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

Exploitation of environmental DNA (eDNA) for ecotoxicological research: A critical review on eDNA metabarcoding in assessing marine pollution

The rise in worldwide population has led to a noticeable spike in the production, consumption, and transportation of energy and food, contributing to elevated environmental pollution. Marine pollution is a significant global environmental issue with ongoing challenges, including plastic waste, oil spills, chemical pollutants, and nutrient runoff, threatening marine ecosystems, biodiversity, and human health. Pollution detection and assessment are crucial to understanding the state of marine ecosystems. Conventional approaches to pollution evaluation usually represent laborious and prolonged physical and chemical assessments, constraining their efficacy and expansion. The latest advances in environmental DNA (eDNA) are valuable methods for the detection and surveillance of pollution in the environment, offering enhanced sensibility, efficacy, and involvement. Molecular approaches allow genetic information extraction from natural resources like water, soil, or air. The application of eDNA enables an expanded evaluation of the environmental condition by detecting both identified and unidentified organisms and contaminants. eDNA methods are valuable for assessing community compositions, providing indirect insights into the intensity and quality of marine pollution through their effects on ecological communities. While eDNA itself is not direct evidence of pollution, its analysis offers a sensitive tool for monitoring changes in biodiversity, serving as an indicator of environmental health and allowing for the indirect estimation of the impact and extent of marine pollution on ecosystems. This review explores the potential of eDNA metabarcoding techniques for detecting and identifying marine pollutants. This review also provides evidence for the efficacy of eDNA assessment in identifying a diverse array of marine pollution caused by oil spills, harmful algal blooms, heavy metals, ballast water, and microplastics. In this report, scientists can expand their knowledge and incorporate eDNA methodologies into ecotoxicological research.

Environmental DNA metabarcoding reveals the effect of environmental selection on phytoplankton community structure along a subtropical river

The Dongjiang River, a major tributary of the Pearl River system that supplies water to more than 40 million people in Guangdong Province and neighboring regions of China, harbors rich biodiversity, including many endemic and endangered species. However, human activities such as urbanization, agriculture, and industrialization have posed serious threats to its water quality and biodiversity. To assess the status and drivers of phytoplankton diversity, which is a key indicator of aquatic ecosystem health, this study used Environmental DNA (eDNA) metabarcoding combined with machine learning methods to explore spatial variations in the composition and structure of phytoplankton communities along the Dongjiang River, including its estuary. The results showed that phytoplankton diversity exhibited spatial distribution patterns, with higher community structure similarity and lower network complexity in the upstream than in the downstream regions. Environmental selection was the main mechanism shaping phytoplankton community composition, with natural factors driving the dominance of Pyrrophyta, Ochrophyta, and Cryptophyta in the upstream regions and estuaries. In contrast, the downstream regions was influenced by high concentrations of pollutants, resulting in increased abundance of Cryptophyta. The random forest model identified temperature, dissolved oxygen, chlorophyll a, NO2-, and NH4+ as the main factors influencing the primary phytoplankton communities and could be used to predict changes during wet periods. This study provides valuable insights into the factors influencing phytoplankton diversity and community composition in the Dongjiang River, and demonstrates the application value of eDNA metabarcoding technique in large-scale, long-distance river biodiversity monitoring.

Assessing the impact of river connectivity on fish biodiversity in the Yangtze River Basin using a multi-index evaluation framework

The Yangtze River Basin, the world's third-largest river basin and a hot spot for global biodiversity conservation, is facing biodiversity crisis caused by reduced river connectivity. The deterioration arises from four dimensions: longitudinal, lateral, vertical and temporal. However, limited research has quantified the spatiotemporal connectivity of the Yangtze River Basin and further evaluated the consequent impact on fish biodiversity. In our study, a multi-index evaluation framework was developed to assess the variations in the four-dimensional connectivity of the Yangtze River Basin from 1980 to 2020, and fish biodiversity affected by reduced connectivity was detected by environmental DNA metabarcoding. Our results showed that the Yangtze River Basin suffers from a pronounced connectivity reduction, with 67% of assessed rivers experiencing deteriorated connectivity in recent years. The lost fish biodiversity along the river reaches with the worst connectivity was likely attributed to the construction of hydropower plants. The headwaters and the downstreams of most hydropower plants had a higher fish biodiversity compared with reservoirs. The free-flowing reaches in the downstream of the lowest hydropower station, had higher lotic fish abundance compared with that in the upstream. As for the entire Yangtze River Basin, 67% of threatened fish species, with 70% endemic species, were threatened by reduced river connectivity. Our result indicates that the massive loss of river connectivity changes the spatiotemporal patterns of fish community and threatens protected fish. More effective measures to restore the populations of affected fish in rivers with reduced river connectivity are required.

Estimating the temporal and spatial distribution and threats of bisphenol A in temperate lakes using machine learning models

Bisphenol A (BPA) is easily enriched in many human-disturbed watersheds, particularly lakes with poor water mobility, which is posing a threat to aquatic biota. While previous studies have focused on the concentration of BPA in water and its toxicity to aquatic organisms, a small amount of measured data is not enough to reveal the temporal and spatial distribution and threats of BPA, and estimate the ecological risk in watersheds. Therefore, we collected 164 measured BPA data points from Taihu Lake to develop machine learning models using random forest (RF), support vector machine (SVM) and least square regression (LSR) and created month-by-month watershed prediction maps in temperate lakes to estimate the spatiotemporal distribution and threats of BPA. Due to RF's superior robustness to noisy data, the RF model exhibits the best performance among the three algorithms. The RF model showed acceptable predictive performance on the modeling dataset (coefficients of determination and root-mean-square error for the training set were 0.927 and 17.499, respectively, and 0.607, 39.645 for the validation set, respectively). The maps indicated that areas susceptible to anthropogenic activities were more severely polluted by BPA, and rainy climate may favor the migration of BPA to aquatic ecosystems. The model was also applied to predict 42 data points of BPA collected from Dianchi Lake, and the results showed that most predicted data were within a factor of 10 of the measured data, but the prediction accuracy of the model has declined. The ecological risks in the two lakes were evaluated and attention should be paid to the regions with higher risks. Our study provided a novel idea for comprehensive monitoring of an unconventional trace pollutant with endocrine disrupting effects in aquatic ecosystems and analyzing their spatiotemporal distribution, which will contribute to the scientific assessment of the ecological risk of BPA.

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.

Generative adversarial networks for detecting contamination events in water distribution systems using multi-parameter, multi-site water quality monitoring

Contamination events in water distribution networks (WDNs) can have a huge impact on water supply and public health; increasingly, online water quality sensors are deployed for real-time detection of contamination events. Machine learning has been used to integrate multivariate time series water quality data at multiple stations for contamination detection; however, accurate extraction of spatial features in water quality signals remains challenging. This study proposed a contamination detection method based on generative adversarial networks (GANs). The GAN model was constructed to simultaneously consider the spatial correlation between sensor locations and temporal information of water quality indicators. The model consists of two networks-a generator and a discriminator-the outputs of which are used to measure the degree of abnormality of water quality data at each time step, referred to as the anomaly score. Bayesian sequential analysis is used to update the likelihood of event occurrence based on the anomaly scores. Alarms are then generated from the fusion of single-site and multi-site models. The proposed method was tested on a WDN for various contamination events with different characteristics. Results showed high detection performance by the proposed GAN method compared with the minimum volume ellipsoid benchmark method for various contamination amplitudes. Additionally, the GAN method achieved high accuracy for various contamination events with different amplitudes and numbers of anomalous water quality parameters, and water quality data from different sensor stations, highlighting its robustness and potential for practical application to real-time contamination events.

The Application of Reference Dose Prediction Model to Human Health Water Quality Criteria and Risk Assessment

Oral reference dose (RfD) is a key parameter for deriving the human health ambient water quality criteria (AWQC) for non-carcinogenic substances. In this study, a non-experimental approach was used to calculate the RfD values, which explore the potential correlation between toxicity and physicochemical characteristics and the chemical structure of pesticides. The molecular descriptors of contaminants were calculated using T.E.S.T software from EPA, and a prediction model was developed using a stepwise multiple linear regression (MLR) approaches. Approximately 95% and 85% of the data points differ by less than 10-fold and 5-fold between predicted values and true values, respectively, which improves the efficiency of RfD calculation. The model prediction values have certain reference values in the absence of experimental data, which is beneficial to the advancement of contaminant health risk assessment. In addition, using the prediction model constructed in this manuscript, the RfD values of two pesticide substances in the list of priority pollutants are calculated to derive human health water quality criteria. Furthermore, an initial assessment of the health risk was performed by the quotient value method based on the human health water quality criteria calculated by the prediction model.

Phytoplankton and Microzooplankton Community Structure and Assembly Mechanisms in Northwestern Pacific Ocean Estuaries with Environmental Heterogeneity and Geographic Segregation

Phytoplankton and microzooplankton are crucial players in marine ecosystems and first responders to environmental changes, but their community structures and how they are shaped by environmental conditions have rarely been studied simultaneously. In this study, we conducted an eDNA metabarcoding sequencing combined with multiple statistical methods to simultaneously analyze the phytoplankton and microzooplankton in Liaohe (LH) and Yalujiang (YLJ) estuaries. The major objective was to examine how plankton community structure and assembly mechanism may differ between two estuaries with similar latitudinal position and climate but geographical segregation and differential level of urbanization (more in LH). Clear differences in diversity and composition of phytoplankton and microzooplankton communities between LH and YLJ estuaries were observed. Richness of phytoplankton was significantly higher in LH than YLJ, while richness of microzooplankton was higher in YLJ. The magnitude of intrahabitat variations in phytoplankton communities was significantly stronger than that of microzooplankton. Some phytoplankton and microzooplankton taxa also showed interhabitat differences in their relative abundances. Phytoplankton showed a stronger geographic distance-decay of similarity than microzooplankton, while significant environmental distance-decay of similarity in microzooplankton was found in the less urbanized YLJ estuary. Community assembly of phytoplankton was, based on the neutral community models, driven primarily by stochastic processes, while deterministic processes contributed more for microzooplankton. Furthermore, we detected wider habitat niche breadths and stronger dispersal abilities in phytoplankton than in microzooplankton. These results suggest that passive dispersal shapes the phytoplankton community whereas environmental selection shapes the microzooplankton community. IMPORTANCE Understanding the underlying mechanisms shaping a metacommunity is useful to management for improving the ecosystem function. The research presented in the manuscript mainly tried to address the effects of habitat geography and environmental conditions on the phytoplankton and microzooplankton communities, and the underlying mechanisms of community assembly in temperate estuaries. In order to achieve this purpose, we developed a metabarcoding sequencing method based on 18S rRNA gene. The phytoplankton and microzooplankton communities from two estuaries with similar latitude and climatic conditions but obvious geographical segregation and significant environmental heterogeneity were investigated. The results of our study could lay a solid foundation for ascertaining phytoplankton and microzooplankton communities in estuaries with obvious environmental heterogeneity and geographic segregation and mechanisms underlying community assembly.

Environmental DNA sequencing reveals the regional difference in diversity and community assembly mechanisms of eukaryotic plankton in coastal waters

The diversity and community assembly mechanisms of eukaryotic plankton in coastal waters is so far not clear. In this study, we selected the coastal waters of Guangdong-Hong Kong-Macao Greater Bay Area, which is a highly developed region in China, as the research area. By use of high-throughput sequencing technologies, the diversity and community assembly mechanisms of eukaryotic marine plankton were studied in which a total of 7,295 OTUs were obtained, and 2,307 species were annotated by doing environmental DNA survey of 17 sites consist of surface and bottom layer. Ultimately, the analysis reveals that the species abundance of bottom layer is, by and large, higher than that in the surface layer. In the bottom, Arthropoda is the first largest group, accounting for more than 20% while Arthropoda and Bacillariophyta are dominant groups in surface waters accounting for more than 40%. It is significant of the variance in alpha-diversity between sampling sites, and the difference of alpha-diversity between bottom sites is greater than that of surface sites. The result suggests that the environmental factors that have significant influence on alpha-diversity are total alkalinity and offshore distance for surface sites, and water depth and turbidity for bottom sites. Likewise, the plankton communities obey the typical distance-decay pattern. Analysis about community assembly mechanisms reveals that, overall, dispersal limitation is the major pattern of community formation, which accounts for more than 83% of the community formation processes, suggesting that stochastic processes are the crucial assembly mechanism of the eukaryotic plankton community in the study area.

Versatile in silico modeling of XAD-air partition coefficients for POPs based on abraham descriptor and temperature

The concentration of persistent organic pollutants (POPs) makes remarkable difference to environmental fate. In the field of passive sampling, the partition coefficients between polystyrene-divinylbenzene resin (XAD) and air (i.e., K_XAD-A) are indispensable to obtain POPs concentration, and the K_XAD-A is generally thought to be governed by temperature and molecular structure of POPs. However, experimental determination of K_XAD-A is unrealistic for countless and novel chemicals. Herein, the Abraham solute descriptors of poly parameter linear free energy relationship (pp-LFER) and temperature were utilized to develop models, namely pp-LFER-T, for predicting K_XAD-A values. Two linear (MLR and LASSO) and four nonlinear (ANN, SVM, kNN and RF) machine learning algorithms were employed to develop models based on a data set of 307 sample points. For the aforementioned six models, R²_adj and Q²_ext were both beyond 0.90, indicating distinguished goodness-of-fit and robust generalization ability. By comparing the established models, the best model was observed as the RF model with R²_adj = 0.991, Q²_ext = 0.935, RMSE_tra = 0.271 and RMSE_ext = 0.868. The mechanism interpretation revealed that the temperature, size of molecules and dipole-type interactions were the predominant factors affecting K_XAD-A values. Concurrently, the developed models with the broad applicability domain provide available tools to fill the experimental data gap for untested chemicals. In addition, the developed models were helpful to preliminarily evaluate the environmental ecological risk and understand the adsorption behavior of POPs between XAD membrane and air.

Reference dose prediction by using CDK molecular descriptors: A non-experimental method

Reference dose (RfD) is an estimate of a daily dose that individual can be exposed chronically without obvious deleterious effects during a lifetime. In the area of toxicology, researchers always use the traditional approach by employing NOAEL/LOAEL or the benchmark dose (BMD) and other dose-response approaches to estimate RfD. These methods have, despite their typicalness, certain limitations. In this study, we present a novel method of the estimation of reference dose without experiments. The information of the organic chemicals is available from the Integrated Risk Information System (IRIS) of USEPA. Molecular descriptors for each molecular structure were calculated by an integrated platform, and the chemicals were classified into four categories based on molecular similarity: 128 contained benzene rings, 47 were heteroaromatics, 104 contained halogen substituents and 44 were halogenated aliphatic hydrocarbons. The predictive model of RfD was constructed by the multiple linear stepwise regression (MLR) method. Approximately 95% and 82% of the data points differ by less than 10-fold and 5-fold between the predicted values and the true values respectively. The non-experimental method improves the estimation efficiency and has a certain reference value to predict.

A catchment-scale model of river water quality by Machine Learning

Water quality is a concern in most river basins worldwide due to the widespread release of pollutants which impacts the freshwater ecosystems. Exploring the relationships between drivers and water quality parameters at the regional scale is key in the identification of appropriate actions for the reduction of water pollution. Regional models are the appropriate tool to achieve this, though their development poses relevant challenges because of the complexity and non-linearity of such relationships. Among the available approaches, Machine Learning (ML) is promising because of its capability to detect complex nonlinear relationships and flexibility in the parameterization, which is learned from data. In this work, we developed regional models of water temperature, dissolved oxygen, arsenic, sulfate and chloride concentrations, as well as electrical conductivity, by using two ML algorithms, Random Forest and Deep feed-forward Neural Network, and compared their performances against the standard Linear Regression model. Our results indicate that the two ML algorithms are much more accurate models for such variables than the classical Linear Regression model, with Deep feed-forward Neural Network being the most effective in identifying the reciprocal importance of the drivers and capturing nonlinear relationships between drivers and water quality variables. Our analysis also revealed that the Julian day and year at which the sample was taken surrogate the air temperature in modeling water temperature and dissolved oxygen, with only a slight performance reduction. Arsenic, sulfate, and chloride show more complex behaviors in which geogenic and anthropogenic sources are intertwined. Dilution exerts a role chiefly for arsenic concentration, which suggests a non-uniform, in space, geogenic origin for this variable.

Combining eDNA and morphological approaches to reveal the impacts of long-term discharges of shale gas wastewaters on receiving waters

The potential threats of shale gas wastewater discharges to receiving waters is of great concern. In this study, chemical analyses and biomonitoring were performed three times in a small river that received treated wastewater over a two-year period. The results of chemical analyses showed that the concentrations of chloride, conductivity, barium, and strontium increased at the discharge site, but their concentrations decreased considerably farther downstream (≥500 m). The concentrations of toxic organic compounds (16 US EPA priority polycyclic aromatic hydrocarbons and 6 priority phthalates), trace metals (strontium, arsenic, zinc, copper, chromium, lead, cadmium, nickel, and neodymium), and natural radionuclides (⁴⁰K, ²³⁸U, ²²⁶Ra, and ²³²Th) were comparable to the corresponding background values or did not exhibit obvious accumulation in sediments with continued discharge. Morphological and environmental DNA approaches were used to reveal the potential effects of wastewater discharges on aquatic ecosystems. The results showed that the community structure of benthic invertebrates was not altered by the long-term discharges of shale gas wastewaters. However, the biodiversity indices (richness and Shannon) from the two approaches showed inconsistencies, which were caused by multiple reasons, and that substrates had a strong influence on the morphological biodiversity indices. A multimetric index was proposed to further analyze morphological and environmental DNA data, and the results showed no significant difference between the upstream and downstream sites. Generally, the chemical and biological results both demonstrated that the discharges of shale gas wastewaters had limited impacts on river ecosystems within two years.

Developing stacking ensemble models for multivariate contamination detection in water distribution systems

This study presents a new stacking ensemble model for contamination event detection using multiple water quality parameters. The stacking model consists of a number of machine learning base predictors and a meta-predictor, and it is trained using cross-validation to capture different features in multiple water quality parameters and then used for water quality predictions. For each water quality parameter, the residuals between predicted and measured data are classified to identify anomalies with thresholds derived from the sequential model-based optimization method and detection probabilities updated using Bayesian analysis. Alarms derived from individual water quality parameters are fused to enhance the anomaly signals and improve the detection accuracy. The proposed stacking-based method is evaluated using a data set of six water quality parameters from a real water distribution system with randomly simulated events. The stacking-based method could detect 2496 events out of a total 2500 events without a false alarm. The results show that the stacking method outperforms an artificial neural network (ANN) benchmark method in contamination event detection. The stacking method has a higher true positive rate, lower false positive rate and higher F1 score than the ANN method. This implies that the stacking method has great promise of detecting contamination events in the water distribution system.

A bacterial index to estimate lake trophic level: National scale validation

Lakes and their catchments have been subjected to centuries to millennia of exploitation by humans. Efficient monitoring methods are required to promote proactive protection and management. Traditional monitoring is time consuming and expensive, which limits the number of lakes monitored. Lake surface sediments provide a temporally integrated representation of environmental conditions and contain high microbial biomass. Based on these attributes, we hypothesized that bacteria associated with lake trophic states could be identified and used to develop an index that would not be confounded by non-nutrient stressor gradients. Metabarcoding (16S rRNA gene) was used to assess bacterial communities present in surface sediments from 259 non-saline lakes in New Zealand encompassing a range of trophic states from alpine microtrophic lakes to lowland hypertrophic lakes. A subset of lakes (n = 96) with monitoring data was used to identify indicator amplicon sequence variants (ASVs) associated with different trophic states. A total of 10,888 indicator taxa were identified and used to develop a Sediment Bacterial Trophic Index (SBTI), which signficantly correlated (r² = 0.842, P < 0.001) with the Trophic Lake Index. The SBTI was then derived for the remaining 163 lakes, providing new knowledge of the trophic state of these unmonitored lakes. This new, robust DNA-based tool provides a rapid and cost-effective method that will allow a greater number of lakes to be monitored and more effectively managed in New Zealand and globally. The SBTI could also be applied in a paleolimnological context to investigate changes in trophic status over centuries to millennia.

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

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

Prediction of chemical reproductive toxicity to aquatic species using a machine learning model: An application in an ecological risk assessment of the Yangtze River, China

The endocrine disrupting chemicals (EDCs) have been at the forefront of environmental issues for over 20 years and are a principle factor considered in every ecological risk assessment, but this kind of risk assessment faces difficulties. The expense, time cost of in vivo tests, and lack of toxicity data are key limiting factors for the ability to conduct ecological risk assessments of EDCs to aquatic species. In this study, a machine learning model named the support vector machine (SVM) was used to predict the reproductive toxicity of EDCs, and the performance of the models was evaluated. The results showed that the SVM model provided more accurate toxicity prediction data compared with the interspecies correlation estimation (ICE) model developed by previous study to predict the reproductive toxicity. The application of the predicted toxicity data was an important supplement to the observed data for the ecological risk assessment of EDCs in the Yangtze River, where estrogens and phenolic compounds have been found at some sampling sites in the middle and lower reaches. The results showed that the ecological risk of estrone, 17β-estradiol, and ethinyl estradiol were significant. This study revealed the application potential of machine learning models for the prediction of reproductive toxicity effects of EDCs. This can provide reliable alternative toxicity data for the ecological risk assessments of EDCs.

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.

Environmental DNA and environmental RNA: Current and prospective applications for biological monitoring

Traditional environmental biomonitoring approaches have limitations in terms of species detectability and their capacity to account for spatial and temporal variation. Furthermore, as invasive techniques they can be harmful to individual organisms, populations and habitats. The application of non-invasive sampling methods that extract, isolate and identify nucleic acid sequences (i.e. DNA, RNA) from environmental matrices have significant potential for complementing, or even ultimately replacing, current methods of biological environmental assessment. These environmental DNA (eDNA) and environmental RNA (eRNA) techniques increase spatial and temporal acuity of monitoring, and in the case of the latter, may provide functional information regarding the health of individuals, and thus ecosystems. However, these assessments require robust analysis of factors such as the detectability and specificity of the developed assays. The presented work highlights the current and future uses of nucleic acid-based biomonitoring regimes, with a focus on fish and aquatic invertebrates and their utility for water quality, biodiversity and species-specific monitoring. These techniques are compared to traditional approaches, with a particular emphasis on the potential insights that could be provided by eRNA analysis, including the benefits of microRNAs as assay targets.

Estimating aquatic plant diversity and distribution in rivers from Jingjinji region, China, using environmental DNA metabarcoding and a traditional survey method

Traditional survey methods (TSMs) are difficult to use to perform a census of aquatic plant diversity completely in river ecosystems, and improved aquatic plant community monitoring programs are becoming increasingly crucial with a continuous decline in diversity. Although environmental DNA (eDNA) metabarcoding has been applied successfully to assess aquatic biodiversity, limited work has been reported regarding aquatic plant diversity in rivers. In this study, the efficiency of eDNA to estimate the aquatic plant diversity and spatial distribution of rivers from the Jingjinji (JJJ) region was evaluated by comparing results obtained by the TSM. Based on a combination of the two methods, 157 aquatic plant species, including 24 hydrophytes, 61 amphibious plants, and 72 mesophytes, were identified. The spatial patterns in species richness and abundance by eDNA exhibited agreement with the TSM results with a gradual decline from the mountain area (MA) to the agricultural area (AA) and then to the urban area (UA). Compared to the TSM, eDNA identified a significantly greater number of species per site (p < 0.01) and obtained a significantly higher abundance in hydrophytes (p < 0.01), supplementing the unavailable abundance data from the TSM. Furthermore, the aquatic plant assemblages from the different areas were discriminated well using eDNA (p < 0.05), but they were better discriminated by the TSM (p < 0.01). Thus, our study provides more detailed data on aquatic plant diversity in rivers from the JJJ region, which is essential for biodiversity conservation. Our findings also highlight that eDNA can be reliable for evaluating aquatic plant diversity and has the potential to respond to landscape heterogeneity in river ecosystems.

Evaluation of V2 18S rDNA barcode marker and assessment of sample collection and DNA extraction methods for metabarcoding of autotrophic euglenids

Even though the interest in metabarcoding in environmental studies is growing, euglenids are still underrepresented in both sea and freshwater bodies researches. The reason for this situation could be the unsuitability of universal eukaryotic DNA barcodes and primers as well as the lack of a verified protocol, suitable to assess euglenid diversity. In this study, using specific primers for the V2 hypervariable region of 18S rDNA for metabarcoding resulted in obtaining a high fraction (85%) of euglenid reads and species‐level identification of almost 90% of them. Fifty species were detected by the metabarcoding method, including almost all species observed using a light microscope. We investigated three biomass harvesting methods (filtering, centrifugation and scraping the side of a collection vessel) and determined that centrifugation and filtration outperformed scrapes, but the choice between them is not crucial for the reliability of the analysis. In addition, eight DNA extraction methods were evaluated. We compared five commercially available DNA isolation kits, two CTAB‐based protocols and a chelating resin. For this purpose, the efficiency of extraction, quality of obtained DNA, preparation time and generated costs were taken into consideration. After examination of the aforementioned criteria, we chose the GeneMATRIX Soil DNA Purification Kit as the most suitable for DNA isolation.

Application of attached algae flow-ways for coupling biomass production with the utilization of dilute non-point source nutrients in the Upper Laguna Madre, TX

The purpose of this study is to determine the potential for an attached algae flow-way system to efficiently produce algal biomass in estuarine surface waters by utilizing dilute non-point source nutrients from local urban, industrial, and agricultural discharges into the Upper Laguna Madre, Corpus Christi, Texas. The study was conducted over the course of two years to establish seasonal base-line biomass productivity and composition for bioproducts applications, and to identify key environmental factors and flow-way cohorts impacting biomass production. For the entire cultivation period, continuous ash-free biomass production at 4 to 10 g/m²/day (corresponding to nutrient recovery at 300 to 500 mg of nitrogen/m²/day and 15 to 30 mg of phosphorus/m²/day) was successfully achieved without system restart. Upon start-up, a latency period was observed which indicates roles for species succession from relatively low productivity, high ash content pioneer periphytic culture composed primarily of benthic diatoms from the source waters to higher productivity, reduced ash content, and more resilient culture mainly composed of filamentous chlorophyta, Ulva lactuca. Principal Component Analysis (PCA) was used to identify environmental factors driving biomass production, and machine learning (ML) models were constructed to assess the predictive capability of the data set for system performance using the local multi-season environmental variations. Environmental datasets were segregated for ML training, validation, and testing using three methods: regression tree, ensemble regression, and Gaussian process regression (GPR). The predicted ash-free biomass productivity using ML models resulted in root-squared-mean-errors (RSME) from 1.78 to 1.86 g/m²/day, and R² values from 0.67 to 0.75 using different methods. The greatest contributor to net productivity was total solar irradiation, followed by air temperature, salinity, and pH. The results of the study should be useful as a decision-making tool to application of attached algae flow-ways for biomass production while preventing algal blooms in the environment.