Exploiting taxonomic information from metagenomes to infer bacterial bioindicators and environmental quality at salmon aquaculture installations

Environmental DNA (eDNA) metabarcoding has emerged as a powerful method for assessing the environmental impacts of marine Atlantic salmon aquaculture by identifying bacterial bioindicators and inferring biotic indices. However, because this approach relies on the PCR amplification of 16S rRNA gene fragments, it may introduce errors that compromise bioindicator reliability. In contrast, metagenomic analysis which captures the complete set of genetic material directly extracted from environmental samples circumvents biases inherent to PCR amplification. We hypothesized that metagenomic data could offer superior assessments of benthic environmental impacts associated with salmon aquaculture compared to metabarcoding. To test this, we compared bacterial community structures derived from both metabarcoding and metagenomic analyses of 68 sediment samples obtained from aquaculture installation sites characterized by varying degrees of benthic impact as determined by macroinvertebrate inventories. Bacterial bioindicators were identified from each dataset, and Random Forest models were used to predict the degrees of benthic impacts. Metagenomics identified a greater number of bioindicators at both the family and individual sequence variant levels, resulting in higher predictive accuracy for impact assessments. Notably, only a few bioindicators were common to both methods, suggesting that methodological limitations and distorted abundance patterns in metabarcoding data may lead to spurious indicators. These findings highlight both the challenges and potential advantages of employing metagenomics for reliable environmental impact assessments.

Developing and testing a new Ecological Quality Status index based on marine nematode metabarcoding: A proof of concept

Nematodes are the most diverse and dominant group of marine meiofauna with high potential as bioindicators of the ecological quality status (EcoQS). The present study explores, for the first time, the applicability of the nematode metabarcoding to infer EcoQS index based on the calibration of ecological behaviors of nematodes Amplicon Sequence Variants (ASVs). To achieve this, we analyzed the nematode community in sediment eDNA samples collected in 2018 and 2021 in areas around three offshore oil platforms in the Danish west coast of the North Sea. One training dataset based on eDNA and environmental data from the three platforms in 2021 covering a wide range of environmental gradients has been used as a training dataset to assign the nematodes ASVs to Ecological Groups. These assignments then allowed us to infer the EcoQS both around these three platforms and in an independent dataset (one of the platforms sampled in 2018). The EcoQS inferred from the nema-gAMBI is perfectly in line with the pollution gradient of the platforms. In fact, stations located close to the platforms (i.e., 100 m and 250 m) show a relatively lower EcoQS than those at greater distance (i.e., reference or 3000 m). The nema-gAMBI seems to capture well the EcoQS variability around platforms and correlates well with the environmental parameters (e.g., trace element and hydrocarbon pollution). Indeed, the nema-gAMBI is positively and significantly correlated with the traditional macrofauna-based AMBI. The present proof of concept strongly advocates for the application of the nematode eDNA-based index in the evaluation of EcoQS.

Environmental gradients and optimal fixation time revealed with DNA metabarcoding of benthic sample fixative

Assessments of biodiversity and ecosystem status can benefit from DNA metabarcoding as a means to streamline sample processing and specimen identification. Moreover, processing the fixation medium instead of the precious material introduces straightforward protocols that allow subsequent focus on certain organisms detected among the preserved specimens. In this study, we present a proof of concept via the analysis of freshwater invertebrate samples from the Tatra Mountain lakes (Slovakia). Besides highlighting a match between the lake-specific environmental conditions and the results of our fixative DNA metabarcoding, we observed an option to fine-tune the fixation time: to prefer two weeks over a day or a month. This effect emerged from the presence/absence of individual taxa rather than from coarse per-sample records of taxonomic richness, demonstrating that metabarcoding studies-and efforts to optimize their protocols-can use the robust metrics to explore even subtle trends. We also provide evidence that fixative DNA might better capture large freshwater species than terrestrial or meiofauna.

Assessing the potential of nematode metabarcoding for benthic monitoring of offshore oil platforms

Environmental DNA metabarcoding is gaining momentum as a time and cost-effective tool for biomonitoring and environmental impact assessment. Yet, its use as a replacement for the conventional marine benthic monitoring based on morphological analysis of macrofauna is still challenging. Here we propose to study the meiofauna, which is much better represented in sediment DNA samples. We focus on nematodes, which are the most numerous and diverse group of meiofauna. Our aim is to assess the potential of nematode metabarcoding to monitor impacts associated with offshore oil platform activities. To achieve this goal, we used nematode-optimized marker (18S V1V2-Nema) and universal eukaryotic marker (18S V9) region to analyse 252 sediment DNA samples collected near three offshore oil platforms in the North Sea. For both markers, we analysed changes in alpha and beta diversity in relation to distance from the platforms and environmental variables. We also defined three impact classes based on selected environmental variables that are associated with oil extraction activities and used random forest classifiers to compare the predictive performance of both datasets. Our results show that alpha- and beta-diversity of nematodes varies with the increasing distance from the platforms. The variables directly related to platform activity, such as Ba and THC, strongly influence the nematode community. The nematode metabarcoding data provide more robust predictive models than eukaryotic data. Furthermore, the nematode community appears more stable in time and space, as illustrated by the overlap of nematode datasets obtained from the same platform three years apart. A significative negative correlation between distance and Shannon diversity also advocates for higher performance of the V1V2-Nema over the V9. Overall, these results suggest that the sensitivity of nematodes is higher compared to the eukaryotic community. Hence, nematode metabarcoding has the potential to become an effective tool for benthic monitoring in marine environment.

Measuring the state of aquatic environments using eDNA-upscaling spatial resolution of biotic indices

Aquatic macroinvertebrates, including many aquatic insect orders, are a diverse and ecologically relevant organismal group yet they are strongly affected by anthropogenic activities. As many of these taxa are highly sensitive to environmental change, they offer a particularly good early warning system for human-induced change, thus leading to their intense monitoring. In aquatic ecosystems there is a plethora of biotic monitoring or biomonitoring approaches, with more than 300 assessment methods reported for freshwater taxa alone. Ultimately, monitoring of aquatic macroinvertebrates is used to calculate ecological indices describing the state of aquatic systems. Many of the methods and indices used are not only hard to compare, but especially difficult to scale in time and space. Novel DNA-based approaches to measure the state and change of aquatic environments now offer unprecedented opportunities, also for possible integration towards commonly applicable indices. Here, we first give a perspective on DNA-based approaches in the monitoring of aquatic organisms, with a focus on aquatic insects, and how to move beyond traditional point-based biotic indices. Second, we demonstrate a proof-of-concept for spatially upscaling ecological indices based on environmental DNA, demonstrating how integration of these novel molecular approaches with hydrological models allows an accurate evaluation at the catchment scale. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Revolutionizing aquatic eco-environmental monitoring: Utilizing the RPA-Cas-FQ detection platform for zooplankton

The integration of recombinase polymerase amplification (RPA) with CRISPR/Cas technology has revolutionized molecular diagnostics and pathogen detection due to its unparalleled sensitivity and trans-cleavage ability. However, its potential in the ecological and environmental monitoring scenarios for aquatic ecosystems remains largely unexplored, particularly in accurate qualitative/quantitative detection, and its actual performance in handling complex real environmental samples. Using zooplankton as a model, we have successfully optimized the RPA-CRISPR/Cas12a fluorescence detection platform (RPA-Cas-FQ), providing several crucial "technical tips". Our findings indicate the sensitivity of CRISPR/Cas12a alone is 5 × 109 copies/reaction, which can be dramatically increased to 5 copies/reaction when combined with RPA. The optimized RPA-Cas-FQ enables reliable qualitative and semi-quantitative detection within 50 min, and exhibits a good linear relationship between fluorescence intensity and DNA concentration (R2 = 0.956-0.974***). Additionally, we developed a rapid and straightforward identification procedure for single zooplankton by incorporating heat-lysis and DNA-barcode techniques. We evaluated the platform's effectiveness using real environmental DNA (eDNA) samples from the Three Gorges Reservoir, confirming its practicality. The eDNA-RPA-Cas-FQ demonstrated strong consistency (Kappa = 0.43***) with eDNA-Metabarcoding in detecting species presence/absence in the reservoir. Furthermore, the two semi-quantitative eDNA technologies showed a strong positive correlation (R2 = 0.58-0.87***). This platform also has the potential to monitor environmental pollutants by selecting appropriate indicator species. The novel insights and methodologies presented in this study represent a significant advancement in meeting the complex needs of aquatic ecosystem protection and monitoring.

Environmental DNA: The next chapter

Molecular tools are an indispensable part of ecology and biodiversity sciences and implemented across all biomes. About a decade ago, the use and implementation of environmental DNA (eDNA) to detect biodiversity signals extracted from environmental samples opened new avenues of research. Initial eDNA research focused on understanding population dynamics of target species. Its scope thereafter broadened, uncovering previously unrecorded biodiversity via metabarcoding in both well-studied and understudied ecosystems across all taxonomic groups. The application of eDNA rapidly became an established part of biodiversity research, and a research field by its own. Here, we revisit key expectations made in a land-mark special issue on eDNA in Molecular Ecology in 2012 to frame the development in six key areas: (1) sample collection, (2) primer development, (3) biomonitoring, (4) quantification, (5) behaviour of DNA in the environment and (6) reference database development. We pinpoint the success of eDNA, yet also discuss shortfalls and expectations not met, highlighting areas of research priority and identify the unexpected developments. In parallel, our retrospective couples a screening of the peer-reviewed literature with a survey of eDNA users including academics, end-users and commercial providers, in which we address the priority areas to focus research efforts to advance the field of eDNA. With the rapid and ever-increasing pace of new technical advances, the future of eDNA looks bright, yet successful applications and best practices must become more interdisciplinary to reach its full potential. Our retrospect gives the tools and expectations towards concretely moving the field forward.

Machine learning modeling for the prediction of phosphorus and nitrogen removal efficiency and screening of crucial microorganisms in wastewater treatment plants

The effectiveness of wastewater treatment plants (WWTPs) is largely determined by the microbial community structure in their activated sludge (AS). Interactions among microbial communities in AS systems and their indirect effects on water quality changes are crucial for WWTP performance. However, there is currently no quantitative method to evaluate the contribution of microorganisms to the operating efficiency of WWTPs. Traditional assessments of WWTP performance are limited by experimental conditions, methods, and other factors, resulting in increased costs and experimental pollutants. Therefore, an effective method is needed to predict WWTP efficiency based on AS community structure and quantitatively evaluate the contribution of microorganisms in the AS system. This study evaluated and compared microbial communities and water quality changes from WWTPs worldwide by meta-analysis of published high-throughput sequencing data. Six machine learning (ML) models were utilized to predict the efficiency of phosphorus and nitrogen removal in WWTPs; among them, XGBoost showed the highest prediction accuracy. Cross-entropy was used to screen the crucial microorganisms related to phosphorus and nitrogen removal efficiency, and the modeling confirmed the reasonableness of the results. Thirteen genera with nitrogen and phosphorus cycling pathways obtained from the screening were considered highly appropriate for the simultaneous removal of phosphorus and nitrogen. The results showed that the microbes Haliangium, Vicinamibacteraceae, Tolumonas, and SWB02 are potentially crucial for phosphorus and nitrogen removal, as they may be involved in the process of phosphorus and nitrogen removal in sewage treatment plants. Overall, these findings have deepened our understanding of the relationship between microbial community structure and performance of WWTPs, indicating that microbial data should play a critical role in the future design of sewage treatment plants. The ML model of this study can efficiently screen crucial microbes associated with WWTP system performance, and it is promising for the discovery of potential microbial metabolic pathways.

Simultaneous analysis of seven 16S rRNA hypervariable gene regions increases efficiency in marine bacterial diversity detection

Environmental DNA sequencing is the gold standard to reveal microbial community structures. In most applications, a one-fragment PCR approach is applied to amplify a taxonomic marker gene, usually a hypervariable region of the 16S rRNA gene. We used a new reverse complement (RC)-PCR-based assay that amplifies seven out of the nine hypervariable regions of the 16S rRNA gene, to interrogate bacterial communities in sediment samples collected from different coastal marine sites with an impact gradient. In parallel, we employed a traditional one-fragment analysis of the hypervariable V3-V4 region to investigate whether the RC-PCR reveals more of the 'unseen' diversity obtained by the one-fragment approach. As a benchmark for the full deck of diversity, we subjected the samples to PCR-free metagenomic sequencing. None of the two PCR-based approaches recorded the full taxonomic repertoire obtained from the metagenomics datasets. However, the RC-PCR approach detected 2.8 times more bacterial genera compared to the near-saturation sequenced V3-V4 samples. RC-PCR is an ideal compromise between the standard one-fragment approach and metagenomics sequencing and may guide future environmental sequencing studies, in which bacterial diversity is a central subject.

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.

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.

Bacterial bioindicators enable biological status classification along the continental Danube river

Despite the importance of bacteria in aquatic ecosystems and their predictable diversity patterns across space and time, biomonitoring tools for status assessment relying on these organisms are widely lacking. This is partly due to insufficient data and models to identify reliable microbial predictors. Here, we show metabarcoding in combination with multivariate statistics and machine learning allows to identify bacterial bioindicators for existing biological status classification systems. Bacterial beta-diversity dynamics follow environmental gradients and the observed associations highlight potential bioindicators for ecological outcomes. Spatio-temporal links spanning the microbial communities along the river allow accurate prediction of downstream biological status from upstream information. Network analysis on amplicon sequence veariants identify as good indicators genera Fluviicola, Acinetobacter, Flavobacterium, and Rhodoluna, and reveal informational redundancy among taxa, which coincides with taxonomic relatedness. The redundancy among bacterial bioindicators reveals mutually exclusive taxa, which allow accurate biological status modeling using as few as 2-3 amplicon sequence variants. As such our models show that using a few bacterial amplicon sequence variants from globally distributed genera allows for biological status assessment along river systems.

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.

Evaluation and comparison of the benthic and microbial indices of biotic integrity for urban lakes based on environmental DNA and its management implications

With the intensification of human disturbance in urban lakes, the loss of eukaryotic biodiversity (macroinvertebrates, etc.) reduces the accuracy of the index of biotic integrity (IBI) assessment. Therefore, how to accurately evaluate the ecological status of urban lakes based on IBI has become an important issue. In this study, 17 sampling sites from four lakes in Wuhan City, China were selected to analyze the composition and diversity characteristics of benthic and microbial communities and their relationship with environmental factors based on eDNA high-throughput sequencing, and compare the application effects of the benthic index of biotic integrity (B-IBI) and the microbial index of biotic integrity (M-IBI). Canonical correspondence analysis showed that the key environmental factors affecting benthic family/genus composition were temperature, conductivity, total phosphorus (TP), and total nitrogen (TN). Redundancy analysis showed that pH, TP, conductivity, and ammonia nitrogen had the greatest impact on microbial phyla/genera. After screening, four and six core metrics were determined from candidate parameters to establish B-IBI and M-IBI. The B-IBI evaluation results showed that healthy, sub-heathy, and poor accounted for 58.8%, 35.3%, and 5.9%, respectively, in the sites. The results of the M-IBI evaluation showed that 29.4% of the sites were healthy, 47.1% were sub-healthy, and 23.5% were common. M-IBI was positively correlated with water quality (r = 0.74, P < 0.001), whereas B-IBI was not. Further results showed that M-IBI was negatively correlated with the relative abundance of bloom-forming cyanobacteria Planktothrix (r = -0.54, P < 0.05). Therefore, M-IBI is more sensitive than B-IBI and can better reflect the actual water pollution status. This study can provide a new perspective for ecological assessment and management of urban lakes strongly disturbed by human activities.

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.

Using Machine Learning to make nanomaterials sustainable

Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.

Genotyping by sequencing for estimating relative abundances of diatom taxa in mock communities

BACKGROUND

Diatoms are present in all waters and are highly sensitive to pollution gradients. Therefore, they are ideal bioindicators for water quality assessment. Current indices used in these applications are based on identifying diatom species and counting their abundances using traditional light microscopy. Several molecular techniques have been developed to help automate different steps of this process, but obtaining reliable estimates of diatom community composition and species abundance remains challenging.

RESULTS

Here, we evaluated a recently developed quantification method based on Genotyping by Sequencing (GBS) for the first time in diatoms to estimate the relative abundances within a species complex. For this purpose, a reference database comprised of thousands of genomic DNA clusters was generated from cultures of Nitzschia palea. The sequencing reads from calibration and mock samples were mapped against this database for parallel quantification. We sequenced 25 mock diatom communities containing up to five taxa per sample in different abundances. Taxon abundances in these communities were also quantified by a diatom expert using manual counting of cells on light microscopic slides. The relative abundances of strains across mock samples were over- or under-estimated by the manual counting method, and a majority of mock samples had stronger correlations using GBS. Moreover, one previously recognized putative hybrid had the largest number of false positive detections demonstrating the limitation of the manual counting method when morphologically similar and/or phylogenetically close taxa are analyzed.

CONCLUSIONS

Our results suggest that GBS is a reliable method to estimate the relative abundances of the N. palea taxa analyzed in this study and outperformed traditional light microscopy in terms of accuracy. GBS provides increased taxonomic resolution compared to currently available quantitative molecular approaches, and it is more scalable in the number of species that can be analyzed in a single run. Hence, this is a significant step forward in developing automated, high-throughput molecular methods specifically designed for the quantification of [diatom] communities for freshwater quality assessments.

Towards the fully automated monitoring of ecological communities

High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components-for example, individual behaviours and traits, and species abundance and distribution-is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.

Methodological 'lenses' influence the characterization of phytoplankton dynamics in a coastal upwelling ecosystem

New technologies enable the opportunity to improve our monitoring and understanding of marine phytoplankton communities. However, careful consideration for how different methodological approaches, or 'lenses', influence our interpretation of phytoplankton ecology is important, particularly when drawing conclusions about change over time or space. Using both high-throughput 18S rRNA gene sequencing and microscopy, we explored how phytoplankton community structure varied over the course of a year within a nearshore semi-enclosed coastal embayment along the Central Coast of California. The seasonal shift in the relative community dominance (i.e., diatom vs. dinoflagellate dominance) was captured in the microscopy results but not effectively captured in the molecular-based findings. However, the molecular approach explained more of the variability in composition across seasons relative to the microscopy approach. Temporal dynamics of specific bloom-forming taxa also differed between the molecular and microscopy results. Overall, the observed differences between the molecular- and microscopy-derived characterization of phytoplankton dynamics suggest that the approaches are best suited to answer different research questions. Moreover, the approaches complement each other for a more comprehensive perspective of a coastal phytoplankton ecosystem. Therefore, identifying the biases of each approach within natural communities is necessary to effectively and accurately characterize phytoplankton communities.

Key factors to consider in the use of environmental DNA metabarcoding to monitor terrestrial ecological restoration

Ecological restoration of terrestrial environments is a globally important process to combat the loss of biodiversity and ecosystem services. Holistic monitoring of restored biota and active management of restoration is necessary to improve restoration processes and outcomes, and provide evidence to stakeholders that targets are being achieved. Increasingly, environmental DNA (eDNA) metabarcoding is used as a restoration monitoring tool because it is able to generate biodiversity data rapidly, accurately, non-destructively, and reliably, on a wide breadth of organisms from soil microbes to mammals. The overall objective of this review is to discuss the key factors to consider in the use of environmental DNA for monitoring of restored terrestrial ecosystems, hopefully improving monitoring, and ultimately, restoration outcomes. We identified that the majority of eDNA based studies of ecosystem restoration are currently conducted in Europe, North America, and Australia, and that almost half of total studies were published in 2021-22. Soil was the most popular sample substrate, soil microbial communities the most targeted taxa, and forests the most studied ecosystem. We suggest there is no 'one size fits all' approach to restoration monitoring using eDNA, and discuss survey design. Factors to consider include substrate selection, sample collection and storage, assay selection, and data interpretation, all of which require careful planning to obtain reliable, and accurate information that can be used for restoration monitoring and decision making. We explore future directions for research and argue that eDNA metabarcoding can be a useful tool in the restoration monitoring 'toolkit', but requires informed application and greater accessibility to data by a wide spectrum of stakeholders.

Environmental DNA-based profiling of benthic bacterial and eukaryote communities along a crude oil spill gradient in a coral reef in the Persian Gulf

Coral reef ecosystems in the Persian Gulf are frequently exposed to crude oil spills. We investigated benthic bacterial and eukaryote community structures at such coral reef sites subjected to different degrees of polycyclic aromatic hydrocarbon (PAH) pollution using environmental DNA (eDNA) metabarcoding. Both bacterial and eukaryote communities responded with pronounced shifts to crude oil pollution and distinguished control sites, moderately and heavily impacted sites with significant confidentiality. The observed community patterns were predominantly driven by Alphaproteobacteria and metazoans. Among these, we identified individual genera that were previously linked to oil spill stress, but also taxa, for which a link to hydrocarbon still remains to be established. Considering the lack of an early-warning system for the environmental status of coral reef ecosystems exposed to frequent crude-oil spills, our results encourage further research towards the development of an eDNA-based biomonitoring tool that exploits benthic bacterial and eukaryote communities as bioindicators.

Supervised Machine Learning Enables Geospatial Microbial Provenance

The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applications within forensic science and public health. To determine the regional specificity for environmental metagenomes, we examined 4305 shotgun-sequenced samples from the MetaSUB Consortium dataset-the most extensive public collection of urban microbiomes, spanning 60 different cities, 30 countries, and 6 continents. We were able to identify city-specific microbial fingerprints using supervised machine learning (SML) on the taxonomic classifications, and we also compared the performance of ten SML classifiers. We then further evaluated the five algorithms with the highest accuracy, with the city and continental accuracy ranging from 85-89% to 90-94%, respectively. Thereafter, we used these results to develop Cassandra, a random-forest-based classifier that identifies bioindicator species to aid in fingerprinting and can infer higher-order microbial interactions at each site. We further tested the Cassandra algorithm on the Tara Oceans dataset, the largest collection of marine-based microbial genomes, where it classified the oceanic sample locations with 83% accuracy. These results and code show the utility of SML methods and Cassandra to identify bioindicator species across both oceanic and urban environments, which can help guide ongoing efforts in biotracing, environmental monitoring, and microbial forensics (MF).

Composition and functionality of bacterioplankton communities in marine coastal zones adjacent to finfish aquaculture

Finfish aquaculture is a fast-growing primary industry and is increasingly common in coastal ecosystems. Bacterioplankton is ubiquitous in marine environment and respond rapidly to environmental changes. Changes in bacterioplankton community are not well understood in semi-enclosed stratified embayments. This study aims to examine aquaculture effects in the composition and functional profiles of the bacterioplankton community using amplicon sequencing along a distance gradient from two finfish leases in a marine embayment. Results revealed natural stratification in bacterioplankton associated to NOx, conductivity, salinity, temperature and PO₄. Among the differentially abundant bacteria in leases, we found members associated with nutrient enrichment and aquaculture activities. Abundant predicted functions near leases were assigned to organic matter degradation, fermentation, and antibiotic resistance. This study provides a first effort to describe changes in the bacterioplankton community composition and function due to finfish aquaculture in a semi-enclosed and highly stratified embayment with a significant freshwater input.

Environmental DNA Metabarcoding: A Novel Contrivance for Documenting Terrestrial Biodiversity

Simple Summary

The innovative concept of environmental DNA has found its foot in aquatic ecosystems but remains an unexplored area of research concerning terrestrial ecosystems. When making management choices, it is important to understand the rate of eDNA degradation, the persistence of DNA in terrestrial habitats, and the variables affecting eDNA detectability for a target species. Therefore an attempt has been made to provide comprehensive information regarding the exertion of eDNA in terrestrial ecosystems from 2012 to 2022. The information provided will assist ecologists, researchers and decision-makers in developing a holistic understanding of environmental DNA and its applicability as a biodiversity monitoring contrivance.

Abstract

The dearth of cardinal data on species presence, dispersion, abundance, and habitat prerequisites, besides the threats impeded by escalating human pressure has enormously affected biodiversity conservation. The innovative concept of eDNA, has been introduced as a way of overcoming many of the difficulties of rigorous conventional investigations, and is hence becoming a prominent and novel method for assessing biodiversity. Recently the demand for eDNA in ecology and conservation has expanded exceedingly, despite the lack of coordinated development in appreciation of its strengths and limitations. Therefore it is pertinent and indispensable to evaluate the extent and significance of eDNA-based investigations in terrestrial habitats and to classify and recognize the critical considerations that need to be accounted before using such an approach. Presented here is a brief review to summarize the prospects and constraints of utilizing eDNA in terrestrial ecosystems, which has not been explored and exploited in greater depth and detail in such ecosystems. Given these obstacles, we focused primarily on compiling the most current research findings from journals accessible in eDNA analysis that discuss terrestrial ecosystems (2012–2022). In the current evaluation, we also review advancements and limitations related to the eDNA technique.

The relationship between land cover and microbial community composition in European lakes

Microbes are essential for element cycling and ecosystem functioning. However, many questions central to understanding the role of microbes in ecology are still open. Here, we analyze the relationship between lake microbiomes and the lakes' land cover. By applying machine learning methods, we quantify the covariance between land cover categories and the microbial community composition recorded in the largest amplicon sequencing dataset of European lakes available to date. Our results show that the aggregation of environmental features or microbial taxa before analysis can obscure ecologically relevant patterns. We observe a comparatively high covariation of the lakes' microbial community with herbaceous and open spaces surrounding the lake; nevertheless, the microbial covariation with land cover categories is generally lower than the covariation with physico-chemical parameters. Combining land cover and physico-chemical bioindicators identified from the same amplicon sequencing dataset, we develop analytical data structures that facilitate insights into the ecology of the lake microbiome. Among these, a list of the environmental parameters sorted by the number of microbial bioindicators we have identified for them points towards apparent environmental drivers of the lake microbial community composition, such as the altitude, conductivity, and area covered herbaceous vegetation surrounding the lake. Furthermore, the response map, a similarity matrix calculated from the Jaccard similarity of the environmental parameters' lists of bioindicators, allows us to study the ecosystem's structure from the standpoint of the microbiome. More specifically, we identify multiple clusters of highly similar and possibly functionally linked ecological parameters, including one that highlights the importance of the calcium-bicarbonate equilibrium for lake ecology. Taken together, we demonstrate the use of machine learning approaches in studying the interplay between microbial diversity and environmental factors and introduce novel approaches to integrate environmental molecular diversity into monitoring and water quality assessments.

Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges

Microbial communities are ubiquitous and carry an exceptionally broad metabolic capability. Upon environmental perturbation, microbes are also amongst the first natural responsive elements with perturbation-specific cues and markers. These communities are thereby uniquely positioned to inform on the status of environmental conditions. The advent of microbial omics has led to an unprecedented volume of complex microbiological data sets. Importantly, these data sets are rich in biological information with potential for predictive environmental classification and forecasting. However, the patterns in this information are often hidden amongst the inherent complexity of the data. There has been a continued rise in the development and adoption of machine learning (ML) and deep learning architectures for solving research challenges of this sort. Indeed, the interface between molecular microbial ecology and artificial intelligence (AI) appears to show considerable potential for significantly advancing environmental monitoring and management practices through their application. Here, we provide a primer for ML, highlight the notion of retaining biological sample information for supervised ML, discuss workflow considerations, and review the state of the art of the exciting, yet nascent, interdisciplinary field of ML-driven microbial ecology. Current limitations in this sphere of research are also addressed to frame a forward-looking perspective toward the realization of what we anticipate will become a pivotal toolkit for addressing environmental monitoring and management challenges in the years ahead.

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.

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.

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.

Environmental DNA gives comparable results to morphology-based indices of macroinvertebrates in a large-scale ecological assessment

Anthropogenic activities are changing the state of ecosystems worldwide, affecting community composition and often resulting in loss of biodiversity. Rivers are among the most impacted ecosystems. Recording their current state with regular biomonitoring is important to assess the future trajectory of biodiversity. Traditional monitoring methods for ecological assessments are costly and time-intensive. Here, we compared monitoring of macroinvertebrates based on environmental DNA (eDNA) sampling with monitoring based on traditional kick-net sampling to assess biodiversity patterns at 92 river sites covering all major Swiss river catchments. From the kick-net community data, a biotic index (IBCH) based on 145 indicator taxa had been established. The index was matched by the taxonomically annotated eDNA data by using a machine learning approach. Our comparison of diversity patterns only uses the zero-radius Operational Taxonomic Units assigned to the indicator taxa. Overall, we found a strong congruence between both methods for the assessment of the total indicator community composition (gamma diversity). However, when assessing biodiversity at the site level (alpha diversity), the methods were less consistent and gave complementary data on composition. Specifically, environmental DNA retrieved significantly fewer indicator taxa per site than the kick-net approach. Importantly, however, the subsequent ecological classification of rivers based on the detected indicators resulted in similar biotic index scores for the kick-net and the eDNA data that was classified using a random forest approach. The majority of the predictions (72%) from the random forest classification resulted in the same river status categories as the kick-net approach. Thus, environmental DNA validly detected indicator communities and, combined with machine learning, provided reliable classifications of the ecological state of rivers. Overall, while environmental DNA gives complementary data on the macroinvertebrate community composition compared to the kick-net approach, the subsequently calculated indices for the ecological classification of river sites are nevertheless directly comparable and consistent.

Capture by hybridization for full-length barcode-based eukaryotic and prokaryotic biodiversity inventories of deep sea ecosystems

Biodiversity inventory of marine systems remains limited due to unbalanced access to the three ocean dimensions. The use of environmental DNA (eDNA) for metabarcoding allows fast and effective biodiversity inventory and is forecast as a future biodiversity research and biomonitoring tool. However, in poorly understood ecosystems, eDNA results remain difficult to interpret due to large gaps in reference databases and PCR bias limiting the detection of some major phyla. Here, we aimed to circumvent these limitations by avoiding PCR and recollecting larger DNA fragments to improve assignment of detected taxa through phylogenetic reconstruction. We applied capture by hybridization (CBH) to enrich DNA from deep-sea sediment samples and compared the results with those obtained through an up-to-date metabarcoding PCR-based approach (MTB). Originally developed for bacterial communities and targeting 16S rDNA, the CBH approach was applied to 18S rDNA to improve the detection of species forming benthic communities of eukaryotes, with a particular focus on metazoans. The results confirmed the possibility of extending CBH to metazoans with two major advantages: (i) CBH revealed a broader spectrum of prokaryotic, eukaryotic, and particularly metazoan diversity, and (ii) CBH allowed much more robust phylogenetic reconstructions of full-length barcodes with up to 1900 base pairs. This is particularly important for taxa whose assignment is hampered by gaps in reference databases. This study provides a database and probes to apply 18S CBH to diverse marine systems, confirming this promising new tool to improve biodiversity assessments in data-poor ecosystems such as those in the deep sea.

Improving metabarcoding taxonomic assignment: A case study of fishes in a large marine ecosystem

DNA metabarcoding is an important tool for molecular ecology. However, its effectiveness hinges on the quality of reference sequence databases and classification parameters employed. Here we evaluate the performance of MiFish 12S taxonomic assignments using a case study of California Current Large Marine Ecosystem fishes to determine best practices for metabarcoding. Specifically, we use a taxonomy cross-validation by identity framework to compare classification performance between a global database comprised of all available sequences and a curated database that only includes sequences of fishes from the California Current Large Marine Ecosystem. We demonstrate that the regional database provides higher assignment accuracy than the comprehensive global database. We also document a tradeoff between accuracy and misclassification across a range of taxonomic cutoff scores, highlighting the importance of parameter selection for taxonomic classification. Furthermore, we compared assignment accuracy with and without the inclusion of additionally generated reference sequences. To this end, we sequenced tissue from 597 species using the MiFish 12S primers, adding 252 species to GenBank's existing 550 California Current Large Marine Ecosystem fish sequences. We then compared species and reads identified from seawater environmental DNA samples using global databases with and without our generated references, and the regional database. The addition of new references allowed for the identification of 16 additional native taxa representing 17.0% of total reads from eDNA samples, including species with vast ecological and economic value. Together these results demonstrate the importance of comprehensive and curated reference databases for effective metabarcoding and the need for locus-specific validation efforts.

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.

Biases in bulk: DNA metabarcoding of marine communities and the methodology involved

With the growing anthropogenic pressure on marine ecosystems, the need for efficient monitoring of biodiversity grows stronger. DNA metabarcoding of bulk samples is increasingly being implemented in ecosystem assessments and is more cost-efficient and less time-consuming than monitoring based on morphology. However, before raw sequences are obtained from bulk samples, a profound number of methodological choices must be made. Here, we critically review the recent methods used for metabarcoding of marine bulk samples (including benthic, plankton and diet samples) and indicate how potential biases can be introduced throughout sampling, preprocessing, DNA extraction, marker and primer selection, PCR amplification and sequencing. From a total of 64 studies evaluated, our recommendations for best practices include to (a) consider DESS as a fixative instead of ethanol, (b) use the DNeasy PowerSoil kit for any samples containing traces of sediment, (c) not limit the marker selection to COI only, but preferably include multiple markers for higher taxonomic resolution, (d) avoid touchdown PCR profiles, (e) use a fixed annealing temperature for each primer pair when comparing across studies or institutes, (f) use a minimum of three PCR replicates, and (g) include both negative and positive controls. Although the implementation of DNA metabarcoding still faces several technical complexities, we foresee wide-ranging advances in the near future, including improved bioinformatics for taxonomic assignment, sequencing of longer fragments and the use of whole-genome information. Despite the bulk of biases involved in metabarcoding of bulk samples, if appropriate controls are included along the data generation process, it is clear that DNA metabarcoding provides a valuable tool in ecosystem assessments.

Deep learning and computer vision will transform entomology

Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is sparse. Insect populations are challenging to study, and most monitoring methods are labor intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors can effectively, continuously, and noninvasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the laboratory. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behavior, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to exceptionally large datasets to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) validation of image-based taxonomic identification; 2) generation of sufficient training data; 3) development of public, curated reference databases; and 4) solutions to integrate deep learning and molecular tools.

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.

Integrating eDNA metabarcoding and simultaneous underwater visual surveys to describe complex fish communities in a marine biodiversity hotspot

Marine biodiversity can be surveyed using underwater visual censuses and recently with eDNA metabarcoding. Although a promising tool, eDNA studies have shown contrasting results related to its detection scale and the number of species identified compared to other survey methods. Also, its accuracy relies on complete reference databases used for taxonomic assignment and, as other survey methods, species detection may show false-negative and false-positive errors. Here, we compared results from underwater visual censuses and simultaneous eDNA metabarcoding fish surveys in terms of observed species and community composition. We also assess the effect of a custom reference database in the taxonomic assignment, and evaluate occupancy, capture and detection probabilities, as well as error rates of eDNA survey data. We amplified a 12S rRNA fish barcode from 24 sampling sites in the gulf of California. More species were detected with eDNA metabarcoding than with UVC. Because each survey method largely detected different sets of species, the combined approach doubled the number of species registered. Both survey methods recovered a known biodiversity gradient and a biogeographic break, but eDNA captured diversity over a broader geographic and bathymetric scale. Furthermore, the use of a modest-sized custom reference database significantly increased taxonomic assignment. In a subset of species, occupancy models revealed eDNA surveys provided similar or higher detection probabilities compared to UVC. The occupancy value of each species had a large influence on eDNA detectability, and in the false positive and negative error. Overall, these results highlight the potential of eDNA metabarcoding in complementing other established ecological methods for studies of marine fishes.

Quantification of the covariation of lake microbiomes and environmental variables using a machine learning-based framework

It is known that microorganisms are essential for the functioning of ecosystems, but the extent to which microorganisms respond to different environmental variables in their natural habitats is not clear. In the current study, we present a methodological framework to quantify the covariation of the microbial community of a habitat and environmental variables of this habitat. It is built on theoretical considerations of systems ecology, makes use of state-of-the-art machine learning techniques and can be used to identify bioindicators. We apply the framework to a data set containing operational taxonomic units (OTUs) as well as more than twenty physicochemical and geographic variables measured in a large-scale survey of European lakes. While a large part of variation (up to 61%) in many environmental variables can be explained by microbial community composition, some variables do not show significant covariation with the microbial lake community. Moreover, we have identified OTUs that act as "multitask" bioindicators, i.e., that are indicative for multiple environmental variables, and thus could be candidates for lake water monitoring schemes. Our results represent, for the first time, a quantification of the covariation of the lake microbiome and a wide array of environmental variables for lake ecosystems. Building on the results and methodology presented here, it will be possible to identify microbial taxa and processes that are essential for functioning and stability of lake ecosystems.

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.

A novel metabarcoding primer pair for environmental DNA analysis of Cephalopoda (Mollusca) targeting the nuclear 18S rRNA region

Cephalopods are pivotal components of marine food webs, but biodiversity studies are hampered by challenges to sample these agile marine molluscs. Metabarcoding of environmental DNA (eDNA) is a potentially powerful technique to study oceanic cephalopod biodiversity and distribution but has not been applied thus far. We present a novel universal primer pair for metabarcoding cephalopods from eDNA, Ceph18S (Forward: 5'-CGC GGC GCT ACA TAT TAG AC-3', Reverse: 5'-GCA CTT AAC CGA CCG TCG AC-3'). The primer pair targets the hypervariable region V2 of the nuclear 18S rRNA gene and amplifies a relatively short target sequence of approximately 200 bp in order to allow the amplification of degraded DNA. In silico tests on a reference database and empirical tests on DNA extracts from cephalopod tissue estimate that 44-66% of cephalopod species, corresponding to about 310-460 species, can be amplified and identified with this primer pair. A multi-marker approach with the novel Ceph18S and two previously published cephalopod mitochondrial 16S rRNA primer sets targeting the same region (Jarman et al. 2006 Mol. Ecol. Notes. 6, 268-271; Peters et al. 2015 Mar. Ecol. 36, 1428-1439) is estimated to amplify and identify 89% of all cephalopod species, of which an estimated 19% can only be identified by Ceph18S. All sequences obtained with Ceph18S were submitted to GenBank, resulting in new 18S rRNA sequences for 13 cephalopod taxa.

Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring

Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets of taxonomic and functional diversity are key to better understanding microbial ecology. Machine learning has proven to be a useful approach for analyzing microbial community data and making predictions about outcomes including human and environmental health. Machine learning applied to microbial community profiles has been used to predict disease states in human health, environmental quality and presence of contamination in the environment, and as trace evidence in forensics. Machine learning has appeal as a powerful tool that can provide deep insights into microbial communities and identify patterns in microbial community data. However, often machine learning models can be used as black boxes to predict a specific outcome, with little understanding of how the models arrived at predictions. Complex machine learning algorithms often may value higher accuracy and performance at the sacrifice of interpretability. In order to leverage machine learning into more translational research related to the microbiome and strengthen our ability to extract meaningful biological information, it is important for models to be interpretable. Here we review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understanding microbial communities.

Metabarcoding quantifies differences in accumulation of ballast water borne biodiversity among three port systems in the United States

Characterizing biodiversity conveyed in ships' ballast water (BW), a global driver of biological invasions, is critically important for understanding risks posed by this key vector and establishing baselines to evaluate changes associated with BW management. Here we employ high throughput sequence (HTS) metabarcoding of the 18S small subunit rRNA to test for and quantify differences in the accumulation of BW-borne biodiversity among three distinct recipient port systems in the United States. These systems were located on three different coasts (Pacific, Gulf, and Atlantic) and chosen to reflect distinct trade patterns and source port biogeography. Extensive sampling of BW tanks (n = 116) allowed detailed exploration of molecular diversity accumulation. Our results indicate that saturation of introduced zooplankton diversity may be achieved quickly, with fewer than 25 tanks needed to achieve 95% of the total extrapolated diversity, if source biogeography is relatively limited. However, as predicted, port systems with much broader source geographies require more extensive sampling to estimate diversity, which continues to accumulate after sampling >100 discharges. The ability to identify BW sources using molecular indicators was also found to depend on the breadth of source biogeography and the extent to which sources had been sampled. These findings have implications both for the effort required to fully understand introduced diversity and for projecting risks associated with future changes to maritime traffic that may increase source biogeography for many recipient ports. Our data also suggest that molecular diversity may not decline significantly with BW age, indicating either that some organisms survive longer than recognized in previous studies or that nucleic acids from dead organisms persist in BW tanks. We present evidence for detection of potentially invasive species in arriving BW but discuss important caveats that preclude strong inferences regarding the presence of living representatives of these species in BW tanks.

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.