, as well as the keyword type: the conceptual category (Biodiversity, Environmental DNA, eDNA, Diversity, and Conservation), technical vocabulary categories (Metabarcoding, Barcode, Temperature, Primers, and Quality), classification types (Macroinvertebrates, Invertebrates, Fish, Benthic Invertebrates, and Bivalvia), and study site categories (Marine, Freshwater, Streams, and Sediments). The results of the clustering keywords are shown in Figure 3.
From the data, the top 4 keywords (Biodiversity, eDNA, Environmental DNA, and Diversity) with a frequency of more than 300 occurrences all first appeared in 2012 and 2013, while the remaining keywords demonstrate a more dispersed temporal distribution, with initial appearances spanning from 2012 to 2021. The results of a timeline analysis of the keywords are shown in Figure 4.
An analysis of the keyword temporal distribution and frequency trends reveals distinct evolutionary patterns in the research focus within this field. Core technical terms such as “DNA” and “Sample” demonstrate a persistent presence across all study periods, while methodological keywords like “DNA barcoding”, “Amplification”, “Species Detection”, and “Ecology” emerged early and maintained a high frequency through both the initial and mid-phase research cycles. Notably, the most recent five-year dataset exhibits significant thematic divergence, with prominent terms shifting towards environmental dimensions (“Climate Change” and “Ecological Status”) and taxonomic specificity (“Decapoda” and “Dynamics”), alongside emerging methodological frameworks.

3.5. Analysis of Research Directions

A Sankey Diagram is a visual representation that illustrates the magnitude of data flows between stages or categories using bands of varying widths, thereby visualizing transitions and flows [41]. From macroinvertebrate studies employing eDNA methodologies, 279 original research papers were selected after excluding reviews, conference proceedings, and non-research articles. These papers were analyzed to identify the trends in research objectives, target taxa, sample species, and study environments, with their temporal evolution and interrelationships comprehensively mapped in Figure 5.
Regarding the distribution of research purposes over time, the majority of studies focused on diversity monitoring, followed by invasive species research and methodological studies. Since 2021, the annual publication count for invasive species detection and methodological development studies has shown a consistent upward trend. In contrast, studies on rare species conservation and species-specific detection remained comparatively limited. Regarding the association between the research objectives and taxonomic focus on target taxa, diversity monitoring has mainly focused on all macroinvertebrates and metazoan, with fewer relevant studies targeting specific taxa. In the study on invasive species detection, the target groups were mainly molluscs and crustaceans. Studies on the conservation of rare species mainly focused on molluscs, especially on the phylum Lamellibranchia and Bivalvia. In addition, by reading articles that study this group (Lamellibranchia and Bivalvia), it was found that many of their species are very sensitive to environmental changes and have a weaker ability to cope with environmental changes and species invasion. Therefore, the monitoring and protection of these species are an important part of the benthic research using eDNA metabarcoding technology. Methodological and species-specific studies are mainly focused on molluscs and aquatic insects. The methodological studies on mollusks, which are species of economic value, are aimed at the development of more suitable primers and multi-marker assays, while the related studies on aquatic insects are mainly focused on Diptera. In terms of the research trends of the sample types, water samples accounted for 70.2% of all studies, which mainly consisted of studies focusing on metazoan, macroinvertebrates, molluscs, crustaceans, and aquatic insects. In contrast, the sediment research was mainly concerned with macroinvertebrates and metazoans. The passive collection substrate mainly consists of natural or artificial porous materials, and the studies of metazoan and macroinvertebrates are mainly carried out. A total of 146 studies were conducted in the ocean (including deep sea, coastal, and river estuary), which accounted for 45.2% of all studies. In the study of sediment samples, 41.7% of the study sites were in the intertidal zone. Most studies on eDNA using passive substrates using metal nodules as research samples are conducted in marine environments. Some of these passive substrates are artificial, such as settling plates, while others are natural, such as sponges or naturally formed metal nodules. There are a total of seven studies conducted under laboratory conditions using aquariums. The remaining studies are all carried out in freshwater environments, including rivers, lakes, reservoirs, streams, wetlands, caves, groundwater, and other special environments (Figure 5).

3.6. The Main Research Directions of Monitoring Articles

3.6.1. Diversity Studies

The first biodiversity study of macroinvertebrates employing eDNA methodology was conducted in 2012. Hajibabaei et al. used high-throughput sequencing technology to sequence the DNA obtained from the ethanol preservation solution of macroinvertebrates and employed a multiplex PCR with three primers to detect and analyze the diversity of macroinvertebrates [38]. After its pioneering implementation in biodiversity assessment, eDNA approaches demonstrated growing potential through accelerated methodological refinements and expanding utilization in varied ecosystems and taxonomic contexts. For example, Guardiol et al. demonstrated the method’s efficacy in deep-sea environments through a sediment analysis at a 2250 m depth, where 18S rRNA gene sequencing revealed 1629 operational taxonomic units (OTUs) [42]. eDNA metabarcoding technology has become more mature, and the diversity studies have gradually evolved from a single study of community diversity to the use of eDNA to solve practical problems. Lisa et al. used the eDNA methodology to investigate the diversity of eukaryotic taxa in shallow groundwater to determine the effects of magnesium ions and sulphate, which are abundantly contained in the groundwater at the site, on the community composition of the biological communities in the groundwater at the site [43]. Conventional biodiversity surveys face limitations including taxonomic resolution constraints, organismal stress induction, and habitat disturbance. The eDNA methodology provides a new approach to assessing biodiversity, requiring only a small number of water samples to reliably detect target organisms in the aquatic environment.

3.6.2. Invasive Species Detection

Biological invasions are a major threat to global biodiversity [44]. Since eDNA metabarcoding technology was first applied to invasive species detection in 2008 [12], the use of eDNA metabarcoding technology for the early detection and early warning of invasive benthos has gradually received more and more attention. Representative of early studies on invasive species detection in macroinvertebrates using eDNA metabarcoding technology are the articles published by Goldberg et al. in 2013, including “Environmental DNA as a new method for early detection of New Zealand mudsnails (Potamopyrgus antipodarum)”. The authors investigated the detection method for the invasive species Potamopyrgus antipodarum using qPCR in a laboratory setting and validated the specific detection method in an open aquatic environment. The results showed that the substance could be detected in low-density water samples and remained detectable 21 days after the removal of Potamopyrgus antipodarum [45]. eDNA metabarcoding technology has now reached the application phase in the research of detecting invasive macroinvertebrate organisms. Numerous studies have conducted the eDNA detection of invasive macroinvertebrates on a larger scale, such as within river basin strata. For example, Laura et al. conducted invasive species detection in six rivers and four lakes in the Rhine River Basin in 2021, and detected a total of eight invasive species, including six macroinvertebrates, including two invasive arthropods: the velvet shrimp Dikerogammarus villosus, the shrimp Limnomysis benedeni, and four invasive molluscs: zebra mussel (Dreissena polymorpha), Asian clam (Corbicula fluminea), New Zealand mudsnail (Potamopyrgus antipodarum), and spiny snail (Physella acuta) [46]. One of the main reasons for the spread of invasive species is anthropogenic activities such as marine traffic, and the management of vessels engaged in marine transport is one of the most important ways to deal with invasions of exogenous organisms. Maggio et al. conducted eDNA testing of seaward-carrying water and waters near harbors and detected a variety of invasive macroinvertebrates [47].

3.6.3. Protection of Rare Species

In surveys of rare macroinvertebrates, because rare species often have low species densities, large sampling areas are needed to obtain sufficient data to assess the species, and the use of a bottom trawl or other collection equipment to collect macroinvertebrates for species detection can be costly and damaging to the target species, as well as to the species’ habitat to a certain extent. In contrast, eDNA metabarcoding technology allows species surveys to be sampled non-destructively, with improved detection costs and efficiency [48]. Using a non-invasive anisotropic qPCR analysis of water samples, Lor et al. achieved the successful detection of the endangered freshwater mussel Margaritifera monodonta across three Midwestern U.S. River systems, demonstrating the efficacy of eDNA-based monitoring without physical specimen collection [49].

3.7. Research and Technological Advances

Since the first eDNA study of macroinvertebrates was published in 2010, the technology has continued to advance and various new techniques have made eDNA studies more sensitive and efficient. In this study, after reviewing the literature, we identified 58 kits from 24 companies that have been used in macroinvertebrate eDNA studies to extract DNA from environmental samples. Among them are kits specifically designed for water and sediment samples, as well as kits for the purification of DNA and RNA samples specifically developed to ensure PCR reactions. The top 10 DNA extraction kits that appeared with a high frequency in investigative articles on macroinvertebrate research involving eDNA metabarcoding technology are shown (Table 3). Qiagen (Hilden, Germany) DNeasy Blood & Tissue Kit was the most prevalent, being employed in 94 out of 279 analyzed studies. The Qiagen company accounted for 6 of the top 10 most frequently used kits, and also included the DNeasy PowerWater Kit, developed specifically for water samples, and the DNeasy PowerSoil Kit and DNeasy PowerMax Soil Kit, developed specifically for soil and sediment samples, as well as the QIAamp® DNA Mini Kit, which uses QIAamp technology. MP Biomedicals (Santa Clarita, United States) FastDNA™ Spin Kit for Soil ranked fifth, being utilized in 7 studies. The E.Z.N.A.® Mollusc DNA Kit developed by Omega Bio-tek (Norcross, GA, USA) was ranked tenth as a specially developed kit for mollusc and arthropod taxa. Four papers used this kit. Various kits effectively remove salt, metal ions, humic substances, and other inhibitory compounds present in aquatic and sedimentary matrices to facilitate downstream analyses.
There have also been many advances in DNA amplification. Advancements in qPCR technology have significantly enhanced the amplification accuracy and detection sensitivity, and DNA amplification technologies have progressed from conventional PCR to including real-time PCR and microfluidic digital PCR. Many studies have used qPCR and dPCR for the specific amplification of a species or a particular taxon, and a positive correlation between the detected eDNA concentration and population size has been initially established, including field sampling experiments and controlled experiments in laboratory aquarium environments [50,51,52,53]. eDNA metabarcoding technology can be accurately implemented in the detection of eDNA, often relying on the design and selection of primers that are adapted to the target species. The accuracy of eDNA metabarcoding technology depends on the design and selection of primers that are adapted to the target species [54]. Macroinvertebrates encompass phylogenetically diverse taxa with substantial genetic divergence, which places high demands on the primer design. Often, multiple sets of universal primers, or the design of primers specific to the target taxa to be detected, are required for detection. Nowadays, researchers have developed a variety of universal primers for macroinvertebrates as well as primers specific to a taxon or a single species [55,56]. Most studies employ mtCOI gene fragments, while 16S rRNA and 18S rRNA encoding genes are also frequently utilized. A smaller number of studies have adopted the Cytb gene for amplification purposes [57]. To mitigate primer bias and improve taxonomic coverage, multilocus approaches employing two or more genetic markers are increasingly adopted to obtain a higher detection sensitivity [58,59]. eDNA metabarcoding sequencing generates a large amount of sequencing data, i.e., many short fragments of DNA sequences. The current standard processing procedure is to cluster the data into OTUs after processing and then obtain species information by a sequence comparison with existing genetic databases [60]. Incomplete reference databases lead to numerous unassigned sequences, significantly reducing the informational value of sequencing data in environmental monitoring assessments. To solve this problem, researchers have challenged the problem in terms of improving databases and enriching algorithms, respectively. With the continuous development of computer technology, data processing for high-throughput sequencing and methods for analyzing the relationship between eDNA results and the environment have been updated. Supervised machine learning has been used in the construction of predictive models for macroinvertebrate monitoring [39], and the emergence of new methods has increased the stability of data for biodiversity assessment and monitoring. Deep-learning algorithms based on neural network models have once again optimized the analysis process [61], making eDNA data parsing more efficient and precise, and more able to accurately represent the data’s ability to reflect the current state of the environment, improving biodiversity monitoring.

4. Discussion

In recent years, the monitoring research of macroinvertebrates based on environmental DNA (eDNA) metabarcoding technology has undergone a rapid transformation from technical verification to large-scale application. This study found that, since the first research was published in 2010, the annual publication volume in this field has shown rapid growth, breaking through 100 articles in 2019, marking the synergistic drive of technological maturity and policy demand. Countries such as the United States and France, as core contributors, not only led the high-impact research but also promoted the technological standardization and regional application through intensive international cooperation networks. Technical tools represented by QIAgen’s DNeasy-series kits have become the mainstream choice, with their customized DNA extraction solutions for aquatic environments and sediments significantly reducing the risk of PCR inhibition. The combined use of multiple gene markers such as COI, 18S rRNA, and 16S rRNA has improved the coverage of taxa, especially reaching a new height in the detection sensitivity of key macroinvertebrates such as mollusks and crustaceans. However, technical bottlenecks still exist: although dPCR and machine-learning algorithms have shown potential in quantitative analysis and data interpretation, the long-distance migration of eDNA leading to false positives and false negatives in complex matrixes remain major obstacles in practical applications. In addition, the fragmentation of reference databases (such as the absence of region-specific species sequences) has limited the efficiency of metabarcoding annotation, and the “dark diversity” issue in some studies (where the proportion of unannotated OTUs reaches 35–42%) highlights the urgent need for global data sharing and standardized annotation protocols.
The evolution of research focal points further reveals the multidimensional value of eDNA metabarcoding technology in ecological management. A keyword network analysis shows that early studies focused on basic concepts such as “biodiversity” and “metabarcoding,” while the surge in technical terms like “qPCR” and “high-throughput sequencing” in the past five years reflects a shift from qualitative screening to precise quantification and multi-omics integration. For instance, invasive species detection has evolved from the laboratory validation of single species to the simultaneous monitoring of multiple taxa at the watershed scale, with an efficiency that is three to five times higher than traditional trawl surveys. Rare species conservation benefits from the ethical advantages of non-invasive sampling, providing a new paradigm for the protection of vulnerable ecosystems. It is noteworthy that the disparity in the proportion of monitoring between marine and freshwater environments exposes the regional imbalance in technology application. The lack of unified standards for the processing of deep-sea and sediment samples may affect the comparability of cross-habitat data. Furthermore, although supervised machine-learning and deep-learning algorithms perform excellently in community prediction, their generalization ability in complex benthic food webs still needs to be validated, especially as a mature framework for multi-trophic level interactions and functional trait association analysis has not yet been established.
In the future, eDNA metabarcoding technology holds both potential and challenges in macroinvertebrates monitoring. With the enhancement in the ability to extract high-purity DNA from complex matrices, and the advancement in amplification and sequencing technologies, data acquisition and credibility will be further improved. On the other hand, interdisciplinary collaborations (such as the combination of eDNA and stable isotopes) are expected to reveal the ecological functional dimensions of macroinvertebrate communities, going beyond traditional species list records. However, the limitations of the current research should not be overlooked: the Euro-American-centric bias of databases (such as the coverage of Asian-specific species being less than 30% in NCBI) may lead to regional monitoring biases, and the differences between laboratory-controlled experiments (such as aquarium simulations) and actual field conditions (such as the impact of water flow disturbance on eDNA distribution) still need to be systematically quantified. In the future, it is necessary that we promote open-source data sharing through global observation networks (such as the Global eDNA Observatory) and establish standardized operational procedures across habitats, in order to fully leverage the core role of eDNA metabarcoding technology in biodiversity conservation, invasive species control, and ecosystem service assessment.

5. Conclusions

eDNA metabarcoding has become a pivotal tool in macroinvertebrate biomonitoring. It is an indispensable survey tool in the fields of diversity inventories, invasive species detection, and rare and endangered species conservation. The number of research publications has begun to increase rapidly since 2018, and the exponential growth in research publications provides a promising trajectory for the future of the field. The core publishing for the macroinvertebrate research based on eDNA metabarcoding technology include PLOS ONE, SCIENCE OF THE TOTAL ENVIRONMENT, and FRONTIERS IN MARINE SCIENCE. PLOS ONE is ranked relatively high in terms of the number of articles published, number of citations, CPP value, and H-index, indicating that the journal has an important role in this research field. While the United States leads in terms of publication output, European nations exhibit stronger intra-regional collaborative networks, with the countries demonstrating closer research partnerships. The keyword co-occurrence analysis reveals methodological evolution—from general eDNA surveys to purpose-specific monitoring employing qPCR, high-throughput sequencing, and advanced bioinformatics, which is more sensitive and efficient. Studies using eDNA metabarcoding technology to investigate macroinvertebrates are not only limited to this taxonomic group but also frequently encompass broader metazoan communities. At the same time, studies on economic benthic species and studies on invasive species make up a large part of the total number of articles. As the demand for eDNA monitoring continues to grow, new monitoring technologies are being developed. Qiagen’s DNA extraction kits are widely adopted by researchers for their reliability in eDNA studies. DNA amplification instruments have been upgraded, and the latest dPCR technology is essential for the quantitative analysis of eDNA studies and for suppressing false negatives and false positives. Databases continue to improve, and methods of data analysis are being introduced. Overall, our study systematically visualizes the literature on benthos-related studies based on eDNA metabarcoding technology, and highlights the main key points and research directions of the discipline, as well as the technological advances, and provides directions and references for eDNA metabarcoding technology and its application in macroinvertebrate research.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/d17060402/s1. Table S1: Top 15 countries with at least 17 articles published; Table S2: Top 15 institutions with at least 6 articles published; Table S3: Top 17 authors with at least 5 publications; Code S1: File retrieval.py; Code S2: Sankey diagram plotting.py.

Author Contributions

Conceptualization, X.G., W.L. and C.Y.; methodology, X.G. and J.Z.; software, X.G. and J.Z.; validation, X.G., J.Z. and Z.S.; formal analysis, J.Z., Z.S. and D.Y.; investigation, X.G., D.Y. and H.Z.; data curation, L.C., J.N. and H.Z.; writing—original draft preparation, X.G.; writing—review and editing, X.G., W.L. and C.Y.; visualization, J.N. and L.C.; supervision, X.G., W.L. and C.Y.; funding acquisition, X.G., W.L. and C.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (32170473, 32370489, 32400357).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

All generated data are presented in the article and Supplementary Materials.

Acknowledgments

We sincerely thank the editors and reviewers for their valuable comments on this study, and we also thank Yiwen Wang from Tianjin University for his help.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Carter, J.L.; Resh, V.H.; Hannaford, M.J. Macroinvertebrates as Biotic Indicators of Environmental Quality. In Methods in Stream Ecology, 3rd ed.; Lamberti, G.A., Hauer, F.R., Eds.; Academic Press: San Diego, CA, USA, 2017; Volume 3, pp. 293–318. ISBN 978-0-12-813047-6. [Google Scholar]
  2. Birk, S.; Bonne, W.; Borja, A.; Brucet, S.; Courrat, A.; Poikane, S.; Solimini, A.; van de Bund, W.; Zampoukas, N.; Hering, D. Three Hundred Ways to Assess Europe’s Surface Waters: An Almost Complete Overview of Biological Methods to Implement the Water Framework Directive. Ecol. Indic. 2012, 18, 31–41. [Google Scholar] [CrossRef]
  3. Dudgeon, D.; Arthington, A.H.; Gessner, M.O.; Kawabata, Z.-I.; Knowler, D.J.; Lévêque, C.; Naiman, R.J.; Prieur-Richard, A.-H.; Soto, D.; Stiassny, M.L.J.; et al. Freshwater Biodiversity: Importance, Threats, Status and Conservation Challenges. Biol. Rev. 2006, 81, 163–182. [Google Scholar] [CrossRef] [PubMed]
  4. Cooper, M.J.; Uzarski, D.G.; Burton, T.M. Macroinvertebrate Community Composition in Relation to Anthropogenic Disturbance, Vegetation, and Organic Sediment Depth in Four Lake Michigan Drowned River-Mouth Wetlands. Wetlands 2007, 27, 894–903. [Google Scholar] [CrossRef]
  5. Chaves-Ulloa, R.; Umaña-Villalobos, G.; Springer, M. Downstream Effects of Hydropower Production on Aquatic macroinvertebrate Assemblages in Two Rivers in Costa Rica. Rev. Biol. Trop. 2014, 62 (Suppl. S2), 179–201. [Google Scholar] [CrossRef]
  6. Meyer, C.K.; Whiles, M.R. macroinvertebrate Communities in Restored and Natural Platte River Slough Wetlands. J. N. Am. Benthol. Soc. 2008, 27, 626–639. [Google Scholar] [CrossRef]
  7. Pfrender, M.E.; Hawkins, C.P.; Bagley, M.; Courtney, G.W.; Creutzburg, B.R.; Epler, J.H.; Fend, S.; Schindel, D.; Ferrington, L.C.; Hartzell, P.L.; et al. Assessing macroinvertebrate Biodiversity in Freshwater Ecosystems: Advances and Challenges in DNA-Based Approaches. Q. Rev. Biol. 2010, 85, 319–340. [Google Scholar] [CrossRef]
  8. Beng, K.C.; Corlett, R.T. Applications of Environmental DNA (eDNA) in Ecology and Conservation: Opportunities, Challenges and Prospects. Biodivers. Conserv. 2020, 29, 2089–2121. [Google Scholar] [CrossRef]
  9. Barnes, M.A.; Chadderton, W.L.; Jerde, C.L.; Mahon, A.R.; Turner, C.R.; Lodge, D.M. Environmental Conditions Influence eDNA Particle Size Distribution in Aquatic Systems. Environ. DNA 2021, 3, 643–653. [Google Scholar] [CrossRef]
  10. Carvalho, D.C.; Leal, C.G. Steps Forward in Biomonitoring 2.0: eDNA Metabarcoding and Community-Level Modelling Allow the Assessment of Complex Drivers of Neotropical Fish Diversity. Glob. Change Biol. 2023, 29, 1688–1690. [Google Scholar] [CrossRef]
  11. Tsuji, S.; Takahara, T.; Doi, H.; Shibata, N.; Yamanaka, H. The Detection of Aquatic Macroorganisms Using Environmental DNA Analysis—A Review of Methods for Collection, Extraction, and Detection. Environ. DNA 2019, 1, 99–108. [Google Scholar] [CrossRef]
  12. Ficetola, G.F.; Miaud, C.; Pompanon, F.; Taberlet, P. Species Detection Using Environmental DNA from Water Samples. Biol. Lett. 2008, 4, 423–425. [Google Scholar] [CrossRef]
  13. Lee, A.H.; Lee, J.; Noh, J.; Lee, C.; Hong, S.; Kwon, B.-O.; Kim, J.-J.; Khim, J.S. Characteristics of Long-Term Changes in Microbial Communities from Contaminated Sediments along the West Coast of South Korea: Ecological Assessment with eDNA and Physicochemical Analyses. Mar. Pollut. Bull. 2020, 160, 111592. [Google Scholar] [CrossRef]
  14. Aprilia, M.; Effendi, H.; Hariyadi, S.; Permatasari, P.A. Aquatic eDNA Metabarcoding Reveals Biodiversity and Plankton Composition in River Ecosystems. Pol. J. Environ. Stud. 2023, 32, 3491–3500. [Google Scholar] [CrossRef] [PubMed]
  15. Brantschen, J.; Fopp, F.; Adde, A.; Keck, F.; Guisan, A.; Pellissier, L.; Altermatt, F. Habitat Suitability Models Reveal the Spatial Signal of Environmental DNA in Riverine Networks. Ecography 2024, 2024, e07267. [Google Scholar] [CrossRef]
  16. Espinosa Prieto, A.; Hardion, L.; Debortoli, N.; Bournonville, T.; Mathot, T.; Marescaux, J.; Chanez, E.; Staentzel, C.; Beisel, J.-N. A Comparative Analysis of eDNA Metabarcoding and Field Surveys: Exploring Freshwater Plant Communities in Rivers. Sci. Total Environ. 2024, 954, 176200. [Google Scholar] [CrossRef]
  17. Vautier, M.; Chardon, C.; Goulon, C.; Guillard, J.; Domaizon, I. A Quantitative eDNA-Based Approach to Monitor Fish Spawning in Lakes: Application to European Perch and Whitefish. Fish. Res. 2023, 264, 106708. [Google Scholar] [CrossRef]
  18. Métris, K.L.; Métris, J. Aircraft Surveys for Air eDNA: Probing Biodiversity in the Sky. PeerJ 2023, 11, e15171. [Google Scholar] [CrossRef]
  19. Ariza, M.; Engelstad, M.; Lieungh, E.; Laux, M.; Ready, J.; Mauvisseau, Q.; Halvorsen, R.; de Boer, H.J. Evaluating the Feasibility of Using Plant-Specific Metabarcoding to Assess Forest Types from Soil eDNA. Appl. Veg. Sci. 2024, 27, e12806. [Google Scholar] [CrossRef]
  20. Zhou, C.; Zhu, C.; Cheng, Y.; Lei, Y.; Nan, Y.; Ouyang, S.; Wu, X. Exploring Freshwater Snail Diversity and Community Structure in China’s Largest Lake Using eDNA Technology. Ecol. Indic. 2024, 158, 111577. [Google Scholar] [CrossRef]
  21. Li, C.; Long, H.; Yang, S.; Zhang, Y.; Tang, F.; Jin, W.; Wang, G.; Chang, W.; Pi, Y.; Gao, L.; et al. eDNA Assessment of Pelagic Fish Diversity, Distribution, and Abundance in the Central Pacific Ocean. Reg. Stud. Mar. Sci. 2022, 56, 102661. [Google Scholar] [CrossRef]
  22. Minamoto, T.; Miya, M.; Sado, T.; Seino, S.; Doi, H.; Kondoh, M.; Nakamura, K.; Takahara, T.; Yamamoto, S.; Yamanaka, H.; et al. An Illustrated Manual for Environmental DNA Research: Water Sampling Guidelines and Experimental Protocols. Environ. DNA 2021, 3, 8–13. [Google Scholar] [CrossRef]
  23. Kelly, R.P.; Lodge, D.M.; Lee, K.N.; Theroux, S.; Sepulveda, A.J.; Scholin, C.A.; Craine, J.M.; Andruszkiewicz Allan, E.; Nichols, K.M.; Parsons, K.M.; et al. Toward a National eDNA Strategy for the United States. Environ. DNA 2024, 6, e432. [Google Scholar] [CrossRef]
  24. Thomsen, P.F.; Willerslev, E. Environmental DNA—An Emerging Tool in Conservation for Monitoring Past and Present Biodiversity. Biol. Conserv. 2015, 183, 4–18. [Google Scholar] [CrossRef]
  25. Furlan, E.M.; Gleeson, D.; Wisniewski, C.; Yick, J.; Duncan, R.P. eDNA Surveys to Detect Species at Very Low Densities: A Case Study of European Carp Eradication in Tasmania, Australia. J. Appl. Ecol. 2019, 56, 2505–2517. [Google Scholar] [CrossRef]
  26. Sun, X.; Guo, N.; Gao, J.; Xiao, N. Using eDNA to Survey Amphibians: Methods, Applications, and Challenges. Biotechnol. Bioeng. 2024, 121, 456–471. [Google Scholar] [CrossRef]
  27. Garlapati, D.; Charankumar, B.; Ramu, K.; Madeswaran, P.; Murthy, M.V.R. A Review on the Applications and Recent Advances in Environmental DNA (eDNA) Metagenomics. Rev. Environ. Sci. Bio-Technol. 2019, 18, 389–411. [Google Scholar] [CrossRef]
  28. VOSviewer. Available online: https://www.vosviewer.com/ (accessed on 6 November 2024).
  29. Hirsch, J.E. An index to quantify an individual’s scientific research output. Proc. Natl. Acad. Sci. USA 2005, 102, 16569–16572. [Google Scholar] [CrossRef] [PubMed]
  30. Fernandez-Llimos, F. Differences and Similarities between Journal Impact Factor and CiteScore. Pharm. Pract. 2018, 16, 1282. [Google Scholar] [CrossRef] [PubMed]
  31. SCImago Graphica. Available online: https://www.graphica.app/ (accessed on 6 November 2024).
  32. Pajek. Available online: http://mrvar.fdv.uni-lj.si/pajek/ (accessed on 6 November 2024).
  33. CiteSpace. Available online: https://citespace.podia.com/ (accessed on 6 November 2024).
  34. Sankey. Available online: https://gallery.pyecharts.org/#/Sankey/ (accessed on 7 February 2025).
  35. Deiner, K.; Altermatt, F. Transport Distance of Invertebrate Environmental DNA in a Natural River. PLoS ONE 2014, 9, e88786. [Google Scholar] [CrossRef]
  36. Hering, D.; Borja, A.; Jones, J.I.; Pont, D.; Boets, P.; Bouchez, A.; Bruce, K.; Drakare, S.; Hanfling, B.; Kahlert, M.; et al. Implementation Options for DNA-Based Identification into Ecological Status Assessment under the European Water Framework Directive. Water Res. 2018, 138, 192–205. [Google Scholar] [CrossRef]
  37. Treguier, A.; Paillisson, J.-M.; Dejean, T.; Valentini, A.; Schlaepfer, M.A.; Roussel, J.-M. Environmental DNA Surveillance for Invertebrate Species: Advantages and Technical Limitations to Detect Invasive Crayfish Procambarus Clarkii in Freshwater Ponds. J. Appl. Ecol. 2014, 51, 871–879. [Google Scholar] [CrossRef]
  38. Hajibabaei, M.; Spall, J.L.; Shokralla, S.; van Konynenburg, S. Assessing Biodiversity of a Freshwater benthic macroinvertebrate Community through Non-Destructive Environmental Barcoding of DNA from Preservative Ethanol. BMC Ecol. 2012, 12, 28. [Google Scholar] [CrossRef] [PubMed]
  39. Cordier, T.; Esling, P.; Lejzerowicz, F.; Visco, J.; Ouadahi, A.; Martins, C.; Cedhagen, T.; Pawlowski, J. Predicting the Ecological Quality Status of Marine Environments from eDNA Metabarcoding Data Using Supervised Machine Learning. Environ. Sci. Technol. 2017, 51, 9118–9126. [Google Scholar] [CrossRef]
  40. Moshobane, M.C.; Khoza, T.T.; Niassy, S. The Period of Insect Research in the Tropics: A Bibliometric Analysis. Int. J. Trop. Insect Sci. 2022, 42, 989–998. [Google Scholar] [CrossRef]
  41. Li, M.-J.; Chien, T.-W.; Liao, K.-W.; Lai, F.-J. Using the Sankey Diagram to Visualize Article Features on the Topics of Whole-Exome Sequencing (WES) and Whole-Genome Sequencing (WGS) since 2012: Bibliometric Analysis. Medicine 2022, 101, e30682. [Google Scholar] [CrossRef]
  42. Guardiola, M.; Uriz, M.J.; Taberlet, P.; Coissac, E.; Wangensteen, O.S.; Turon, X. Correction: Deep-Sea, Deep-Sequencing: Metabarcoding Extracellular DNA from Sediments of Marine Canyons. PLoS ONE 2016, 11, e0153836. [Google Scholar] [CrossRef]
  43. Chandler, L.; Harford, A.J.; Hose, G.C.; Humphrey, C.L.; Chariton, A.; Greenfield, P.; Davis, J. Saline Mine Water Influences Eukaryote Life in Shallow Groundwater of a Tropical Sandy Stream. Sci. Total Environ. 2024, 946, 174101. [Google Scholar] [CrossRef]
  44. Chown, S.L.; Hodgins, K.A.; Griffin, P.C.; Oakeshott, J.G.; Byrne, M.; Hoffmann, A.A. Biological Invasions, Climate Change and Genomics. Evol. Appl. 2015, 8, 23–46. [Google Scholar] [CrossRef] [PubMed]
  45. Goldberg, C.S.; Sepulveda, A.; Ray, A.; Baumgardt, J.; Waits, L.P. Environmental DNA as a New Method for Early Detection of New Zealand Mudsnails (Potamopyrgus Antipodarum). Freshw. Sci. 2013, 32, 792–800. [Google Scholar] [CrossRef]
  46. Clusa, L.; Garcia-Vazquez, E.; Fernandez, S.; Meyer, A.; Machado-Schiaffino, G. Nuisance Species in Lake Constance Revealed through eDNA. Biol. Invasions 2021, 23, 1619–1636. [Google Scholar] [CrossRef]
  47. Maggio, T.; Cattapan, F.; Falautano, M.; Julian, D.; Malinverni, R.; Poloni, E.; Sanseverino, W.; Todesco, S.; Castriota, L. eDNA Metabarcoding Analysis as Tool to Assess the Presence of Non-Indigenous Species (NIS): A Case Study in the Bilge Water. Diversity 2023, 15, 1117. [Google Scholar] [CrossRef]
  48. Jerde, C.L.; Mahon, A.R.; Chadderton, W.L.; Lodge, D.M. “Sight-Unseen” Detection of Rare Aquatic Species Using Environmental DNA. Conserv. Lett. 2011, 4, 150–157. [Google Scholar] [CrossRef]
  49. Lor, Y.; Schreier, T.M.; Waller, D.L.; Merkes, C.M. Using Environmental DNA (eDNA) to Detect the Endangered Spectaclecase Mussel (Margaritifera Monodonta). Freshw. Sci. 2020, 39, 837–847. [Google Scholar] [CrossRef]
  50. Uthicke, S.; Doyle, J.R.; Cabrera, M.G.; Patel, F.; Mclatchie, M.J.; Doll, P.C.; Chandler, J.F.; Pratchett, M.S. eDNA Monitoring Detects New Outbreak Wave of Corallivorous Seastar (Acanthaster Cf. Solaris) at Lizard Island, Great Barrier Reef. Coral Reefs 2024, 43, 857–866. [Google Scholar] [CrossRef]
  51. Chucholl, F.; Fiolka, F.; Segelbacher, G.; Epp, L.S. eDNA Detection of Native and Invasive Crayfish Species Allows for Year-Round Monitoring and Large-Scale Screening of Lotic Systems. Front. Environ. Sci. 2021, 9, 639380. [Google Scholar] [CrossRef]
  52. Yip, Z.T.; Lim, C.S.; Tay, Y.C.; Tan, Y.; Beng, S.; Tun, K.; Teo, S.; Huang, D. Environmental DNA Detection of the Invasive Mussel Mytella Strigata as a Surveillance Tool. Manag. Biol. Invasion 2021, 12, 578–598. [Google Scholar] [CrossRef]
  53. Yang, C.; Du, Y.; Zeng, X.; Ni, G. Development and Testing of Species-Specific Primers for Detecting the Presence of the Northern Pacific Sea Star (Asterias Amurensis) from Environmental DNA. Mar. Biotechnol. 2024, 26, 215–222. [Google Scholar] [CrossRef] [PubMed]
  54. Coissac, E.; Riaz, T.; Puillandre, N. Bioinformatic Challenges for DNA Metabarcoding of Plants and Animals. Mol. Ecol. 2012, 21, 1834–1847. [Google Scholar] [CrossRef] [PubMed]
  55. de Jonge, D.S.W.; Merten, V.; Bayer, T.; Puebla, O.; Reusch, T.B.H.; Hoving, H.-J.T. A Novel Metabarcoding Primer Pair for Environmental DNA Analysis of Cephalopoda (Mollusca) Targeting the Nuclear 18S rRNA Region. R. Soc. Open Sci. 2021, 8, 201388. [Google Scholar] [CrossRef]
  56. Clusa, L.; Ardura, A.; Gower, F.; Miralles, L.; Tsartsianidou, V.; Zaiko, A.; Garcia-Vazquez, E. An Easy Phylogenetically Informative Method to Trace the Globally Invasive Potamopyrgus Mud Snail from River’s eDNA. PLoS ONE 2016, 11, e0162899. [Google Scholar] [CrossRef]
  57. Takahara, T.; Ikebuchi, T.; Doi, H.; Minamoto, T. Using Environmental DNA to Estimate the Seasonal Distribution and Habitat Preferences of a Japanese Basket Clam in Lake Shinji, Japan. Estuar. Coast. Shelf Sci. 2019, 221, 15–20. [Google Scholar] [CrossRef]
  58. West, K.M.; Stat, M.; Harvey, E.S.; Skepper, C.L.; DiBattista, J.D.; Richards, Z.T.; Travers, M.J.; Newman, S.J.; Bunce, M. eDNA Metabarcoding Survey Reveals Fine-Scale Coral Reef Community Variation across a Remote, Tropical Island Ecosystem. Mol. Ecol. 2020, 29, 1069–1086. [Google Scholar] [CrossRef] [PubMed]
  59. Martins, F.M.S.; Feio, M.J.; Porto, M.; Filipe, A.F.; Bonin, A.; Serra, S.R.Q.; Alves, P.C.; Taberlet, P.; Beja, P. Assessing Changes in Stream macroinvertebrate Communities across Ecological Gradients Using Morphological versus DNA Metabarcoding Approaches. Sci. Total Environ. 2021, 797, 149030. [Google Scholar] [CrossRef]
  60. Deiner, K.; Bik, H.M.; Machler, E.; Seymour, M.; Lacoursiere-Roussel, A.; Altermatt, F.; Creer, S.; Bista, I.; Lodge, D.M.; de Vere, N.; et al. Environmental DNA Metabarcoding: Transforming How We Survey Animal and Plant Communities. Mol. Ecol. 2017, 26, 5872–5895. [Google Scholar] [CrossRef] [PubMed]
  61. Lamperti, L.; Sanchez, T.; Si Moussi, S.; Mouillot, D.; Albouy, C.; Flück, B.; Bruno, M.; Valentini, A.; Pellissier, L.; Manel, S. New Deep Learning-Based Methods for Visualizing Ecosystem Properties Using Environmental DNA Metabarcoding Data. Mol. Ecol. Resour. 2023, 23, 1946–1958. [Google Scholar] [CrossRef]
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Figure 1. Annual and cumulative number of publications from 2010 to 2024.
Figure 1. Annual and cumulative number of publications from 2010 to 2024.
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Figure 2. Map of national cooperation networks: (a) world, and (b) Europe. The size of the markers representing countries indicates the number of publications; larger markers represent a greater number of articles. The color of the connecting lines represents the strength of collaboration, with red indicating the strongest collaboration and yellow the weakest.
Figure 2. Map of national cooperation networks: (a) world, and (b) Europe. The size of the markers representing countries indicates the number of publications; larger markers represent a greater number of articles. The color of the connecting lines represents the strength of collaboration, with red indicating the strongest collaboration and yellow the weakest.
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Figure 3. Keyword clustering is carried out based on keyword types.
Figure 3. Keyword clustering is carried out based on keyword types.
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Figure 4. Top 30 keywords with the strongest citation bursts.
Figure 4. Top 30 keywords with the strongest citation bursts.
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Figure 5. Research trend analysis based on publication year, research objectives, target taxa, sample types, and research settings.
Figure 5. Research trend analysis based on publication year, research objectives, target taxa, sample types, and research settings.
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Table 1. List of search terms used for Python screening. When an order contains multiple families, only one family is listed, and the remainder are indicated by “etc.”.
Table 1. List of search terms used for Python screening. When an order contains multiple families, only one family is listed, and the remainder are indicated by “etc.”.
Organism GroupOrderFamilyMajor Taxonomic GroupCollection Method
Aquatic BeetleActiniariaActiniidae, etc.AnnelidaBenthic dredge
Aquatic InsectAlcyoniaceaXeniidae, etc.Anthozoabenthic grab
BenthicAmphipodaGammaridae, etc.AsteroideaBenthic sampler
BenthosApodidaSynaptidae, etc.BivalviaBenthic sediment sampler
CaddisflyArchaeogastropodaTrochidae, etc.Bryozoabenthic survey
ClamArcoidaArcidae, etc.Calcareabenthic trawl
CoralsArhynchobdellidaHirudinidae, etc.CnidariaBenthic trawl net
CrabsBasommatophoraAncylidae, etc.CrustaceaBox corer
CrustaceansBlattariaBlattidae, etc.CtenophoraD-frame net
LobstersColeopteraPsephenidae, etc.CubozoaD-shaped net
MacrobenthosDecapodaPotamidae, etc.Demospongiaekicking net
MacroinvertebrateDendroceratidaDarwinellidae, etc.EchinodermataMulti-corer
MayflyDendrochirotidaPhyllophoridae, etc.EchinoideaSediment core
MusselDipteraChaoboridae, etc.Gastropoda
MusselsEphemeropteraEphemeridae, etc.Hexactinellida
OysterErrantiaNereidae, etc.Hirudinea
EPTForcipulatidaAsteriidae, etc.Holothuroidea
PolychaeteHalichondridaAxinellidae, etc.Hydrozoa
Sea anemonesHemipteraAphelocheiridae, etc.Insecta
Sea cucumbersHexactinosidaEuretidae, etc.Mollusca
Sea urchinsIsopodaAnthuridae, etc.Nemertea
ShrimpsLepidopteraPyralidae, etc.Oligochaeta
SnailMegalopteraCorydalidae, etc.Ophiuroidea
SpongeMesogastropodaAmpullariidae, etc.Platyhelminthes
StarfishMyoidaCorbulidae, etc.Polychaeta
StoneflyMytiloidaMytilidae, etc.Polyplacophora
WormNeogastropodaMuricidae, etc.Porifera
NereididaHesionidae, etc.Scaphopoda
NeuropteraSisyridae, etc.Scyphozoa
OdonataCalopterygidae, etc.Staurozoa
OphiuridaOreasteridae, etc.Trematoda
PhyllodocidaAcoetidae, etc.Turbellaria
PlecopteraPerlidae, etc.Urochordata
PoeciloscleridaEsperiopsidae, etc.
PterioidaLimidae, etc.
ScleractiniaOculinidae, etc.
TrichopteraRhyacophilidae, etc.
UnionoidaUnionidae, etc.
VeneroidaSphaeriidae, etc.
Table 2. Top 15 documents with 132 or more citations.
Table 2. Top 15 documents with 132 or more citations.
RankPublicationFirst Address CountryFirst Address Participating InstitutionsCitationsPublication Year
1Monitoring endangered freshwater biodiversity using environmental DNADenmarkUniversity of Copenhagen7832012
2Can DNA-Based Ecosystem Assessments Quantify Species Abundance? Testing Primer Bias and Biomass-Sequence Relationships with an Innovative Metabarcoding ProtocolGermanyRuhr-Universität Bochum4612015
3Transport Distance of Invertebrate Environmental DNA in a Natural RiverSwitzerlandEawag Swiss Federal Institute of Aquatic Science and Technology4202014
4DNA barcoding and metabarcoding of standardized samples reveal patterns of marine benthic diversityUSASmithsonian Institution3682015
5Environmental DNA as a new method for early detection of New Zealand mudsnails (Potamopyrgus antipodarum)USAUniversity of Idaho2882013
6Second-generation environmental sequencing unmasks marine metazoan biodiversityWalesBangor University2712010
7Implementation options for DNA-based identification into ecological status assessment under the European Water Framework DirectiveGermanyUniversity of Duisburg-Essen2442018
8Environmental DNA surveillance for invertebrate species: advantages and technical limitations to detect invasive crayfish Procambarus clarkii in freshwater pondsFranceInstitut National de la Recherche Agronomique2022014
9Annual time-series analysis of aqueous eDNA reveals ecologically relevant dynamics of lake ecosystem biodiversityWalesBangor University1922017
10Environmental DNA reveals seasonal shifts and potential interactions in a marine communityUSAUniversity of South Florida1702020
11Assessing biodiversity of a freshwater benthic macroinvertebrate community through non-destructive environmental barcoding of DNA from preservative ethanolCanadaUniversity of Guelph1482012
12eDNA metabarcoding survey reveals fine-scale coral reef community variation across a remote, tropical island ecosystemAustraliaCurtin University1462020
13Deep-Sea, Deep-Sequencing: Metabarcoding Extracellular DNA from Sediments of Marine CanyonsSpainCentre for Advanced Studies of Blanes—CEAB-CSIC1432015
14Predicting the Ecological Quality Status of Marine Environments from eDNA Metabarcoding Data Using Supervised Machine LearningSwitzerlandUniversity of Geneva1342017
15The downside of eDNA as a survey tool in water bodiesFranceInstitut National de la Recherche Agronomique1322015
Table 3. The top 10 commercial kits are most frequently used in research-based papers.
Table 3. The top 10 commercial kits are most frequently used in research-based papers.
RankKit NameManufacturerDocuments
1DNeasy Blood & Tissue KitQiagen94
2DNeasy PowerSoil KitQiagen33
3DNeasy PowerWater KitQiagen26
4DNeasy PowerMax Soil kitQiagen21
5FastDNA™ Spin kit for SoilMP Biomedicals7
6DNeasy PowerWater Sterivex kitQiagen5
7NucleoSpin Soil, Mini kit for DNA from soilMacherey-Nagel5
8NucleoSpin Tissue, Mini kit for DNA from cells and tissueMacherey-Nagel5
9QIAamp® DNA Mini KitQiagen4
10E.Z.N.A.® Mollusc DNA KitOmega Bio-tek4
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MDPI and ACS Style

Ge, X.; Zhang, J.; Shao, Z.; Chai, L.; Nie, J.; Yin, D.; Zhang, H.; Liu, W.; Yan, C. Research Trends and Hotspots in eDNA-Based Surveys of Macroinvertebrates: A Bibliometric Analysis. Diversity 2025, 17, 402. https://doi.org/10.3390/d17060402

AMA Style

Ge X, Zhang J, Shao Z, Chai L, Nie J, Yin D, Zhang H, Liu W, Yan C. Research Trends and Hotspots in eDNA-Based Surveys of Macroinvertebrates: A Bibliometric Analysis. Diversity. 2025; 17(6):402. https://doi.org/10.3390/d17060402

Chicago/Turabian Style

Ge, Xinyu, Junyu Zhang, Ziming Shao, Lu Chai, Jiaxin Nie, Dan Yin, Haoran Zhang, Wenbin Liu, and Chuncai Yan. 2025. "Research Trends and Hotspots in eDNA-Based Surveys of Macroinvertebrates: A Bibliometric Analysis" Diversity 17, no. 6: 402. https://doi.org/10.3390/d17060402

APA Style

Ge, X., Zhang, J., Shao, Z., Chai, L., Nie, J., Yin, D., Zhang, H., Liu, W., & Yan, C. (2025). Research Trends and Hotspots in eDNA-Based Surveys of Macroinvertebrates: A Bibliometric Analysis. Diversity, 17(6), 402. https://doi.org/10.3390/d17060402

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