1. Introduction
Oceans cover approximately 71% of the Earth’s surface area. As one of the major repositories of the Earth’s biodiversity, oceans have significant ecological, economic, and social value for humankind [1]. Each species in the marine food web and the diverse marine communities play a crucial role in maintaining the balance and health of the marine ecosystem, providing important ecological services to the Earth, such as oxygen production, carbon conservation, and nutrient cycling [2]. Therefore, marine biodiversity is critical to maintaining global biodiversity and environmental health. However, the oceans are facing enormous threats and challenges from climate change and overexploitation, making marine environmental conservation an urgent worldwide concern [3,4]. To address biodiversity decline and to protect and restore marine ecosystems and biodiversity [5], the United Nations has implemented the Strategic Plan for Biodiversity 2011–2020 [6] and followed with the next Decade of Marine Science for Sustainable Development (2021–2030). Real-time, accurate, comprehensive, and large-scale monitoring of the oceans’ physicochemical, biodiversity, and health status is fundamental and a critical foundation for the enhanced protection and management of the marine environment [7].
Traditional biomonitoring methods such as electrofishing, netting, trapping, bottom trawling, etc., have been serving as crucial and useful tools for understanding species composition and abundance in the marine environment. However, these traditional biomonitoring methods are mostly capture-oriented in their means of obtaining samples for analysis, which are invasive and potentially destructive when applied to reflect the status of the environment [8]. Furthermore, morphological identification-based biomonitoring methods can be limited in their ability to identify and quantify rare species at low densities and differentiate morphologically similar or identical species (i.e., cryptic species) [8]. For important but vulnerable marine environments, such as the coral reef ecosystems and other marine protected areas, capture-based methods will cause significant damage and destabilize the inhabiting community, which increases the environmental burden and offsets the protection initiatives. Consequently, it is crucial to develop noninvasive marine monitoring technologies for enhanced marine protection and management strategies.
In recent years, various environmentally friendly and high-resolution monitoring platforms have emerged, such as optical (sensing) and acoustic methods, biosensors, and molecular biology-based monitoring methods (metagenomics, environmental DNA (eDNA) metabarcoding) [8,9]. Automated vehicles/equipment that reduce human labor in front-end operations have also been incorporated into such monitoring platforms to improve monitoring outcomes [10]. For example, the integration of high-resolution image capture equipment on unmanned platforms has profoundly transformed the field of underwater optical imaging-based monitoring to achieve high-resolution imaging with labor-saving operations [11]. No matter how cryptic a species may be, it is bound to leave its DNA in the environment; it is theoretically possible to detect these species by sampling and extracting environmental DNA. Moreover, by using multiple primers to amplify target DNA fragments after collecting eDNA from environmental samples, it is also possible to simultaneously obtain compositional or structural information of multiple taxa, such as the “tree of life metabarcoding” [12,13]. Hence, eDNA metabarcoding-based monitoring has also emerged as a promising tool for enhanced marine monitoring because of its noninvasive nature in sampling. Furthermore, integrating eDNA metabarcoding-based monitoring with morphology-based optical monitoring can complement each other further to empower the monitoring outcomes with enhanced monitoring efficiency.
Artificial intelligence (AI) systems leverage data and algorithms to enable capabilities such as perception, learning, reasoning, and decision-making, allowing them to emulate and surpass human performance in tackling complex problems and tasks [14]. Machine learning, a fundamental branch of AI, aims to develop models and optimize algorithms through training on large datasets. By learning and improving predictions autonomously using neural networks and deep learning techniques, these optimized algorithms and models can perform direct calculations on new input data to obtain predicted results [15]. In the ecological domain, AI has been used to estimate ecosystem status [16,17] and predict ecosystem responses to management changes [18]. The growing availability of large datasets from optical imaging platforms and environmental DNA (eDNA) metabarcoding has further highlighted the crucial role of AI in assisting with the processing and analysis of these rich data sources. For instance, integrating imaging and sequencing data with AI-powered techniques, such as using machine learning to cross-validate eDNA-based taxonomic classification and identification against extensive image libraries, can significantly improve the efficiency and reliability of these advanced biomonitoring platforms [19].
In this review, a literature search for marine environment monitoring methods was conducted on the Web of Science (
2. High-Resolution Underwater Optical Imaging-Based Monitoring Technology as a Powerful Monitoring Tool for Marine Ecosystem
High-resolution underwater optical imaging-based monitoring technology utilizes advanced optical instruments and techniques to capture detailed and high-quality images or videos of underwater environments. This technology provides observational data that are high resolution and noninvasive in nature and have been proven to have advantages in capturing details that traditional methods tend to overlook. These methods are also much less labor-intensive as they can be operated remotely; specialized devices are highly efficient, capable of storing and effectively processing large amounts of data and transmitting data in real time. This technology has been helpful in studying underwater ecosystems, biodiversity, and various physical and biological processes with precision and accuracy [20].
2.1. High-Resolution Underwater Optical Imaging-Based Monitoring Platform for Marine Environmental Monitoring
The key components of high-resolution underwater optical imaging-based monitoring include specialized underwater cameras, remote-operated vehicles (ROVs), and autonomous underwater vehicles (AUVs) equipped with cameras, lights, illumination systems, image and video processing systems, data transmission and storage systems. Underwater cameras are designed with high-resolution sensors, lenses, and durable housings to withstand the underwater environment and capture fine details. ROVs and AUVs, on the other hand, can navigate underwater, collect data, and transmit them in real time for analysis. The advantages of using underwater ROVs for monitoring are to reduce labor-intensive work, human safety risks, and high costs associated with field investigations. Underwater ROVs also enable the analysis of collected videos or images to extract the spatial distribution characteristics, which is crucial for image signal analysis. Additionally, the proposed deep learning-based scheme using underwater ROVs can accurately reveal the biological diversity and distribution, making it a practical alternative to traditional inspection methods [21,22]. However, elevated pressure, poor visibility, streamflow regime, brightness, turbidity, and the importance of structural tightness are some factors that must be considered during the design and later in the implementation phase [23]. Underwater target detection via ROVs has been widely used for purposes such as fish cage inspection [24].
To ensure high-resolution imaging, additional lighting systems are used to compensate for the lack of natural light at depth. Advanced image and video processing techniques can further enhance the quality of obtained images and video through techniques such as removing visual noise and enabling accurate and relevant information to be extracted from the captured data. Real-time data transmission from the underwater environment to the surface is achieved using underwater communication systems, while efficient storage and management systems handle the large volumes of data generated. For example, researchers have adopted the iterative sparse reconstruction theory to study orthogonal frequency-division multiplexing communication and estimated the channel of the system [25]. Then, a new FFT technology was introduced and implemented to reduce the inter-carrier interference of time-varying underwater acoustic channels [26] and achieve high-efficiency transmission.
Marine ecology research benefits from high-resolution imaging, enabling investigations into the behavior of marine organisms, species interactions, and ecosystem dynamics. Biodiversity monitoring aims to study and document underwater species and habitats, contributing valuable information for biodiversity conservation [27]. High-resolution underwater optical imaging-based monitoring technology is vital in advancing our understanding of underwater ecosystems and their dynamics. It empowers scientists to gather detailed data, visualize underwater environments, and make informed decisions for marine resource conservation and sustainable management.
2.2. Artificial Intelligence (AI)-Based Data Processing Strategies to Overcome Limitations of Captured Image or Video Data
Traditional methods require a significant amount of manual effort to examine features and patterns in images that are relevant to ecological reality. Such manual approaches cannot efficiently handle the ever-increasing volume of raw data generated by modern sensing technologies and are not suitable for large-scale monitoring of complex ecosystems [28]. To address this challenge, ecological studies increasingly rely on advanced computational methods to automate data processing and extract knowledge from ecological records [29]. Thus, obtaining image and video data and its processing technologies are essential in vision-based underwater monitoring and biodiversity assessment.
With the introduction of machine learning and artificial intelligence techniques, the identification task can be intuitively modeled as a target detection and recognition task in the field of image signal processing. The statistics of biological species and quantity can be completed during image processing by providing a clear and high-quality image basis for underwater organism identification and statistical work after it. For example, with the help of target detection technology, AI-driven deep-sea creature sampling missions [30] and underwater biodiversity data collection [31] are already possible. Advanced object detection frameworks, such as You Only Look Once (YOLO), are also being applied to underwater bio-detection [32], while applications such as fish size monitoring [33], penguins and their prey detection [34], and marine animals tracking [35] also demonstrated the important role of image processing techniques in vision-based underwater monitoring and biodiversity assessment. However, due to the harsh, unexplored, and unpredictable nature of the marine environment, impact due to waves, absence of light, and photos taken at close range, reliable and long-term monitoring remains a challenge for smart underwater monitoring systems. The use of computer vision and machine learning algorithms, which heavily rely on the reliability and quality of image data, also imposes pressure on data processing considerations such as correcting color distortion and handling image clarity in turbid water areas.
Camera performance limitations, such as resolution and color distortion, have constrained long-term monitoring of optical image diversity in the marine environment. In other words, the outcome of monitoring tasks depends on the quality of the image. In recent years, advancements in technology and equipment have significantly enhanced optical imaging-based marine monitoring [36]. The design, optimization, transmission, enhancement, and renovation of the optical image system, along with intensity retrieval and allotment, encompass a series of critical steps that are refined through image quality assessment (IQA) and underwater-image-quality-measurement (UIQM) methods. Incomplete and low-quality data in underwater images and videos are primarily due to insufficient information. To address this deficiency, single-modal and multi-modal approaches are commonly employed, as depicted in Figure 1.
2.2.1. Single-Modal Methods
Single-modal strategies focus on mining deep into the information from the visual modality itself, thereby focusing more attention on improving the quality of the raw data. One of the most representative techniques is image enhancement, covering dehazing [37], color restoration [38], and feature enhancement [37] under the empowerment of artificial intelligence, which has been applied in underwater animal detection and classification at cabled observatories [39]. Noise reduction methods also behave as image enhancement strategies [40]. Super-resolution (SR) enhances high-resolution images from low-resolution counterparts and has been increasingly exploited for perceptual image quality improvement, but underwater images with SR are relatively under-explored as most existing methods are unable to reduce the adverse effects of being underwater, and corresponding techniques are yet to be developed [41]. Within these methods, techniques such as transfer learning, multiscale, and residual learning are also introduced for better information mining [42,43].
2.2.2. Multi-Modal Methods
Multi-modal strategies are the current trend for method development, making better use of the multidimensional information carried by the sample and fusing its multi-view and context knowledge. Most existing methods find both models from the visual perspective [44] or decompose existing images [45]. The image fusion of infrared and visible video can guide the complementary advantages of different modal image information, which significantly improve image quality and feature engineering efficiency, and extending the idea to the underwater environment will be a very meaningful exploration [46]. In terms of noise reduction, multi-modal methods need to be able to process complex noise in different models simultaneously instead of separating different views for separate processing in traditional methods [47]. It is necessary to design novel end-to-end processing network models to combat different frequencies of ocean noise through attention mechanisms, adaptive algorithms, ADMM algorithms, etc. [48].
Regarding the architecture of deep methods, the framework derived from CNN and its extended architecture are still the main ones [49,50]. Although GAN is helpful in supplementing information, correcting color and contrast, and reconstructing occluded objects, the generated fake samples limit the authenticity of underwater exploration tasks [51,52]. In response to the multiple challenges of underwater tasks, researchers have focused on new network architecture based on various technologies. A cascade of neural networks has been designed for automated marking of underwater animals [53]. Meanwhile, FA+Net [54], SFGNet [55], PA-UIENet [56], are advanced architectures emerging in recent years.
2.3. The Promising Use of AI in Underwater Optical Imaging-Based Monitoring Application
Relevant ecological information has been recorded through noninvasive and nondestructive underwater optical imaging-based methods as a meaningful new way to monitor and evaluate biodiversity. At the same time, the trend of underwater optical imaging-based monitoring applications is leading to multi-modal methods, which obtain and utilize multi-dimensional information, even beyond pure visual modal analysis. The empowering role of artificial intelligence has been given research attention to improve the overall intelligence of models and processes, such as creature reidentification, real-time tracking, etc. [57]. Although it is desirable to select multiple models with as wide a gap as possible, such as among text, visual, acoustic, and even eDNA signals, to complement multi-dimensional information from a broader perspective, it is currently difficult to integrate multi-modal heterogeneous feature information.
From the perspective of underwater animal detection and recognition application, a multimodal-based, scene-aware, framework for aquatic animal segmentation has been proposed [58]. However, there are many challenges and limitations in the generalization of methods in the underwater environment. For example, the morphological characteristics of underwater organisms can be significantly different, and the aforementioned limitations of camera hardware conditions will also be obstacles. To sum up, developing multi-modal underwater optical imaging-based monitoring and biodiversity assessment in complex underwater environments requires introducing and combining more advanced technologies [59,60]. With the help of artificial intelligence, multi-modal methods have advantages in the ability to examine the same sample from various perspectives, extract and fuse crucial feature information from each modal, and leverage intermodal and intramodal knowledge to enhance underwater object detection.
3. eDNA Metabarcoding as the Next-Generation Biomonitoring and Biodiversity Conservation Tools: Its Noninvasive Nature and Advantages
eDNA metabarcoding-based monitoring methods involve collecting and extracting DNA from environmental samples such as air, water, sediment, soil, etc., and then utilizing molecular techniques such as polymerase chain reaction (PCR) and high-throughput sequencing (HTS) to obtain biological information from the environmental samples. eDNA-based methods only require collecting environmental samples, which is advantageous in being noninvasive and minimizes the destruction or invasion of the environment compared to traditional capture-based methods. Environmental samples typically contain multiple types and sources of DNA, such as those from captured microbes, living cell DNA released into the environment through urine, feces, mucus, etc., and extracellular DNA released due to cell fragmentation after the death of organisms [61]. These eDNA contain biological information of their hosts, which when coupled with bioinformatics, can serve as evidence for the presence of various species in the environment and further provide information on the distribution and functional characteristics of a community without the need to directly observe or capture any organisms [61,62]. Furthermore, eDNA metabarcoding enables simultaneous monitoring of diverse taxa by concurrently amplifying multiple specific DNA fragments using single or multiple universal primers. Combined with HTS technology, eDNA metabarcoding thus can detect a wide range of taxa rapidly and at lower costs than traditional methods [63,64]. In addition, the use of eDNA metabarcoding also promotes the transition of species identification from morphological recognition, which can be inherently deceptive, to molecular biology, which can be more reliable and reproducible. The new concepts and approaches for noninvasive biomonitoring [65,66], which eDNA provides over visual and morphology-based monitoring, are appealing for detecting many marine species that are naturally rare and cryptic, endangered, or at juvenile stages, which are essential to be protected from invasive and destructive activities [67]. Given the characteristics and advantages described above, eDNA metabarcoding has been widely used in ecological environment monitoring and biodiversity conservation, with the potential to further integrate with new concepts and technologies such as citizen science and machine learning (ML).
4. Progress in Key eDNA Metabarcoding Methodologies
eDNA metabarcoding is conducted based on the assumption that all organisms leave behind DNA and the DNA present in the environmental samples could realistically reflect and represent the biodiversity in the environment [62]. The use of eDNA metabarcoding in marine ecosystems is considered more challenging than in freshwater ecosystems, because eDNA dilution and mixing are higher in the bulk water environment of the ocean due to ocean currents, salinity, tides, and other abiotic factors that can affect the migration and degradation of genetic materials [68,69]. In the methodology of eDNA metabarcoding, there are various factors, in addition to the sampling environment, that could affect how informative the samples represent the biodiversity in the environment. Thus, ongoing efforts have been dedicated to optimizing the sampling process, the efficiency of DNA collection and extraction, the selection and design of PCR primers, and the completeness of the reference database. For instance, the sampling process and sample size, according to the study’s purpose, the study site’s environmental factors, and target species, must be thoroughly considered before the experiment to optimize the result outcomes. Over the past decade, eDNA metabarcoding-associated procedures have advanced significantly. For example, methods for collecting environmental samples have evolved from manual sampling to a wide variety of automated sampling to facilitate standardization. Therefore, this section summarizes the progress made in sampling methods, reference databases, and primers development, which are the key processes and foundations for eDNA metabarcoding-based monitoring (Figure 2).
4.1. The Nondestructive Sampling in eDNA Metabarcoding-Based Monitoring
In the marine environment, eDNA metabarcoding is most frequently carried out by collecting water and sediment samples [70], though new forms of sampling such as biofilm sampling have emerged [71]. Water and sediment samples cover different environmental compartments and behave differently in reflecting target types of organisms or communities [72,73,74]. For example, the DNA of aquatic insects, mollusks, and oligochaetes is preferentially derived from sediments compared to amphibians, fish, mammals, etc. [75]. Tagliabue et al. [74] collected both sediment and benthic water samples to detect benthic communities, and the structure of benthic communities reflected differed significantly between samples, with more pelagic and nektonic species in the water samples. Thus, the appropriate selection of water or sediment samples, or the simultaneous collection of both samples, is critical for obtaining eDNA representative of scientific objectives.
For water sampling, surface seawater is often directly collected from sampling sites using simple tools such as buckets [76,77,78], wide-mouth containers [79], or Niskin bottles [80], while larger volume samplers tend to be used for deep seawater sampling, such as Nansen metal water sampler [81] or Niskin-style rosette sampler [82]. Similarly, depending on water depth and substrate type, different types of corers or grabs for sediment sampling can be employed, such as Kajak corer, piston corer, gravity corer, or a Van Veen grab, which are operated by hand or by line [83,84]. Though these samplers have been widely used to collect water or sediment samples, they still need manual effort for operation and transport. In recent years, various semi- or full-automatic and passive samplers that can be deployed in marine environments have been used for sampling in eDNA metabarcoding, which reduces labor and allows for the standardization potential of the sampling procedure.
4.1.1. Active and Automatic Sampling
Currently, the major challenge of employing eDNA metabarcoding remains in the standardization of sampling and processing methods. Several methods and guidelines for eDNA monitoring in aquatic environments have been published by the United States Geological Survey (USGS) [85], the Federal Office for the Environment (FOEN) of Switzerland [75], European Cooperation in Science & Technology (COST) [86] etc., providing a reference for standardized operation for eDNA applications. However, applying these guidance methods rigorously in practice is often not feasible. For example, the volume of water required in sampling may conflict with the need for timely transportation of samples back to the laboratory to reduce risks of DNA degradation [87]. Methods to preserve eDNA in environmental samples, such as the addition of storage reagents to samples and field-based filtration systems to reduce the need for laboratory-dependent work, are constantly revised and explored for improved sampling effectiveness.
To improve sampling efficiency and reduce labor, a portable eDNA sampling system (
Representative eDNA sampling coverage can be challenging in the field, especially in relatively inaccessible environments. In recent years, ROVs or Unmanned Aerial Vehicle (UAV; drone) coupled with sampling containers and filtration instruments have been used to collect water samples for eDNA studies. For example, 1 L water bottles attached to the flying drones submerging into the surface water have been used to collect water samples [89]. Further, a drone combining a filter capsule that can take one sample consisting of 10 to 20 sub-samples of 50 or 100 mL water was developed (
Meanwhile, a series of fully automated eDNA samplers have been designed to sample and preserve eDNA samples without researchers on-site. The environmental sampling processor (ESP) generation 3 is one of the most advanced automated samplers that can be coupled with an autonomous underwater vehicle (AUV) and contains 60 filter cartridges that filter water samples under a set program which can be kept stable for 21 days [91]. The ESP sampler provides high-frequency and continuous sampling and, because of its capability for unmanned operation, can travel to remote and inaccessible field sites, greatly reducing the cost of time, labor, and other resources. A test of eDNA quantification for ESP has confirmed its comparable capability and feasibility with manual sampling methods [91]. ESP has also been employed on an uncrewed surface vessel (USV) surveyor SD 1200 to collect water samples for regular intervals over a 4200-km, 29-day transit, which would otherwise require enormous human and material resources using traditional manual sampling methods [92]. However, it is undeniable that ESP samplers are expensive, especially for small research groups, and are still in research and development phases without widescale implementation potential yet. Thus, a range of automated eDNA samplers were also recommended for a lower cost. A representative, open-sourced, single-filter system, subsurface automated sampler for eDNA (SASe) with a relatively low cost (~280 USD) was designed by the National Oceanic and Atmospheric Administration (NOAA, USA), which is submersible to 55 m and can filter and preserve eDNA samples in situ [93]. There are also relatively low-cost samplers with a multi-filter system for eDNA collection, such as the Large Volume eDNA Samples [94], as well as a compact and automated eDNA sampler that have both sample preservation and self-cleaning capabilities designed by Hendricks et al. [95]. The development of these automated samplers can be highly beneficial to reduce logistic challenges in eDNA monitoring.
4.1.2. Passive Sampling
In active sampling, water and sediment samples are commonly collected manually or via automated samplers, paired with filtration systems to concentrate DNA. While labor efficiency compared with conventional capture-based monitoring methods have considerably improved, some of the limitations of active sampling, such as the need for pump equipment, could be time-consuming or costly. Thus, passive sampling, a low-cost and easily deployable alternative method, was developed using natural (e.g., sponge) or artificial materials. For example, two types of passive samplers, including positively charged nylon and non-charged cellulose ester membranes fixed in a pearl oyster aquaculture frame, were submerged in seawater ~1 m below the water surface for 24 h, and presented a 97% detection rate of fish taxa compared with active eDNA sampling [96]. Membranes submerged underwater for 24 h to collect eDNA also proved able to detect comparable species richness with eDNA metabarcoding conducted by active sampling [97]. Furthermore, the sorbent material used to capture eDNA is a key factor in passive sampling. Chen et al. [98] reported that the best-performing material was glass fiber filters after comparing the ability to capture the eDNA of the Chinese giant salamander (Andrias davidianus) among 12 artificial materials. These studies demonstrated the usefulness and potential of passive eDNA sampling. However, limitations remain, such as the fixation of sorbent materials mentioned above in inaccessible water or remote deep seawater and the potential of biofilm development and fouling process.
In summary, both widely accepted manual sampling methods and emerging automated samplers or passive sampling strategies have their advantages and limitations. Choosing and optimizing appropriate eDNA sampling methods and maximizing and rationalizing the sampling process according to experimental needs to avoid errors and risk are crucial steps for high-efficiency monitoring.
4.2. The Reference Database for Marine Species and the Practical Standards for eDNA Metabarcoding-Based Marine Monitoring
Following amplification of the target DNA fragments, the sequence information obtained by sequencing needs to be aligned with known species genomic sequence information, i.e., reference databases. Moreover, the selection of appropriate marker genes and the design of appropriate primers, as well as the subsequent taxonomic assignment of sequenced marker genes, are all tightly bound to the coverage and quality of the reference databases. Thus, the integrity and breadth of the reference sequence database are crucial for exact species identification [99].
4.2.1. Globally Accessible Reference Databases
A collection of publicly available reference databases that can be used for eDNA metabarcoding has been established and is constantly updated. For example, Barcode of Life Database (BOLD) (
4.2.2. Localized References Databases
Although many reference databases have been established and are widely used, current reference databases are still insufficient considering the great diversity of species [100]. Such databases currently suffer from a huge redundancy of data volume, frequent comparison errors due to low upload thresholds, and an imbalance in both geospatial and species coverage [101]. A recently realized need is for excessive data to be summarized into more effectively accessible and processible information such as class-specific or pathogen-specific databases. For example, Kasmi et al. [102] targeting mitochondrial genes in fish, have extracted all 12S rRNA and COI fish sequences in databases such as NCBI, European Nucleotide Archive (ENA), BOLD, etc. and revised corresponding taxonomic information according to the Integrated Taxonomic Information System (ITIS) and FishBase, create the Mare-MAGE database. Gold et al. [103] reported that regional databases using sequences of local fishes provide higher alignment accuracy than comprehensive global databases such as GenBank. Therefore, establishing regional, indigenous DNA databases and updating DNA barcodes globally are crucial for species monitoring and conservation. Several regional databases have been established, such as the Canadian Centre for DNA Barcoding (CCDB) and the European Molecular Biology Laboratory database (EMBL). Recently, the Wuhan Institute of Aquatic Biology, Chinese Academy of Sciences, also constructed China’s first comprehensive aquatic organisms eDNA database, AeDNA (
Summary of commonly used public genome reference database.
| Database | Main Content | Source | Website * |
|---|---|---|---|
| GenBank | Nucleotide sequences for over 478,000 officially classified species | National Center for Biotechnology Information (NCBI) | |
| European Molecular Biology Laboratory database (EMBL) | Data resources and analysis tools to support life science research | 29 member states | |
| Barcode of Life Database (BOLD) | An assembly of DNA barcode data with primers, electropherograms, images and sequences.; sub-database for fish (FISH-BOL), mammals, bird species, plant species, and more. | International Barcode of Life (iBOL) Project; Canada | |
| Greengenes 2—16S rRNA database | A reference tree that unifies the genome and 16S rRNA databases in a consistent, integrated resource by inserting sequences into a genome-wide phylogenetic tree | Lawrence Berkeley National Laboratory, Berkeley, CA, USA | |
| SILVA rRNA database | Aligned small (16S/18S, SSU) and large subunit (23S/28S, LSU) ribosomal RNA (rRNA) sequences for all three domains of life (Bacteria, Archaea and Eukarya). | Free online resource from Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures GmbH, Braunschweig, Germany. | |
| Ribosomal Database Project (RDP) | Quality-controlled, aligned and annotated Bacterial and Archaeal 16S rRNA sequences, and Fungal 28S rRNA sequences; A series of analysis tools | United States | |
| PR2-metaPR2 | Eukaryotic 18S rRNA metabarcodes that have been reprocessed and assigned using PR2 | Vaulot et al., 2022 [106] | |
| UNITE | Eukaryotic nuclear ribosomal ITS region | Northern European initiative | |
| MIDORI Reference 2 | DNA and amino acid sequences used for taxonomic assignments of Eukaryota mitochondrial DNA sequences | Biodiversity Research Center, Academia Sinica, Taiwan | |
| MitoFish | Standardized fish mitochondrial genome | Atmosphere and Ocean Research Institute, the University of Tokyo, Japan. | |
| AeDNA | Aquatic DNA barcodes and genomes; Habitat types cover rivers, lakes, seas, glaciers and hot springs. | Institute of Aquatic Biology, Chinese Academy of Sciences, Wuhan, China | |
* All URLs were accessed on 27 August 2024.
4.3. eDNA Metabarcoding-Based Monitoring from the Use of Single to Multiple Primers
eDNA is naturally degrading and can be minuscule in quantity in aquatic environments. A recent study showed that fish-derived eDNA fragments extracted from natural seawater samples accounted for only 0.004% of total sequences when using shotgun sequencing [12]. Thus, following the DNA collection from environmental samples, specific/targeted DNA fragments known as the “barcode”, which involves highly conserved gene regions across species and highly variable regions capable of distinguishing species, need to be amplified for subsequent high-throughput sequencing and analysis. Appropriate PCR primers for PCR amplification are critical in eDNA metabarcoding-based methods as they determine the taxonomic coverage of amplified DNA, which can ultimately determine monitoring outcomes [107]. Significantly different diversity was detected when different primers were used for samples obtained in the same study area and with the same eDNA metabarcoding workflow [63]. A well-designed primer must consider the variable and conserved regions of the gene for its intended target coverage and specificity, in addition to other practical considerations such as amplicon size, structure, and primer compatibility. Sources of gene sequences that can be referenced for primer design include verified genome sequences in established reference databases or genome sequences of target species obtained by sequencing. The performance of designed primers can then be tested by in silico evaluation [108], in vitro experimental tests [109], and/or surveys in laboratory aquariums or natural environments [110].
Mitochondrial genes are generally more widely used as marker genes than nuclear genes, as the copy number of mitochondrial DNA (mtDNA) is larger than that of nuclear DNA. Since the concentration of genetic materials in the environment is low, mtDNA copies are more likely to be detected [111,112]. Moreover, current genome databases contain many mitochondrial DNA sequence information, which eases species identification. The cytochrome b (Cytb) gene, the D-loop gene, and the mitochondrial 12S and 16S ribosomal DNA are now widely used as molecular markers for eukaryotic species identification, particularly in fish [113]. A universal primer that targets a specific DNA fragment across a wide range of taxonomic groups is often used in eDNA metabarcoding-based methods to facilitate broad-scale community-level study in the marine environment [76]. Targeting teleost fish, Zhang et al. [13] compared 22 sets of primers for eDNA metabarcoding analysis using in silico PCR and metabarcoding. In general, commonly used primers designed to target 12S rRNA genes, such as MiFish-U [114] and Teleo [115], compared to primers targeting 16S rRNA or COI genes, such as Ve16S [116], FishCBL/CBR [76], etc., were able to detect higher fish diversity [117]. As reported by Xing et al. [113], the Cytb gene is the most used marker for species-specific fish detection, followed by COI. For other marine invertebrates, some primers are commonly used, such as mlCOIintF/jgHCO2198 [118] targeting COI genes, 16S_Inv_For/16S_Inv_Rev [119] targeting 16S rRNA. New sets of primers are also constantly developed, such as MollCOI154/MollCOI255 for marine mollusks [120].
Simultaneous amplification of multiple target fragments using multiple universal primers enables monitoring of multiple taxa-groups and overall biodiversity in the ecosystem. This is the prominent advantage demonstrated by eDNA metabarcoding-based methods. For example, Liu and Zhang [121] recommended the simultaneous use of multiple markers for eDNA metabarcoding analyses after investigating biodiversity in deep-sea sediments using 18S rRNA (V1–2 and V9) and 28S rRNA primers in conjunction. In a systematic review, groupings of (a) COI and 18S rRNA, and (b) COI, 16S rDNA and 18S rRNA were the most commonly used strategy for marine eDNA metabarcoding [122]. In addition, one of the most recognized examples by Stat et al. [12] simultaneously used 10 pairs of primers with nuclear 18S rDNA, mitochondrial COI, mitochondrial 16S rDNA, and chloroplast 23S rDNA as marker genes targeting Eukaryotes, Metazoans, Fish, Mammals, Crustaceans, Cephalopods, Symbiodinium, Plants, and Prokaryotes, etc., to achieve “tree of life” metabarcoding at the ecosystem level.
5. eDNA Metabarcoding-Based Monitoring for Marine Conservation and Management
eDNA metabarcoding, which benefits from the development of molecular technologies and bioinformatics [123], has shown great potential to be used for population- and community-level or large-scale monitoring with implications for conservation strategies and policy-making through biodiversity monitoring. This section focuses on complex marine environments such as coral reef ecosystems and deep sea to show the application of eDNA metabarcoding for marine conservation and management. Further, we introduced regional and/or global monitoring trends by integrating citizen science or machine learning with eDNA metabarcoding (Figure 2).
5.1. eDNA Metabarcoding for Marine Biodiversity Monitoring
eDNA metabarcoding significantly facilitates and improves the assessment of biodiversity in marine ecosystems. It effectively captures community dynamics and has emerged as a crucial tool for informing management decisions. In 2012, eDNA metabarcoding was first applied in marine environments for the purpose of fish biodiversity monitoring [76]. The detection coverage achieved with the eDNA metabarcoding method, utilizing only 0.5 L water samples from a temperate marine ecosystem, was reported to be equal to or superior to that of nine conventional methods, including bottom trawling. Currently, eDNA metabarcoding is widely employed for monitoring community composition and structure across spatiotemporal gradients, ecosystem-level assessments [13], ecological status evaluations [63,124], early warnings of harmful algal blooms [125], and the impacts of environmental changes and human activities. This method targets various marine taxa, including fish [126], invertebrates [127], elasmobranchs, cetaceans, and even entire eukaryotic communities [128]. The below section summarizes the applications of eDNA metabarcoding in the context of rare or threatened marine species of high conservation concern, as well as sensitive marine ecosystems and deep-sea environments. It highlights the advantages and applications of eDNA metabarcoding-based methodologies for marine environmental protection and management.
5.1.1. eDNA Metabarcoding-Based Monitoring for Rare, Protected, or Threatened Marine Species
Monitoring the status and population trends of rare, protected, or endangered marine species is essential for informing conservation efforts and guiding effective management strategies to prevent further declines and promote recovery. Many of these rare, protected, or threatened species occur at naturally low population densities or are challenging to observe, making traditional survey methods ineffective, time-consuming, and resource-intensive. Recent studies have demonstrated that eDNA metabarcoding-based monitoring serves as a rapid, safe, sensitive, and cost-effective method for detecting and studying rare marine species [129]. For instance, the impact of increased human fishing activities on elasmobranch diversity and richness was revealed through the use of two forward primers, FishF2 and VF2, along with one reverse primer, “Shark COI-MINIR”, in eDNA metabarcoding analyses [130]. For species such as sharks, which are challenging to capture for survey purposes, eDNA metabarcoding detected approximately 44% more shark species than underwater visual census (UVC) and baited video methods, with a sampling effort that was two orders of magnitude lower [131]. Notably, sharks were identified in human-impacted areas where no shark species had previously been recorded using these methods [131]. Similarly, for threatened cetaceans that exhibit low abundance and elusive behavior, such as dwarf sperm whales [132], humpback whales, and bottlenose dolphins [133], diversity can be effectively monitored using 12S rDNA primers, including Vert01 and Mamm01, within eDNA metabarcoding frameworks. In addition to diversity assessments, eDNA metabarcoding employing universal primer MiFish-U and modified primer Elas02 provided rapid and extensive insights into the abundance and temporal variation of sharks and rays around an Indian Ocean island, surpassing traditional visual and capture-based techniques [134]. Furthermore, leveraging the advantages of eDNA metabarcoding to monitor multiple taxa allows for the simultaneous assessment of the spatiotemporal distribution and abundance of marine mammals, such as bottlenose dolphins, minke whales, and harbor porpoises, along with their foraging fish. This is achieved using two sets of primers, MarVer1 and MarVer3, offering critical information for understanding and predicting their distribution and informing conservation strategies [135].
With the enhancement of methodological infrastructure, such as the establishment of local reference databases, eDNA metabarcoding methods can detect a broader range of species diversity, including those that have not been previously observed or recorded in certain areas. For instance, de la Hoz Schilling et al. [136] identified 27 elasmobranch species using the MiFish-E (12S) primer set in the Banc d’Arguin National Park, Mauritania, of which 12 species were newly documented. Notably, 67.9% of the detected species are listed in the IUCN Red List of Threatened Species. Additionally, the implementation of passive filter-collection eDNA samplers within nets used for routine fishing activities allows for the detection of bycatch, including elasmobranchs, while further reducing sampling effort [137]. These vulnerable marine organisms can act as valuable bioindicators, offering insights into the overall health and functioning of the broader marine ecosystem. Their presence, abundance, and behavioral patterns may indicate changes in environmental conditions. Thus, eDNA metabarcoding methods hold significant potential for providing enhanced and comprehensive information for the future monitoring and protection of these important marine species.
5.1.2. eDNA Metabarcoding-Based Monitoring for the Vulnerable Marine Ecosystem: Coral Reef Ecosystem
eDNA metabarcoding methods have proven effective in monitoring community composition and structure across diverse habitats [74,126,138]. Marine ecosystems, such as coral reefs and seagrass beds, are recognized as biodiversity hotspots and are particularly sensitive to disturbances. The noninvasive nature of eDNA metabarcoding maximizes its potential for conservation-oriented monitoring. In vulnerable marine environments like coral ecosystems, which are among the most hyper-diverse and sensitive, traditional underwater visual census (UVC) methods conducted via scuba diving may be inadequate for effectively monitoring cryptic organisms residing within coral structures. Visual obstructions and limitations in taxonomic identification further compound these challenges. For example, Brandl et al. [139] reported that half of all reef fish are cryptic species. In contrast, eDNA-based methods, which rely on the DNA released by organisms into the environment, mitigate biases associated with visual obstacles. A study collecting 226 seawater samples from five tropical regions (the Caribbean, Central and Southwest Pacific, Coral Triangle, and Western Indian Ocean) demonstrated that eDNA metabarcoding could monitor a greater diversity of reef-associated and cryptobenthic species compared to UVC methods [140]. Furthermore, coral spawning and sexual reproduction are critical events for maintaining coral reef ecosystems, typically occurring at night. Visual observations during these events are limited by nighttime light conditions, inaccessibility of certain locations, and the labor-intensive nature of such surveys, which are also prone to bias and may disrupt coral activities. By comparing eDNA abundance before and after coral spawning events, eDNA metabarcoding methods successfully monitored the spawning activities of 37 coral species and an associated 133 fish species that serve as indicators of spawning, all with reduced physical disturbance to coral reef organisms [141].
Recent advancements in eDNA-based monitoring methods have led to the development of coral-specific primers aimed at assessing species diversity (at least to the genus level) and relative abundance of corals. The efficacy of eDNA metabarcoding for coral monitoring has been evaluated in comparison to underwater visual census (UVC) methods. Nichols and Marko et al. [77] were among the first to design primers for amplifying the COI and 16S genes of coral genera in Hawaii specifically for eDNA metabarcoding. They created two corresponding primers for relatively short (~120 bp) and long (~400 bp) amplified fragments for each gene, as detailed in Table 2. When these primers were employed to estimate coral relative abundance, the eDNA read counts exhibited a high correlation with coral coverage measured via diving techniques, yielding consistent information regarding dominant species. Similarly, Shinzato et al. [142] designed two pairs of primers (Scle_12S and Scle_COI) based on the mitochondrial genomes of 71 scleractinian coral species (representing 36 genera and 15 families) sourced from the NCBI database. Their findings revealed that 93% and 72% of the PCR-amplified fragments matched 12S and COI sequences in the database, respectively, demonstrating greater specificity than previous studies. Currently, the most commonly utilized primers are those developed by Alexander et al. [143], which include one reverse primer capable of detecting corals, excluding the genus Acropora, and another primer specifically designed for Acropora detection, based on ITS gene reference sequences of scleractinian corals in the NCBI database. eDNA metabarcoding employing these two primer pairs detected coral genera comparably to results obtained from scuba diving surveys. West et al. [144] further applied these primer pairs in eDNA metabarcoding monitoring of Lalanggarram marine parks. Although these methods were able to detect corals at the species level, they required the collection of benthic tissue samples, which conflicts with the original aim of being environmentally friendly. To enhance species-level monitoring, further enrichment of the ITS reference database is essential. Dugal et al. [145] established a reference database for 70 local coral species based on the ITS sequences of 94 coral species, demonstrating that utilizing local reference databases significantly improved the accuracy of coral species identification. There remains an urgent need for additional eDNA metabarcoding research focused on coral monitoring, with objectives to refine methodological processes, including sample optimization and the enrichment of primers and reference databases, to achieve high efficiency and resolution in coral monitoring.
Monitoring associated and symbiotic organisms in coral reef ecosystems is equally crucial, as these organisms are integral to the food web that supports trophic transfer. Thus, a multi-primer approach is necessary to target various taxa, including both micro- and macro-organism groups. For instance, Dugal et al. [146] using a pair of universal primers (18S_uni_1F and 18S_uni_400R) targeting the V1–3 hypervariable region of the 18S rRNA genes have successfully detected 14 metazoan phyla and 57 species within coral reef environments. By employing multiple primers that target mitochondrial 16S rRNA, COI genes, and the nuclear 18S rRNA gene, a diverse array of taxa—including bony fish, elasmobranchs, crustaceans, mollusks, and echinoderms—has been simultaneously identified in coral ecosystems [144,146]. Stat et al. [12] utilized ten pairs of primers to detect numerous target marine taxa in a single tropical coral reef in Western Australia. These multi-primer strategies yield more comprehensive outcomes and are increasingly adopted to meet marine biomonitoring needs. Furthermore, several studies have indicated that eDNA metabarcoding demonstrates comparable or superior detection performance while requiring less effort than traditional underwater visual census methods for monitoring corals [147] and reef fishes [148]. Utilizing eDNA metabarcoding allows for the assessment of coral community structure [77,142], species diversity, and β/γ biodiversity of reef fishes [73,140], as well as coral symbionts [149], echinoderms [150], and their larvae [151]. This approach provides detailed information that can enhance the conservation of coral reef ecosystems.
5.1.3. eDNA Metabarcoding-Based Monitoring for Deep-Sea Environment
The deep-sea environment, defined as ocean depths below 200 m, encompasses nearly two-thirds of the Earth’s surface and represents one of the planet’s largest and most biodiverse yet least explored ecosystems [121,152]. The significant depths and immense pressure of the deep sea pose challenges for the deployment of widely available monitoring tools. Remote deep-sea areas are often unfeasible for transportation of monitoring equipment, such as trawls [81]. Additionally, the complex topography of deep-sea environments—including steep slopes, rocky substrates, and areas inhabited by sensitive fauna such as corals and sponges—renders traditional trawling methods inappropriate [64,82]. Visual survey methods utilizing remotely operated vehicles (ROVs) may be compromised by light requirements and noise, potentially skewing species observations [152]. In contrast, eDNA metabarcoding methods are relatively noninvasive, adaptable to the intricacies of deep-sea environments, and require minimal effort for sampling. For example, Thomsen et al. [81] demonstrated that eDNA metabarcoding could detect the equivalent of 26 fish families compared to trawling, achieved through straightforward water sample collection at specified depths (188–918 m). Notably, eDNA metabarcoding revealed biodiversity levels approximately 3.9 and 10.1 times greater than those detected by vertical nets and trawls, respectively. Additionally, the community patterns (beta diversity) identified through eDNA analyses aligned with conventional net sampling for deep-ocean pelagic zooplankton and fish across depth ranges of approximately 500 to 2500 m [64]. Seamounts, recognized as biodiversity hotspots within deep-sea ecosystems, are increasingly threatened by climate change and human activities, including mineral resource exploitation. eDNA metabarcoding has been employed to monitor discrepancies in prokaryotic and eukaryotic communities across various contexts of seamounts and adjacent abyssal plains [153], as well as to assess benthic indicator organisms like foraminiferal communities [154]. These efforts provide a crucial biodiversity baseline for the protection and management of deep-sea environments. However, the current lack of knowledge regarding deep-sea organisms, coupled with the scarcity of relevant reference databases, remains a significant constraint to effective biodiversity monitoring in these ecosystems using eDNA metabarcoding methods [155].
5.2. Integrating Citizen Science with eDNA Metabarcoding: Regional/National to Global Biodiversity Monitoring
Citizen science programs are increasingly recognized for their ability to combine accessible methods with public engagement, facilitating environmental monitoring over broader spatial and temporal scales than traditional research allows. By lowering technical barriers to sampling, these initiatives have seen numerous successful implementations, such as the Reef Life Survey, which focuses on marine biodiversity [156]. In recent years, several citizen science projects employing eDNA metabarcoding have emerged at both national and international levels, targeting freshwater and marine ecosystems. These programs leverage public participation to enhance monitoring effectiveness. For instance, Miya et al. [99] engaged volunteers in a citizen science project across six temperate sites in Japan. Participants received training on eDNA collection and filtration techniques using syringes and filter cartridges, followed by the collection of water samples. Subsequent DNA extraction, eDNA metabarcoding, and data analysis revealed 66 families and 118 genera of fish, illustrating distinct spatial biodiversity patterns within the region. Similarly, Agersnap et al. [157] coordinated a national “BioBlitz” citizen science initiative utilizing eDNA metabarcoding to assess marine fish species diversity in Denmark’s coastal zone. This project mobilized 360 citizen scientists to collect seawater samples from 100 sites, which were then frozen and dispatched to Aarhus University for analysis within 24 to 72 h. The project achieved a high sample return rate of 94% and identified a total of 52 fish species, representing approximately 80% of Danish coastal fish diversity and 25% of all marine fish species. These initiatives demonstrate the advantages of integrating citizen science with eDNA methodologies, particularly in terms of the volume of samples that can be collected simultaneously—something often challenging for professional researchers. Moreover, eDNA metabarcoding has shown resilience to procedural failures, enabling citizens with varying levels of training to conduct biodiversity monitoring with considerable accuracy. This approach is poised to play an essential role in future large-scale species assessments. Beyond regional and national efforts, advancements in technology and decreasing costs have positioned eDNA metabarcoding within the framework of global conservation initiatives [158]. For example, the United Nations Educational, Scientific and Cultural Organization (UNESCO) has launched the Environmental DNA Expeditions in UNESCO World Heritage Marine Sites project (
6. Machine Learning (ML)-Assisted eDNA Metabarcoding for Large-Scale Marine Biodiversity Monitoring
Over the past decade, AI has significantly transformed our capacity to automatically, accurately, and reliably identify features and patterns within ecological datasets. Machine learning focuses on developing models from pre-processed training datasets by selecting suitable algorithms—such as classification, regression, and clustering. These models are evaluated using a test dataset, allowing for the optimization of the trained model before applying it to new datasets for accurate predictions [159]. eDNA metabarcoding typically generates vast amounts of sequencing data, and optimizing machine learning techniques for predictions from this data can substantially reduce the labor-intensive data processing typically required by researchers. For instance, Cordier et al. [16] employed supervised machine learning to analyze eDNA metabarcoding data to predict biological indices (BIs) that assess Ecological Quality Status. They obtained eDNA data from 144 sediment samples collected from five salmon farms in Norway, which included operational taxa units (OTUs) and OTU richness. Using Random Forest and Self-Organizing Map algorithms, they trained models on data from different samples designated as either the training or test dataset to predict four BIs related to alpha and beta diversity. Their comparative analysis revealed that the machine learning predictions closely aligned with results derived from traditional morphological identification methods. Subsequently, Cordier et al. [17] further extended their work by training eDNA data from five different molecular markers using a similar machine learning approach, demonstrating the broad applicability of supervised machine learning across various molecular markers, with all tested markers generating accurate predictive models. Similarly, Dully et al. [160] investigated the impact of sequencing depth on machine learning predictions derived from eDNA metabarcoding, utilizing K-means clustering and Random Forest algorithms. Their results indicated that a sequencing depth of 40,000 to 80,000 sequences per sample was necessary for the machine learning methods to achieve over 80% classification accuracy.
Machine learning techniques utilizing coding algorithms have also been applied in biodiversity monitoring [161]. In this approach, three coding methods were developed to encode the four nucleotide bases, along with genus and species names. A traditional three-layer perceptron neural network was then employed, where the encoded bases served as the input layer and the corresponding genus and species names as the output layer. Data from the database were fed into the neural network, which learned through backpropagation. Following training, the model was capable of automatically recognizing and classifying new eDNA sequencing data as they were input. These studies demonstrate that machine learning can effectively manage large volumes of eDNA metabarcoding data, enhancing overall processing efficiency. The successful integration of machine learning into biodiversity assessments based on sequencing data alleviates researchers from labor-intensive tasks and highlights the necessity for further exploration of ML applications in eDNA metabarcoding for ecological monitoring and conservation. The effectiveness of machine learning-assisted eDNA metabarcoding and species identification relies heavily on high-quality training data, careful feature selection, and thorough model evaluation.
7. Centralized Environmental Database for Enhanced Marine Environmental Baseline and Impact Monitoring
Local biodiversity and species inventories serve as essential reference databases for the double verification of emerging monitoring techniques. Benefiting from long-term local monitoring efforts, a substantial volume of environmental data—including biodiversity, water quality, and land use—can be systematically organized into centralized environmental databases. Such Centralized Environmental Databases facilitate data analysis and visualization, enabling stakeholders to gain insights into the current state of the environment, assess potential risks, predict future changes, formulate regulations to address existing issues, and implement effective environmental protection and management strategies. The integration of environmental data with Geographic Information Systems (GIS) and analytical tools enhances the accessibility, comprehensiveness, and efficiency of environmental data management, potentially reducing the need for redundant environmental surveys. For instance, the Centralized Environmental Database (CED) developed by the Environmental Protection Department (EPD) of Hong Kong (
8. The Integration of Underwater Optical Imaging-Based Monitoring and eDNA Metabarcoding-Based Monitoring for Marine Environmental Conservation
As discussed in previous sections, both underwater optical imaging and eDNA metabarcoding methods possess distinct advantages. Integrating these two approaches for marine environmental monitoring can leverage their strengths while mitigating some limitations, providing more comprehensive data and enhancing monitoring efficiency. eDNA metabarcoding serves as a complementary method to optical monitoring, expanding the range of detectable species [164]. For instance, eDNA metabarcoding can effectively supplement underwater visual surveys in coral reef ecosystems, where cryptic fish and other species may obscure themselves within the complex structures of coral, making direct observation challenging. This synergy allows for the detection of a greater diversity of pelagic and benthic fishes [165]. Notably, the simultaneous use of eDNA metabarcoding and baited remote underwater video systems has resulted in over a 30% increase in the abundance of detected fish genera compared to either method used independently. Due to the inherent limitations and biases of each technique, one method frequently identifies taxa that the other does not [166]. Valdivia-Carrillo et al. [167] observed that the number of fish species detected doubled when integrating underwater visual census with eDNA metabarcoding.
Overall, as described in Figure 3, eDNA metabarcoding methods facilitate extensive spatial and temporal monitoring on regional and global scales, owing to their standardizable nature across geographical boundaries. These methods can also be effectively combined with citizen science initiatives and artificial intelligence technologies. In contrast, underwater visual monitoring allows for the capture of detailed information about marine organisms and their environments, particularly when complemented by real-time data transmission and processing systems, which yield high-resolution image data. This capability compensates for the limitations and cost constraints associated with high-resolution underwater visual monitoring equipment. In summary, the combination of underwater optical imaging and eDNA metabarcoding methods creates a synergistic approach that validates and enhances each technique, offering more reliable baseline information. This integrated methodology provides a robust framework for marine environmental monitoring, yielding valuable insights into biodiversity, ecosystem health, and the impacts of human activities.
9. Future Perspectives
Traditional biomonitoring has evolved from simple ecological observations aimed at identifying and assessing species diversity and abundance to a more comprehensive approach focused on understanding the underlying functions and interactions among species and communities. Contemporary biomonitoring is increasingly moving away from manual, visual, and capture-based methods, which are not only disruptive and unsustainable but also often fail to provide a holistic view of the ecosystem. The emergence of what we term “next-generation biomonitoring” may only be achievable through scalable approaches that harness large-scale data from next-generation sequencing and machine learning tools. These technologies enable the interpretation of visual data and the reconstruction of ecological networks through globally distributed, automated sampling stations and collaborative efforts [168]. Thomsen et al. [169] have proposed a global eDNA biomonitoring framework that leverages advancements in sequencing technologies—such as long-read and birdshot sequencing—alongside refined reference databases and artificial intelligence for data analysis. This framework emphasizes local sampling, regional sequencing, and global information sharing, supported by autosamplers, drones, and large-scale citizen science initiatives to facilitate comprehensive eDNA-based monitoring. Concurrently, advancements in acoustic and optical technologies have paved the way for the simultaneous use of multiple monitoring tools to achieve global oversight. Besson et al. [168] advocate for the integration of diverse automated recording instruments—such as eDNA metabarcoding and digital cameras—to collect large-scale, high-resolution data. Machine learning can then be employed to detect, identify, and analyze this data, creating accessible pipelines for real-time, multidimensional, and continuous monitoring of various species. Hartig et al. [170] have introduced new sensing technologies, including eDNA, acoustic, and optical sensors, collectively termed the “Novel Community”, to facilitate real-time and long-term monitoring of species and trophic interactions. Looking forward, we anticipate that future trends in next-generation biomonitoring will be characterized by (1) the integration and simultaneous deployment of multiple monitoring techniques; (2) the utilization of machine learning and artificial intelligence to enhance ecosystem-level understanding and connectivity; and (3) the implementation of automated monitoring equipment, citizen science projects, and data-sharing platforms to enable global, real-time, and high-resolution biomonitoring. This review highlights the urgent need for effective and strategic biomonitoring as a global imperative, necessitating public engagement and interdisciplinary collaboration. Only through the combined efforts of the scientific community, the public, and various stakeholders can we begin to better understand and manage our environment.
Conceptualization, J.Y., C.L., H.Y. and J.C.; methodology and investigation, J.Y., C.L., X.Z., H.Y. and J.C.; writing—original draft preparation, J.Y., C.L., X.Z., H.Y. and J.C.; writing—review and editing, L.S.H.L., Z.C., C.U., Z.D., M.N., J.G., H.Y., J.Y. and J.C.; visualization, J.Y. and C.L.; supervision, H.Y. and J.C.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.
The authors declare no conflicts of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 1. Flow chart summaring the components (blue boxes) and existing problems (orange boxes) involved in high-resolution underwater optical imaging-based monitoring technology. The collected high-resolution underwater data, images, and video data are generally incomplete, low quality, high noise, and other problems. Through the use of deep learning methods, such as Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and new network architectures, construction, single-modal/multi-modal methods, purposes such as real-time tracking, creature reidentification, and more can be achieved.
Enlarge this image.Figure 2. Thematic diagram of environmental DNA (eDNA) metabarcoding-based monitoring in marine protection and management. PCR: polymerase chain reaction; HTS: High-throughput sequencing.
Enlarge this image.Figure 3. The integration of underwater optical imaging-based monitoring and eDNA metabarcoding-based monitoring methods for enhanced marine environmental monitoring and conservation.
Reported primers targeting scleractinian corals monitoring using eDNA metabarcoding.
| Markers | Primer Name | Primer Sequence (5′-3′) | Target Length (bp) | References |
|---|---|---|---|---|
| 16S rRNA | HICOR16S_F1 | CCGGTATGAATGGTRTCMCGA | Nichols & Marko [ | |
| HICOR16S_R1 | TMCAGTAAAGYTCCATGGGG | 120 | ||
| HICOR16S_R2 | GTAACTTTTATTTGYTTATC | 400 | ||
| COI | HICORCOX_F1 | GAACAAGGRGCKGGBAC | ||
| HICORCOX_R1 | CCVGGRGCYCKCATRTTAAA | 120 | ||
| HICORCOX_R2 | GCAACAAAAGTYGGKATTAT | 400 | ||
| Nuclear ITS | SCLER5.8SFor | GARTCTTTGAACGCAAATGGC | Alexander et al. [ | |
| SCLER28SRev | GCTTATTAATATGCTTAAATTCAGCG | |||
| Coralacro_874Rev | TCGCCGTTACTGAGGGAATC | |||
| 12S rRNA | Scle_12S_Fw | CCAGCMGACGCGGTRANACTTA | ~366–465 | Shinzato et al. [ |
| Scle_12S_Rv | AAWTTGACGACGGCCATGC | |||
| COI | Scle_CO1_Fw | ATTGTNTGRGCNCAYCATATGTTTA | ~296–302 | |
| Scle_CO1_Rv | CCCATAGARAGNACATARTGAAA |
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; Zhang, Xu 3 ; Chen, Zhikui 3 ; Gao, Jing 3 ; Clara, U 4 ; Dai, Zhijun 5 ; Nakaoka, Masahiro 6
; Yang, Huayong 7 ; Cheng, Jinping 1
1 Department of Science and Environmental Studies, The Education University of Hong Kong, New Territories, Hong Kong SAR, China;
2 Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China;
3 DUT School of Software Technology, DUT-RU International School of Information Science and Engineering, the Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian University of Technology, Dalian 116620, China;
4 DUT School of Software Technology, DUT-RU International School of Information Science and Engineering, the Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian University of Technology, Dalian 116620, China;
5 State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, China;
6 Field Science Center for Northern Biosphere, Hokkaido University, 1 Aikappu, Akkeshi, Hokkaido 088-1113, Japan;
7 Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China;