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Review

Application of Environmental DNA in Aquatic Ecosystem Monitoring: Opportunities, Challenges and Prospects

1
College of Life Sciences and Engineering, Henan University of Urban Construction, Pingdingshan 467036, China
2
School of Energy and Building Environment Engineering, Henan University of Urban Construction, Pingdingshan 467036, China
*
Author to whom correspondence should be addressed.
Water 2025, 17(5), 661; https://doi.org/10.3390/w17050661
Submission received: 21 January 2025 / Revised: 21 February 2025 / Accepted: 23 February 2025 / Published: 24 February 2025

Abstract

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Environmental DNA (eDNA) technology is a method for identifying specific biological species by monitoring the presence of DNA fragments in the environment. This technology has the capacity to detect a wide range of species, including elusive and cryptic organisms, by analyzing the genetic material in the environment. The advantages of high sensitivity, wide spatial coverage and non-invasiveness provide many opportunities for its application in identifying and monitoring aquatic organisms, improving our ability to detect and quantify biodiversity. Furthermore, eDNA technology can provide an accurate, convenient and standardizable solution for regularly monitoring aquatic ecosystems. The utilization of eDNA in ecology and conservation has witnessed substantial growth in recent years. However, eDNA still faces numerous challenges, including DNA degradation, risk of contamination and the absence of standardized protocols. Nonetheless, the application of eDNA in aquatic ecosystem monitoring holds considerable promise, particularly in light of technological advancements. As technology evolves, the accuracy, scalability and applicability of eDNA in diverse ecosystems are steadily improving. This paper aims to provide a comprehensive review of the application of eDNA technology in aquatic ecosystem monitoring, addressing its technical limitations and potential future developments.

1. Introduction

The global biodiversity crisis is widely regarded as one of our time’s most pressing environmental issues, primarily driven by anthropogenic activities such as habitat destruction, pollution, overexploitation of resources and climate change [1]. Human activities have led to an unprecedented loss of species and significant degradation of ecosystems, with aquatic environments being especially vulnerable [2]. Aquatic biodiversity is particularly susceptible to external stressors, such as changes in water quality, temperature and pollution [3,4,5]. Aquatic species are frequently observed to demonstrate a high degree of sensitivity to environmental changes, and the ecological health of aquatic environments can be effectively assessed by monitoring these organisms [4,6,7,8]. However, conventional methods of monitoring aquatic organisms, namely, the collection and counting of organisms by morphological identification, are characterized by numerous limitations, including being labor-intensive and time-consuming [9,10,11].
Environmental DNA (eDNA) refers to the DNA released by an organism into the environment [12]. Currently, two definitions of eDNA are employed in ecological studies in parallel. Firstly, the definition of eDNA sensu lato is used in global biodiversity surveys that analyze microbial, meiofauna and macrofauna communities, focusing on their ecological interactions [13,14] and temporal and spatial dynamics [15,16]. This definition is also commonly used in environmental biomonitoring studies that target different groups of bioindicators to infer or predict biotic indices [17,18]. On the other hand, eDNA sensu stricto only refers to (mainly or even exclusively extracellular) the DNA of macrobial organisms. It is mainly used in conservation biology to monitor invasive and/or endangered species [19,20] and in ecology to survey animal and plant communities and study biodiversity patterns in aquatic ecosystems [13,21]. In this study, the original concept of eDNA is retained and utilized, which is defined as the total pool of DNA isolated from environmental samples [12,22,23]. This general concept assumes that the eDNA is determined primarily by its origin, not by its taxonomic composition or specific structural state (intra- or extracellular). This definition encompasses eDNA from diverse taxonomic sources, including living microorganisms, meiofauna-size taxa, macrofauna traces and possible larval stages or gametes [22]. According to the environmental origin of the DNA sample in question, eDNA can be divided into the following categories: water eDNA, sediment eDNA, soil eDNA or air eDNA [22]. Water eDNA refers to DNA molecules released by organisms in water [24,25,26]. This study focuses on eDNA in the aquatic environment.
The extraction of DNA directly from environmental samples (water, soil, sediment, etc.) is referred to as eDNA technology. This technique can be used for species monitoring purposes and is complementary to traditional methods [22]. However, given the technique’s inherent limitations, its practical applications must be carefully considered [8,12,22]. The application of eDNA in water environments is a significant area of research. The process involves the collection of water environment samples, the extraction of DNA and the performance of high-throughput sequencing (HTS). The resulting data are then matched against known databases to identify species-matching DNA fragments in the samples [12,23,25]. This process enables the estimation of the biological species present in the samples (Figure 1).
The continuous advances in sequencing technology have rendered eDNA technology more precise and reliable, thus establishing it as a pivotal instrument in biodiversity research and environmental management [8,24,26,27,28,29]. According to the various methods and research objects, the aquatic biological surveys of eDNA can be divided into three aspects: primer-specific amplification sequencing, universal primer amplification sequencing and total DNA sequencing. Primer-specific amplification sequencing is a method that uses primers specific for the target species to amplify the DNA of the target biological species present in the environment. Polymerase chain reaction (PCR), real-time fluorescence quantitative PCR (qPCR) and digital microdrop PCR (ddPCR) are the main methods for the species-specific analysis of environmental DNA [30,31,32,33,34]. Analyzing eDNA in the natural environment through specific primers is a highly efficient and sensitive method of revealing target organisms, including rare, endangered, protected and newly emerged invasive species [7,25,26,35,36]. Nevertheless, the capacity of eDNA technology is subject to limitations in detecting rare or invasive species when species abundance is low [37].
The primary general primer amplification sequencing method is metabarcoding, an eDNA monitoring technique for identifying multiple species in environmental samples. Metabarcoding is a specialized application of HTS that involves amplifying and sequencing numerous DNA fragments. This technique enables researchers to overview species diversity thoroughly [38]. Using primers that target specific gene regions, such as 12S or 16S rRNA, enhances the precision of species identification through metabarcoding. Consequently, it has become an indispensable tool for ecological research [39]. Furthermore, metabarcoding facilitates the detection of rare or invasive species that might otherwise go unnoticed with traditional survey methods [28,40,41]. Recent studies have clarified the relationship between barcodes, DNA metabarcoding (i.e., analysis of bulk samples) and eDNA metabarcoding (defined as a study that allows the identification of multiple taxa using eDNA as a template material) [22]. The findings of these studies concluded that eDNA metabarcoding is a powerful tool for long-term ecological monitoring, especially in tracking ecosystem recovery after conservation interventions [42,43].
Total DNA sequencing methods, known as metagenomics, sequence all the DNA in an environmental sample, not just the parts amplified by specific primers. This method can simultaneously monitor and identify the DNA of multiple species in the environment. Metagenomic methods provide a wider range of species information than metabarcoding techniques [25,44]. However, metagenomic sequencing is expensive, generates large amounts of data, is complex to analyze and process, and requires powerful computing resources and professional analysis methods. Different methods of eDNA technology provide a variety of powerful tools. They have essential application value in studying aquatic organisms and can be selected according to research objectives, sample characteristics, budget and research resources [25,38]. In the future, these eDNA technology approaches will continue to play a key role in ecology, environmental monitoring and species conservation.
Studies have rapidly increased using eDNA isolated from the environment in recent years, particularly in freshwater and marine aquatic ecosystems. These aquatic ecosystems are subject to substantial anthropogenic pressures, which have a detrimental effect on biodiversity and the associated ecosystem processes and services [1,3,4]. Therefore, there is a need for the effective management of biomes and for calculating their status and change by describing their biodiversity or using it as a proxy for describing the state of the environment [24,26,27]. However, a significant limitation of previous assessment methods is their high cost, the diversity across taxa and the inability to scale up methods in time and space. There is a critical need for high-resolution biomonitoring data, which may depend on new technologies. The use of molecular techniques, particularly the study of eDNA, is an example of such advances, which are already recognized as a game changer for bioassessment and biodiversity monitoring [8,23,26,45]. Numerous studies focusing on the bioassessment of aquatic systems have recently adopted and advanced eDNA methodologies. However, the objectives, methodologies, eDNA sources and target organisms of these studies can vary considerably [11,28,30]. Concurrently, these novel technologies and the integration of eDNA in bioassessment have engendered elevated expectations, which are being progressively realized in the context of ongoing biodiversity monitoring programs and bioassessment studies [8,12,22,28]. While many of these expectations may be realistic, there are often misconceptions about the potential and limitations of eDNA research [22].
Although the application fields, research methods, technical characteristics and other aspects of eDNA have been discussed in existing papers [12,24,45,46,47], to realize the potential of eDNA fully, it is necessary to have a deep understanding of the application, limitations and future development trend of this technology in the field of water ecology. A comprehensive literature survey of studies on aquatic ecosystems was conducted using the Web of Science with the keywords “environmental DNA” and “eDNA”. The final literature search was completed on 10 October 2024 and included representative peer-reviewed journal articles published in the past decade relevant to this review’s content. According to the content, the references were read and analyzed individually to ensure their reliability and relevance to the content of this review. This review offers a comprehensive overview of the opportunities presented by eDNA as a new and non-invasive method for monitoring aquatic ecosystems. It further discusses the challenges and prospects for future development, including its application. This will facilitate a more comprehensive and in-depth understanding of the development trends and trends in the field of eDNA, and provide a reference basis for carrying out relevant cutting-edge research. The potential benefits of this approach include improving the efficiency of ecological monitoring and protection, optimizing the application process and promoting technological upgrading.

2. Opportunities of eDNA in Aquatic Ecosystems

The field of eDNA has undergone a significant transformation in monitoring aquatic ecosystems. This is because eDNA has been shown to offer a highly sensitive, non-invasive and cost-effective method for detecting species and assessing biodiversity [48]. The device’s sensitivity, scalability and cost-effectiveness make it a vital tool for species detection, large-scale monitoring and the early identification of invasive species [9,43]. Furthermore, its non-invasive nature and capacity to evaluate ecosystem health provide ethical and effective alternatives to traditional methods [12,49]. As technological advancements continue to enhance eDNA analysis, its potential to transform aquatic biodiversity monitoring and significantly aid global conservation efforts is expanding, providing numerous opportunities for using eDNA in detecting aquatic ecosystems [26,35] (Figure 1).

2.1. Detecting Rare and Elusive Species

The presence of rare and elusive species in biological surveys poses a significant challenge due to their low biomass and density, which conventional methods often fail to detect. However, using eDNA analysis has emerged as a promising approach to address these challenges [50,51,52]. For instance, eDNA has been successfully employed to monitor endangered amphibian and deep-sea fish species, including the Hula painted frog (Latonia nigriventer) [53], European weather loach (Misgurnus fossilis) [54] and European eel (Macquaria australasica) [55].
It is noteworthy that eDNA has demonstrated efficacy in species detection, even in challenging environmental conditions, such as cold or turbid waters, where traditional monitoring techniques often prove unsuccessful [52,56]. The ability to detect rare species, including the noble crayfish (Astacus astacus) [57] and the Yangtze finless porpoise (Neophocaena asiaeorientalis asiaeorientalis) [7], even at low densities, has been demonstrated to enhance conservation efforts for endangered native species [58]. However, the diversity and abundance of different organisms affect the shedding and decay rates of their eDNA, affecting the likelihood of that DNA being detected in water samples [18]. The sensitivity of eDNA detection also varies depending on the target species, their abundance and their behavior [37]. Organisms with higher biomass or that shed more DNA are more likely to be detected, while some rare or elusive species may not shed enough DNA into the environment for detection [59,60]. Fortunately, the development of eDNA quantification methods, such as ddPCR, has led to enhanced detection and monitoring of elusive species, including the corallivorous crown-of-thorns seastar (Acanthaster cf. solaris) [34]. In conclusion, the application of eDNA in detecting rare and elusive species is affected by many factors [18,61], and further studies are needed to explore it in greater depth.

2.2. Large-Scale Biodiversity Monitoring

The biodiversity assessment can be facilitated by eDNA monitoring, which offers significant advantages over traditional methods in scope and temporal and geographical coverage [10,62]. Collecting eDNA from diverse samples, including water, sediment and ice, ensures comprehensive geographic coverage with minimal fieldwork [12]. The application of eDNA has been demonstrated in monitoring fish populations in rivers, lakes and oceans, providing essential data on species distribution and abundance [40,48,51,60,63].
The portability of eDNA technology facilitates its application in remote areas, including Arctic coastal regions and ice-covered ecosystems [64]. This approach enables the acquisition of crucial insights into biodiversity. In addition, this method has been demonstrated to effectively monitor changes in species composition over time, thereby providing a more accurate assessment of biodiversity shifts, which would be challenging to obtain through conventional logistics methods [65].

2.3. Early Detection of Invasive Species

One of the most significant applications of eDNA is in the early detection of invasive species [25]. Such species have the potential to pose a threat to native biodiversity and the overall function of ecosystems. Invasive species have been shown to outcompete native species, alter habitats and disrupt food webs. This can lead to both ecological and economic consequences [10,36,41,54]. Furthermore, the use of eDNA has been demonstrated to be an effective method of detecting other invasive species across diverse habitats, including New Zealand mudsnails (Potamopyrgus antipodarum) [66] and rusty crayfish (Orconectes rusticus) [56], even at very low densities. This capacity facilitates the implementation of expeditious response methodologies [67].
eDNA promises to detect invasive species at low population densities, enabling earlier interventions. In addition, using eDNA has been demonstrated to be an effective method for detecting species even in early life stages, such as larvae or juveniles [20]. The early detection of invasive species using eDNA is vital for the management; for instance, eDNA has proven highly effective in detecting bigheaded carps (Hypophthalmichthys nobilis and Hypophthalmichthys molitrix), providing an opportunity to monitor and control the spread of this invasive species in the Great Lakes region [20]. This early detection offers a vital chance for implementing control measures, preventing the establishment of invasive populations and reducing their impact on native ecosystems [36,68]. Nevertheless, early detection can be difficult as only a few individuals may initiate the expansion of a species’ range, and, thus, observations of individuals will be rare. Furthermore, there is often a lag time between initial colonization and expansive population growth, resulting in low detectability. However, eradication efforts by management may be most successful at eliminating invasive species early in an invasion [9,19,69], thus increasing the sensitivity of detection methods is essential. While numerous factors may influence the quantity of eDNA detected [18], eDNA holds considerable potential for detecting invasive species. Further research is necessary to comprehensively elucidate the relationship between organisms and the detection and quantification of eDNA released by them.

2.4. Species Mapping and Habitat Monitoring

The analysis of eDNA in water has been shown to have significant potential for species mapping. By analyzing eDNA, it is possible to monitor the presence of various species in a given water body (such as lentic ecosystems), including fish [70], benthos [71], zooplankton [37,72], phytoplankton [73] and microorganisms [74]. This analysis can provide insights into these species’ presence and distribution ranges, thereby facilitating the monitoring of biodiversity [14,21]. Furthermore, this approach can contribute to our understanding of the health of the habitat and the signs of environmental change and provide data and references for habitat monitoring [71,72,74].
The non-invasive nature of eDNA sampling facilitates expeditious assessments across vast geographical areas, thereby assisting researchers in monitoring species within ecologically fragile ecosystems, such as seagrass, mangroves, coral reefs and freshwater ponds [50,75]. The ability of eDNA to detect species presence over large areas is crucial for monitoring changes in biodiversity due to environmental stressors, such as pollution, habitat loss and climate change [45,76]. Integrating eDNA data with remote sensing technologies facilitates the creation of relatively precise species distribution maps, essential for conservation planning and habitat monitoring [7,48,77].
Furthermore, eDNA supplies pivotal information for monitoring ecosystems affected by climate change, including changes in species distributions due to rising temperatures or habitat loss [7,78]. This method enables conservationists to track current biodiversity and monitor ecosystem recovery after conservation efforts, such as habitat restoration and pollution mitigation [8].

2.5. Monitoring Ecosystem Health

The application of eDNA in ecological health monitoring covers many key areas. eDNA can effectively evaluate the impact of chemical pollutants on ecosystems by monitoring the distribution and abundance of wildlife, biome and ecosystem function [23]. eDNA has been successfully used to monitor individuals or multiple species in different environments (freshwater, ocean, soil, etc.) and various periods (ancient and modern), allowing for a more relevant approach to assessing the risks posed by toxic substances to ecosystems [79]. For example, studies have found that benthic bacterial communities in marine pastures are highly correlated with eukaryotic communities and nutrient levels in the corresponding area [80], and eDNA can also be used to assess the impact of oil spills on ecosystems [81].
The presence or absence of specific species can indicate ecosystem stability, with sensitive or keystone species frequently used as bioindicators of the ecosystem’s condition [82,83]. Changes in microbial populations often signal more extensive shifts in water quality and ecosystem health, which can be pivotal for the early detection of ecological disturbances [82,84]. The researchers studied the significant stressors of environmental pollution with the microbial community as the monitoring object. They found that the microbial community changes with the change of the considerable pollution stressors in the environment, which provides better information for formulating environmental protection policies [74]. It has been demonstrated that the joint analysis of multi-community data can predict pollution in the water environment with an accuracy of up to 79% [17]. It has also been shown that biological communities are susceptible to copper concentrations, with significant changes even at low concentrations (1.5 μg/L) [85], suggesting that microbial communities in the environment can be used to study the effects of toxic substances on ecosystems and that the study of eDNA will be a good complement to the evaluation of the impact of chemical pollution on ecological health. Furthermore, eDNA has been found to provide information about changes in microbial community composition, offering insights into water quality and nutrient cycling processes [86]. Theoretical models, for example, the biodiversity-ecosystem functioning (BEF) framework [87] and the community assembly and the functioning of ecosystems (CAFE) approach [88], provide promising tools for predicting the effects of toxic substances on ecosystem function using community characteristics (biodiversity and community composition) of eDNA methods such as metabarcoding [23]. The combination of biodiversity assessment and ecosystem function monitoring is expected to make eDNA an integrated approach for assessing species composition and ecosystem health so that proactive conservation measures can be taken [11,72,73].
eDNA technology has been demonstrated in supporting research into food webs, aiding comprehension of food chains and networks within ecosystems, and exposing the intricacies of food webs [89,90]. It also plays a pivotal role in studying ecosystem services, which are deployed to evaluate the functions of ecosystems, including water purification, soil fertility and natural pest control [91]. This enhances the management of sustainable resources. In addition, eDNA technology has essential applications in ecosystem restoration, where it is used to monitor the effects of vegetation and wildlife reintroduction, ensuring rapid and effective recovery of damaged ecosystems and providing a powerful tool for ecosystem conservation and restoration [72]. These studies promote the deeper application of eDNA technology in ecosystem monitoring, pollution diagnosis and ecological restoration [23].

3. Challenges of Using eDNA in Aquatic Ecosystems

Using eDNA has resulted in substantial progress in biodiversity monitoring. Nevertheless, several technical and methodological challenges must be addressed to enhance its utility and accuracy [46]. The degradation of eDNA poses a significant challenge, potentially resulting in false negatives or biased interpretations regarding species presence [92]. The measurement of species abundance through eDNA presents challenges, primarily due to the variable rates at which species shed DNA and the differing decay rates that DNA undergoes [93,94]. Another challenge is the introduction of biases during the PCR amplification process [11,95,96]. Contamination represents a significant issue, especially during sample collection and analysis, as even trace amounts of external DNA can result in false positives [97,98]. Finally, the absence of standardized protocols in various studies restricts the ability to effectively compare their results [99,100,101] (Figure 2).

3.1. Difficulty in Quantifying Species Abundance

Whilst eDNA boasts a high level of sensitivity in detecting species presence, it is subject to limitations in accurately estimating species abundance or biomass [37]. The concentration of eDNA in a sample does not directly correlate with population size. This discrepancy can be attributed to various factors, including species-specific shedding rates, variations in life stages and differing environmental conditions. For instance, larger organisms or those actively shedding DNA, such as during spawning, may release more DNA than smaller or less active individuals. Consequently, reliance on eDNA concentration as a sole metric for population size estimation can be problematic [59,60]. eDNA technology can infer the relative abundance of species from the number of sequences. Still, studies have shown that sequence abundance may also be related to the biomass of different taxa, further complicating the interpretation of relationships between sequence abundance and species abundance or biomass [42]. Additionally, eDNA from different taxa may behave differently at any point in the process from its release into the environment until it is finally sequenced (e.g., differing rates of release, degradation or capture by and extraction from filters) so that each taxon has a unique relationship between sequence abundance and species abundance or biomass [102]. These relationships may also vary by site or by season [42,72].
Numerous studies have adopted qPCR and ddPCR to quantify population abundance [30,31,68]. For instance, ddPCR methods have been employed to monitor species such as the invasive American bullfrog (Lithobates catesbeianus) and to quantify temporal changes in species abundance [31]. These methods have been particularly effective for species exhibiting consistent eDNA emission rates throughout their life stages, enhancing the accuracy of essential population monitoring efforts [32]. ddPCR has been shown to have advantages over traditional qPCR, especially at low eDNA concentrations [31,33,34]. Notwithstanding the advancements above, the challenge of establishing a correlation between eDNA concentration and population size remains [103]. Environmental factors such as water flow, sedimentation and temperature have been demonstrated to affect the concentration of eDNA in aquatic systems, resulting in variability in detection and estimation accuracy [94,95]. These factors complicate efforts to estimate species abundance accurately [104]. When developing more robust models to estimate abundance, it is essential to consider factors such as the varying DNA shedding rates among species and the impact of environmental variables [10,105].

3.2. False Negatives

It is well established that DNA molecules are vulnerable to physical, chemical and biological processes that result in degradation over time. Consequently, this can lead to false negatives or an underestimation of species presence. Temperature plays a vital role in the degradation of eDNA, and the findings suggest that the degradation rate increases with rising temperatures, with warmer water resulting in a faster degradation rate compared to cooler conditions [99,106,107]. In addition, decay rates for eDNA were significantly higher in neutral and alkaline conditions than in acidic conditions [92,98]. Salinity has been shown to influence the persistence of eDNA. In freshwater environments, higher salt concentrations have been observed to reduce eDNA degradation, suggesting that salinity affects microbial activity, which is a crucial factor in the breakdown of DNA [108].
Another significant factor to consider is the abundance of microorganisms. It has been demonstrated that elevated levels of bacteria can substantially accelerate the process of eDNA degradation, as these microorganisms can decompose DNA particles in water. Research findings indicate that both the activity of microorganisms and the microbial community’s composition influence eDNA degradation rates [98]. Increased levels of organic matter have been shown to contribute to faster DNA decay due to higher microbial growth [109]. In conclusion, alterations in temperature, pH, salinity and microbial abundance can influence the degradation of eDNA [92,98]. The interaction of these factors can complicate eDNA assessments, as the persistence and degradation of eDNA vary across different ecosystems. Consequently, researchers must consider these variables to enhance eDNA results’ reliability and improve species detection accuracy in varying environments [108,110].
The capacity for taxonomic resolution in eDNA studies is constrained by the availability of reference databases, which frequently lack essential genetic sequences for certain species, particularly those inhabiting understudied ecosystems [111,112]. Without comprehensive reference libraries, eDNA data may yield ambiguous or erroneous taxonomic assignments, impeding precise biodiversity assessments [63,113]. Consequently, developing enhanced primer design and database coverage is imperative to promote the broader application of eDNA technology [112,114,115].

3.3. False Positives

False positives occur when the target species is not present in the environment but gives a positive result by eDNA analysis. The process of sampling in aquatic ecosystems presents a particular challenge, given the capacity of flowing water to transport DNA from diverse locations, resulting in erroneous detections [116]. Extensive research has demonstrated that eDNA is characterized by its extreme sensitivity and high susceptibility to contamination [9]. Even trace amounts of external DNA from researchers, equipment or other environmental sources can interfere with samples, resulting in false positives [117]. The presence of contaminants, whether from field or laboratory sources, poses a considerable threat to the integrity of eDNA studies. Thus, specialized field collection and laboratory processing protocols are essential to reduce contamination risks and avoid false positives [118,119]. Such measures include using sterile equipment, wearing gloves, handling samples in a controlled, contamination-free environment, and using deionized water to check for contamination of equipment [120]. Employing negative controls throughout the sampling and analysis process is instrumental in identifying and mitigating potential contamination sources [121].
The degradation of DNA and the cross-amplification of non-target species during the process of PCR may produce false positives. It is imperative to ensure primer specificity to prevent such issues and guarantee that only target DNA is amplified [122]. Synthetic oligonucleotide controls have been developed to differentiate false positives caused by contamination during PCR amplification [119]. To minimize contamination and false positives, it is also essential to use high-fidelity enzymes during PCR, thoroughly clean laboratory spaces and include blank samples at various workflow stages [123].

3.4. Lack of Standardization in eDNA Protocols

A significant barrier to the widespread use of eDNA is the absence of standardized protocols for sample collection, processing and analysis [101]. The heterogeneity of methodologies employed across disparate studies engenders difficulty in comparing results from diverse ecosystems or geographic regions. This inconsistency hinders our capacity to draw broader conclusions about global biodiversity trends. Current research emphasizes establishing best practices in eDNA monitoring to ensure consistency. This necessitates the formulation of guidelines delineating the volumes of water samples to be collected, the most efficacious filtration methods and the most efficient DNA extraction techniques [97,124].
On-site filtration is frequently advocated as a preventative measure against DNA degradation during transportation, given that delays between sample collection and analysis can compromise the detectability of eDNA [97]. Portable filtration systems have been demonstrated to effectively preserve eDNA concentrations and reduce contamination risks, thereby enabling more accurate and reliable biodiversity assessments [101]. The selection of filtration pore size, storage conditions and extraction methods must be performed with great care, according to the objectives of the study in question [32,125]. The eDNA recovery study conducted on Oriental weatherloach (Misgurnus anguillicaudatus) indicates that enhanced DNA retrieval can be achieved through the utilization of the cellulose nitrate filter paper stored in ethanol or a refrigerator set at −20 °C in conjunction with specific DNA kits. The study further suggests that water samples should be filtered within 24 h. However, if this is not feasible, short-term storage (i.e., 3–5 days) may be viable, with refrigeration being a preferable option to freezing [125].
There is an increasing acknowledgment of the necessity to harmonize bioinformatic workflows for eDNA analysis to ensure reproducibility and consistency across research studies [126,127]. The optimization of protocols for specific environments, such as turbid waters or high-salinity ecosystems, is imperative to enhance the reliability of eDNA as a monitoring tool [128,129]. Establishing standardized protocols that can be utilized globally necessitates collaboration among researchers, policymakers and conservation organizations.

3.5. Temporal and Spatial Variability in eDNA Dynamics

Factors such as water currents, sedimentation and temperature can influence the types and amounts of eDNA present over time and across different locations. This variability can lead to biases and errors, making it challenging to accurately determine species presence and abundance from a sample [15]. Hydrodynamic processes have been demonstrated to significantly influence the detection of eDNA across both spatial and temporal scales. Furthermore, eDNA can be transferred within water bodies, which complicates the interpretation of its origins [62].
Hydrodynamic and sediment models have effectively considered the influence of tidal forces, sedimentation and seasonal changes in aquatic systems, which impact both the spatial and temporal distribution of eDNA [130,131]. Integrating hydrodynamic models with eDNA data offers a promising approach to address the challenges posed by spatial complexities. These models can simulate water flow and predict how eDNA is transported, thereby assisting researchers in gaining a more nuanced understanding of the spatial distribution of eDNA concentrations about the locations of various species [132]. This integration clarifies the origins of eDNA in large or rapidly moving water bodies [133]. Additionally, temporal variability is influenced by species-specific behaviors, such as increased DNA shedding during spawning periods, which can lead to higher concentrations of eDNA [134]. The degradation of eDNA, caused by factors such as temperature and microbial activity, can reduce its detectability over time [135]. This temporal variability necessitates meticulous planning of sampling timeframes to ensure precise assessments.

4. Prospects and Technological Advancements in eDNA

The future of eDNA in environmental monitoring appears to be highly promising. Continuous advancements in the field are enhancing the accuracy, scalability and application of eDNA across various ecosystems. As these technologies continue to evolve, it is anticipated that eDNA will assume a pivotal role in global conservation initiatives. This section provides a comprehensive overview of the most significant technological developments, including HTS, portable sequencing devices, real-time analysis and artificial intelligence (AI)-assisted data processing. Furthermore, it discusses the integration of eDNA with other monitoring tools, such as remote sensing (Figure 3).

4.1. High-Throughput Sequencing and Metabarcoding

HTS technologies, including next-generation sequencing (NGS), metabarcoding and metagenomics, have significantly impacted the analysis of eDNA. These advancements enable concurrently identifying multiple species from a single environmental sample [8,24,26,27,38,44]. Within river systems, the combination of eDNA sampling with HTS facilitates the detection of biodiversity across ecological gradients, thereby providing insights into the reactions of species distributions to changing habitats [136]. In lake ecosystems, time-series analyses of eDNA have demonstrated the ability to track seasonal biodiversity dynamics and identify early signs of ecosystem stress [137]. Despite the financial constraints currently hindering the widespread application of HTS analysis in environmental science, the declining costs of sequencing represent a promising development. It is anticipated that, in the future, as HTS technologies become more affordable and accessible, they will assume a central role in biodiversity monitoring. Analyzing large DNA sequences in a single process provides insights into ecological dynamics, including species interactions, habitat preferences and trophic relationships [138,139]. Generating such detailed datasets facilitates monitoring biodiversity changes caused by environmental stressors such as pollution, habitat loss and climate change [140].
Nanopore sequencing can enhance conventional sequencing technologies by the long-read capabilities it provides, thus leading to an improvement in taxonomic resolution. As more efficient sampling strategies and portable sequencing devices, such as the MinION, emerge, HTS will broaden its scope, facilitating field-based analyses that deliver timely data to guide conservation strategies [141]. The utilization of MinION for the sequencing of extended mitochondrial regions has resulted in enhanced species identification within both freshwater and marine ecosystems [142]. Despite the higher error rates observed in nanopore sequencing compared to Illumina, the integration of HTS with AI-based bioinformatics pipelines has been demonstrated to significantly enhance the application of HTS and address these challenges [143]. The employment of AI algorithms has been shown to augment the efficiency and precision of taxonomic assignments, particularly in the context of substantial datasets. This development can potentially optimize the broader biodiversity assessment process [144]. Advancements in AI and machine learning now allow for real-time processing of raw sequence data, enabling expeditious biodiversity assessments in dynamic environments such as marine aquaculture systems [145]. These technologies facilitate the efficient monitoring of entire biological communities and the detection of shifts in biodiversity. This capability is essential for addressing global conservation challenges and understanding the intricate dynamics of ecosystems.

4.2. Portable Sequencing Devices and Real-Time Analysis

The advent of portable sequencing devices signifies a substantial progression in eDNA monitoring for biodiversity. These handheld devices, such as the Oxford Nanopore MinION, facilitate real-time analysis in field settings, thereby reducing the time required for species identification compared to conventional laboratory methods. The immediate data generation through on-site analysis enables rapid decision-making, a crucial aspect of conservation and ecosystem management [146,147]. Using portable sequencing devices is of particular significance in monitoring invasive species. A study that utilized MinION sequencing successfully identified invasive aquatic bivalves, completing the entire process from sample collection to identification in a mere 3.5 days [29]. This real-time capability is crucial for timely interventions to prevent or reduce the ecological impact of invasive species.
The advent of portable devices has significantly impacted the accessibility of eDNA technologies in remote and resource-constrained regions. For instance, MinION sequencing has been utilized in the rainforest of Ecuador, facilitating expeditious identification of rare and endangered species. A notable example is the rediscovery of the Jambato toad (Atelopus ignescens), which was believed to be extinct for 28 years, facilitated by this technology [146]. This rapid and accessible sequencing capability enhances biodiversity monitoring even in challenging field conditions, empowering local researchers and conservationists [148]. Real-time eDNA analysis with portable sequencers is essential for evaluating ecosystem recovery following restoration efforts. The monitoring of changes in biodiversity in near real-time offers valuable insights into the effectiveness of ecological interventions. MinION sequencing has been utilized in marine environments to detect white sharks in remote open ocean regions, underscoring the efficacy of portable sequencing for species detection in conservation initiatives [27].
Mobile sequencing platforms now incorporate cloud computing and AI-based bioinformatics tools to enhance data interpretation. This integration enables real-time notifications and adaptive ecosystem management, facilitating timely responses to biodiversity threats and strengthening conservation efforts [149]. Combining portable sequencing devices with The Internet of Things (IoT) frameworks simultaneously enhances decentralized biodiversity monitoring across multiple locations [150].

4.3. Integration of eDNA with Artificial Intelligence and Machine Learning

As eDNA datasets become larger and more complex, the need for bioinformatics tools to efficiently process and interpret this data grows. To enhance the precision of species identification and mitigate the prevalence of false positives and negatives, AI and machine learning algorithms have been integrated into bioinformatics workflows [151,152]. These algorithms can enhance species identification by tackling primer bias and improving taxonomic resolution [153]. The application of AI holds considerable potential in predicting species abundance. This process involves the consideration of variables such as species-specific DNA shedding rates, prevailing environmental conditions and the rate of DNA degradation [104]. Furthermore, AI models have enhanced biodiversity assessments by incorporating various data sources, including visual observations, to verify the outcomes of eDNA analyses [63].
Integrating AI and machine learning with eDNA has been demonstrated to enhance the early detection of invasive species. This is achieved by creating new assays that reflect read abundance and ecological trends [10,69]. Machine learning algorithms have been shown to address uncertainties in eDNA studies effectively. They reduce the risk of false positives by improving data processing and analysis tools [152]. Recent studies have demonstrated that occupancy models with eDNA data yield more accurate estimates of species presence and distribution [154]. Furthermore, the development of probabilistic methods for species identification has resulted in more reliable taxonomic assignments and increased confidence levels [155]. AI techniques, such as supervised machine learning, have been employed to predict ecological quality status and monitor benthic species effectively, irrespective of the taxonomic classification of eDNA sequences [145]. Supervised learning algorithms effectively identify patterns in eDNA datasets, allowing for the distinction of cryptic species even in dynamic environments such as large aquatic ecosystems [136].

4.4. Advancements in Quantifying Species Abundance

A significant constraint associated with using eDNA is the challenge of accurately quantifying species abundance or biomass based on the detected DNA concentrations [59,60]. Advances in eDNA-based quantification models are enhancing technology from merely species detection to providing accurate biomass and population size estimates [10,105]. These models aim to improve the use of eDNA for estimating population sizes, a process essential for understanding ecosystem dynamics, managing invasive species and evaluating conservation efforts [10,105]. Recent research has made significant progress in developing models that correlate eDNA concentration with species abundance more effectively by considering environmental factors such as water flow, sedimentation and DNA shedding rates [130,131,132]. Research has demonstrated that incorporating hydrodynamic models that consider the river networks’ water flow can enhance the precision of species distribution and abundance estimates [47]. Furthermore, reservoir sedimentation has been observed to impede DNA detection in aquatic systems [11]. Advanced techniques, such as ddPCR, have been demonstrated to enhance the precision of low-concentration eDNA detection, thereby improving the reliability of abundance estimates in ecosystems characterized by sparse populations [156]. Allometric scaling models have been employed to enhance predictions by accounting for the nonlinear relationship between individual size and eDNA production [72].
Challenges still exist despite the potential of advances in modeling and detection techniques. Variability in DNA shedding rates among different species and environmental conditions complicates the development of universal abundance models. Consequently, future research is necessary to refine these models [104]. Enhancing collaboration among research institutions and conservation organizations is crucial to validate these models and broaden their applicability over large spatial scales [151].

4.5. Combining eDNA with Remote Sensing and Geographic Information Systems (GISs)

Integrating eDNA with remote sensing technologies and GIS significantly advances environmental monitoring. The utilization of remote sensing tools, such as satellite imagery and drones, facilitates the acquisition of detailed information regarding environmental conditions, including vegetation cover, temperature and water quality. When combined with the species-level precision offered by eDNA, these technologies create high-resolution biodiversity maps and facilitate monitoring changes over large spatial and temporal scales [157]. Integrating satellite-derived variables, including vegetation indices and pollutant concentrations, with eDNA data facilitate predicting species distributions and assessing habitat conditions [158]. This integrated approach enables conservationists to effectively manage biodiversity by monitoring species responses to environmental changes, such as habitat fragmentation and climate variability [26].
Employing unmanned aerial vehicles (UAVs) equipped with sensors and sampling apparatuses facilitates the expeditious and precise acquisition of environmental data encompassing eDNA. To illustrate this point, the utilization of drones in the collection of eDNA from tree canopies and bodies of water has been demonstrated, thereby enhancing the efficacy of surveys in areas that are otherwise difficult to access [159]. This integration facilitates real-time ecosystem health assessments, enabling conservation managers to detect invasive species and habitat degradation promptly. Integrating eDNA with GIS facilitates the development of dynamic biodiversity models that adapt to environmental changes, including those induced by climate change. For instance, integrating eDNA data with satellite observations has effectively tracked coral reef biodiversity and predicted shifts in communities due to changing climatic conditions [160]. These insights are vital for creating adaptive management strategies for ecosystems under stress.
While integrating eDNA with remote sensing holds great promise, there are challenges, particularly the necessity for standardized protocols to ensure data compatibility across different regions [137]. Enhancing eDNA sampling techniques and expanding reference libraries are imperative for comprehensively utilizing these methodologies [138].

4.6. Standardization of Protocols and Global Collaboration

As the utilization of eDNA methodologies expands, a mounting awareness has emerged concerning the imperative for standardized protocols across diverse studies and geographical regions. The current lack of standardization in sampling, processing and analysis methods results in a paucity of comparable results between ecosystems. This inconsistency in methods limits the effectiveness of eDNA in global biodiversity assessments [49]. The creation of standardized protocols is, therefore, essential for ensuring the consistency of data collection, thus facilitating effective biodiversity monitoring across diverse regions. Standardizing eDNA protocols and promoting global collaboration are crucial for advancing environmental monitoring. By enhancing interoperability, establishing centralized genetic databases and encouraging collaborative efforts, eDNA can significantly transform conservation science and policy [151].
Collaboration among researchers, conservation organizations and policymakers is crucial for developing and implementing standardized protocols. Initiatives such as the French National eDNA Repository establish a data interoperability framework, facilitating regional data alignment with international standards [161]. Platforms like the International Barcode of Life (iBOL, the Secretariat of the International Barcode of Life Consortium is based at the Centre for Biodiversity Genomics in Guelph, Canada, https://ibol.org/) aim to create comprehensive reference libraries that enhance species identification in biodiversity hotspots [162]. Notwithstanding the advances that have been made, challenges remain in adapting standardized protocols to diverse ecosystems. Fluctuations in environmental conditions, including water quality and temperature, have been demonstrated to influence DNA detection and complicate the development of universal workflows [163].
Expanding genetic reference databases is imperative for enhancing the precision of eDNA-based species identification [112]. However, the limitations of existing reference libraries impede the efficacy of eDNA applications in hyper-diverse tropical ecosystems [164,165]. The advent of sophisticated data platforms such as Anacapa has paved the way for customizing genetic reference libraries, fostering standardized workflows and elevating data quality [166]. Consequently, there is a necessity for future efforts to concentrate on the expansion of databases and the creation of flexible protocols that can be adapted to local ecological contexts while ensuring global compatibility [112,131,138].

4.7. eDNA for Early Warning Systems and Long-Term Monitoring

The utilization of eDNA holds considerable promise as a mechanism for the early identification of ecological disturbances. Such shifts may include alterations in species composition, the arrival of invasive species and changes in microbial communities [35,40,139]. The continuous monitoring of eDNA facilitates the rapid identification of changes, often before their recognition through conventional methods, thereby enabling the timely implementation of conservation actions [9]. This is of crucial importance in the mitigation of their ecological and economic impacts. For instance, studies utilizing eDNA metabarcoding in the Great Lakes successfully identified invasive mollusk species at early stages, demonstrating its potential as an effective early warning tool [10]. Furthermore, eDNA monitoring in coastal lagoons has been shown to provide early warnings about biological risks by detecting invasive species and indicators of pollution, thereby guiding timely restoration efforts [167].
Furthermore, the non-invasive nature of eDNA sampling enables the long-term monitoring of ecosystems, thereby providing continuous data on biodiversity and ecosystem health. This approach has been successfully applied in various habitats, ranging from freshwater to tropical rainforests. It has been demonstrated that this method can be used with traditional survey techniques, thereby providing a more comprehensive assessment of biodiversity [11,48,165,168,169]. This ongoing monitoring enables conservationists to track population trends and identify changes in community composition over time, which is crucial for effective ecosystem management [7,12].
The increasing utilization of eDNA in ecological monitoring indicates the alignment with national and international biodiversity policies. For instance, eDNA-based tracking is consistent with the Aichi Biodiversity Targets, providing standardized biodiversity indicators to inform conservation efforts [168]. Furthermore, integrating eDNA with citizen science initiatives has enhanced biodiversity monitoring, enabling non-experts to contribute to conservation efforts by collecting samples for eDNA analysis [170].

5. Conclusions

The potential of eDNA technology in water ecological monitoring is significant. It offers an efficient, accurate and non-invasive method for species monitoring and protection, water environmental health monitoring, biodiversity assessment and water quality monitoring [169]. Although eDNA technology has challenges, such as non-standard processes and imperfect databases, its advantages, such as time-saving efficiency, high sensitivity and environmental friendliness, are promoting this technology to become a powerful tool for ecological monitoring and assessment. The combination of eDNA technology with emerging technologies is expected to further advance conservation efforts by providing researchers with more detailed and comprehensive data on species presence, abundance and ecosystem health, enabling large-scale, efficient biological monitoring on a global scale [37,73,171].
eDNA technology has been demonstrated to enhance human capacity to obtain ecosystem data. Future development of this technology is required to address challenges relating to technological processes, monitoring accuracy, aquatic species databases and associated issues. This development should be informed by research and innovation in emerging sequencing technologies and platforms. Integrating gene sequencing and supervised machine learning is expected to facilitate the intelligence of aquatic ecological monitoring and enhance monitoring efficiency. Furthermore, there is an urgent need to strengthen the application of eDNA technology in the reconstruction of food chains/webs in watersheds, nutrient control in watersheds and ecological regulation and management in watersheds while steadily promoting the application of eDNA technology in routine research on the detection of organisms in watersheds and monitoring of water ecology. Concurrently, exploratory studies should be undertaken to ascertain the viability of incorporating eDNA technology into the water environment and water ecology monitoring system. Additionally, guidelines and methodologies related to applying and implementing eDNA technology should be collated and refined to expand the scope of eDNA technology in water ecology and environmental monitoring. As tools and methods are developed, eDNA will play an essential role in addressing the challenges of biodiversity loss and climate change. It will play a key role in the sustainable management and conservation of global ecosystems.

Author Contributions

Conceptualization, H.C. and X.L.; methodology, all authors; investigation, H.C. and X.L.; resources, H.C. and Z.X.; writing—original draft preparation, H.C.; writing—review and editing, T.Y., Z.X. and X.L.; visualization, H.C. and T.Y.; supervision, X.L.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Henan Provincial Science and Technology Research Project (Grant Number 242102321116), Foundation of Henan Educational Committee (Grant Number 24B180001) and Henan University of Urban Construction (Grant Number K-Q2023039).

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Pörtner, H.O.; Scholes, R.J.; Arneth, A.; Barnes, D.K.A.; Burrows, M.T.; Diamond, S.E.; Duarte, C.M.; Kiessling, W.; Leadley, P.; Managi, S.; et al. Overcoming the coupled climate and biodiversity crises and their societal impacts. Science 2023, 380, eabl4881. [Google Scholar] [CrossRef] [PubMed]
  2. Hogue, A.S.; Breon, K. The greatest threats to species. Conserv. Sci. Pract. 2022, 4, e12670. [Google Scholar] [CrossRef]
  3. Arya, S. Freshwater Biodiversity and Conservation Challenges: A Review. Int. J. Biol. Innov. 2021, 3, 75–78. [Google Scholar] [CrossRef]
  4. Baranov, V.; Jourdan, J.; Pilotto, F.; Wagner, R.; Haase, P. Complex and nonlinear climate-driven changes in freshwater insect communities over 42 years. Conserv. Biol. 2020, 34, 1241–1251. [Google Scholar] [CrossRef] [PubMed]
  5. Sun, D.; Li, S.; Xiong, W.; Du, X.; Qiao, K.; Zhan, A. Monitoring bloom-forming Aphanizomenon using environmental DNA metabarcoding: Method development, validation, and field application. J. Environ. Sci. 2025, 150, 477–489. [Google Scholar] [CrossRef]
  6. Sullivan, G.T.; Ozman-Sullivan, S.K. Global mite diversity is in crisis: What can we do about it? Zoosymposia 2022, 22, 089–093. [Google Scholar] [CrossRef]
  7. Qiao, Q.; Zhou, Q.; Wang, J.; Lin, H.-J.; Li, B.-Y.; Du, H.; Yan, Z.-G. Environmental DNA reveals the spatiotemporal distribution and migration characteristics of the Yangtze finless porpoise, the sole aquatic mammal in the Yangtze River. Environ. Res. 2024, 263, 120050. [Google Scholar] [CrossRef]
  8. Deiner, K.; Yamanaka, H.; Bernatchez, L. The future of biodiversity monitoring and conservation utilizing environmental DNA. Environ. DNA 2020, 3, 3–7. [Google Scholar] [CrossRef]
  9. Fonseca, V.G.; Davison, P.I.; Creach, V.; Stone, D.; Bass, D.; Tidbury, H.J. The Application of eDNA for Monitoring Aquatic Non-Indigenous Species: Practical and Policy Considerations. Diversity 2023, 15, 631. [Google Scholar] [CrossRef]
  10. Doi, H.; Klymus, K.E.; Marshall, N.T.; Stepien, C.A. Environmental DNA (eDNA) metabarcoding assays to detect invasive invertebrate species in the Great Lakes. PLoS ONE 2017, 12, e0177643. [Google Scholar] [CrossRef]
  11. Lim, N.K.M.; Tay, Y.C.; Srivathsan, A.; Tan, J.W.T.; Kwik, J.T.B.; Baloğlu, B.; Meier, R.; Yeo, D.C.J. Next-generation freshwater bioassessment: eDNA metabarcoding with a conserved metazoan primer reveals species-rich and reservoir-specific communities. R. Soc. Open Sci. 2016, 3, 160635. [Google Scholar] [CrossRef] [PubMed]
  12. 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]
  13. Deiner, K.; Fronhofer, E.A.; Mächler, E.; Walser, J.-C.; Altermatt, F. Environmental DNA reveals that rivers are conveyer belts of biodiversity information. Nat. Commun. 2016, 7. [Google Scholar] [CrossRef] [PubMed]
  14. Djurhuus, A.; Closek, C.J.; Kelly, R.P.; Pitz, K.J.; Michisaki, R.P.; Starks, H.A.; Walz, K.R.; Andruszkiewicz, E.A.; Olesin, E.; Hubbard, K.; et al. Environmental DNA reveals seasonal shifts and potential interactions in a marine community. Nat. Commun. 2020, 11, 254. [Google Scholar] [CrossRef] [PubMed]
  15. Carraro, L.; Mächler, E.; Wüthrich, R.; Altermatt, F. Environmental DNA allows upscaling spatial patterns of biodiversity in freshwater ecosystems. Nat. Commun. 2020, 11, 3585. [Google Scholar] [CrossRef]
  16. Bálint, M.; Nowak, C.; Márton, O.; Pauls, S.U.; Wittwer, C.; Aramayo, J.L.; Schulze, A.; Chambert, T.; Cocchiararo, B.; Jansen, M. Accuracy, limitations and cost efficiency of eDNA-based community survey in tropical frogs. Mol. Ecol. Resour. 2018, 18, 1415–1426. [Google Scholar] [CrossRef]
  17. Li, F.; Peng, Y.; Fang, W.; Altermatt, F.; Xie, Y.; Yang, J.; Zhang, X. Application of Environmental DNA Metabarcoding for Predicting Anthropogenic Pollution in Rivers. Environ. Sci. Technol. 2018, 52, 11708–11719. [Google Scholar] [CrossRef]
  18. Andruszkiewicz Allan, E.; Zhang, W.G.; Lavery, A.C.; Govindarajan, A.F. Environmental DNA shedding and decay rates from diverse animal forms and thermal regimes. Environ. DNA 2020, 3, 492–514. [Google Scholar] [CrossRef]
  19. Harper, K.; Anucha, P.; Turnbull, J.; Bean, C.; Leaver, M. Searching for a signal: Environmental DNA (eDNA) for the detection of invasive signal crayfish, Pacifastacus leniusculus (Dana, 1852). Manag. Biol. Invasions 2018, 9, 137–148. [Google Scholar] [CrossRef]
  20. Klymus, K.E.; Richter, C.A.; Chapman, D.C.; Paukert, C. Quantification of eDNA shedding rates from invasive bighead carp Hypophthalmichthys nobilis and silver carp Hypophthalmichthys molitrix. Biol. Conserv. 2015, 183, 77–84. [Google Scholar] [CrossRef]
  21. Nguyen, B.N.; Shen, E.W.; Seemann, J.; Correa, A.M.S.; O’Donnell, J.L.; Altieri, A.H.; Knowlton, N.; Crandall, K.A.; Egan, S.P.; McMillan, W.O.; et al. Environmental DNA survey captures patterns of fish and invertebrate diversity across a tropical seascape. Sci. Rep. 2020, 10, 6729. [Google Scholar] [CrossRef] [PubMed]
  22. Pawlowski, J.; Apothéloz-Perret-Gentil, L.; Altermatt, F. Environmental DNA: What’s behind the term? Clarifying the terminology and recommendations for its future use in biomonitoring. Mol. Ecol. 2020, 29, 4258–4264. [Google Scholar] [CrossRef] [PubMed]
  23. Wang, P.; Yan, Z.; Yang, S.; Wang, S.; Zheng, X.; Fan, J.; Zhang, T. Environmental DNA: An Emerging Tool in Ecological Assessment. Bull. Environ. Contam. Toxicol. 2019, 103, 651–656. [Google Scholar] [CrossRef] [PubMed]
  24. Sahu, A.; Kumar, N.; Pal Singh, C.; Singh, M. Environmental DNA (eDNA): Powerful technique for biodiversity conservation. J. Nat. Conserv. 2023, 71, 126325. [Google Scholar] [CrossRef]
  25. Rishan, S.T.; Kline, R.J.; Rahman, M.S. Applications of environmental DNA (eDNA) to detect subterranean and aquatic invasive species: A critical review on the challenges and limitations of eDNA metabarcoding. Environ. Adv. 2023, 12, 100370. [Google Scholar] [CrossRef]
  26. Stephenson, P.J. The use of environmental DNA in monitoring aquatic biodiversity for conservation: A review of challenges and opportunities. ARPHA Conf. Abstr. 2021, 4, e65283. [Google Scholar] [CrossRef]
  27. Truelove, N.K.; Andruszkiewicz, E.A.; Block, B.A.; Gilbert, M.T.P. A rapid environmental DNA method for detecting white sharks in the open ocean. Methods Ecol. Evol. 2019, 10, 1128–1135. [Google Scholar] [CrossRef]
  28. Miya, M.; Gotoh, R.O.; Sado, T. MiFish metabarcoding: A high-throughput approach for simultaneous detection of multiple fish species from environmental DNA and other samples. Fish. Sci. 2020, 86, 939–970. [Google Scholar] [CrossRef]
  29. Egeter, B.; Veríssimo, J.; Lopes-Lima, M.; Chaves, C.; Pinto, J.; Riccardi, N.; Beja, P.; Fonseca, N.A. Speeding up the detection of invasive bivalve species using environmental DNA: A Nanopore and Illumina sequencing comparison. Mol. Ecol. Resour. 2022, 22, 2232–2247. [Google Scholar] [CrossRef]
  30. Wu, L.; Li, J.; Tong, F.; Zhang, J.; Li, M.; Ding, S. Resource Assessment of Larimichthys crocea in the East China Sea Based on eDNA Analysis. Front. Mar. Sci. 2022, 9, 890756. [Google Scholar] [CrossRef]
  31. Everts, T.; Halfmaerten, D.; Neyrinck, S.; De Regge, N.; Jacquemyn, H.; Brys, R. Accurate detection and quantification of seasonal abundance of American bullfrog (Lithobates catesbeianus) using ddPCR eDNA assays. Sci. Rep. 2021, 11, 11282. [Google Scholar] [CrossRef] [PubMed]
  32. Palsson, A.; Capo, E.; Spong, G.; Norman, S.; Königsson, H.; Bartels, P.; Byström, P. Droplet digital PCR assays for the quantification of brown trout (Salmo trutta) and Arctic char (Salvelinus alpinus) from environmental DNA collected in the water of mountain lakes. PLoS ONE 2019, 14, e0226638. [Google Scholar] [CrossRef]
  33. Nathan, L.M.; Simmons, M.; Wegleitner, B.J.; Jerde, C.L.; Mahon, A.R. Quantifying Environmental DNA Signals for Aquatic Invasive Species Across Multiple Detection Platforms. Environ. Sci. Technol. 2014, 48, 12800–12806. [Google Scholar] [CrossRef] [PubMed]
  34. Uthicke, S.; Lamare, M.; Doyle, J.R. eDNA detection of corallivorous seastar (Acanthaster cf. solaris) outbreaks on the Great Barrier Reef using digital droplet PCR. Coral Reefs 2018, 37, 1229–1239. [Google Scholar] [CrossRef]
  35. Liu, B.; Wang, F.; Li, S.; Xiong, W.; Zhan, A. Environmental DNA-Based Identification of Non-Native Fish in Beijing: Diversity, Geographical Distribution, and Interactions with Native Taxa. Animals 2024, 14, 2532. [Google Scholar] [CrossRef] [PubMed]
  36. Doi, H.; Anglès d’Auriac, M.B.; Strand, D.A.; Mjelde, M.; Demars, B.O.L.; Thaulow, J. Detection of an invasive aquatic plant in natural water bodies using environmental DNA. PLoS ONE 2019, 14, e0219700. [Google Scholar] [CrossRef]
  37. Jo, T.S.; Sasaki, Y. Evaluating the quantitative performance of environmental DNA metabarcoding for freshwater zooplankton community: A case study in Lake Biwa, Japan. Environ. Sci. Pollut. Res. 2024, 31, 58069–58082. [Google Scholar] [CrossRef]
  38. Miya, M. Environmental DNA Metabarcoding: A Novel Method for Biodiversity Monitoring of Marine Fish Communities. Annu. Rev. Mar. Sci. 2022, 14, 161–185. [Google Scholar] [CrossRef]
  39. Valsecchi, E.; Bylemans, J.; Goodman, S.J.; Lombardi, R.; Carr, I.; Castellano, L.; Galimberti, A.; Galli, P. Novel universal primers for metabarcoding environmental DNA surveys of marine mammals and other marine vertebrates. Environ. DNA 2020, 2, 460–476. [Google Scholar] [CrossRef]
  40. Roblet, S.; Priouzeau, F.; Gambini, G.; Cottalorda, J.-M.; Gastaldi, J.M.; Pey, A.; Raybaud, V.; Suarez, G.R.; Serre, C.; Sabourault, C.; et al. From sight to sequence: Underwater visual census vs environmental DNA metabarcoding for the monitoring of taxonomic and functional fish diversity. Sci. Total Environ. 2024, 956, 177250. [Google Scholar] [CrossRef]
  41. Jurecki, S. Application and Validation of the eDNA-Metabarcoded MiFish/MitoFish Pipeline for Assessment of Native and Non-Native Fish Communities of Lake Michigan. Master’s Thesis, Purdue University, West Lafayette, IN, USA, 2020. [Google Scholar]
  42. Coble, A.A.; Flinders, C.A.; Homyack, J.A.; Penaluna, B.E.; Cronn, R.C.; Weitemier, K. eDNA as a tool for identifying freshwater species in sustainable forestry: A critical review and potential future applications. Sci. Total Environ. 2019, 649, 1157–1170. [Google Scholar] [CrossRef] [PubMed]
  43. Saenz-Agudelo, P.; Delrieu-Trottin, E.; DiBattista, J.D.; Martínez-Rincon, D.; Morales-González, S.; Pontigo, F.; Ramírez, P.; Silva, A.; Soto, M.; Correa, C. Monitoring vertebrate biodiversity of a protected coastal wetland using eDNA metabarcoding. Environ. DNA 2021, 4, 77–92. [Google Scholar] [CrossRef]
  44. New, F.N.; Brito, I.L. What Is Metagenomics Teaching Us, and What Is Missed? Annu. Rev. Microbiol. 2020, 74, 117–135. [Google Scholar] [CrossRef] [PubMed]
  45. Yang, J.; Zhang, X.; Jin, X.; Seymour, M.; Richter, C.; Logares, R.; Khim, J.S.; Klymus, K. Recent advances in environmental DNA-based biodiversity assessment and conservation. Divers. Distrib. 2021, 27, 1876–1879. [Google Scholar] [CrossRef]
  46. 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]
  47. Carraro, L.; Hartikainen, H.; Jokela, J.; Bertuzzo, E.; Rinaldo, A. Estimating species distribution and abundance in river networks using environmental DNA. Proc. Natl. Acad. Sci. USA 2018, 115, 11724–11729. [Google Scholar] [CrossRef]
  48. Oliver, J.-C.; Shum, P.; Mariani, S.; Sink, K.J.; Palmer, R.; Matcher, G.F. Enhancing African coelacanth monitoring using environmental DNA. Biol. Lett. 2024, 20, 20240415. [Google Scholar] [CrossRef]
  49. Harper, L.R.; Buxton, A.S.; Rees, H.C.; Bruce, K.; Brys, R.; Halfmaerten, D.; Read, D.S.; Watson, H.V.; Sayer, C.D.; Jones, E.P.; et al. Prospects and challenges of environmental DNA (eDNA) monitoring in freshwater ponds. Hydrobiologia 2018, 826, 25–41. [Google Scholar] [CrossRef]
  50. Mwamburi, S.M.; Uku, J.; Wambiji, N.; Kairo, J.; Oketch, F.; Oduor, K.O.; Amondi, L.; Ishmael, N. Integration of environmental DNA metabarcoding technique to reinforce fish biodiversity assessments in seagrass ecosystems: A case study of Gazi bay seagrass meadows. Environ. DNA 2023, 5, 1574–1588. [Google Scholar] [CrossRef]
  51. Keck, F.; Hürlemann, S.; Locher, N.; Stamm, C.; Deiner, K.; Altermatt, F. A triad of kicknet sampling, eDNA metabarcoding, and predictive modeling to assess richness of mayflies, stoneflies and caddisflies in rivers. Metabarcoding Metagenomics 2022, 6, 117–131. [Google Scholar] [CrossRef]
  52. Clarke, L.J.; Suter, L.; Deagle, B.E.; Polanowski, A.M.; Terauds, A.; Johnstone, G.J.; Stark, J.S. Environmental DNA metabarcoding for monitoring metazoan biodiversity in Antarctic nearshore ecosystems. PeerJ 2021, 9, e12458. [Google Scholar] [CrossRef] [PubMed]
  53. Renan, S.; Gafny, S.; Perl, R.G.B.; Roll, U.; Malka, Y.; Vences, M.; Geffen, E. Living quarters of a living fossil-Uncovering the current distribution pattern of the rediscovered Hula painted frog (Latonia nigriventer) using environmental DNA. Mol. Ecol. 2017, 26, 6801–6812. [Google Scholar] [CrossRef] [PubMed]
  54. Brys, R.; Halfmaerten, D.; Neyrinck, S.; Mauvisseau, Q.; Auwerx, J.; Sweet, M.; Mergeay, J. Reliable eDNA detection and quantification of the European weather loach (Misgurnus fossilis). J. Fish Biol. 2020, 98, 399–414. [Google Scholar] [CrossRef] [PubMed]
  55. Burgoa Cardás, J.; Deconinck, D.; Márquez, I.; Peón Torre, P.; Garcia-Vazquez, E.; Machado-Schiaffino, G. New eDNA based tool applied to the specific detection and monitoring of the endangered European eel. Biol. Conserv. 2020, 250, 108750. [Google Scholar] [CrossRef]
  56. Dougherty, M.M.; Larson, E.R.; Renshaw, M.A.; Gantz, C.A.; Egan, S.P.; Erickson, D.M.; Lodge, D.M.; Frid, C. EnvironmentalDNA(eDNA) detects the invasive rusty crayfishOrconectes rusticusat low abundances. J. Appl. Ecol. 2016, 53, 722–732. [Google Scholar] [CrossRef]
  57. Johnsen, S.I.; Strand, D.A.; Rusch, J.C.; Vrålstad, T. Environmental DNA (eDNA) Monitoring of Noble Crayfish Astacus astacus in Lentic Environments Offers Reliable Presence-Absence Surveillance—But Fails to Predict Population Density. Front. Environ. Sci. 2020, 8, 612253. [Google Scholar] [CrossRef]
  58. Doi, H.; Katano, I.; Sakata, Y.; Souma, R.; Kosuge, T.; Nagano, M.; Ikeda, K.; Yano, K.; Tojo, K. Detection of an endangered aquatic heteropteran using environmental DNA in a wetland ecosystem. R. Soc. Open Sci. 2017, 4, 170568. [Google Scholar] [CrossRef]
  59. Mahon, A.R.; Doi, H.; Uchii, K.; Takahara, T.; Matsuhashi, S.; Yamanaka, H.; Minamoto, T. Use of Droplet Digital PCR for Estimation of Fish Abundance and Biomass in Environmental DNA Surveys. PLoS ONE 2015, 10, e0122763. [Google Scholar] [CrossRef]
  60. Lacoursière-Roussel, A.; Rosabal, M.; Bernatchez, L. Estimating fish abundance and biomass from eDNA concentrations: Variability among capture methods and environmental conditions. Mol. Ecol. Resour. 2016, 16, 1401–1414. [Google Scholar] [CrossRef]
  61. Harrison, J.B.; Sunday, J.M.; Rogers, S.M. Predicting the fate of eDNA in the environment and implications for studying biodiversity. Proc. R. Soc. B Biol. Sci. 2019, 286, 20191409. [Google Scholar] [CrossRef]
  62. Fukaya, K.; Murakami, H.; Yoon, S.; Minami, K.; Osada, Y.; Yamamoto, S.; Masuda, R.; Kasai, A.; Miyashita, K.; Minamoto, T.; et al. Estimating fish population abundance by integrating quantitative data on environmental DNA and hydrodynamic modelling. Mol. Ecol. 2020, 30, 3057–3067. [Google Scholar] [CrossRef] [PubMed]
  63. Port, J.A.; O’Donnell, J.L.; Romero-Maraccini, O.C.; Leary, P.R.; Litvin, S.Y.; Nickols, K.J.; Yamahara, K.M.; Kelly, R.P. Assessing vertebrate biodiversity in a kelp forest ecosystem using environmental DNA. Mol. Ecol. 2015, 25, 527–541. [Google Scholar] [CrossRef] [PubMed]
  64. Lacoursière-Roussel, A.; Howland, K.; Normandeau, E.; Grey, E.K.; Archambault, P.; Deiner, K.; Lodge, D.M.; Hernandez, C.; Leduc, N.; Bernatchez, L. eDNA metabarcoding as a new surveillance approach for coastal Arctic biodiversity. Ecol. Evol. 2018, 8, 7763–7777. [Google Scholar] [CrossRef] [PubMed]
  65. Sales, N.G.; McKenzie, M.B.; Drake, J.; Harper, L.R.; Browett, S.S.; Coscia, I.; Wangensteen, O.S.; Baillie, C.; Bryce, E.; Dawson, D.A.; et al. Fishing for mammals: Landscape-level monitoring of terrestrial and semi-aquatic communities using eDNA from riverine systems. J. Appl. Ecol. 2020, 57, 707–716. [Google Scholar] [CrossRef]
  66. 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]
  67. Robson, H.L.A.; Noble, T.H.; Saunders, R.J.; Robson, S.K.A.; Burrows, D.W.; Jerry, D.R. Fine-tuning for the tropics: Application of eDNA technology for invasive fish detection in tropical freshwater ecosystems. Mol. Ecol. Resour. 2016, 16, 922–932. [Google Scholar] [CrossRef]
  68. Lauretta, M.; Coulter, D.P.; Wang, P.; Coulter, A.A.; Van Susteren, G.E.; Eichmiller, J.J.; Garvey, J.E.; Sorensen, P.W. Nonlinear relationship between Silver Carp density and their eDNA concentration in a large river. PLoS ONE 2019, 14, e0218823. [Google Scholar] [CrossRef]
  69. Doi, H.; Nevers, M.B.; Byappanahalli, M.N.; Morris, C.C.; Shively, D.; Przybyla-Kelly, K.; Spoljaric, A.M.; Dickey, J.; Roseman, E.F. Environmental DNA (eDNA): A tool for quantifying the abundant but elusive round goby (Neogobius melanostomus). PLoS ONE 2018, 13, e0191720. [Google Scholar] [CrossRef]
  70. Doi, H.; Hervé, A.; Domaizon, I.; Baudoin, J.-M.; Dejean, T.; Gibert, P.; Jean, P.; Peroux, T.; Raymond, J.-C.; Valentini, A.; et al. Spatio-temporal variability of eDNA signal and its implication for fish monitoring in lakes. PLoS ONE 2022, 17, e0272660. [Google Scholar] [CrossRef]
  71. Lanzén, A.; Dahlgren, T.G.; Bagi, A.; Hestetun, J.T. Benthic eDNA metabarcoding provides accurate assessments of impact from oil extraction, and ecological insights. Ecol. Indic. 2021, 130, 108064. [Google Scholar] [CrossRef]
  72. Yang, J.; Zhang, X. eDNA metabarcoding in zooplankton improves the ecological status assessment of aquatic ecosystems. Environ. Int. 2020, 134, 105230. [Google Scholar] [CrossRef] [PubMed]
  73. Li, X.; Chen, K.; Wang, C.; Zhuo, T.; Li, H.; Wu, Y.; Lei, X.; Li, M.; Chen, B.; Chai, B. Deciphering environmental factors influencing phytoplankton community structure in a polluted urban river. J. Environ. Sci. 2025, 148, 375–386. [Google Scholar] [CrossRef] [PubMed]
  74. Lee, A.H.; Lee, J.; Hong, S.; Kwon, B.-O.; Xie, Y.; Giesy, J.P.; Zhang, X.; Khim, J.S. Integrated assessment of west coast of South Korea by use of benthic bacterial community structure as determined by eDNA, concentrations of contaminants, and in vitro bioassays. Environ. Int. 2020, 137, 105569. [Google Scholar] [CrossRef] [PubMed]
  75. Wee, A.K.S.; Salmo Iii, S.G.; Sivakumar, K.; Then, A.Y.H.; Basyuni, M.; Fall, J.; Habib, K.A.; Isowa, Y.; Leopardas, V.; Peer, N.; et al. Prospects and challenges of environmental DNA (eDNA) metabarcoding in mangrove restoration in Southeast Asia. Front. Mar. Sci. 2023, 10, 1033258. [Google Scholar] [CrossRef]
  76. Belle, C.C.; Stoeckle, B.C.; Geist, J. Taxonomic and geographical representation of freshwater environmental DNA research in aquatic conservation. Aquat. Conserv. Mar. Freshw. Ecosyst. 2019, 29, 1996–2009. [Google Scholar] [CrossRef]
  77. Zong, S.; Brantschen, J.; Zhang, X.; Albouy, C.; Valentini, A.; Zhang, H.; Altermatt, F.; Pellissier, L. Combining environmental DNA with remote sensing variables to map fish species distributions along a large river. Remote Sens. Ecol. Conserv. 2023, 10, 220–235. [Google Scholar] [CrossRef]
  78. Meyer, R.S.; Ramos, M.M.; Lin, M.; Schweizer, T.M.; Gold, Z.; Ramos, D.R.; Shirazi, S.; Kandlikar, G.; Kwan, W.-Y.; Curd, E.E.; et al. The CALeDNA program: Citizen scientists and researchers inventory California’s biodiversity. Calif. Agric. 2021, 75, 20–32. [Google Scholar] [CrossRef]
  79. Zhang, X. Environmental DNA Shaping a New Era of Ecotoxicological Research. Environ. Sci. Technol. 2019, 53, 5605–5612. [Google Scholar] [CrossRef]
  80. Keeley, N.; Wood, S.A.; Pochon, X. Development and preliminary validation of a multi-trophic metabarcoding biotic index for monitoring benthic organic enrichment. Ecol. Indic. 2018, 85, 1044–1057. [Google Scholar] [CrossRef]
  81. Xie, Y.; Zhang, X.; Yang, J.; Kim, S.; Hong, S.; Giesy, J.P.; Yim, U.H.; Shim, W.J.; Yu, H.; Khim, J.S. eDNA-based bioassessment of coastal sediments impacted by an oil spill. Environ. Pollut. 2018, 238, 739–748. [Google Scholar] [CrossRef]
  82. Downie, A.T.; Bennett, W.W.; Wilkinson, S.; de Bruyn, M.; DiBattista, J.D. From land to sea: Environmental DNA is correlated with long-term water quality indicators in an urbanized estuary. Mar. Pollut. Bull. 2024, 207, 116887. [Google Scholar] [CrossRef] [PubMed]
  83. Gu, S.; Zhang, P.; Luo, S.; Chen, K.; Jiang, C.; Xiong, J.; Miao, W. Microbial Community Colonization Process Unveiled through eDNA-PFU Technology in Mesocosm Ecosystems. Microorganisms 2023, 11, 2498. [Google Scholar] [CrossRef] [PubMed]
  84. Liu, Q.; Zhang, Y.; Wu, H.; Liu, F.; Peng, W.; Zhang, X.; Chang, F.; Xie, P.; Zhang, H. A Review and Perspective of eDNA Application to Eutrophication and HAB Control in Freshwater and Marine Ecosystems. Microorganisms 2020, 8, 417. [Google Scholar] [CrossRef] [PubMed]
  85. Yang, J.; Xie, Y.; Jeppe, K.; Long, S.; Pettigrove, V.; Zhang, X. Sensitive community responses of microbiota to copper in sediment toxicity test. Environ. Toxicol. Chem. 2018, 37, 599–608. [Google Scholar] [CrossRef]
  86. Sehnal, L.; Brammer-Robbins, E.; Wormington, A.M.; Blaha, L.; Bisesi, J.; Larkin, I.; Martyniuk, C.J.; Simonin, M.; Adamovsky, O. Microbiome Composition and Function in Aquatic Vertebrates: Small Organisms Making Big Impacts on Aquatic Animal Health. Front. Microbiol. 2021, 12, 567408. [Google Scholar] [CrossRef]
  87. De Laender, F.; Rohr, J.R.; Ashauer, R.; Baird, D.J.; Berger, U.; Eisenhauer, N.; Grimm, V.; Hommen, U.; Maltby, L.; Meliàn, C.J.; et al. Reintroducing Environmental Change Drivers in Biodiversity–Ecosystem Functioning Research. Trends Ecol. Evol. 2016, 31, 905–915. [Google Scholar] [CrossRef]
  88. Bannar-Martin, K.H.; Kremer, C.T.; Ernest, S.K.M.; Leibold, M.A.; Auge, H.; Chase, J.; Declerck, S.A.J.; Eisenhauer, N.; Harpole, S.; Hillebrand, H.; et al. Integrating community assembly and biodiversity to better understand ecosystem function: The Community Assembly and the Functioning of Ecosystems (CAFE) approach. Ecol. Lett. 2017, 21, 167–180. [Google Scholar] [CrossRef]
  89. Li, F.; Guo, F.; Gao, W.; Cai, Y.; Zhang, Y.; Yang, Z. Environmental DNA Biomonitoring Reveals the Interactive Effects of Dams and Nutrient Enrichment on Aquatic Multitrophic Communities. Environ. Sci. Technol. 2022, 56, 16952–16963. [Google Scholar] [CrossRef]
  90. Visser, F.; Merten, V.J.; Bayer, T.; Oudejans, M.G.; de Jonge, D.S.W.; Puebla, O.; Reusch, T.B.H.; Fuss, J.; Hoving, H.J.T. Deep-sea predator niche segregation revealed by combined cetacean biologging and eDNA analysis of cephalopod prey. Sci. Adv. 2021, 7, eabf5908. [Google Scholar] [CrossRef]
  91. Xie, Y.; Wang, J.; Yang, J.; Giesy, J.P.; Yu, H.; Zhang, X. Environmental DNA metabarcoding reveals primary chemical contaminants in freshwater sediments from different land-use types. Chemosphere 2017, 172, 201–209. [Google Scholar] [CrossRef]
  92. Kagzi, K.; Hechler, R.M.; Fussmann, G.F.; Cristescu, M.E. Environmental RNA degrades more rapidly than environmental DNA across a broad range of pH conditions. Mol. Ecol. Resour. 2022, 22, 2640–2650. [Google Scholar] [CrossRef] [PubMed]
  93. Jo, T.; Murakami, H.; Masuda, R.; Sakata, M.K.; Yamamoto, S.; Minamoto, T. Rapid degradation of longer DNA fragments enables the improved estimation of distribution and biomass using environmental DNA. Mol. Ecol. Resour. 2017, 17, e25–e33. [Google Scholar] [CrossRef] [PubMed]
  94. Shogren, A.J.; Tank, J.L.; Egan, S.P.; August, O.; Rosi, E.J.; Hanrahan, B.R.; Renshaw, M.A.; Gantz, C.A.; Bolster, D. Water flow and biofilm cover influence environmental DNA detection in recirculating streams. Environ. Sci. Technol. 2018, 52, 8530–8537. [Google Scholar] [CrossRef] [PubMed]
  95. Jane, S.F.; Wilcox, T.M.; McKelvey, K.S.; Young, M.K.; Schwartz, M.K.; Lowe, W.H.; Letcher, B.H.; Whiteley, A.R. Distance, flow and PCR inhibition: eDNA dynamics in two headwater streams. Mol. Ecol. Resour. 2014, 15, 216–227. [Google Scholar] [CrossRef]
  96. Ushio, M.; Murakami, H.; Masuda, R.; Sado, T.; Miya, M.; Sakurai, S.; Yamanaka, H.; Minamoto, T.; Kondoh, M. Quantitative monitoring of multispecies fish environmental DNA using high-throughput sequencing. Metabarcoding Metagenom. 2018, 2, e23297. [Google Scholar] [CrossRef]
  97. Yamanaka, H.; Motozawa, H.; Tsuji, S.; Miyazawa, R.C.; Takahara, T.; Minamoto, T. On-site filtration of water samples for environmental DNA analysis to avoid DNA degradation during transportation. Ecol. Res. 2016, 31, 963–967. [Google Scholar] [CrossRef]
  98. Zhao, B.; van Bodegom, P.M.; Trimbos, K.B. Bacterial abundance and pH associate with eDNA degradation in water from various aquatic ecosystems in a laboratory setting. Front. Environ. Sci. 2023, 11, 1025105. [Google Scholar] [CrossRef]
  99. McCartin, L.J.; Vohsen, S.A.; Ambrose, S.W.; Layden, M.; McFadden, C.S.; Cordes, E.E.; McDermott, J.M.; Herrera, S. Temperature Controls eDNA Persistence across Physicochemical Conditions in Seawater. Environ. Sci. Technol. 2022, 56, 8629–8639. [Google Scholar] [CrossRef]
  100. Saito, T.; Doi, H. A Model and Simulation of the Influence of Temperature and Amplicon Length on Environmental DNA Degradation Rates: A Meta-Analysis Approach. Front. Ecol. Evol. 2021, 9, 623831. [Google Scholar] [CrossRef]
  101. DeHart, H.M.; Gasser, M.T.; Dixon, J.; Thielen, P. An aquatic environmental DNA filtration system to maximize recovery potential and promote filtration approach standardization. PeerJ 2023, 11, e15360. [Google Scholar] [CrossRef]
  102. Elbrecht, V.; Peinert, B.; Leese, F. Sorting things out: Assessing effects of unequal specimen biomass on DNA metabarcoding. Ecol. Evol. 2017, 7, 6918–6926. [Google Scholar] [CrossRef] [PubMed]
  103. Adams, C.I.M.; Knapp, M.; Gemmell, N.J.; Jeunen, G.-J.; Bunce, M.; Lamare, M.D.; Taylor, H.R. Beyond Biodiversity: Can Environmental DNA (eDNA) Cut It as a Population Genetics Tool? Genes 2019, 10, 192. [Google Scholar] [CrossRef] [PubMed]
  104. Sassoubre, L.M.; Yamahara, K.M.; Gardner, L.D.; Block, B.A.; Boehm, A.B. Quantification of Environmental DNA (eDNA) Shedding and Decay Rates for Three Marine Fish. Environ. Sci. Technol. 2016, 50, 10456–10464. [Google Scholar] [CrossRef] [PubMed]
  105. Tillotson, M.D.; Kelly, R.P.; Duda, J.J.; Hoy, M.; Kralj, J.; Quinn, T.P. Concentrations of environmental DNA (eDNA) reflect spawning salmon abundance at fine spatial and temporal scales. Biol. Conserv. 2018, 220, 1–11. [Google Scholar] [CrossRef]
  106. Lance, R.; Klymus, K.; Richter, C.; Guan, X.; Farrington, H.; Carr, M.; Thompson, N.; Chapman, D.; Baerwaldt, K. Experimental observations on the decay of environmental DNA from bighead and silver carps. Manag. Biol. Invasions 2017, 8, 343–359. [Google Scholar] [CrossRef]
  107. Sales, N.G.; Wangensteen, O.S.; Carvalho, D.C.; Mariani, S. Influence of preservation methods, sample medium and sampling time on eDNA recovery in a neotropical river. Environ. DNA 2019, 1, 119–130. [Google Scholar] [CrossRef]
  108. Saito, T.; Doi, H. Effect of salinity and water dilution on environmental DNA degradation in freshwater environments. bioRxiv 2021. [Google Scholar] [CrossRef]
  109. van Bochove, K.; Bakker, F.T.; Beentjes, K.K.; Hemerik, L.; Vos, R.A.; Gravendeel, B. Organic matter reduces the amount of detectable environmental DNA in freshwater. Ecol. Evol. 2020, 10, 3647–3654. [Google Scholar] [CrossRef]
  110. Collins, R.A.; Wangensteen, O.S.; O’Gorman, E.J.; Mariani, S.; Sims, D.W.; Genner, M.J. Persistence of environmental DNA in marine systems. Commun. Biol. 2018, 1, 185. [Google Scholar] [CrossRef]
  111. Jackman, J.M.; Benvenuto, C.; Coscia, I.; Oliveira Carvalho, C.; Ready, J.S.; Boubli, J.P.; Magnusson, W.E.; McDevitt, A.D.; Guimarães Sales, N. eDNA in a bottleneck: Obstacles to fish metabarcoding studies in megadiverse freshwater systems. Environ. DNA 2021, 3, 837–849. [Google Scholar] [CrossRef]
  112. Deng, J.; Zhang, X.; Yao, X.; Rao, J.; Dai, F.; Wang, H.; Wang, Y.; Jiang, W. eDNA metabarcoding reveals differences in fish diversity and community structure in Danjiang River. Sci. Rep. 2024, 14, 29460. [Google Scholar] [CrossRef] [PubMed]
  113. Le Joncour, A.; Mouchet, M.; Boussarie, G.; Lavialle, G.; Pennors, L.; Bouche, L.; Le Bourdonnec, P.; Morandeau, F.; Kopp, D. Is it worthy to use environmental DNA instead of scientific trawling or video survey to monitor taxa in soft-bottom habitats? Mar. Environ. Res. 2024, 200, 106667. [Google Scholar] [CrossRef] [PubMed]
  114. Schroeter, J.C.; Maloy, A.P.; Rees, C.B.; Bartron, M.L. Fish mitochondrial genome sequencing: Expanding genetic resources to support species detection and biodiversity monitoring using environmental DNA. Conserv. Genet. Resour. 2019, 12, 433–446. [Google Scholar] [CrossRef]
  115. Polanco, F.A.; Richards, E.; Flück, B.; Valentini, A.; Altermatt, F.; Brosse, S.; Walser, J.C.; Eme, D.; Marques, V.; Manel, S.; et al. Comparing the performance of 12S mitochondrial primers for fish environmental DNA across ecosystems. Environ. DNA 2021, 3, 1113–1127. [Google Scholar] [CrossRef]
  116. Doi, H.; Stoeckle, B.C.; Beggel, S.; Cerwenka, A.F.; Motivans, E.; Kuehn, R.; Geist, J. A systematic approach to evaluate the influence of environmental conditions on eDNA detection success in aquatic ecosystems. PLoS ONE 2017, 12, e0189119. [Google Scholar] [CrossRef]
  117. Shah, V.; Schultz, M.T.; Lance, R.F. Modeling the Sensitivity of Field Surveys for Detection of Environmental DNA (eDNA). PLoS ONE 2015, 10, e0141503. [Google Scholar] [CrossRef]
  118. Goldberg, C.S.; Turner, C.R.; Deiner, K.; Klymus, K.E.; Thomsen, P.F.; Murphy, M.A.; Spear, S.F.; McKee, A.; Oyler-McCance, S.J.; Cornman, R.S.; et al. Critical considerations for the application of environmental DNA methods to detect aquatic species. Methods Ecol. Evol. 2016, 7, 1299–1307. [Google Scholar] [CrossRef]
  119. Wilson, C.C.; Wozney, K.M.; Smith, C.M.; Yu, D. Recognizing false positives: Synthetic oligonucleotide controls for environmental DNA surveillance. Methods Ecol. Evol. 2015, 7, 23–29. [Google Scholar] [CrossRef]
  120. Sepulveda, A.J.; Hutchins, P.R.; Forstchen, M.; McKeefry, M.N.; Swigris, A.M. The Elephant in the Lab (and Field): Contamination in Aquatic Environmental DNA Studies. Front. Ecol. Evol. 2020, 8, 609973. [Google Scholar] [CrossRef]
  121. Hutchins, P.R.; Simantel, L.N.; Sepulveda, A.J. Time to get real with qPCR controls: The frequency of sample contamination and the informative power of negative controls in environmental DNA studies. Mol. Ecol. Resour. 2021, 22, 1319–1329. [Google Scholar] [CrossRef]
  122. Willson, R.C.; Wilcox, T.M.; McKelvey, K.S.; Young, M.K.; Jane, S.F.; Lowe, W.H.; Whiteley, A.R.; Schwartz, M.K. Robust Detection of Rare Species Using Environmental DNA: The Importance of Primer Specificity. PLoS ONE 2013, 8, e59520. [Google Scholar] [CrossRef]
  123. Lahoz-Monfort, J.J.; Guillera-Arroita, G.; Tingley, R. Statistical approaches to account for false-positive errors in environmental DNA samples. Mol. Ecol. Resour. 2015, 16, 673–685. [Google Scholar] [CrossRef] [PubMed]
  124. Xing, Y.; Gao, W.; Shen, Z.; Zhang, Y.; Bai, J.; Cai, X.; Ouyang, J.; Zhao, Y. A Review of Environmental DNA Field and Laboratory Protocols Applied in Fish Ecology and Environmental Health. Front. Environ. Sci. 2022, 10, 725360. [Google Scholar] [CrossRef]
  125. Doi, H.; Hinlo, R.; Gleeson, D.; Lintermans, M.; Furlan, E. Methods to maximise recovery of environmental DNA from water samples. PLoS ONE 2017, 12, e0179251. [Google Scholar] [CrossRef]
  126. Mathieu, C.; Hermans, S.M.; Lear, G.; Buckley, T.R.; Lee, K.C.; Buckley, H.L. A Systematic Review of Sources of Variability and Uncertainty in eDNA Data for Environmental Monitoring. Front. Ecol. Evol. 2020, 8, 135. [Google Scholar] [CrossRef]
  127. Hiraoka, S.; Ijichi, M.; Takeshima, H.; Kumagai, Y.; Yang, C.C.; Makabe-Kobayashi, Y.; Fukuda, H.; Yoshizawa, S.; Iwasaki, W.; Kogure, K.; et al. Probe Capture Enrichment Sequencing of amoA Genes Improves the Detection of Diverse Ammonia-Oxidising Archaeal and Bacterial Populations. Mol. Ecol. Resour. 2024, e14042. [Google Scholar] [CrossRef]
  128. Zaiko, A.; von Ammon, U.; Stuart, J.; Smith, K.; Yao, R.; Welsh, M.; Pochon, X.; Bowers, H. Performance and cost-efficiency of eDNA and eRNA capture methodologies: Experimental assessment using cultured microalgae. ARPHA Conf. Abstr. 2021, 4, e65098. [Google Scholar] [CrossRef]
  129. Sanches, T.M.; Schreier, A.M. Optimizing an eDNA protocol for monitoring endangered Chinook Salmon in the San Francisco Estuary: Balancing sensitivity, cost and time. bioRxiv 2019. [Google Scholar] [CrossRef]
  130. Monteban, D.; Pedersen, J.O.P.; Nielsen, M.H. Physical oceanographic conditions and a sensitivity study on meltwater runoff in a West Greenland fjord: Kangerlussuaq. Oceanologia 2020, 62, 460–477. [Google Scholar] [CrossRef]
  131. Vilmin, L.; Mogollón, J.M.; Beusen, A.H.W.; van Hoek, W.J.; Liu, X.; Middelburg, J.J.; Bouwman, A.F. Modeling Process-Based Biogeochemical Dynamics in Surface Fresh Waters of Large Watersheds With the IMAGE-DGNM Framework. J. Adv. Model. Earth Syst. 2020, 12, e2019MS001796. [Google Scholar] [CrossRef]
  132. Dunn, R.; Zigic, S.; Burling, M.; Lin, H.-H. Hydrodynamic and Sediment Modelling within a Macro Tidal Estuary: Port Curtis Estuary, Australia. J. Mar. Sci. Eng. 2015, 3, 720–744. [Google Scholar] [CrossRef]
  133. Reid, D.A.; Hassan, M.A.; Bird, S.; Pike, R.; Tschaplinski, P. Does variable channel morphology lead to dynamic salmon habitat? Earth Surf. Process. Landf. 2020, 45, 295–311. [Google Scholar] [CrossRef]
  134. Sevellec, M.; Lacoursière-Roussel, A.; Bernatchez, L.; Normandeau, E.; Solomon, E.; Arreak, A.; Fishback, L.; Howland, K. Detecting community change in Arctic marine ecosystems using the temporal dynamics of environmental DNA. Environ. DNA 2020, 3, 573–590. [Google Scholar] [CrossRef]
  135. Troth, C.R.; Sweet, M.J.; Nightingale, J.; Burian, A. Seasonality, DNA degradation and spatial heterogeneity as drivers of eDNA detection dynamics. Sci. Total Environ. 2021, 768, 144466. [Google Scholar] [CrossRef]
  136. Bylemans, J.; Gleeson, D.M.; Lintermans, M.; Hardy, C.M.; Beitzel, M.; Gilligan, D.M.; Furlan, E.M. Monitoring riverine fish communities through eDNA metabarcoding: Determining optimal sampling strategies along an altitudinal and biodiversity gradient. Metabarcoding Metagenom. 2018, 2, e30457. [Google Scholar] [CrossRef]
  137. Bista, I.; Carvalho, G.R.; Walsh, K.; Seymour, M.; Hajibabaei, M.; Lallias, D.; Christmas, M.; Creer, S. Annual time-series analysis of aqueous eDNA reveals ecologically relevant dynamics of lake ecosystem biodiversity. Nat. Commun. 2017, 8, 14087. [Google Scholar] [CrossRef]
  138. Ruppert, K.M.; Kline, R.J.; Rahman, M.S. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: A systematic review in methods, monitoring, and applications of global eDNA. Glob. Ecol. Conserv. 2019, 17, e00547. [Google Scholar] [CrossRef]
  139. Li, J.; Wang, S.; Liu, P.; Peng, J.; Liu, X.; Sun, Q.; Zhou, B.; Lei, K. Environmental DNA metabarcoding reveals the influence of environmental heterogeneity on microeukaryotic plankton in the offshore waters of East China Sea. Environ. Res. 2024, 262, 119921. [Google Scholar] [CrossRef]
  140. Garlapati, D.; Charankumar, B.; Ramu, K.; Madeswaran, P.; Ramana Murthy, M.V. 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]
  141. Othman, N.; Muniar, K.; Haris, H.; Ramli, F.F.; Sariyat, N.H.; Najmuddin, M.F.; Abdul-Latiff, M.A.B. A Review of Next-Generation Wildlife Monitoring using Environmental DNA (eDNA) Detection and Next-Generation Sequencing in Malaysia. Sains Malays. 2023, 52, 17–33. [Google Scholar] [CrossRef]
  142. Doorenspleet, K.; Jansen, L.; Oosterbroek, S.; Kamermans, P.; Bos, O.; Wurz, E.; Murk, A.; Nijland, R. The long and the short of it: Nanopore based eDNA metabarcoding of marine vertebrates works; sensitivity and specificity depend on amplicon lengths. BioRxiv 2021, 11, 470087. [Google Scholar] [CrossRef]
  143. Benítez-Páez, A.; Sanz, Y. Multi-locus and long amplicon sequencing approach to study microbial diversity at species level using the MinION™ portable nanopore sequencer. GigaScience 2017, 6, gix043. [Google Scholar] [CrossRef] [PubMed]
  144. Flück, B.; Mathon, L.; Manel, S.; Valentini, A.; Dejean, T.; Albouy, C.; Mouillot, D.; Thuiller, W.; Murienne, J.; Brosse, S.; et al. Applying convolutional neural networks to speed up environmental DNA annotation in a highly diverse ecosystem. Sci. Rep. 2022, 12, 10247. [Google Scholar] [CrossRef] [PubMed]
  145. Tristan, C.; Philippe, E.; Franck, L.; Joana, V.; Amine, O.; Catarina, M.; Tomas, C.; Jan, P. 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]
  146. Pomerantz, A.; Peñafiel, N.; Arteaga, A.; Bustamante, L.; Pichardo, F.; Coloma, L.A.; Barrio-Amorós, C.L.; Salazar-Valenzuela, D.; Prost, S. Real-time DNA barcoding in a rainforest using nanopore sequencing: Opportunities for rapid biodiversity assessments and local capacity building. GigaScience 2018, 7, giy033. [Google Scholar] [CrossRef]
  147. Chen, W.; Wang, J.; Zhao, Y.; He, Y.; Chen, J.; Dong, C.; Liu, L.; Wang, J.; Zhou, L. Contrasting pollution responses of native and non-native fish communities in anthropogenically disturbed estuaries unveiled by eDNA metabarcoding. J. Hazard. Mater. 2024, 480, 136323. [Google Scholar] [CrossRef]
  148. Gadagkar, S.R.; Menegon, M.; Cantaloni, C.; Rodriguez-Prieto, A.; Centomo, C.; Abdelfattah, A.; Rossato, M.; Bernardi, M.; Xumerle, L.; Loader, S.; et al. On site DNA barcoding by nanopore sequencing. PLoS ONE 2017, 12, e0184741. [Google Scholar] [CrossRef]
  149. Urban, L.; Holzer, A.; Baronas, J.J.; Hall, M.B.; Braeuninger-Weimer, P.; Scherm, M.J.; Kunz, D.J.; Perera, S.N.; Martin-Herranz, D.E.; Tipper, E.T.; et al. Freshwater monitoring by nanopore sequencing. eLife 2021, 10, e61504. [Google Scholar] [CrossRef]
  150. Merelli, I.; Morganti, L.; Corni, E.; Pellegrino, C.; Cesini, D.; Roverelli, L.; Zereik, G.; D’Agostino, D. Low-power portable devices for metagenomics analysis: Fog computing makes bioinformatics ready for the Internet of Things. Future Gener. Comput. Syst. 2018, 88, 467–478. [Google Scholar] [CrossRef]
  151. Mousavi-Derazmahalleh, M.; Stott, A.; Lines, R.; Peverley, G.; Nester, G.; Simpson, T.; Zawierta, M.; De La Pierre, M.; Bunce, M.; Christophersen, C.T. eDNAFlow, an automated, reproducible and scalable workflow for analysis of environmental DNA sequences exploiting Nextflow and Singularity. Mol. Ecol. Resour. 2021, 21, 1697–1704. [Google Scholar] [CrossRef]
  152. Burian, A.; Mauvisseau, Q.; Bulling, M.; Domisch, S.; Qian, S.; Sweet, M. Improving the reliability of eDNA data interpretation. Mol. Ecol. Resour. 2021, 21, 1422–1433. [Google Scholar] [CrossRef] [PubMed]
  153. Mathon, L.; Valentini, A.; Guérin, P.E.; Normandeau, E.; Noel, C.; Lionnet, C.; Boulanger, E.; Thuiller, W.; Bernatchez, L.; Mouillot, D.; et al. Benchmarking bioinformatic tools for fast and accurate eDNA metabarcoding species identification. Mol. Ecol. Resour. 2021, 21, 2565–2579. [Google Scholar] [CrossRef] [PubMed]
  154. Ficetola, G.F.; Pansu, J.; Bonin, A.; Coissac, E.; Giguet-Covex, C.; De Barba, M.; Gielly, L.; Lopes, C.M.; Boyer, F.; Pompanon, F.; et al. Replication levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data. Mol. Ecol. Resour. 2014, 15, 543–556. [Google Scholar] [CrossRef] [PubMed]
  155. Wilkinson, S.; Davy, S.; Bunce, M.; Stat, M. Taxonomic identification of environmental DNA with informatic sequence classification trees. PeerJ 2018, 6, e26812v1. [Google Scholar] [CrossRef]
  156. Hoshino, T.; Nakao, R.; Doi, H.; Minamoto, T. Simultaneous absolute quantification and sequencing of fish environmental DNA in a mesocosm by quantitative sequencing technique. Sci. Rep. 2021, 11, 4372. [Google Scholar] [CrossRef]
  157. Lin, M.; Simons, A.L.; Harrigan, R.J.; Curd, E.E.; Schneider, F.D.; Ruiz-Ramos, D.V.; Gold, Z.; Osborne, M.G.; Shirazi, S.; Schweizer, T.M.; et al. Landscape analyses using eDNA metabarcoding and Earth observation predict community biodiversity in California. Ecol. Appl. 2021, 31, e02379. [Google Scholar] [CrossRef]
  158. Luque, S.; Pettorelli, N.; Vihervaara, P.; Wegmann, M.; Vamosi, J. Improving biodiversity monitoring using satellite remote sensing to provide solutions towards the 2020 conservation targets. Methods Ecol. Evol. 2018, 9, 1784–1786. [Google Scholar] [CrossRef]
  159. Aucone, E.; Kirchgeorg, S.; Valentini, A.; Pellissier, L.; Deiner, K.; Mintchev, S. Drone-assisted collection of environmental DNA from tree branches for biodiversity monitoring. Sci. Robot. 2023, 8, eadd5762. [Google Scholar] [CrossRef]
  160. 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]
  161. Lacoeuilhe, A.; Pamerlon, S.; Archambeau, A.-S.; Denys, G.; Le Bras, Y.; Norvez, O. An Overview of the French eDNA Data Landscape: Focus on a national technical repository of reference genetic sequences. Biodivers. Inf. Sci. Stand. 2023, 7, e110103. [Google Scholar] [CrossRef]
  162. Mashaphu, M.F.; O’Brien, G.C.; Downs, C.T.; Willows-Munro, S. The status of COI and 12S rRNA DNA barcode reference libraries for freshwater fish in South Africa: Implications for future eDNA projects. Afr. Zool. 2023, 58, 97–105. [Google Scholar] [CrossRef]
  163. Schenekar, T.; Schletterer, M.; Lecaudey, L.A.; Weiss, S.J. Reference databases, primer choice, and assay sensitivity for environmental metabarcoding: Lessons learnt from a re-evaluation of an eDNA fish assessment in the Volga headwaters. River Res. Appl. 2020, 36, 1004–1013. [Google Scholar] [CrossRef]
  164. Banerjee, P.; Stewart, K.A.; Dey, G.; Antognazza, C.M.; Sharma, R.K.; Maity, J.P.; Saha, S.; Doi, H.; de Vere, N.; Chan, M.W.Y.; et al. Environmental DNA analysis as an emerging non-destructive method for plant biodiversity monitoring: A review. AoB Plants 2022, 14, 987–999. [Google Scholar] [CrossRef] [PubMed]
  165. Bálint, M.; Tumusiime, J.; Nakintu, J.; Baranski, D.; Schardt, L.; Romahn, J.; Dusabe, M.-C.; Tolo, C.U.; Kagoro, G.R.; Ssenkuba, F.; et al. Environmental DNA barcoding reveals general biodiversity patterns in the large tropical rift Lake Albert. Sci. Total Environ. 2024, 957, 177308. [Google Scholar] [CrossRef]
  166. Curd, E.E.; Gold, Z.; Kandlikar, G.S.; Gomer, J.; Ogden, M.; O’Connell, T.; Pipes, L.; Schweizer, T.M.; Rabichow, L.; Lin, M.; et al. Anacapa Toolkit: An environmental DNA toolkit for processing multilocus metabarcode datasets. Methods Ecol. Evol. 2019, 10, 1469–1475. [Google Scholar] [CrossRef]
  167. Suarez-Menendez, M.; Planes, S.; Garcia-Vazquez, E.; Ardura, A. Early Alert of Biological Risk in a Coastal Lagoon Through eDNA Metabarcoding. Front. Ecol. Evol. 2020, 8, 9. [Google Scholar] [CrossRef]
  168. Rovero, F.; Ahumada, J. The Tropical Ecology, Assessment and Monitoring (TEAM) Network: An early warning system for tropical rain forests. Sci. Total Environ. 2017, 574, 914–923. [Google Scholar] [CrossRef]
  169. Liu, Z.; Hu, C.; You, W.; Li, S.; Wu, Y.; Liang, Y.; Chu, L.; Yan, Y.; Zhang, C. Comparison Between Environmental DNA Metabarcoding and Traditional Survey Method to Identify Community Composition and Assembly of Stream Fish. Ecol. Evol. 2024, 14, e70627. [Google Scholar] [CrossRef]
  170. Larson, E.R.; Graham, B.M.; Achury, R.; Coon, J.J.; Daniels, M.K.; Gambrell, D.K.; Jonasen, K.L.; King, G.D.; LaRacuente, N.; Perrin-Stowe, T.I.N.; et al. From eDNA to citizen science: Emerging tools for the early detection of invasive species. Front. Ecol. Environ. 2020, 18, 194–202. [Google Scholar] [CrossRef]
  171. Valentini, A.; Taberlet, P.; Miaud, C.; Civade, R.; Herder, J.; Thomsen, P.F.; Bellemain, E.; Besnard, A.; Coissac, E.; Boyer, F.; et al. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol. Ecol. 2016, 25, 929–942. [Google Scholar] [CrossRef]
Figure 1. The workflow of eDNA technology. The figure was created with BioGDP.com; some vector images are from PinClipart (www.pinclipart.com, accessed on 24 February 2025).
Figure 1. The workflow of eDNA technology. The figure was created with BioGDP.com; some vector images are from PinClipart (www.pinclipart.com, accessed on 24 February 2025).
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Figure 2. Challenges of using eDNA in aquatic ecosystems.
Figure 2. Challenges of using eDNA in aquatic ecosystems.
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Figure 3. Future prospects and technological advancements in eDNA. The figure was drawn by Figdraw (www.figdraw.com, accessed on 20 January 2025).
Figure 3. Future prospects and technological advancements in eDNA. The figure was drawn by Figdraw (www.figdraw.com, accessed on 20 January 2025).
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MDPI and ACS Style

Chang, H.; Ye, T.; Xie, Z.; Liu, X. Application of Environmental DNA in Aquatic Ecosystem Monitoring: Opportunities, Challenges and Prospects. Water 2025, 17, 661. https://doi.org/10.3390/w17050661

AMA Style

Chang H, Ye T, Xie Z, Liu X. Application of Environmental DNA in Aquatic Ecosystem Monitoring: Opportunities, Challenges and Prospects. Water. 2025; 17(5):661. https://doi.org/10.3390/w17050661

Chicago/Turabian Style

Chang, Huihui, Tao Ye, Zhaohui Xie, and Xinhu Liu. 2025. "Application of Environmental DNA in Aquatic Ecosystem Monitoring: Opportunities, Challenges and Prospects" Water 17, no. 5: 661. https://doi.org/10.3390/w17050661

APA Style

Chang, H., Ye, T., Xie, Z., & Liu, X. (2025). Application of Environmental DNA in Aquatic Ecosystem Monitoring: Opportunities, Challenges and Prospects. Water, 17(5), 661. https://doi.org/10.3390/w17050661

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