Predicting the Ecological Quality Status of Marine Environments from eDNA Metabarcoding Data Using Supervised Machine Learning

Using ozone nanobubbles, and microalgae to promote the removal of nutrients from aquaculture wastewater: Insights from the changes of microbiomes

Integrated aquaculture wastewater treatment systems (IAWTSs) are widely used in treating aquaculture wastewater with the aeration-microalgae unit serving as an important component. In this study, we artificially constructed an IAWTS and applied two aeration-microalgae methods: ordinary aeration or ozone nanobubbles (ONBs) with microalgae (Nannochloropsis oculata). The impact of N.oculata and ONBs on the removal performance of nutrients and the underlying micro-ecological mechanisms were investigated using 16S rRNA gene amplicon sequencing. The results demonstrated that the combined use of ONBs and N.oculata exhibited superior purification effects with 78.25%, 76.59% and 86.71% removal of CODMn, TN and TP. N.oculata played a pivotal role as the primary element in wastewater purification, while ONBs influenced nutrient dynamics by affecting both N.oculata and bacterial communities. N.oculata actively shaped bacterial communities, with a specific focus on nitrogen and phosphorus cycling in the micro-environment remodeled by ONBs. Rare bacterial communities displayed heightened activity in response to the changes in N.oculata, ONBs, and nutrient levels. These findings provide a novel approach to improve the technological processes the IAWTS, contributing to the advancement of sustainable aquaculture practices by offering valuable insights into wastewater purification efficiency and micro-ecological mechanisms.

A novel biosensing strategy on the dynamic and on-site detection of Vibrio coralliilyticus eDNA for coral health warnings

Heat stress and coral diseases are the predominant factors causing the degradation of coral reef ecosystems. Over recent years, Vibrio coralliilyticus was identified as a temperature-dependent pathogen causing tissue lysis in Pocillopora damicornis and one of the primary pathogens causing bleaching and mortality in other corals. Yet current detection techniques for V. coralliilyticus rely primarily on qPCR and ddPCR, which cannot meet the requirements for non-invasive and real-time detection. Herein, we developed an effective electrochemical biosensor modified by an Au-MoS2/rGO (AMG) nanocomposites and a specific capture probe to dynamically detect V. coralliilyticus environment DNA (eDNA) in aquarium experiments, with a lower limit of detection (0.28 fM) for synthetic DNA and a lower limit of quantification (9.8 fg/µL, ∼0.86 copies/µL) for genomic DNA. Its reliability and accuracy were verified by comparison with the ddPCR method (P > 0.05). Notably, coral tissue started to lyse at only 29 °C when the concentration of V. coralliilyticus increased abruptly to 880 copies/µL, indicating the biosensor could reflect the types of pathogen and health risks of corals under heat stress. Overall, the novel and reliable electrochemical biosensing technology provides an efficient strategy for the on-site monitoring and early warning of coral health in the context of global warming.

Tree of life metabarcoding can serve as a biotic benchmark for shifting baselines in urbanized estuaries

Urbanization of estuaries drastically changed existing shorelines and bathymetric contours, in turn modifying habitat for marine foundational species that host critical biodiversity. And yet we lack approaches to characterize a significant fraction of the biota that inhabit these ecosystems on time scales that align with rates of urbanization. Environmental DNA (or eDNA) metabarcoding that combines multiple assays targeting a broad range of taxonomic groups can provide a solution, but we need to determine whether the biological communities it detects ally with different habitats in these changing aquatic environments. In this study, we tested whether tree of life metabarcoding (ToL-metabarcoding) data extracted from filtered seawater samples correlated with four known geomorphic habitat zones across a heavily urbanized estuary (Sydney Harbour, Australia). Using this method, we substantially expanded our knowledge on the composition and spatial distribution of marine biodiversity across the tree of life in Sydney Harbour, particularly for organisms where existing records are sparse. Excluding terrestrial DNA inputs, we identified significant effects of both distance from the mouth of Sydney Harbour and geomorphic zone on biological community structure in the ToL-metabarcoding dataset (entire community), as well as in each of the taxonomic subgroups that we considered (fish, macroinvertebrates, algae and aquatic plants, bacteria). This effect appeared to be driven by taxa as a collective versus a few individual taxa, with each taxon explaining no more than 0.62% of the variation between geomorphic zones. Similarly, taxonomic richness was significantly higher within geomorphic zones with large sample sizes, but also decreased by 1% with each additional kilometer from the estuary mouth, a result consistent with a reduction in tidal inputs and available habitat in upper catchments. Based on these results, we suggest that ToL-metabarcoding can be used to benchmark biological monitoring in other urbanized estuaries globally, and in Sydney Harbour at future time points based on detection of bioindicators across the tree of life. We also suggest that robust biotic snapshots can be archived following extensive curation of taxonomic assignments that incorporates ecological affinities, supported by records from relevant and regional biodiversity repositories.

Changes in the level of biofilm development significantly affect the persistence of environmental DNA in flowing water

As one of the powerful tools of species biomonitoring, the utilization of environmental DNA (eDNA) technology is progressively expanding in both scope and frequency within the field of ecology. Nonetheless, the growing dissemination of this technology has brought to light a multitude of intricate issues. The complex effects of environmental factors on the persistence of eDNA in water have brought many challenges to the interpretation of eDNA information. In this study, the primary objective was to examine how variations in the presence and development of biofilms impact the persistence of grass carp eDNA under different sediment types and flow conditions. This investigation encompassed the processes of eDNA removal and resuspension in water, shedding light on the complex interactions involved. The findings reveal that with an elevated biofilm development level, the total removal rate of eDNA gradually rose, resulting in a corresponding decrease in its residence time within the mesocosms. The influence of biofilms on the persistence of grass carp eDNA is more pronounced under flowing water conditions. However, changes in bottom sediment types did not significantly interact with biofilms. Lastly, in treatments involving alternating flow conditions between flowing and still water, significant resuspension of grass carp eDNA was not observed due to interference from multiple factors, including the effect of biofilms. Our study offers preliminary insights into the biofilm-mediated mechanisms of aquatic eDNA removal, emphasizing the need for careful consideration of environmental factors in the practical application of eDNA technology for biomonitoring in natural aquatic environments.

Metagenomics-Based Microbial Ecological Community Threshold and Indicators of Anthropogenic Disturbances in Estuarine Sediments

Assessing the impacts of cumulative anthropogenic disturbances on estuarine ecosystem health is challenging. Using spatially distributed sediments from the Pearl River Estuary (PRE) in southern China, which are significantly influenced by anthropogenic activities, we demonstrated that metagenomics-based surveillance of benthic microbial communities is a robust approach to assess anthropogenic impacts on estuarine benthic ecosystems. Correlational and threshold analyses between microbial compositions and environmental conditions indicated that anthropogenic disturbances in the PRE sediments drove the taxonomic and functional variations in the benthic microbial communities. An ecological community threshold of anthropogenic disturbances was identified, which delineated the PRE sediments into two groups (H and L) with distinct taxa and functional traits. Group H, located nearshore and subjected to a higher level of anthropogenic disturbances, was enriched with pollutant degraders, putative human pathogens, fecal pollution indicators, and functional traits related to stress tolerance. In contrast, Group L, located offshore and subjected to a lower level of anthropogenic disturbances, was enriched with halotolerant and oligotrophic taxa and functional traits related to growth and resource acquisition. The machine learning random forest model identified a number of taxonomic and functional indicators that could differentiate PRE sediments between Groups H and L. The identified ecological community threshold and microbial indicators highlight the utility of metagenomics-based microbial surveillance in assessing the adverse impacts of anthropogenic disturbances in estuarine sediments, which can assist environmental management to better protect ecosystem health.

Unlocking foraminiferal genetic diversity on estuarine mudflats with eDNA metabarcoding

Environmental biomonitoring is a prerequisite for efficient evaluation and remediation of ecosystem degradation due to anthropogenic pressure or climate change. Estuaries are key habitats subject to multiple anthropogenic and natural stressors. Due to these multiple stressors, the detection of anthropogenic pressure is challenging. The fact that abundant natural stressors often lead to negative quality assessments has been coined the "estuarine quality paradox". To solve this issue, the application of molecular approaches with successful bioindicators like foraminifera is promising. However, sampling protocols, molecular procedures and data analyses need to be validated before such tools can be routinely applied. We conducted an environmental DNA survey of estuarine mudflats along the French Atlantic coast, using a metabarcoding approach targeting foraminifera. Our results demonstrate that estuarine environments have only a few active OTUs dominating the community composition and a large stock of dormant or propagule stages. This last genetic diversity components constitute an important reservoir, with different species which can potentially develop in response to the temporal variability of the multiple stressors. In fact, different OTUs were dominant in the studied estuaries. Our statistical model shows that the physical and chemical characteristics of the sediment and the climatic conditions explain only 43 % of the community composition variance. This suggests that other, less easily quantifiable factors, such as the history and use of the estuaries or the ecological drift could play an important role as well. Environmental DNA biomonitoring opens new perspectives to better characterize the genetic diversity in estuaries.

Unraveling the potential of environmental DNA for deciphering recent advances in plant-animal interactions: a systematic review

MAIN CONCLUSION

Environmental DNA-based monitoring provides critical insights for enhancing our understanding of plant-animal interactions in the context of worldwide biodiversity decrease for developing a global framework for effective plant biodiversity conservation. To understand the ecology and evolutionary patterns of plant-animal interactions (PAI) and their pivotal roles in ecosystem functioning advances in molecular ecology tools such as Environmental DNA (eDNA) provide unprecedented research avenues. These methods being non-destructive in comparison to traditional biodiversity monitoring methods, enhance the discernment of ecosystem health, integrity, and complex interactions. This review intends to offer a systematic and critical appraisal of the prospective of eDNA for investigating PAI. The review thoroughly discusses and analyzes the recent reports (2015-2022) employing preferred reporting items for systematic reviews and meta-analyses (PRISMA) to outline the recent progression in eDNA approaches for elucidating PAI. The current review envisages that eDNA has a significant potential to monitor both plants and associated cohort of prospective pollinators (avian species and flowering plants, bees and plants, arthropods and plants, bats and plants, etc.). Furthermore, a brief description of the factors that influence the utility and interpretation of PAI eDNA is also presented. The review establishes that factors such as biotic and abiotic, primer selection and taxonomic resolution, and indeterminate spatio-temporal scales impact the availability and longevity of eDNA. The study also identified the limitations that influence PAI detection and suggested possible solutions for better execution of these molecular approaches. Overcoming these research caveats will augment the assortment of PAI analysis through eDNA that could be vital for ecosystem health and integrity. This review forms a critical guide and offers prominent insights for ecologists, environmental managers and researchers to assess and evaluate plant-animal interaction through environmental DNA.

Insights in Pharmaceutical Pollution: The Prospective Role of eDNA Metabarcoding

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

Supervised machine learning improves general applicability of eDNA metabarcoding for reservoir health monitoring

Effective and standardized monitoring methodologies are vital for successful reservoir restoration and management. Environmental DNA (eDNA) metabarcoding sequencing offers a promising alternative for biomonitoring and can overcome many limitations of traditional morphological bioassessment. Recent attempts have even shown that supervised machine learning (SML) can directly infer biotic indices (BI) from eDNA metabarcoding data, bypassing the cumbersome calculation process of BI regardless of the taxonomic assignment of eDNA sequences. However, questions surrounding the general applicability of this taxonomy-free approach to monitoring reservoir health remain unclear, including model stability, feature selection, algorithm choice, and multi-season biomonitoring. Here, we firstly developed a novel biological integrity index (Me-IBI) that integrates multitrophic interactions and environmental information, based on taxonomy-assigned eDNA metabarcoding data. The Me-IBI can better distinguish the actual health status of the Three Gorges Reservoir (TGR) than physicochemical assessments and have a clear response to human activity. Then, taking this reliable Me-IBI as a supervised label, we compared the impact of selecting different numbers of features and SML algorithms on the stability and predictive performance of the model for predicting ecological conditions in multiple seasons using taxonomy-free eDNA metabarcoding data. We discovered that even with a small number of features, different SML algorithms can establish a stable model and obtain excellent predictive performance. Finally, we proposed a four-step strategy for standardized routine biomonitoring using SML tools. Our study firstly explores the general applicability problem of the taxonomy-free eDNA-SML approach and establishes a solid foundation for the large-scale and standardized biomonitoring application.

Microbial biomarkers of tree water status for next-generation biomonitoring of forest ecosystems

Next-generation biomonitoring proposes to combine machine-learning algorithms with environmental DNA data to automate the monitoring of the Earth's major ecosystems. In the present study, we searched for molecular biomarkers of tree water status to develop next-generation biomonitoring of forest ecosystems. Because phyllosphere microbial communities respond to both tree physiology and climate change, we investigated whether environmental DNA data from tree phyllosphere could be used as molecular biomarkers of tree water status in forest ecosystems. Using an amplicon sequencing approach, we analysed phyllosphere microbial communities of four tree species (Quercus ilex, Quercus robur, Pinus pinaster and Betula pendula) in a forest experiment composed of irrigated and non-irrigated plots. We used these microbial community data to train a machine-learning algorithm (Random Forest) to classify irrigated and non-irrigated trees. The Random Forest algorithm detected tree water status from phyllosphere microbial community composition with more than 90% accuracy for oak species, and more than 75% for pine and birch. Phyllosphere fungal communities were more informative than phyllosphere bacterial communities in all tree species. Seven fungal amplicon sequence variants were identified as candidates for the development of molecular biomarkers of water status in oak trees. Altogether, our results show that microbial community data from tree phyllosphere provides information on tree water status in forest ecosystems and could be included in next-generation biomonitoring programmes that would use in situ, real-time sequencing of environmental DNA to help monitor the health of European temperate forest ecosystems.

Environmental DNA metabarcoding serves as a promising method for aquatic species monitoring and management: A review focused on its workflow, applications, challenges and prospects

Marine and freshwater biodiversity is under threat from both natural and manmade causes. Biological monitoring is currently a top priority for biodiversity protection. Given present limitations, traditional biological monitoring methods may not achieve the proposed monitoring aims. Environmental DNA metabarcoding technology reflects species information by capturing and extracting DNA from environmental samples, using molecular biology techniques to sequence and analyze the DNA, and comparing the obtained information with existing reference libraries to obtain species identification. However, its practical application has highlighted several limitations. This paper summarizes the main steps in the environmental application of eDNA metabarcoding technology in aquatic ecosystems, including the discovery of unknown species, the detection of invasive species, and evaluations of biodiversity. At present, with the rapid development of big data and artificial intelligence, certain advanced technologies and devices can be combined with environmental DNA metabarcoding technology to promote further development of aquatic species monitoring and management.

A combination of machine-learning and eDNA reveals the genetic signature of environmental change at the landscape levels

The current advances of environmental DNA (eDNA) bring profound changes to ecological monitoring and provide unique insights on the biological diversity of ecosystems. The very nature of eDNA data is challenging yet also revolutionizing how biological monitoring information is analysed. In particular, new metrics and approaches should take full advantage of the extent and detail of molecular data produced by genetic methods. In this perspective, machine learning algorithms are particularly promising as they can capture complex relationships between the multiple environmental pressures and the diversity of biological communities. We investigated the potential of a new generation of biomonitoring tools that implement machine-learning techniques to fully exploit eDNA datasets. We trained a machine learning model to discriminate between reference and impacted communities of freshwater macroinvertebrates and assessed its performances using a large eDNA dataset collected at 64 standard federal monitoring sites across Switzerland. We show that a model trained on eDNA is significantly better than a naive model and performs similarly to a model trained on traditional data. Our proof-of-concept shows that such a combination of eDNA and machine learning approaches has the potential to complement or even replace traditional environmental monitoring, and could be scaled along temporal or spatial dimensions.

Plasmid permissiveness of wastewater microbiomes can be predicted from 16S rRNA sequences by machine learning

MOTIVATION

Wastewater treatment plants (WWTPs) harbor a dense and diverse microbial community. They constantly receive antimicrobial residues and resistant strains, and therefore provide conditions for horizontal gene transfer (HGT) of antimicrobial resistance (AMR) determinants. This facilitates the transmission of clinically important genes between, e.g. enteric and environmental bacteria, and vice versa. Despite the clinical importance, tools for predicting HGT remain underdeveloped.

RESULTS

In this study, we examined to which extent water cycle microbial community composition, as inferred by partial 16S rRNA gene sequences, can predict plasmid permissiveness, i.e. the ability of cells to receive a plasmid through conjugation, based on data from standardized filter mating assays using fluorescent bio-reporter plasmids. We leveraged a range of machine learning models for predicting the permissiveness for each taxon in the community, representing the range of hosts a plasmid is able to transfer to, for three broad host-range resistance IncP plasmids (pKJK5, pB10, and RP4). Our results indicate that the predicted permissiveness from the best performing model (random forest) showed a moderate-to-strong average correlation of 0.49 for pB10 [95% confidence interval (CI): 0.44-0.55], 0.43 for pKJK5 (0.95% CI: 0.41-0.49), and 0.53 for RP4 (0.95% CI: 0.48-0.57) with the experimental permissiveness in the unseen test dataset. Predictive phylogenetic signals occurred despite the broad host-range nature of these plasmids. Our results provide a framework that contributes to the assessment of the risk of AMR pollution in wastewater systems.

AVAILABILITY AND IMPLEMENTATION

The predictive tool is available as an application at https://github.com/DaneshMoradigaravand/PlasmidPerm.

Comparing quantile regression spline analyses and supervised machine learning for environmental quality assessment at coastal marine aquaculture installations

Organic enrichment associated with marine finfish aquaculture is a local stressor of marine coastal ecosystems. To maintain ecosystem services, the implementation of biomonitoring programs focusing on benthic diversity is required. Traditionally, impact-indices are determined by extracting and identifying benthic macroinvertebrates from samples. However, this is a time-consuming and expensive method with low upscaling potential. A more rapid, inexpensive, and robust method to infer the environmental quality of marine environments is eDNA metabarcoding of bacterial communities. To infer the environmental quality of coastal habitats from metabarcoding data, two taxonomy-free approaches have been successfully applied for different geographical regions and monitoring goals, namely quantile regression splines (QRS) and supervised machine learning (SML). However, their comparative performance remains untested for monitoring the impact of organic enrichment introduced by aquaculture on marine coastal environments. We compared the performance of QRS and SML using bacterial metabarcoding data to infer the environmental quality of 230 aquaculture samples collected from seven farms in Norway and seven farms in Scotland along an organic enrichment gradient. As a measure of environmental quality, we used the Infaunal Quality Index (IQI) calculated from benthic macrofauna data (reference index). The QRS analysis plotted the abundance of amplicon sequence variants (ASVs) as a function to the IQI from which the ASVs with a defined abundance peak were assigned to eco-groups and a molecular IQI was subsequently calculated. In contrast, the SML approach built a random forest model to directly predict the macrofauna-based IQI. Our results show that both QRS and SML perform well in inferring the environmental quality with 89% and 90% accuracy, respectively. For both geographic regions, there was high correspondence between the reference IQI and both the inferred molecular IQIs (p < 0.001), with the SML model showing a higher coefficient of determination compared to QRS. Among the 20 most important ASVs identified by the SML approach, 15 were congruent with the good quality spline ASV indicators identified via QRS for both Norwegian and Scottish salmon farms. More research on the response of the ASVs to organic enrichment and the co-influence of other environmental parameters is necessary to eventually select the most powerful stressor-specific indicators. Even though both approaches are promising to infer environmental quality based on metabarcoding data, SML showed to be more powerful in handling the natural variability. For the improvement of the SML model, addition of new samples is still required, as background noise introduced by high spatio-temporal variability can be reduced. Overall, we recommend the development of a powerful SML approach that will be onwards applied for monitoring the impact of aquaculture on marine ecosystems based on eDNA metabarcoding data.

Ultrasensitive and on-site eDNA detection for the monitoring of crown-of-thorns starfish densities at the pre-outbreak stage using an electrochemical biosensor

The coral reef crisis has significantly intensified over the last decades, mainly due to severe outbreaks of crown-of-thorns starfish (COTS). Current ecological monitoring has failed to detect COTS densities at the pre-outbreak stage, thus preventing early intervention. In this work, we developed an effective electrochemical biosensor modified by a MoO₂/C nanomaterial, as well as a specific DNA probe that could detect trace COTS environmental DNA (eDNA) at a lower detection limit (LOD = 0.147 ng/μL) with excellent specificity. The reliability and accuracy of the biosensor were validated against the standard methods by an ultramicro spectrophotometer and droplet digital PCR (p > 0.05). The biosensor was then utilized for the on-site analysis of seawater samples from SYM-LD and SY sites in the South China Sea. For the SYM-LD site suffering an outbreak, the COTS eDNA concentrations were 0.33 ng/μL (1 m, depth) and 0.26 ng/μL (10 m, depth), respectively. According to the ecological survey, the COTS density was 500 ind/hm² at the SYM-LD site, verifying the accuracy of our measurements. At the SY site, COTS eDNA was also detected at 0.19 ng/μL, but COTS was not found by the traditional survey. Hence, larvae were possibly present in this region. Therefore, this electrochemical biosensor could be used to monitor COTS populations at the pre-outbreak stages, and potentially serve as a revolutionary early warning method. We will continue to improve this method for picomolar or even femtomolar detection of COTS eDNA.

Unsupervised biological integrity assessment by eDNA biomonitoring of multi-trophic aquatic taxa

The biological integrity of global freshwater ecosystems is threatened by ever-increasing environmental stressors due to increased human activities, such as land-use change, eutrophication, toxic pollutants, overfishing, and exploitation. Traditional ecological assessments of lake or riverine ecosystems often require human supervision of a pre-selected reference area, using the current state of the reference area as the expected state. However, selecting an appropriate reference area has become increasingly difficult with the expansion of human activities. Here, an unsupervised biological integrity assessment framework based on environmental DNA metabarcoding without a prior reference area is proposed. Taxon richness, species dominance, co-occurrence network density, and phylogenetic distance were used to assess the aquatic communities in the Taihu Lake basin. Multi-gene metabarcoding revealed comprehensive biodiversity at multiple trophic levels including algae, protists, zooplankton, and fish. Fish sequences were mainly derived from 12S, zooplankton mainly from mitochondrial cytochrome C oxidase subunit I, and algae and protists mainly from 18S. There were significant differences in community composition among lakes, rivers, and reservoirs but no significant differences in the four fundamental biological indicators. The algal and zooplankton integrities were positively correlated with protist and fish integrities, respectively. Additionally, the algal integrity of lakes was found to be significantly lower than that of rivers. The unsupervised assessment framework proposed in this study allows different ecosystems, including the same ecosystem in different seasons, to adopt the same indicators and assessment methods, which is more convenient for environmental management and decision-making.

Algal community structure prediction by machine learning

The algal community structure is vital for aquatic management. However, the complicated environmental and biological processes make modeling challenging. To cope with this difficulty, we investigated using random forests (RF) to predict phytoplankton community shifting based on multi-source environmental factors (including physicochemical, hydrological, and meteorological variables). The RF models robustly predicted the algal communities composed by 13 major classes (Bray-Curtis dissimilarity = 9.2 ± 7.0%, validation NRMSE mostly <10%), with accurate simulations to the total biomass (validation R² > 0.74) in Norway's largest lake, Lake Mjosa. The importance analysis showed that the hydro-meteorological variables (Standardized MSE and Node Purity mostly >0.5) were the most influential factors in regulating the phytoplankton. Furthermore, an in-depth ecological interpretation uncovered the interactive stress-response effect on the algal community learned by the RF models. The interpretation results disclosed that the environmental drivers (i.e., temperature, lake inflow, and nutrients) can jointly pose strong influence on the algal community shifts. This study highlighted the power of machine learning in predicting complex algal community structures and provided insights into the model interpretability.

Transition of an estuarine benthic meiofauna assemblage 1.7 and 2.8 years after a mining disaster

Background

Estuaries are transitional coastal ecosystems that are threatened by multiple sources of human pollution. In 2015, mining tailings from an upstream dam failure caused massive metal contamination that impacted benthic assemblages on the Brazilian Rio Doce estuary.

Methods

In this study, we investigate and compare meiofaunal assemblages with eDNA metabarcoding 1.7 years (2017) and 2.8 years (2018) after the initial contamination by mine tailings in order to evaluate the continued impact of sediment mine tailing contaminants on the structure of benthic assemblages after the disaster.

Results

The community was dominated by Arthropoda and Nematoda 1.7 yr after the impacts (42 and 29% of meiofaunal sequence reads, respectively) but after 2.8 years Arthropoda (64.8% of meiofaunal sequence reads) and Rotifera (11.8%) were the most common taxa. This continued impact on meiofaunal assemblage revealed a lower phylogenetic diversity (7.8-fold) in 2018, despite overall decrease in metal concentration (Al, Ba, Cr, As, Fe, Zn, Mn, Pb, Cd, Co) in sediments. Our data suggests that differences in benthic assemblages and loss of diversity may be influenced by contaminants in sediments of this estuary, and indicate that broad eDNA assessments are greatly useful to understand the full range of biodiversity changes in dynamic estuarine ecosystems.

Metabarcoding Approaches for Soil Eukaryotes, Protists, and Microfauna

There have been major developments in the molecular characterization of soil protist and micrometazoan diversity, leading to a better understanding of these minute soil eukaryotes. Like in all newly developing research fields, several approaches are currently used in parallel to study these organisms. Here, we synthesize these various approaches and propose a best practice manual that should help researchers to efficiently target soil eukaryotic diversity as a whole. We cover the whole working pipeline, ranging from sampling to nucleic acids extraction to bioinformatic processing and sequence identification. Synchronous approaches to molecularly survey microbial-sized eukaryotes and other soil biodiversity groups are needed in order to provide a cumulative knowledge of soil biodiversity, as here shown for the soil eukaryome. This will be crucial in understanding the important ecosystem functions provided by soil biodiversity.

Fine-scale differences in eukaryotic communities inside and outside salmon aquaculture cages revealed by eDNA metabarcoding

Aquaculture impacts on marine benthic ecosystems are widely recognized and monitored. However, little is known about the community changes occurring in the water masses surrounding aquaculture sites. In the present study, we studied the eukaryotic communities inside and outside salmonid aquaculture cages through time to assess the community changes in the neighbouring waters of the farm. Water samples were taken biweekly over five months during the production phase from inside the cages and from nearby points located North and South of the salmon farm. Eukaryotic communities were analyzed by eDNA metabarcoding of the partial COI Leray-XT fragment. The results showed that eukaryotic communities inside the cages were significantly different from those in the outside environment, with communities inside the cages having higher diversity values and more indicator species associated with them. This is likely explained by the appearance of fouling species that colonize the artificial structures, but also by other species that are attracted to the cages by other means. Moreover, these effects were highly localized inside the cages, as the communities identified outside the cages, both North and South, had very similar eukaryotic composition at each point in time. Overall, the eukaryotic communities, both inside and outside the cages, showed similar temporal fluctuations through the summer months, with diversity peaks occurring at the end of July, beginning of September, and in the beginning of November, with the latter showing the highest Shannon diversity and richness values. Hence, our study suggests that seasonality, together with salmonid aquaculture, are the main drivers of eukaryotic community structure in surface waters surrounding the farm.

Amplicon sequence variant-based meiofaunal community composition revealed by DADA2 tool is compatible with species composition

The present study is aimed at implementing the morphological identification-free amplicon sequence variant (ASV) concept for describing meiofaunal species composition, while strongly indicating reasonable compatibility with the underlying species. A primer pair was constructed and demonstrated to PCR amplify a 470-490 bp 18S barcode from a variety of meiofaunal taxa, high throughput sequenced using the Illumina 300 × 2 bps platform. Sixteen 18S multi-species HTS assemblies were created from meiofaunal samples and merged to one assembly of ~2,150,000 reads. Five quality scores (q = 35, 30, 25, 20, 15) were implemented to filter five 18S barcode assemblies, which served as inputs for the DADA2 software, ending with five reference ASV libraries. Each of these libraries was clustered, applying 3% dissimilarity threshold, revealed an average number of 1.38 ± 0.078 ASVs / cluster. Hence, demonstrating high level of ASV uniqueness. The libraries which were based on q ≤ 25 reached a near-asymptote number of ASVs which together with the low average number of ASVs / cluster, strongly indicated fair representation of the actual number of the underlying species. Hence, the q = 25 library was selected to be used as metabarcoding reference library. It contained 461 ASVs and 342-3% clusters with average number of 1.34 ± 1.036 ASV / cluster and their BLASTN annotation elucidated a variety of expected meiofaunal taxa. The sixteen assemblies of sample-specific paired reads were mapped to this reference library and sample ASV profiles, namely the list of ASVs and their proportional copy numbers were created and clustered.

A Critical Assessment of the Congruency between Environmental DNA and Palaeoecology for the Biodiversity Monitoring and Palaeoenvironmental Reconstruction

The present study suggests that standardized methodology, careful site selection, and stratigraphy are essential for investigating ancient ecosystems in order to evaluate biodiversity and DNA-based time series. Based on specific keywords, this investigation reviewed 146 publications using the SCOPUS, Web of Science (WoS), PUBMED, and Google Scholar databases. Results indicate that environmental deoxyribose nucleic acid (eDNA) can be pivotal for assessing and conserving ecosystems. Our review revealed that in the last 12 years (January 2008–July 2021), 63% of the studies based on eDNA have been reported from aquatic ecosystems, 25% from marine habitats, and 12% from terrestrial environments. Out of studies conducted in aquatic systems using the environmental DNA (eDNA) technique, 63% of the investigations have been reported from freshwater ecosystems, with an utmost focus on fish diversity (40%). Further analysis of the literature reveals that during the same period, 24% of the investigations using the environmental DNA technique were carried out on invertebrates, 8% on mammals, 7% on plants, 6% on reptiles, and 5% on birds. The results obtained clearly indicate that the environmental DNA technique has a clear-cut edge over other biodiversity monitoring methods. Furthermore, we also found that eDNA, in conjunction with different dating techniques, can provide better insight into deciphering eco-evolutionary feedback. Therefore, an attempt has been made to offer extensive information on the application of dating methods for different taxa present in diverse ecosystems. Last, we provide suggestions and elucidations on how to overcome the caveats and delineate some of the research avenues that will likely shape this field in the near future. This paper aims to identify the gaps in environmental DNA (eDNA) investigations to help researchers, ecologists, and decision-makers to develop a holistic understanding of environmental DNA (eDNA) and its utility as a palaeoenvironmental contrivance.

Environmental DNA metabarcoding for benthic monitoring: A review of sediment sampling and DNA extraction methods

Environmental DNA (eDNA) metabarcoding (parallel sequencing of DNA/RNA for identification of whole communities within a targeted group) is revolutionizing the field of aquatic biomonitoring. To date, most metabarcoding studies aiming to assess the ecological status of aquatic ecosystems have focused on water eDNA and macroinvertebrate bulk samples. However, the eDNA metabarcoding has also been applied to soft sediment samples, mainly for assessing microbial or meiofaunal biota. Compared to classical methodologies based on manual sorting and morphological identification of benthic taxa, eDNA metabarcoding offers potentially important advantages for assessing the environmental quality of sediments. The methods and protocols utilized for sediment eDNA metabarcoding can vary considerably among studies, and standardization efforts are needed to improve their robustness, comparability and use within regulatory frameworks. Here, we review the available information on eDNA metabarcoding applied to sediment samples, with a focus on sampling, preservation, and DNA extraction steps. We discuss challenges specific to sediment eDNA analysis, including the variety of different sources and states of eDNA and its persistence in the sediment. This paper aims to identify good-practice strategies and facilitate method harmonization for routine use of sediment eDNA in future benthic monitoring.

LANDMark: an ensemble approach to the supervised selection of biomarkers in high-throughput sequencing data

Background

Identification of biomarkers, which are measurable characteristics of biological datasets, can be challenging. Although amplicon sequence variants (ASVs) can be considered potential biomarkers, identifying important ASVs in high-throughput sequencing datasets is challenging. Noise, algorithmic failures to account for specific distributional properties, and feature interactions can complicate the discovery of ASV biomarkers. In addition, these issues can impact the replicability of various models and elevate false-discovery rates. Contemporary machine learning approaches can be leveraged to address these issues. Ensembles of decision trees are particularly effective at classifying the types of data commonly generated in high-throughput sequencing (HTS) studies due to their robustness when the number of features in the training data is orders of magnitude larger than the number of samples. In addition, when combined with appropriate model introspection algorithms, machine learning algorithms can also be used to discover and select potential biomarkers. However, the construction of these models could introduce various biases which potentially obfuscate feature discovery.

Results

We developed a decision tree ensemble, LANDMark, which uses oblique and non-linear cuts at each node. In synthetic and toy tests LANDMark consistently ranked as the best classifier and often outperformed the Random Forest classifier. When trained on the full metabarcoding dataset obtained from Canada’s Wood Buffalo National Park, LANDMark was able to create highly predictive models and achieved an overall balanced accuracy score of 0.96 ± 0.06. The use of recursive feature elimination did not impact LANDMark’s generalization performance and, when trained on data from the BE amplicon, it was able to outperform the Linear Support Vector Machine, Logistic Regression models, and Stochastic Gradient Descent models (p ≤ 0.05). Finally, LANDMark distinguishes itself due to its ability to learn smoother non-linear decision boundaries.

Conclusions

Our work introduces LANDMark, a meta-classifier which blends the characteristics of several machine learning models into a decision tree and ensemble learning framework. To our knowledge, this is the first study to apply this type of ensemble approach to amplicon sequencing data and we have shown that analyzing these datasets using LANDMark can produce highly predictive and consistent models.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04631-z.

Evaluating eDNA for Use within Marine Environmental Impact Assessments

In this review, the use of environmental DNA (eDNA) within Environmental Impact Assessment (EIA) is evaluated. EIA documents provide information required by regulators to evaluate the potential impact of a development project. Currently eDNA is being incorporated into biodiversity assessments as a complementary method for detecting rare, endangered or invasive species. However, questions have been raised regarding the maturity of the field and the suitability of eDNA information as evidence for EIA. Several key issues are identified for eDNA information within a generic EIA framework for marine environments. First, it is challenging to define the sampling unit and optimal sampling strategy for eDNA with respect to the project area and potential impact receptor. Second, eDNA assay validation protocols are preliminary at this time. Third, there are statistical issues around the probability of obtaining both false positives (identification of taxa that are not present) and false negatives (non-detection of taxa that are present) in results. At a minimum, an EIA must quantify the uncertainty in presence/absence estimates by combining series of Bernoulli trials with ad hoc occupancy models. Finally, the fate and transport of DNA fragments is largely unknown in environmental systems. Shedding dynamics, biogeochemical and physical processes that influence DNA fragments must be better understood to be able to link an eDNA signal with the receptor’s state. The biggest challenge is that eDNA is a proxy for the receptor and not a direct measure of presence. Nonetheless, as more actors enter the field, technological solutions are likely to emerge for these issues. Environmental DNA already shows great promise for baseline descriptions of the presence of species surrounding a project and can aid in the identification of potential receptors for EIA monitoring using other methods.

Application of machine learning and acoustic predation tags to classify migration fate of Atlantic salmon smolts

Mortality and predation of tagged fishes present a serious challenge to interpreting results of acoustic telemetry studies. There is a need for standardized methods to identify predated individuals and reduce the impacts of "predation bias" on results and conclusions. Here, we use emerging approaches in machine learning and acoustic tag technology to classify out-migrating Atlantic salmon (Salmo salar) smolts into different fate categories. We compared three methods of fate classification: predation tag pH sensors and detection data, unsupervised k-means clustering, and supervised random forest combined with tag pH sensor data. Random forest models increased predation estimates by 9-32% compared to relying solely on pH sensor data, while clustering reduced estimates by 3.5-30%. The greatest changes in fate class estimates were seen in years with large class imbalance (one or more fate classes underrepresented compared to the others) or low model accuracy. Both supervised and unsupervised approaches were able to classify smolt fate; however, in-sample model accuracy improved when using tag sensor data to train models, emphasizing the value of incorporating such sensors when studying small fish. Sensor data may not be sufficient to identify predation in isolation due to Type I and Type II error in predation sensor triggering. Combining sensor data with machine learning approaches should be standard practice to more accurately classify fate of tagged fish.

Microbial diversity alteration reveals biomarkers of contamination in soil-river-lake continuum

Microbial communities inhabiting soil-water-sediment continuum in coastal areas provide important ecosystem services. Their adaptation in response to environmental stressors, particularly mitigating the impact of pollutants discharged from human activities, has been considered for the development of microbial biomonitoring tools, but their use is still in the infancy. Here, chemical and molecular (16S rRNA gene metabarcoding) approaches were combined in order to determine the impact of pollutants on microbial assemblages inhabiting the aquatic network of a soil-water-sediment continuum around the Ichkeul Lake (Tunisia), an area highly impacted by human activities. Samples were collected within the soil-river-lake continuum at three stations in dry (summer) and wet (winter) seasons. The contaminant pressure index (PI), which integrates Polycyclic aromatic hydrocarbons (PAHs), alkanes, Organochlorine pesticides (OCPs) and metal contents, and the microbial pressure index microgAMBI, based on bacterial community structure, showed significant correlation with contamination level and differences between seasons. The comparison of prokaryotic communities further revealed specific assemblages for soil, river and lake sediments. Correlation analyses identified potential "specialist" genera for the different compartments, whose abundances were correlated with the pollutant type found. Additionally, PICRUSt analysis revealed the metabolic potential for pollutant transformation or degradation of the identified "specialist" species, providing information to estimate the recovery capacity of the ecosystem. Such findings offer the possibility to define a relevant set of microbial indicators for assessing the effects of human activities on aquatic ecosystems. Microbial indicators, including the detection of "specialist" and sensitive taxa, and their functional capacity, might be useful, in combination with integrative microbial indices, to constitute accurate biomonitoring tools for the management and restoration of complex coastal aquatic systems.

Consideration of Multitrophic Biodiversity and Ecosystem Functions Improves Indices on River Ecological Status

Biological quality elements have been developed worldwide to assess whether a water body is in a good status or not. However, current studies mainly focus on a single taxonomic group or a small set of species, often limited by methods of morphological identification, and lack further aspects of biodiversity (e.g., across taxa and multiple attributes) and ecosystem functions. Here, we advance a framework for assessing the river's ecological status based on complete biodiversity data measured by environmental DNA (eDNA) metabarcoding and measurements of ecosystem functions in addition to physicochemical elements across a large riverine system in China. We identified 40 indicators of biodiversity and ecosystem functions, covering five taxonomic groups from bacteria to invertebrates, and associated with multiple attributes of biodiversity and ecosystem functions. Our data show that human impact on ecosystems could be accurately predicted by these eDNA-based indicators and ecosystem functions, using cross-validation with a known stressor gradient. Moreover, indices based on these indicators of biodiversity and ecosystem functions not only distinguish the physicochemical characteristics of the sites but also improve the assessment accuracy of 20-30% for the river's ecological status. Overall, by incorporating eDNA-based biodiversity with physicochemical and ecosystem functional elements, the multidimensional perspectives of ecosystem states provide additional information to protect and maintain a good ecological status of rivers.

Assessing the effects of cascade dams on river ecological status using multi-species interaction-based index of biotic integrity (Mt-IBI)

Cascade dams have exerted significant effects on river ecosystems. To quantitatively assess dam-induced effects on river ecological status, a novel multi-species interaction-based index of biotic integrity (Mt-IBI) was developed. Benthic microbiota was selected as a bio-indicator for its sensitivity to the environmental disturbance. An environmental DNA metabarcoding tool was used to identify microbiota (bacteria, protozoan, and metazoan). The Mt-IBI was applied to assess the ecological status of the Hanjiang River, a representative dam-affected river in China. Fifteen sampling sites along the Hanjiang River were sampled in June 2018. Seven core metrics were screened from a total of 364 candidate metrics to calculate the value of the Mt-IBI. The Mt-IBI of the Hanjiang River ranged from 1.90 to 6.39, with a mean value of 4.02. The mean values of Mt-IBI at the reservoir and riverine side of dams were 2.11 and 3.81, respectively. The downstream reach without dam constructions had the highest mean Mt-IBI (5.79). Thus, the continuity of the river was strongly related to the Mt-IBI. Structural equation models (SEMs) were further established to identify the dominant environmental variables in the dam-affected river. The SEMs indicated that flow velocity (coefficient 0.749) was the most important determinant of ecological status in the Hanjiang River. Water organic matter also played a vital role in determining the ecological status of the Hanjiang River, and exerted the strongest direct effect (P < 0.001, r = 0.712). The reliability of SEMs was verified by building a support vector regression model (R² = 0.8141). This study can provide new tools for ecological assessment and diagnosis, and provide a new perspective for the management of cascade dams.

Comparing sediment preservation methods for genomic biomonitoring of coastal marine ecosystems

To avoid loss of genetic information in environmental DNA (eDNA) field samples, the preservation of nucleic acids during field sampling is a critical step. In the development of standard operating procedures (SOPs) for eDNA-based compliance monitoring, the effect of different routinely used sediment preservations on biological community structures serving as bioindicators has gone untested. We compared eDNA metabarcoding results of marine bacterial communities from sample aliquots that were treated with a nucleic acid preservation solution (treated samples) and aliquots that were frozen without further treatment (non-treated samples). Sediment samples were obtained from coastal locations subjected to different stressors (aquaculture, urbanization, industry). DNA extraction efficiency, bacterial community profiles, and measures of alpha- and beta-diversity were highly congruent between treated and non-treated samples. As both preservation methods provide the same relevant information to environmental managers and regulators, we recommend the inclusion of both methods into SOPs for biomonitoring in marine coastal environments.

Human impacts on deep-sea sponge grounds: Applying environmental omics to monitoring

Sponges (Phylum Porifera) are the oldest extant Metazoans. In the deep sea, sponges can occur at high densities forming habitats known as sponge grounds. Sponge grounds can extend over large areas of up to hundreds of km² and are biodiversity hotspots. However, as human activities, including deep-water hydrocarbon extraction, continue to expand into areas harbouring sponge grounds, understanding how anthropogenic impacts affect sponges and the ecosystem services they provide at multiple biological scales (community, individual and (sub)cellular levels) is key for achieving sustainable management. This chapter (1) provides an update to the chapter of Advances in Marine Biology Volume 79 entitled "Potential Impacts of Offshore Oil and Gas Activities on Deep-Sea Sponges and the Habitats They Form" and (2) discusses the use of omics as a future tool for deep-sea ecosystem monitoring. While metagenomics and (meta)transcriptomics studies have contributed to improve our understanding of sponge biology in recent years, metabolomics analysis has mostly been used to identify natural products. The sponge metabolome, therefore, remains vastly unknown despite the fact that the metabolome is a key link between the genotype and phenotype, giving us a unique new insight to how key components of an ecosystem are functioning. As the fraction of the metabolome released into the seawater, the sponge exometabolome has only just started to be characterised in comparative environmental metabolomic studies. Yet, the sponge exometabolome constitute a unique opportunity for the identification of biomarkers of sponge health as compounds can be measured in seawater, bypassing the need for physical samples which can still be difficult to collect in the deep sea. Within sponge grounds, the characterisation of a shared sponge exometabolome could lead to the identification of biomarkers of ecosystem functioning and overall health. Challenges remain in establishing omics approaches in environmental monitoring but constant technological advances and reduction in costs means these techniques will become widely available in the future.

Aquatic Organisms Research with DNA Barcodes

Since their inception, DNA barcodes have become a powerful tool for understanding the biodiversity and biology of aquatic species, with multiple applications in diverse fields such as food security, fisheries, environmental DNA, conservation, and exotic species detection. Nevertheless, most aquatic ecosystems, from marine to freshwater, are understudied, with many species disappearing due to environmental stress, mostly caused by human activities. Here we highlight the progress that has been made in studying aquatic organisms with DNA barcodes, and encourage its further development in assisting sustainable use of aquatic resources and conservation.

Ecosystems monitoring powered by environmental genomics: A review of current strategies with an implementation roadmap

A decade after environmental scientists integrated high-throughput sequencing technologies in their toolbox, the genomics-based monitoring of anthropogenic impacts on the biodiversity and functioning of ecosystems is yet to be implemented by regulatory frameworks. Despite the broadly acknowledged potential of environmental genomics to this end, technical limitations and conceptual issues still stand in the way of its broad application by end-users. In addition, the multiplicity of potential implementation strategies may contribute to a perception that the routine application of this methodology is premature or "in development", hence restraining regulators from binding these tools into legal frameworks. Here, we review recent implementations of environmental genomics-based methods, applied to the biomonitoring of ecosystems. By taking a general overview, without narrowing our perspective to particular habitats or groups of organisms, this paper aims to compare, review and discuss the strengths and limitations of four general implementation strategies of environmental genomics for monitoring: (a) Taxonomy-based analyses focused on identification of known bioindicators or described taxa; (b) De novo bioindicator analyses; (c) Structural community metrics including inferred ecological networks; and (d) Functional community metrics (metagenomics or metatranscriptomics). We emphasise the utility of the three latter strategies to integrate meiofauna and microorganisms that are not traditionally utilised in biomonitoring because of difficult taxonomic identification. Finally, we propose a roadmap for the implementation of environmental genomics into routine monitoring programmes that leverage recent analytical advancements, while pointing out current limitations and future research needs.

Deep exploration of random forest model boosts the interpretability of machine learning studies of complicated immune responses and lung burden of nanoparticles

The development of machine learning provides solutions for predicting the complicated immune responses and pharmacokinetics of nanoparticles (NPs) in vivo. However, highly heterogeneous data in NP studies remain challenging because of the low interpretability of machine learning. Here, we propose a tree-based random forest feature importance and feature interaction network analysis framework (TBRFA) and accurately predict the pulmonary immune responses and lung burden of NPs, with the correlation coefficient of all training sets >0.9 and half of the test sets >0.75. This framework overcomes the feature importance bias brought by small datasets through a multiway importance analysis. TBRFA also builds feature interaction networks, boosts model interpretability, and reveals hidden interactional factors (e.g., various NP properties and exposure conditions). TBRFA provides guidance for the design and application of ideal NPs and discovers the feature interaction networks that contribute to complex systems with small-size data in various fields.

Global Trends of Benthic Bacterial Diversity and Community Composition Along Organic Enrichment Gradients of Salmon Farms

The analysis of benthic bacterial community structure has emerged as a powerful alternative to traditional microscopy-based taxonomic approaches to monitor aquaculture disturbance in coastal environments. However, local bacterial diversity and community composition vary with season, biogeographic region, hydrology, sediment texture, and aquafarm-specific parameters. Therefore, without an understanding of the inherent variation contained within community complexes, bacterial diversity surveys conducted at individual farms, countries, or specific seasons may not be able to infer global universal pictures of bacterial community diversity and composition at different degrees of aquaculture disturbance. We have analyzed environmental DNA (eDNA) metabarcodes (V3-V4 region of the hypervariable SSU rRNA gene) of 138 samples of different farms located in different major salmon-producing countries. For these samples, we identified universal bacterial core taxa that indicate high, moderate, and low aquaculture impact, regardless of sampling season, sampled country, seafloor substrate type, or local farming and environmental conditions. We also discuss bacterial taxon groups that are specific for individual local conditions. We then link the metabolic properties of the identified bacterial taxon groups to benthic processes, which provides a better understanding of universal benthic ecosystem function(ing) of coastal aquaculture sites. Our results may further guide the continuing development of a practical and generic bacterial eDNA-based environmental monitoring approach.

A step towards the validation of bacteria biotic indices using DNA metabarcoding for benthic monitoring

Environmental genomics is a promising field for monitoring biodiversity in a timely fashion. Efforts have increasingly been dedicated to the use of bacteria DNA derived data to develop biotic indices for benthic monitoring. However, a substantial debate exists about whether bacteria-derived data using DNA metabarcoding should follow, for example, a taxonomy-based or a taxonomy-free approach to marine bioassessments. Here, we showcase the value of DNA-based monitoring using the impact of fish farming as an example of anthropogenic disturbances in coastal areas and compare the performance of taxonomy-based and taxonomy-free approaches in detecting environmental alterations. We analysed samples collected near to the farm cages and along distance gradients from two aquaculture installations, and at control sites, to evaluate the effect of this activity on bacterial assemblages. Using the putative response of bacterial taxa to stress we calculated the taxonomy-based biotic index microgAMBI. The distribution of individual amplicon sequence variants (ASVs), as a function of a gradient in sediment acid volatile sulphides, was then used to derive a taxonomy-free bacterial biotic index specific for this data set using a de novo approach based on quantile regression splines. Our results show that microgAMBI revealed a organically enriched environment along the gradient. However, the de novo biotic index outperformed microgAMBI by providing a higher discriminatory power in detecting changes in abiotic factors directly related to fish production, whilst allowing the identification of new ASVs bioindicators. The de novo strategy applied here represents a robust method to define new bioindicators in regions or habitats where no previous information about the response of bacteria to environmental stressors exists.

Identifying the minimum amplicon sequence depth to adequately predict classes in eDNA-based marine biomonitoring using supervised machine learning

Environmental DNA metabarcoding is a powerful approach for use in biomonitoring and impact assessments. Amplicon-based eDNA sequence data are characteristically highly divergent in sequencing depth (total reads per sample) as influenced inter alia by the number of samples simultaneously analyzed per sequencing run. The random forest (RF) machine learning algorithm has been successfully employed to accurately classify unknown samples into monitoring categories. To employ RF to eDNA data, and avoid sequencing-depth artifacts, sequence data across samples are normalized using rarefaction, a process that inherently loses information. The aim of this study was to inform future sampling designs in terms of the relationship between sampling depth and RF accuracy. We analyzed three published and one new bacterial amplicon datasets, using a RF, based initially on the maximal rarefied data available (minimum mean of > 30,000 reads across all datasets) to give our baseline performance. We then evaluated the RF classification success based on increasingly rarefied datasets. We found that extreme to moderate rarefaction (50-5000 sequences per sample) was sufficient to achieve prediction performance commensurate to the full data, depending on the classification task. We did not find that the number of classification classes, data balance across classes, or the total number of sequences or samples, were associated with predictive accuracy. We identified the ability of the training data to adequately characterize the classes being mapped as the most important criterion and discuss how this finding can inform future sampling design for eDNA based biomonitoring to reduce costs and computation time.

COD Optimization Prediction Model Based on CAWOA-ELM in Water Ecological Environment

The change of water quality can reflect the important indicators of ecological environment measurement. Sewage discharge is an important factor causing environmental pollution. Establishing an effective water ecological prediction model can detect changes in the ecological environment system quickly and effectively. In order to detect high error rate and poor convergence of the water ecological chemical oxygen demand (COD) prediction model, combining the limit learning machine (ELM) model and whale optimization algorithm, CAWOA is improved by the sin chaos search strategy, while the ELM optimizes the parameters of the algorithm to improve convergence speed, thus improving the generalization performance of the ELM. In the CAWOA, the global optimization results of the WOA are promoted by introducing a sin chaotic search strategy and adaptive inertia weights. On this basis, the COD prediction model of CAWOA-ELM is established and compared with similar algorithms by using the optimized ELM to predict the water ecological COD in a region. Finally, from the experimental results of the CAWOA-ELM algorithm, it has excellent prediction effect and practical application value.

The Exposome in the Era of One Health

Current studies on environmental chemistry mainly focus on a single stressor or single group of stressors, which does not reflect the multiple stressors in the dynamic exposome we are facing. Similarly, current studies on environmental toxicology mostly target humans, animals, or the environment separately, which are inadequate to solve the grand challenge of multiple receptors in One Health. Though chemical, biological, and physical stressors all pose health threats, the susceptibilities of different organisms are different. As such, significant relationships and interactions of the chemical, biological, and physical stressors in the environment and their holistic environmental and biological consequences remain unclear. Fortunately, the rapid developments in various techniques, as well as the concepts of multistressors in the exposome and multireceptor in One Health provide the possibilities to understand our environment better. Since the combined stressor is location-specific and mixture toxicity is species-specific, more comprehensive frameworks to guide risk assessment and environmental treatment are urgently needed. Here, three conceptual frameworks to categorize unknown stressors, spatially visualize the riskiest stressors, and investigate the combined effects of multiple stressors across multiple species within the concepts of the exposome and One Health are proposed for the first time.

Biotic signals associated with benthic impacts of salmon farms from eDNA metabarcoding of sediments

Environmental DNA (eDNA) metabarcoding can rapidly characterize the composition and diversity of benthic communities, thus it has high potential utility for routine assessments of benthic impacts of marine finfish farming. In this study, 126 sediment grab samples from 42 stations were collected at six salmon farms in British Columbia, Canada. Benthic community changes were assessed by both eDNA metabarcoding of metazoans and macrofaunal polychaete surveys. The latter was done by analysing 11,466 individuals using a combination of morphology-based taxonomy and DNA barcoding. Study objectives were to: (i) compare biotic signals associated with benthic impacts of salmon farming in the two data sources, and (ii) identify potential eDNA indicators to facilitate monitoring in Canada. Alpha diversity parameters were consistently reduced near fish cage edge and negatively correlated with pore-water sulphide concentration, with coefficients ranging from -0.62 to -0.48. Although Polychaeta are a common indicator group, the negative correlation with pore-water sulphide concentration was much stronger for Nematoda OTU richness (correlation coefficient: -0.86) than for Polychaeta (correlation coefficient: -0.38). Presence/absence of Capitella generally agreed well between the two methods despite that they differed in the volume of sediments sampled and the molecular marker used. Multiple approaches were used to identify OTUs related to organic enrichment statuses. We demonstrate that eDNA metabarcoding generates biotic signals that could be leveraged for environmental assessment of benthic impacts of fish farms in multiple ways: both alpha diversity and Nematoda OTU richness could be used to assess the spatial extent of impact, and OTUs related to organic enrichment could be used to develop local biotic indices.

The future of fish-based ecological assessment of European rivers: from traditional EU Water Framework Directive compliant methods to eDNA metabarcoding-based approaches

Most of the present EU Water Framework Directive (WFD) compliant fish-based assessment methods of European rivers are multi-metric indices computed from traditional electrofishing (TEF) samples, but this method has known shortcomings, especially in large rivers. The probability of detecting rare species remains limited, which can alter the sensitivity of the indices. In recent years, environmental (e)DNA metabarcoding techniques have progressed sufficiently to allow applications in various ecological domains as well as eDNA-based ecological assessment methods. A review of the 25 current WFD-compliant methods for river fish shows that 81% of the metrics used in these methods are expressed in richness or relative abundance and thus compatible with eDNA samples. However, more than half of the member states' methods include at least one metric related to age or size structure and would have to adapt their current fish index if reliant solely on eDNA-derived information. Most trait-based metrics expressed in richness are higher when computed from eDNA than when computed from TEF samples. Comparable values are obtained only when the TEF sampling effort increases. Depending on the species trait considered, most trait-based metrics expressed in relative abundance are significantly higher for eDNA than for TEF samples or vice versa due to over-estimation of sub-surface species or under-estimation of benthic and rare species by TEF sampling, respectively. An existing predictive fish index, adapted to make it compatible with eDNA data, delivers an ecological assessment comparable with the current approved method for 22 of the 25 sites tested. Its associated uncertainty is lower than that of current fish indices. Recommendations for the development of future fish eDNA-based indices and the associated eDNA water sampling strategy are discussed.

Monitoring the ecological status of rivers with diatom eDNA metabarcoding: A comparison of taxonomic markers and analytical approaches for the inference of a molecular diatom index

Recently, several studies demonstrated the usefulness of diatom eDNA metabarcoding as an alternative to assess the ecological quality of rivers and streams. However, the choice of the taxonomic marker as well as the methodology for data analysis differ between these studies, hampering the comparison of their results and effectiveness. The aim of this study was to compare two taxonomic markers commonly used in diatom metabarcoding and three distinct analytical approaches to infer a molecular diatom index. We used the values of classical morphological diatom index as a benchmark for this comparison. We amplified and sequenced both a fragment of the rbcL gene and the V4 region of the 18S rRNA gene for 112 epilithic samples from Swiss and French rivers. We inferred index values using three analytical approaches: by computing it directly from taxonomically assigned sequences, by calibrating de novo the ecovalues of all metabarcodes, and by using a supervised machine learning algorithm to train predictive models. In general, the values of index obtained using the two "taxonomy-free" approaches, encompassing molecular assignment and machine learning, were closer correlated to the values of the morphological index than the values based on taxonomically assigned sequences. The correlations of the three analytical approaches were higher in the case of rbcL compared to the 18S marker, highlighting the importance of the reference database which is more complete for the rbcL marker. Our study confirms the effectiveness of diatom metabarcoding as an operational tool for rivers ecological quality assessment and shows that the analytical approaches by-passing the taxonomic assignments are particularly efficient when reference databases are incomplete.

Benthic monitoring of oil and gas offshore platforms in the North Sea using environmental DNA metabarcoding

Since 2010, considerable efforts have been undertaken to monitor the environmental status of European marine waters and ensuring the development of methodological standards for the evaluation of this status. However, the current routine biomonitoring implicates time-consuming and costly manual sorting and morphological identification of benthic macrofauna. Environmental DNA (eDNA) metabarcoding represents an alternative to the traditional monitoring method with very promising results. Here, we tested it further by performing eDNA metabarcoding of benthic eukaryotic communities in the vicinity of two offshore oil and gas platforms in the North Sea. Three different genetic markers (18S V1V2, 18S V9 and COI) were used to assess the environmental pressures induced by the platforms. All markers showed patterns of alpha and beta diversity consistent with morphology-based macrofauna analyses. In particular, the communities' structure inferred from metabarcoding and morphological data significantly changed along distance gradients from the platforms. The impact of the operational discharges was also detected by the variation of biotic index values, AMBI index showing the best correlation between morphological and eDNA data sets. Finally, the sediment physicochemical parameters were used to build a local de novo pressure index that served as benchmark to test the potential of a taxonomy-free approach. Our study demonstrates that metabarcoding approach outperforms morphology-based approach and can be used as a cost and time-saving alternative solution to the traditional morphology-based monitoring in order to monitor more efficiently the impact of industrial activities on marine biodiversity.

Benthic foraminiferal metabarcoding and morphology-based assessment around three offshore gas platforms: Congruence and complementarity

Since the 1960 s, there has been a rapid expansion of drilling activities in the central and northern Adriatic Sea to meet the increasing global energy demand. The discharges of organic and inorganic pollutants, as well as the alteration of the sediment substrate, are among the main impacts associated with these activities. In the present study, we evaluate the response of benthic foraminifera to the activities of three gas platforms in the northwestern Adriatic Sea, with a special focus on the Armida A platform for which extensive geochemical data (organic matter, trace elements, polycyclic aromatic hydrocarbons, other hydrocarbons, and volatile organic compounds) are available. The response to disturbance is assessed by analyzing the foraminiferal diversity using the traditional morphology-based approach and by 18S rDNA-based metabarcoding. The two methods give congruent results, showing relatively lower foraminiferal diversity and higher dominance values at stations closer to the platforms (<50 m). The taxonomic compositions of the morphological and metabarcoding datasets are very different, the latter being dominated by monothalamous, mainly soft-walled species. However, compositional changes consistently occur at 50 m from the platform and can be related to variations in sediment grain-size variation and higher concentrations of Ni, Zn, Ba, hydrocarbons and total organic carbon. Additionally, several morphospecies and Molecular Operational Taxonomic Units (MOTUs) show strong correlations with distance from the platform and with environmental parameters extracted from BIOENV analysis. Some of these MOTUs have the potential to become new bioindicators, complementing the assemblage of hard-shelled foraminiferal species detected through microscopic analyses. The congruence and complementarity between metabarcoding and morphological approaches support the application of foraminiferal metabarcoding in routine biomonitoring surveys as a reliable, time- and cost-effective methodology to assess the environmental impacts of marine industries.

Nematodes as bioindicators of polluted sediments using metabarcoding and microscopic taxonomy

The use of bioindicator species is a widely applied approach to evaluate ecological conditions, and several indices have been designed for this purpose. To assess the impact of pollution, especially in sediments, a pollution-sensitive index based on nematodes, one of the most abundant and species-rich groups of metazoa, was developed. The NemaSPEAR[%] index in its original form relies on the morphological inspection of nematode species. The application of a morphologically based NemaSPEAR[%] at the genus-level was previously validated. The present study evaluated a NemaSPEAR[%] index based on metabarcoding of nematode communities and tested the potential of fragments from the 28S rDNA, 18S rDNA and cytochrome c oxidase subunit I (COI) genes. In general, molecular-based results tended to show a poorer condition than morphology-based results for the investigated sites. At the genus level, NemaSPEAR[%] values based on morphological data strongly correlated with those based on molecular data for both the 28S rDNA and the 18S rDNA gene fragments (R² = 0.86 and R² = 0.74, respectively). Within the dominant genera (>3%) identified by morphology, 68% were detected by at least one of the two ribosomal markers. At the species level, however, concordance was less pronounced, as there were several deviations of the molecular from the morphological data. These differences could mostly be attributed to shortcomings in the reference database used in the molecular-based assignments. Our pilot study shows that a molecularly based, genus-level NemaSPEAR[%] can be successfully applied to evaluate polluted sediment. Future studies need to validate this approach further, e.g. with bulk extractions of whole meiofaunal communities in order to circumvent time-consuming nematode isolation. Further database curation with abundant NemaSPEAR[%] species will also increase the applicability of this approach.

qPCR-based assessment of microfaunal indicators of oil for monitoring benthos around oil and gas platforms

Today's benthic offshore biological monitoring of oil & gas (O&G) activities relies on macrofauna taxa enumeration. For the future, analysis of DNA isolated directly from sediments holds great potential for multi-trophic biodiversity surveys and the monitoring of a larger spectrum of benthic taxa, including micro-fauna. Here, we evaluate more specifically the potential of microfauna-specific gene quantification in relation to both petroleum-related discharge compounds and other seafloor environmental properties. We carried out this evaluation using sediment samples collected at drilling Region III on the Norwegian continental shelf where DNA metabarcoding of eukaryotic diversity was already performed. Generally, the quantification of microfauna indicator taxa related well to the gradient of contamination on the seafloor. Contrary to eukaryotic Euplotida, metabarcoding data and qPCR numbers for indicative prokaryotic taxa showed the same relationship to offshore contaminants (both showed positive relationship). We found absolute numbers of SSU rRNA gene copies of (1) Dinophyceae, Bacillariophyceae and Alcanivorax were correlated with the level of petroleum-related compounds but not with other environmental variables, (2) bacteria closely related to Shewanella were correlated with the concentration of Ba, PAH, as well to percent of gravel, (3) Desulfobacteriales correlated with petroleum-related contaminants, but as well with percent of gravel and grain size. Findings from our study suggest that biomonitoring surveys of O&G activities on benthos could benefit from quantification of specific micro-fauna indicators that is simpler and faster than the methods currently used for impact assessment of benthos.

Modeling the ecological status response of rivers to multiple stressors using machine learning: A comparison of environmental DNA metabarcoding and morphological data

Understanding the ecological status response of rivers to multiple stressors is a precondition for river restoration and management. However, this requires the collection of appropriate data, including environmental variables and the status of aquatic organisms, and analysis via a suitable model that captures the nonlinear relationships between ecological status and various stressors. The morphological approach has been the standard data collection method employed for establishing the status of aquatic organisms. However, this approach is very laborious and restricted to a specific set of organisms. Recently, an environmental DNA (eDNA) metabarcoding data approach has been developed that is far more efficient than the morphological approach and potentially applicable to an unlimited set of organisms. However, it remains unclear how well eDNA metabarcoding data reflects the impacts of environmental stressors on aquatic ecosystems compared with morphological data, which is essential for clarifying the potential applications of eDNA metabarcoding data in the ecological monitoring and management of rivers. The present work addresses this issue by modeling organism diversity based on three indices with respect to multiple environmental variables in both the catchment and reach scales. This is done by corresponding support vector machine (SVM) models constructed from eDNA metabarcoding and morphological data on 24 sampling locations in the Taizi River basin, China. According to the mean absolute percent error (MAPE) between the measured diversity index values and the index values predicted by the SVM models, the SVM models constructed from eDNA metabarcoding data (MAPE = 3.87) provide more accurate predictions than the SVM models constructed from morphological data (MAPE = 28.36), revealing that the eDNA metabarcoding data better reflects environmental conditions. In addition, the sensitivity of SVM model predictions of the ecological indices for both catchment-scale and reach-scale stressors is evaluated, and the stressors having the greatest impact on the ecological status of rivers are identified. The results demonstrate that the ecological status of rivers is more sensitive to environmental stressors at the reach scale than to stressors at the catchment scale. Therefore, our study is helpful in exploring the potential applications of eDNA metabarcoding data and SVM modeling in the ecological monitoring and management of rivers.