A taxonomy-free approach based on machine learning to assess the quality of rivers with diatoms

Using DNA metabarcoding to characterize national scale diatom-environment relationships and to develop indicators in streams and rivers of the United States

Recent advancements in DNA techniques, metabarcoding, and bioinformatics could help expand the use of benthic diatoms in monitoring and assessment programs by providing relatively quick and increasingly cost-effective ways to quantify diatom diversity in environmental samples. However, such applications of DNA-based approaches are relatively new, and in the United States, unknowns regarding their applications at large scales exist because only a few small-scale studies have been done. Here, we present results from the first nationwide survey to use DNA metabarcoding (rbcL) of benthic diatoms, which were collected from 1788 streams and rivers across nine ecoregions spanning the conterminous USA. At the national scale, we found that diatom assemblage structure (1) was strongly associated with total phosphorus and total nitrogen concentrations, conductivity, and pH and (2) had clear patterns that corresponded with differences in these variables among the nine ecoregions. These four variables were strong predictors of diatom assemblage structure in ecoregion-specific analyses, but our results also showed that diatom-environment relationships, the importance of environmental variables, and the ranges of these variables within which assemblage changes occurred differed among ecoregions. To further examine how assemblage data could be used for biomonitoring purposes, we used indicator species analysis to identify ecoregion-specific taxa that decreased or increased along each environmental gradient, and we used their relative abundances of gene reads in samples as metrics. These metrics were strongly correlated with their corresponding variable of interest (e.g., low phosphorus diatoms with total phosphorus concentrations), and generalized additive models showed how their relationships compared among ecoregions. These large-scale national patterns and nine sets of ecoregional results demonstrated that diatom DNA metabarcoding is a robust approach that could be useful to monitoring and assessment programs spanning the variety of conditions that exist throughout the conterminous United States.

Maximising environmental pressure-response relationship signals from diatom-based metabarcoding in rivers

DNA metabarcoding has been performed on a large number of river phytobenthos samples collected from the UK, using rbcL primers optimised for diatoms. Within this dataset the composition of non-diatom sequence reads was studied and the effect of including these in models for evaluating the nutrient gradient was assessed. Whilst many non-diatom taxonomic groups were detected, few contained the full diversity expected in riverine environments. This may be due to the performance of the current primers in characterising the wider phytobenthic community and influenced by the sampling method employed, as both were developed specifically for diatoms. Nevertheless, the study identified considerable diversity in some groups, e.g. Eustigmatophyceae and a wider distribution than previously thought for freshwater Phaeophyceae. These results offer a strong case for the benefits of metabarcoding for expanding knowledge of aquatic biodiversity in the UK and elsewhere. Many of the ASVs associated with non-diatoms showed significant pressure responses; however, models that included non-diatoms had similar predictive strength to those based on diatoms alone. Whilst limitations of the primers for assessing non-diatoms may play a role in explaining these results, the diatoms provide a strong signal along the nutrient gradient and other algae, therefore, add little unique information. We recommend that future developments should use ASVs to calculate metrics, with links to reference databases made as a final step to generate lists of taxa to support interpretation. Any further exploration of the potential of non-diatoms would benefit from access to a well-curated reference database, similar to diat.barcode. Such a database does not yet exist, and we caution against the indiscriminate use of NCBI GenBank as a taxonomic resource as many rbcL sequences deposited have not been curated.

TICI: a taxon-independent community index for eDNA-based ecological health assessment

Global biodiversity is declining at an ever-increasing rate. Yet effective policies to mitigate or reverse these declines require ecosystem condition data that are rarely available. Morphology-based bioassessment methods are difficult to scale, limited in scope, suffer prohibitive costs, require skilled taxonomists, and can be applied inconsistently between practitioners. Environmental DNA (eDNA) metabarcoding offers a powerful, reproducible and scalable solution that can survey across the tree-of-life with relatively low cost and minimal expertise for sample collection. However, there remains a need to condense the complex, multidimensional community information into simple, interpretable metrics of ecological health for environmental management purposes. We developed a riverine taxon-independent community index (TICI) that objectively assigns indicator values to amplicon sequence variants (ASVs), and significantly improves the statistical power and utility of eDNA-based bioassessments. The TICI model training step uses the Chessman iterative learning algorithm to assign health indicator scores to a large number of ASVs that are commonly encountered across a wide geographic range. New sites can then be evaluated for ecological health by averaging the indicator value of the ASVs present at the site. We trained a TICI model on an eDNA dataset from 53 well-studied riverine monitoring sites across New Zealand, each sampled with a high level of biological replication (n = 16). Eight short-amplicon metabarcoding assays were used to generate data from a broad taxonomic range, including bacteria, microeukaryotes, fungi, plants, and animals. Site-specific TICI scores were strongly correlated with historical stream condition scores from macroinvertebrate assessments (macroinvertebrate community index or MCI; R2 = 0.82), and TICI variation between sample replicates was minimal (CV = 0.013). Taken together, this demonstrates the potential for taxon-independent eDNA analysis to provide a reliable, robust and low-cost assessment of ecological health that is accessible to environmental managers, decision makers, and the wider community.

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.

Recommendations for the preservation of environmental samples in diatom metabarcoding studies

Implementation of DNA metabarcoding for diatoms for environmental monitoring is now moving from a research to an operational phase, requiring rigorous guidelines and standards. In particular, the first steps of the diatom metabarcoding process, which consist of sampling and storage, have been addressed in various ways in scientific and pilot studies and now need to be rationalised. The objective of this study was to compare three currently applied preservation protocols through different storage durations (ranging from one day to one year) for phytobenthos and phytoplankton samples intended for diatom DNA metabarcoding analysis. The experimental design used samples from four freshwater and two marine sites of diverse ecological characteristics. The impact of the sample preservation and storage duration was assessed through diatom metabarcoding endpoints: DNA quality and quantity, diversity and richness, diatom assemblage composition and ecological index values (for freshwater samples). The yield and quality of extracted DNA only decreased for freshwater phytobenthos samples preserved with ethanol. Diatom diversity was not affected and their taxonomic composition predominantly reflected the site origin. Only rare taxa (< 100 reads) differed among preservation methods and storage durations. For biomonitoring purposes, freshwater ecological index values were not affected by the preservation method and storage duration tested (including ethanol preservation), all treatments returning the same ecological status for a site. This study contributes to consolidating diatom metabarcoding. Thus, accompanied by operational standards, the method will be ready to be confidently deployed and prescribed in future regulatory monitoring.

Evaluation of two short overlapping rbcL markers for diatom metabarcoding of environmental samples: Effects on biomonitoring assessment and species resolution

Two short diatom rbcL barcodes, 331 bp and 263 bp in length, have frequently been used in diatom metabarcoding studies. They overlap in a common 263-bp region but differ in the presence or absence of a 68-bp tail at the 5' end. Though the effectiveness of both has been demonstrated in separate biomonitoring and diversity studies, the impact of the 68-bp non-shared region has not been evaluated. Here we compare the two barcodes in terms of the values of a biotic index (IPS) and the ecological status classes derived from their application to an extensive metabarcoding dataset from United Kingdom rivers; this comprised 1703 samples and was produced using the 331-bp primers. In addition, we assess the effectiveness of each barcode for discrimination of genetic variants around and below the species level. The strong correlation found in IPS values between barcodes (Pearson's R = 0.98) indicates that the choice of the barcode does not have major implications for current WFD ecological assessments, although a very few sites (55: 3.23% of those analysed) were downgraded from an acceptable WFD class ("Good") to an unacceptable one ("Moderate"). Analyses of the taxonomic resolution of the two barcodes indicate that for many ASVs, the use of either marker - 263-bp and 331-bp - gives unambiguous assignations at species level though with differences in bootstrap confidence values. Such differences are caused by the stochasticity involved in the naïve Bayesian classifier used and by the fact that genetic distance, regarding closely related species, is increased when using the 331-bp barcode. However, in three cases, species differentiation fails with the shorter marker, leading to underestimates of species diversity. Finally, two ASVs from Nitzschia species evidenced that the use of the shorter marker can sometimes lead to false positives when the extent and nature of infraspecific variation are poorly known.

Characterizing temporal variability in streams supports nutrient indicator development using diatom and bacterial DNA metabarcoding

Interest in developing periphytic diatom and bacterial indicators of nutrient effects continues to grow in support of the assessment and management of stream ecosystems and their watersheds. However, temporal variability could confound relationships between indicators and nutrients, subsequently affecting assessment outcomes. To document how temporal variability affects measures of diatom and bacterial assemblages obtained from DNA metabarcoding, we conducted weekly periphyton and nutrient sampling from July to October 2016 in 25 streams in a 1293 km² mixed land use watershed. Measures of both diatom and bacterial assemblages were strongly associated with the percent agriculture in upstream watersheds and total phosphorus (TP) and total nitrogen (TN) concentrations. Temporal variability in TP and TN concentrations increased with greater amounts of agriculture in watersheds, but overall diatom and bacterial assemblage variability within sites-measured as mean distance among samples to corresponding site centroids in ordination space-remained consistent. This consistency was due in part to offsets between decreasing variability in relative abundances of taxa typical of low nutrient conditions and increasing variability in those typical of high nutrient conditions as mean concentrations of TP and TN increased within sites. Weekly low and high nutrient diatom and bacterial metrics were more strongly correlated with site mean nutrient concentrations over the sampling period than with same day measurements and more strongly correlated with TP than with TN. Correlations with TP concentrations were consistently strong throughout the study except briefly following two major precipitation events. Following these events, biotic relationships with TP reestablished within one to three weeks. Collectively, these results can strengthen interpretations of survey results and inform monitoring strategies and decision making. These findings have direct applications for improving the use of diatoms and bacteria, and the use of DNA metabarcoding, in monitoring programs and stream site assessments.

How good is good ecological status? A test across river typologies, diatom indices and biological elements

Good ecological status is the environmental objective of EU water legislation to be achieved by all European water bodies. However, good ecological status varies depending on national criteria, typology approach, and classification systems used. Since nutrient enrichment is the main cause of river degradation, it is important to establish nutrient criteria that consistently support good ecological status across these influential factors. This study analyzes good ecological status, depending on the typology, classification system and biological element used, and it discusses potential implications of the results for river management. We used a database of 425 sites from northern Spain, corresponding to 11 river types of the Spanish typology derived from physiographic data, or to the four river types resulting from NORTIdiat predictive model, derived from regional diatom reference assemblages. PERMANOVA analysis found significant differences among diatom assemblages across the four river types derived from the NORTIdiat system. Among the classification systems currently in use, or of potential use in the area, the upper P-PO₄^3- threshold, established as the P₉₅ of the class distribution for good ecological status, both NORTIdiat (50.7 μg l^-1) and the Multimetric Diatom index (MDIAT; 26.4 μg l^-1) were close to proposed thresholds for good status at the EU level. However, this value was much higher for the Specific Polluosensitivity Index (IPS; 118.1 μg l^-1). Nutrient thresholds for good status also varied among bioindicators, since the predictive invertebrate-based model NORTI classified 67% of samples with high P-PO₄^3- content in good ecological status, whereas the NORTIdiat classified only 33% of them in good status. Results suggest that current nutrient criteria used to establish good ecological status should be revised, accounting for biological specificity and response of biological elements, to provide a more ecologically coherent approach to preserving or restoring good ecological status.

Using integrated models to analyze and predict the variance of diatom community composition in an agricultural area

With the growing demand of assessing the ecological status, there is the need to fully understand the relationship between the planktic diversity and the environmental factors. Species richness and Shannon index have been widely used to describe the biodiversity of a community. Besides, we introduced the first ordination value from non-metric multidimensional scaling (NMDS) as a new index to represent the community similarity variance. In this study, we hypothesized that the variation of diatom community in rivers in an agricultural area was influenced by hydro-chemical variables. We collected daily mixed water samples using ISCO auto water samplers for diatoms and for water-chemistry analysis at the outlet of a lowland river for a consecutive year. An integrated modeling was adopted including random forest (RF) to decide the importance of the environmental factors influencing diatoms, generalized linear models (GLMs) combined with 10-folder cross validation to analyze and predict the diatom variation. The hierarchical analysis highlighted antecedent precipitation index (API) as the controlling hydrological variable while water temperature, Si²⁺ and PO₄-P as the main chemical controlling factors in our study area. The generalized linear models performed better prediction for Shannon index (R² = 0.44) and NMDS (R² = 0.51) than diatom abundance (R² = 0.25) and species richness (R² = 0.25). Our findings confirmed that Shannon index and the NMDS as an index showed good performance in explaining the relationship between stream biota and its environmental factors and in predicting the diatom community development based on the hydro-chemical predictors. Our study showed and highlighted the important hydro-chemical factors in the agricultural rivers, which could contribute to the further understanding of predicting diatom community development and could be implemented in the future water management protocol.

Machine learning methods for imbalanced data set for prediction of faecal contamination in beach waters

Predicting water contamination by statistical models is a useful tool to manage health risk in recreational beaches. Extreme contamination events, i.e. those exceeding normative are generally rare with respect to bathing conditions and thus the data is said to be imbalanced. Modeling and predicting those rare events present unique challenges. Here we introduce and evaluate several machine learning techniques and metrics to model imbalanced data and evaluate model performance. We do so by using a) simulated data-sets and b) a real data base with records of faecal coliform abundance monitored for 10 years in 21 recreational beaches in Uruguay (N ≈ 19000) using in situ and meteorological variables. We discuss advantages and disadvantages of the methods and provide a simple guide to perform models for a general audience. We also provide R codes to reproduce model fitting and testing. We found that most Machine Learning techniques are sensitive to imbalance and require specific data pre-treatment (e.g. upsampling) to improve performance. Accuracy (i.e. correctly classified cases over total cases) is not adequate to evaluate model performance on imbalanced data set. Instead, true positive rates (TPR) and false positive rates (FPR) are recommended. Among the 52 possible candidate algorithms tested, the stratified Random forest presented the better performance improving TPR in 50% with respect to baseline (0.4) and outperformed baseline in the evaluated metrics. Support vector machines combined with upsampling method or synthetic minority oversampling technique (SMOTE) performed well, similar to Adaboost with SMOTE. These results suggests that combining modeling strategies is necessary to improve our capacity to anticipate water contamination and avoid health risk.

How can integrated morphotaxonomy- and metabarcoding-based diatom assemblage analyses best contribute to the ecological assessment of streams?

Environmental conditions, such as nutrient concentrations, salinity, elevation etc., shape diatom assemblages of periphytic biofilms. These assemblages respond rapidly to environmental changes, a fact which makes diatoms valuable bioindicators. Hence, freshwater biomonitoring programmes currently use diatom indices (e.g. EU Water Framework Directive - WFD). To date, microscopy-based assessments require high taxonomic expertise for diatom identification at the species level. High-throughput technologies now provide cost-effective identification approaches that are promising, complementary or alternative tools for bioassessment. The suitability of the metabarcoding method is evaluated for the first time in the Cyprus streams WFD monitoring network, an eastern Mediterranean country with many endemic species and results are compared to the results acquired from the morphotaxonomic analysis. Morphotaxonomic identification was conducted microscopically, using the most updated taxonomic concepts, literature and online resources. At the same time, DNA metabarcoding involved the use of the rbcL 312 bp barcode, high-throughput sequencing and bioinformatic analysis. The ecological status was calculated using the IPS Index. Results show a positive correlation between morpho-taxonomic and molecular IPS scores. Discrepancies between the two methodologies are related to the limitations of both techniques. This study confirmed that Fistulifera saprophila can have a crucial role in key differences observed, as it negatively influences IPS scores and microscopy methods frequently overlook it. Importantly, gaps in the DNA barcoding reference databases lead to a positive overestimation in IPS scores. Overall, we conclude that DNA metabarcoding offsets the morphotaxonomic methodology for the ecological quality assessment of freshwaters.

An interpretable machine learning method for supporting ecosystem management: Application to species distribution models of freshwater macroinvertebrates

Species distribution models (SDMs), in which species occurrences are related to a suite of environmental variables, have been used as a decision-making tool in ecosystem management. Complex machine learning (ML) algorithms that lack interpretability may hinder the use of SDMs for ecological explanations, possibly limiting the role of SDMs as a decision-support tool. To meet the growing demand of explainable MLs, several interpretable ML methods have recently been proposed. Among these methods, SHaply Additive exPlanation (SHAP) has drawn attention for its robust theoretical justification and analytical gains. In this study, the utility of SHAP was demonstrated by the application of SDMs of four benthic macroinvertebrate species. In addition to species responses, the dataset contained 22 environmental variables monitored at 436 sites across five major rivers of South Korea. A range of ML algorithms was employed for model development. Each ML model was trained and optimized using 10-fold cross-validation. Model evaluation based on the test dataset indicated strong model performance, with an accuracy of ≥0.7 in all evaluation metrics for all MLs and species. However, only the random forest algorithm showed a behavior consistent with the known ecology of the investigated species. SHAP presents an integrated framework in which local interpretations that incorporate local interaction effects are combined to represent the global model structure. Consequently, this framework offered a novel opportunity to assess the importance of variables in predicting species occurrence, not only across sites, but also for individual sites. Furthermore, removing interaction effects from variable importance values (SHAP values) clearly revealed non-linear species responses to variations in environmental variables, indicating the existence of ecological thresholds. This study provides guidelines for the use of a new interpretable method supporting ecosystem management.

Raman spectroscopy applied to diatoms (microalgae, Bacillariophyta): Prospective use in the environmental diagnosis of freshwater ecosystems

Diatom species are good pollution bioindicators due to their large distribution, fast response to changes in environmental parameters and different tolerance ranges. These organisms are used in ecological water assessment all over the world using autoecological indices. Such assessments commonly rely on the taxonomic identification of diatom species-specific shape and frustule ornaments, from which cell counts, species richness and diversity indices can be estimated. Taxonomic identification is, however, time-consuming and requires years of expertise. Additionally, though the diatom autoecological indices are region-specific, they are often applied indiscriminately across regions. Raman spectroscopy is a simpler, fast and label-free technique that can be applied to environmental diagnosis with diatoms. However, this approach has been poorly explored. This work reviews Raman spectroscopy studies involving the structure, location and conformation of diatom cell components and their variation under different conditions. A critical appreciation of the pros and cons of its application to environmental diagnosis is also given. This knowledge provides a strong foundation for the development of environmental protocols using Raman spectroscopy in diatoms. Our work aims at stimulating further research on the application of Raman spectroscopy as a tool to assess physiological changes and water quality under a changing climate.

Cyprus' diatom diversity and the association of environmental and anthropogenic influences for ecological assessment of rivers using DNA metabarcoding

Human activities are the leading cause of environmental impairments. Appropriate biomonitoring of ecosystems is needed to assess these activities effectively. In freshwater ecosystems, periphytic and epilithic biofilms have diatom assemblages. These assemblages respond rapidly to environmental changes, making diatoms valuable bioindicators. For this reason, freshwater biomonitoring programs are currently using diatoms (e.g., Water Framework Directive). In the past ten years, DNA metabarcoding coupled with next-generation sequencing and bioinformatics represents a complementary approach for diatom biomonitoring. In this study, this approach is used for the first time in Cyprus by considering the association of environmental and anthropogenic pressures to diatom assemblages. Statistical analysis was then applied to identify the environmental (i.e., river types, geo-morphological) and anthropogenic (i.e., physicochemical, human land-use pressures) variables' role in the observed diatom diversity. Results indicate differences in diatom assemblages between intermittent and perennial rivers. Achnanthidium minutissimum was more abundant in intermittent rivers; whereas Amphora pediculus and Planothidium caputium in perennial ones. Additionally, we could demonstrate the correlation between nutrients (e.g., nitrogen, phosphorus), stations' local characteristics (e.g., elevation), and land use activities on the observed differences in diatom diversity. Finally, we conclude that multi-stressors and anthropogenic pressures together as multiple stressors have a significant statistical relationship to the observed diatom diversity and play a pivotal role in determining Cyprus' rivers' ecological status.

Epiphytic Diatom-Based Biomonitoring in Mediterranean Ponds: Traditional Microscopy versus Metabarcoding Approaches

Benthic diatoms have traditionally been used as bioindicators of aquatic ecosystems. Because diatom-based monitoring of water quality is required by European legislation, molecular-based methods had emerged as useful alternatives to classical methods based on morphological identification using light microscopy. The aim of this study was to test the reliability of DNA metabarcoding combined with High-Throughput Sequencing (HTS) techniques in the bioassessment of the trophic status of 22 Mediterranean shallow ponds in NW Spain. For each pond, the Trophic Diatom Index (TDI) was calculated from inventories obtained by identification using light microscopy (LM) followed by high-throughput sequencing (HTS) at the molecular level. Ponds were subsequently classified into five water quality classes. The results showed a good correspondence between both methods, especially after applying a correction factor that depended on the biovolume of the cells. This correspondence led to the assignment to the same quality class in 59% of the ponds. The determination and quantification of valves or DNA sequences was one of the main pitfalls, which mainly included those related to the variability in the relative abundances of some species. Accordingly, ponds with similar relative abundances for the dominant species were assigned to the same quality class. Moreover, other difficulties leading the discrepancies were the misidentification of some species due to the presence of semi-cryptic taxa, the incompleteness of the reference database and the bioinformatic protocol. Thus, the validation of DNA-based methods for the identification of freshwater diatoms represents an important goal, as an alternative to using traditional methods in Mediterranean shallow ponds.

Expanding ecological assessment by integrating microorganisms into routine freshwater biomonitoring

Bioindication has become an indispensable part of water quality monitoring in most countries of the world, with the presence and abundance of bioindicator taxa, mostly multicellular eukaryotes, used for biotic indices. In contrast, microbes (bacteria, archaea and protists) are seldom used as bioindicators in routine assessments, although they have been recognized for their importance in environmental processes. Recently, the use of molecular methods has revealed unexpected diversity within known functional groups and novel metabolic pathways that are particularly important in energy and nutrient cycling. In various habitats, microbial communities respond to eutrophication, metals, and natural or anthropogenic organic pollutants through changes in diversity and function. In this review, we evaluated the common trends in these changes, documenting that they have value as bioindicators and can be used not only for monitoring but also for improving our understanding of the major processes in lotic and lentic environments. Current knowledge provides a solid foundation for exploiting microbial taxa, community structures and diversity, as well as functional genes, in novel monitoring programs. These microbial community measures can also be combined into biotic indices, improving the resolution of individual bioindicators. Here, we assess particular molecular approaches complemented by advanced bioinformatic analysis, as these are the most promising with respect to detailed bioindication value. We conclude that microbial community dynamics are a missing link important for our understanding of rapid changes in the structure and function of aquatic ecosystems, and should be addressed in the future environmental monitoring of freshwater ecosystems.

Benthic Diatoms in River Biomonitoring—Present and Future Perspectives within the Water Framework Directive

The European Water Framework Directive 2000/60/EC (WFD) has been implemented over the past 20 years, using physicochemical, biological and hydromorphological elements to assess the ecological status of surface waters. Benthic diatoms (i.e., phytobenthos) are one of the most common biological quality elements (BQEs) used in surface water monitoring and are particularly successful in detecting eutrophication, organic pollution and acidification. Herein, we reviewed their implementation in river biomonitoring for the purposes of the WFD, highlighting their advantages and disadvantages over other BQEs, and we discuss recent advances that could be applied in future biomonitoring. Until now, phytobenthos have been intercalibrated by the vast majority (26 out of 28) of EU Member States (MS) in 54% of the total water bodies assessed and was the most commonly used BQE after benthic invertebrates (85% of water bodies), followed by fish (53%), macrophytes (27%) and phytoplankton (4%). To meet the WFD demands, numerous taxonomy-based quality indices have been developed among MS, presenting, however, uncertainties possibly related to species biogeography. Recent development of different types of quality indices (trait-based, DNA sequencing and predictive modeling) could provide more accurate results in biomonitoring, but should be validated and intercalibrated among MS before their wide application in water quality assessments.

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.

Use of Aquatic Biota to Detect Ecological Changes in Freshwater: Current Status and Future Directions

Freshwater ecosystems have been severely damaged worldwide by a multitude of human pressures, such as pollution, nutrient enrichment, damming or overexploitation, and this has been more intense over the past five decades. It is therefore important that the impacts of such stressors can be effectively detected, monitored and assessed in order to provide adequate legislative tools and to protect and restore freshwater ecosystems. The use of aquatic biota to detect, measure and track changes in the environment is often known as freshwater biomonitoring and is based on the premise that the presence or absence of biotic assemblages at a given site reflects its degree of environmental quality. For over a century, since the early pollution-oriented indicators, freshwater monitoring has been developing and testing progressively more complex indicator systems, and increasing the plethora of pressures addressed, using different biological groups, such as benthic macroinvertebrates, macrophytes, fish, phytoplankton and phytobenthos. There is an increasing demand for precision and accuracy in bioassessment. In this Special Issue, five high-quality papers were selected and are briefly presented herein, that cover a wide range of issues and spatial contexts relevant to freshwater biomonitoring.