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

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'.

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.

Fishing eDNA in One of the World's Largest Rivers: A Case Study of Cross-Sectional and Depth Profile Sampling in the Yangtze

The world's largest rivers are home to diverse, endemic, and threatened fish species. However, their sheer sizes make large-scale biomonitoring challenging. While environmental DNA (eDNA) metabarcoding has become an established monitoring approach in smaller freshwater ecosystems, its suitability for large rivers may be challenged by the sheer extent of their cross sections (>1 km wide and tens of meters deep). Here, we sampled fish eDNA from multiple vertical layers and horizontal locations from two cross sections of the lower reach of the Yangtze River in China. Over half of the ASVs (amplicon sequence variants) were detected in only a single combination of the vertical layers and horizontal locations, with ∼7% across all combinations. We estimated the need to sample >100 L of water across the cross-sectional profiles to achieve ASV richness saturation, which translates to ∼60 L of water at the species level. No consistent pattern emerged for prioritizing certain depth and horizontal samples, yet we underline the importance of sampling and integrating different layers and locations simultaneously. Our study highlights the significance of spatially stratified sampling and sampling volumes when using eDNA approaches. Specifically, we developed and tested a scalable and broadly applicable strategy that advances the monitoring and conservation of large rivers.

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

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

Current Status of Omics in Biological Quality Elements for Freshwater Biomonitoring

Freshwater ecosystems have been experiencing various forms of threats, mainly since the last century. The severity of this adverse scenario presents unprecedented challenges to human health, water supply, agriculture, forestry, ecological systems, and biodiversity, among other areas. Despite the progress made in various biomonitoring techniques tailored to specific countries and biotic communities, significant constraints exist, particularly in assessing and quantifying biodiversity and its interplay with detrimental factors. Incorporating modern techniques into biomonitoring methodologies presents a challenging topic with multiple perspectives and assertions. This review aims to present a comprehensive overview of the contemporary advancements in freshwater biomonitoring, specifically by utilizing omics methodologies such as genomics, metagenomics, transcriptomics, proteomics, metabolomics, and multi-omics. The present study aims to elucidate the rationale behind the imperative need for modernization in this field. This will be achieved by presenting case studies, examining the diverse range of organisms that have been studied, and evaluating the potential benefits and drawbacks associated with the utilization of these methodologies. The utilization of advanced high-throughput bioinformatics techniques represents a sophisticated approach that necessitates a significant departure from the conventional practices of contemporary freshwater biomonitoring. The significant contributions of omics techniques in the context of biological quality elements (BQEs) and their interpretations in ecological problems are crucial for biomonitoring programs. Such contributions are primarily attributed to the previously overlooked identification of interactions between different levels of biological organization and their responses, isolated and combined, to specific critical conditions.

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.

Groundwater environmental DNA metabarcoding reveals hidden diversity and reflects land-use and geology

Despite being the most important source of liquid freshwater on the planet, groundwater is severely threatened by climate change, agriculture, or industrial mining. It is thus extensively monitored for pollutants and declines in quantity. The organisms living in groundwater, however, are rarely the target of surveillance programmes and little is known about the fauna inhabiting underground habitats. The difficulties accessing groundwater, the lack of expertise, and the apparent scarcity of these organisms challenge sampling and prohibit adequate knowledge on groundwater fauna. Environmental DNA (eDNA) metabarcoding provides an approach to overcome these limitations but is largely unexplored. Here, we sampled water in 20 communal spring catchment boxes used for drinking water provisioning in Switzerland, with a high level of replication at both filtration and amplification steps. We sequenced a portion of the COI mitochondrial gene, which resulted in 4917 ASVs, yet only 3% of the reads could be assigned to a species, genus, or family with more than 90% identity. Careful evaluation of the unassigned reads corroborated that these sequences were true COI sequences belonging mostly to diverse eukaryotic groups, not present in the reference databases. Principal component analyses showed a strong correlation of the community composition with the surface land-use (agriculture vs. forest) and geology (fissured rock vs. unconsolidated sediment). While incomplete reference databases limit the assignment of taxa in groundwater eDNA metabarcoding, we showed that taxonomy-free approaches can reveal large hidden diversity and couple it with major land-use drivers, revealing their imprint on chemical and biological properties of groundwater.

Assessing arthropod diversity metrics derived from stream environmental DNA: spatiotemporal variation and paired comparisons with manual sampling

Background

Benthic invertebrate (BI) surveys have been widely used to characterize freshwater environmental quality but can be challenging to implement at desired spatial scales and frequency. Environmental DNA (eDNA) allows an alternative BI survey approach, one that can potentially be implemented more rapidly and cheaply than traditional methods.

Methods

We evaluated eDNA analogs of BI metrics in the Potomac River watershed of the eastern United States. We first compared arthropod diversity detected with primers targeting mitochondrial 16S (mt16S) and cytochrome c oxidase 1 (cox1 or COI) loci to that detected by manual surveys conducted in parallel. We then evaluated spatial and temporal variation in arthropod diversity metrics with repeated sampling in three focal parks. We also investigated technical factors such as filter type used to capture eDNA and PCR inhibition treatment.

Results

Our results indicate that genus-level assessment of eDNA compositions is achievable at both loci with modest technical noise, although database gaps remain substantial at mt16S for regional taxa. While the specific taxa identified by eDNA did not strongly overlap with paired manual surveys, some metrics derived from eDNA compositions were rank-correlated with previously derived biological indices of environmental quality. Repeated sampling revealed statistical differences between high- and low-quality sites based on taxonomic diversity, functional diversity, and tolerance scores weighted by taxon proportions in transformed counts. We conclude that eDNA compositions are efficient and informative of stream condition. Further development and validation of scoring schemes analogous to commonly used biological indices should allow increased application of the approach to management needs.

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.